Sensor Networks

IBM launches Mote Runner for sensor networks

2011-07-01T18:52:00.000-07:00

Memsic Iris sensor mote
(Credit: Memsic)

IBM on Monday rolled out a software development kit for an application dubbed Mote Runner with the aim of spurring the adoption of sensors in various devices, products, and systems. The real goal is to enable the so-called Internet of things by making sensor networks easier to deploy and manage.

Mote Runner is a free download. IBM made the announcement at the 2010 Sensors Expo & Conference. The Mote Runner moniker refers to motes--or wireless sensor nodes--that gather information including temperature, movement, and light and refer back to a network.

Meanwhile, Memsic, which makes these micro sensor systems, will include Mote Runner on one of its Iris sensors. The Memsic Iris is a 2.4GHz wireless sensor mote used for enabling low-power wireless sensor networks in buildings or traffic patterns at an intersection.

New wearable wireless sensors can improve healthcare

2011-07-01T18:50:00.000-07:00

Members of the public could form the backbone of powerful new mobile internet networks by carrying wearable sensors.
According to researchers from Queen's University Belfast, the novel sensors could create new ultra high bandwidth mobile internet infrastructures and reduce the density of mobile phone base stations.

The engineers from Queen's renowned Institute of Electronics, Communications and Information Technology (ECIT), are working on a new project based on the rapidly developing science of body centric communications.
Social benefits from the work could include vast improvements in mobile gaming and remote healthcare, along with new precision monitoring of athletes and real-time tactical training in team sports.

The researchers at ECIT are investigating how small sensors carried by members of the public, in items such as next generation smartphones, could communicate with each other to create potentially vast body-to-body networks (BBNs).
The new sensors would interact to transmit data, providing 'anytime, anywhere' mobile network connectivity.

Dr Simon Cotton, from ECIT's wireless communications research group said: "In the past few years a significant amount of research has been undertaken into antennas and systems designed to share information across the surface of the human body. Until now, however, little work has been done to address the next major challenge which is one of the last frontiers in wireless communication - how that information can be transferred efficiently to an off-body location.
"The availability of body-to-body networks could bring great social benefits, including significant healthcare improvements through the use of bodyworn sensors for the widespread, routine monitoring and treatment of illness away from medical centres. This could greatly reduce the current strain on health budgets and help make the Government's vision of healthcare at home for the elderly a reality.

"If the idea takes off, BBNs could also lead to a reduction in the number of base stations needed to service mobile phone users, particularly in areas of high population density. This could help to alleviate public perceptions of adverse health associated with current networks and be more environmentally friendly due to the much lower power levels required for operation."

Dr Cotton has been awarded a prestigious joint five-year Research Fellowship by the Royal Academy of Engineering and the Engineering and Physical Research Council (EPSRC) to examine how the new technology can be harnessed to become part of everyday life.

He added: "Our work at Queen's involves collaborating with national and international academic, industrial and institutional experts to develop a range of models for wireless channels required for body centric communications. These will provide a basis for the development of the antennas, wireless devices and networking standards required to make BBNs a reality.

"Success in this field will not only bring major social benefits it could also bring significant commercial rewards for those involved. Even though the market for wearable wireless sensors is still in its infancy, it is expected to grow to more than 400 million devices annually by 2014."

'Body-to-body' networks could serve healthcare, make Internet more mobile

2011-07-01T18:46:00.000-07:00

Researchers at the Institute of Electronics, Communications and Information Technology at Queens University in Belfast, Northern Ireland, are studying how wearable sensors might not only help keep elderly and chronically ill patients out of nursing homes, but also form the backbone of future mobile "body-to-body networks." Says Simon Cotton, one of the researchers, "If the idea takes off, BBNs could also lead to a reduction in the number of base stations needed to service mobile phone users, particularly in areas of high population density. This could help to alleviate public perceptions of adverse health associated with current networks and be more environmentally friendly due to the much lower power levels required for operation."

Central pattern generators

2010-01-20T22:13:00.001-08:00

Even simple insects can generate quite different movement patterns with their six legs. The animal uses various gaits depending on whether it crawls uphill or downhill, slowly or fast. Scientists from Göttingen have now developed a walking robot, which -- depending on the situation -- can flexibly and autonomously switch between different gaits. The success of their solution lies in its simplicity: a small and simple network with just a few connections can create very diverse movement patterns.

To this end, the robot uses a mechanism for "chaos control." This interdisciplinary work was carried out by a team of scientists at the Bernstein Center for Computational Neuroscience Göttingen, the Physics Department of the Georg-August-University of Göttingen and the Max Planck Institute for Dynamics and Self-Organization.

In humans and animals, periodically recurring movements like walking or breathing are controlled by small neural circuits called "central pattern generators" (CPG). Scientists have been using this principle in the development of walking machines. To date, typically one separate CPG was needed for every gait. The robot receives information about its environment via several sensors -- about whether there is an obstacle in front of it or whether it climbs a slope. Based on this information, it selects the CPG controlling the gait that is appropriate for the respective situation.

One single pattern generator for many gaits

The robot developed by the Göttingen scientists now manages the same task with only one CPG that generates entirely different gaits and which can switch between these gaits in a flexible manner. This CPG is a tiny network consisting of two circuit elements. The secret of its functioning lies in the so-called "chaos control." If uncontrolled, the CPG produces a chaotic activity pattern. This activity, however, can very easily be controlled by the sensor inputs into periodic patterns that determine the gait. Depending on the sensory input signal, different patterns -- and thus different gaits -- are generated.

The connection between sensory properties and CPG can either be preprogrammed or learned by the robot from experience. The scientists use a key example to show how this works: the robot can autonomously learn to walk up a slope with as little energy input as possible. As soon as the robot reaches a slope, a sensor shows that the energy consumption is too high. Thereupon, the connection between the sensor and the control input of the CPG is varied until a gait is found that allows the robot to consume less energy. Once the right connections have been established, the robot has learned the relation between slope and gait. When it tries to climb the hill a second time, it will
immediately adopt the appropriate gait.

In the future, the robot will also be equipped with a memory device which will enable it to complete movements even after the sensory input ceases to exist. In order to walk over an obstacle, for instance, the robot would have to take a large step with each of its six legs. "Currently, the robot would not be able to handle this task -- as soon as the obstacle is out of sight, it no longer knows which gait to use," says Marc Timme, scientist at the Max Planck Institute for Dynamics and Self-Organization. "Once the robot is equipped with a motor memory, it will be capable to use foresight and plan its movements."

Wireless Sensor Networks: Security Requirements

2009-09-18T22:17:00.000-07:00

1. Security Requirements

As mentioned in the previous articles (Introduction and Limitations), sensor networks are used in a number of domains that handle sensitive information. Due to this, there are many considerations that should be investigated and are related with protecting sensitive information traveling between nodes (which are either sensor nodes or the base station) from been disclosure to unauthorized third parties.

The scope of this article is to analyze basic security concepts before moving into a detail discussion of the various security issues. It is essential to first understand the security requirements that are raised in a sensor environment; by doing so, we could apply appropriate security techniques to ensure the protection and safety of data and systems involved in a more spherical approach. By knowing what we are trying to protect, we could develop a comprehensive and strong security approach to overcome possible security breaches; after all, in order to protect something you must first know that is in danger. Since sensor networks are still a developing technology. Researchers and developers agree that their efforts should be concentrated in developing and integrating security from the initial phases of sensor applications development; by doing so, they hope to provide a stronger and complete protection against illegal activities maintaining at the same time the stability of the system, rather than adding on security functionality after the application is finished.

Moving on, next section analyzes the security requirements that constitute fundamental objectives based on which every sensor application should adhere in order to guarantee an appropriate level of security.

1.1 Confidentiality

Confidentiality requirement is needed to ensure that sensitive information is well protected and not revealed to unauthorized third parties.

The confidentiality objective is required in sensors’ environment to protect information traveling between the sensor nodes of the network or between the sensors and the base station from disclosure, since an adversary having the appropriate equipment may eavesdrop on the communication. By eavesdropping, the adversary could overhear critical information such as sensing data and routing information. Based on the sensitivity of the data stolen, an adversary may cause severe damage since he can use the sensing data for many illegal purposes i.e. sabotage, blackmail. For example, competitors may use the data to produce a better product i.e. safety monitoring sensor application. Furthermore, by stealing routing information the adversary could introduce his own malicious nodes into the network in an attempt to overhear the entire communication.

If we consider eavesdropping to be a network level threat, then a local level threat could be a compromised node that an adversary has in his possession. Compromised nodes are a big threat to confidentiality objective since the adversary could steal critical data stored on nodes such as cryptographic keys that are used to encrypt the communication.

THE FUTURE OF SENSOR NETWORKS

2009-08-24T03:24:00.000-07:00

From thermostats in building automation to computer numerical controls in factory automation, device and sensor information is traveling over the same technology that is powering our e-world. But how well is it working, and where is the trend taking us?

Mark Fondl, ICT and Lynn Linse, Lantronix, Inc.

The volume of data carried on a network increases as the devices on it become more sophisticated. Low-end devices may transmit data in 1 bit increments, indicating a simple on/off condition. High-end sensors, on the other hand, contain local intelligence and transmit complex data types measured in bytes (see Figure 1.)

To meet the need for more complex data communications, the industry has looked to other networks. In the process, many have asked: Can Ethernet/TCP/IP be used to replace some of these networks? Can some of the networks be integrated into higher level Ethernet architectures (e.g., DeviceNet over Ethernet, Interbus-S over Ethernet, LonWorks over Ethernet)? Some of the answers to these questions can be found in an examination of implementation costs, ease of use, performance, and vendor support.

Implemention Costs

Ethernet costs are not inherently lower than the other networks. For the foreseeable future, cost can be justified only by concentrating multiple sensors on one Ethernet interface.

Other factors contributing to the cost of implementation are the CPU resources. Here, Ethernet does not compare favorably with an architecture such as DeviceNet. For example, DeviceNet can run on a CPU with 4000 bytes of code and 176 bytes of RAM. Ethernet, though, requires a minimum of 64,000 bytes of code and 64,000 bytes of RAM. Here, many implementers insist the minimum is more like 256 KB each, but they would prefer 2–4 MB of code and RAM. If the volumes are low and the margins are high, the simpler software offsets Ethernet’s greater CPU requirements. But as volumes rise and margins shrink, the lower resource needs of something like DeviceNet will force a price premium for Ethernet with the same sales volumes.

Consideration of connection costs—especially for bit-level sensors in industrial environments—causes some to favor ASI and DeviceNet wiring. Optimized for machines in which many discrete sensors are located in a relatively small area (50 m), these sensor networks are ideal. But extending their range poses some difficulty, and based on response times of these clusters, bridging with Ethernet may provide value.

Ease of Use

Here the focus is on long-term support of software configuration. This has multiple facets:

TCP/IP ease of use is based on the wide availability of skilled technicians and tools.
But TCP/IP (at the moment) lacks the high-level standards that allow auto-replacement, which is supported in DeviceNet and ASI.
The complexity of the options found in TCP/IP can overwhelm inexperienced users.
Systems such as DeviceNet and ASI are well suited for applications in which the communications are kept on a local scale. But when the data travel into extended areas and applications in which specialized network skills are required, then a commercial TCû/IP network becomes attractive. Network evaluation can be as simple as a “ping” from almost any computer. Commercial technologies aimed at simple diagnostics will eventually become common. Training individuals to support TCP/IP will be much easier. Device networks feeling this pressure will undoubtedly develop simpler and even browser-based tools.

Performance

With a well-designed network, TCP/IP will perform quite well. But the network must be well designed. An isolated subnet with limited or master/slave functions can expect reliable 2–5 ms times. But the instant you add routers or other noncontrol traffic (including Web servers), you can expect delays of 500 ms or more. So Ethernet performs only as well as the user designs it to perform.

A sparsely designed Ethernet, which underuses its capacity, can rival or beat any deterministic control network. But a poorly designed Ethernet can be an operational nightmare.

Web access via TCP/IP is a common unrealistic hope. With control traffic running at 5–10,000 Bps, users often overlook the fact that a Web page can attempt to force millions of bytes of data through a network at the same time that control data are being transmitted, dwarfing the control traffic. Users and vendors still have to learn the tradeoffs here. Some Web access is wonderful, but this needs to be shared/supplemented with Web resources stored off the control network.

To improve performance on the sensor level, automation companies are experimenting with UDP and variations of limited TCP/IP stacks. These stacks listen only to certain types of transmissions, ignoring others and eliminating a retry structure for a high-speed master-slave structure. This is similar to how I/O has worked for years with PLCs. The architecture and wiring are Ethernet, but the openness is traded for performance.

Most systems don’t need this level of performance and should stay with standard Ethernet. As the technology continues to improve, you can imagine a time when a conventional approach will surpass proprietary methods.

Service and Support
Support for Ethernet TCP/IP systems is good, but the actual media (e.g., cables, connectors, and power) are rather unindustrial. Many TCP/IP experts have an IT mentality, not a plant-floor mindset, so they misunderstand what users want or modify existing systems in ways that hurt the industrial user in an effort to improve the system according to other criteria.

«o users still need to learn about the technology and watch over the shoulders of the experts. Ask questions, and make sure the IT experts learn how you view the problem.

Openness

Ethernet TCP/IP systems provide an open network platform, but high-level application standards are still in flux. TCP/IP is often viewed as a false open standard because its higher layers are proprietary. Modbus/TCP and some of the new encapsulations of òeviceNet, Foundation Fieldbus, and Profibus will help in this area by providing interfaces that will allow different protocols to communicate with each other. But that still leaves a lot of application standards that are not interoperable.

Many of these network architectures encapsulate other protocols, but the interoperability does not extend to the physical and transport layers. This prevents the various buses from communicating with each other. So there is still a great deal of work to be done in this area.

Flexibility

Just about anything is possible with global TCP/IP, but the reliability of its performance depends on the skill of the implementer. However, there is still a need for a more industrial and optimized sensor bus.

As data requirements increase, hybrid and direct Ethernet systems will become commonplace. High-level sensors with serial ports are already being linked over Ethernet. The protocols are transported transparently on top of TCP/IP and delivered to a host, which in some cases is unaware that they were carried over a LAN.

Mobile Sensors

2008-11-10T03:31:00.000-08:00

Mobile Sensor Cam

We were asked by a colleague in Environmental Science about using a mobile phone to relay pictures back of one of there remote sites every half hour for hopefully about a month. We have created not only bespoke mobile application that allows any suitable mobile camera phone to be used for functionality but is programmable for a variety of operating parameters time of operation, frequency etc. The resultant images can be viewed as a series of time lapse photographs at the project site

Mobile sensor cam project in ealy test phase using additional solar panel

River Flow Monitoring

River flow monitoring system

Our influence on the changing natural environment poses many challenges for future generations. One obvious environmental change relates to the increasing regularity of flooding. Monitoring river and stream velocity enables the accurate modelling of flood planes, river bank erosion, and mans influence over the natural environment. The remote monitoring unit developed for this project gives the environmental science community a ‘real time’ cost effective data collection system, freeing up large amounts of time spent out in the field gathering data and eliminating the potential for human error during the recording process. The system incorporates a sensor placed in the river that users Doppler shift to esimate the flow rate of the river. A unit on the bank relays the sensor reading plus a GPS time stamp to a central server using a GPRS connection to the celluar network. With cellular coverage now extended to even the most remote rural of areas systems this project highlights that mass monitoring of stream and river networks is now a practical solution.

Bus ETA

Application showing ETA of bus at selected stop

Whilst it is readily accepted that mobile phones enable users to obtain information quickly and easily in any location, there are a great many ways in which this information can be accessed and provided. Although mobile phone manufacturers are embracing standardization of mobile phone operating systems, as yet there is no clear market leader, which coupled with extremely variable phone feature sets, make porting applications to different platforms a challenging experience. Further to this, is the fact that even within technologically advanced societies large sections of the population are technologically naïve. Thus, the development of information systems that can be accessed by large numbers of the general public from mobile phones, can be problematic, and all too often such systems provide only a single mode access solution, which limits both their acceptance and usefulness. In this project we developed a mobile information system which has been designed so that it can accommodate both the variation in mobile phone features and the technical sophistication of individual users and can be easily deployed for a wide variety of services. The system was demonstrated through an example service which demonstrated information related to the Estimated Time of Arrival (ETA) of vehicles in a metro-bus public transport system. The solution illustrates that information can be provided in different forms, to suit individual users preferences, from the same mobile information system, without significant increases in the underlying infrastructure.

WayCon – your specialist for professional displacement transducers

2008-11-10T03:30:00.001-08:00

WayCon is a manufacturer of accurat measurement systems and professional sensors for industry and research. We are your contact for all applications in areas of position and distance measurement techniques, level measurements and angel measurements.

Configure your sensor individually?... then we recommend you contact us for special advise, before you start your application. In particular we can advise on which technology to use for cost effectiveness and desired accuracy.

This is why you will not find a catalog with prefabricated stock products, but a product overview with detailed datasheets where you can configure your product individually. We build the equipment to your specification and, if required, even on the same day.

TinyNode

2008-10-25T02:01:00.000-07:00

Introducing TinyNode

Shockfish SA has developed the TinyNode platform with real industrial application in mind. Our mission is to bridge results from academic research with industrial needs in the area of wireless sensor networks.

The design philosophy of TinyNode is to provide a platform for both academic projects and industrial applications. The TinyNode 584 and TinyNode 184 core module are versatile lowest-power sensor nodes and come with an array of extension hardware offering a wide set of connectivity, storage, energy and interfacing options.

TinyNode 584

The TinyNode 584 is an ultra-low power OEM module that provides a simple and reliable way to add wireless communication to sensors, actuators and controllers.

TinyNode 584 is optimized to run TinyOS and packaged as a complete wireless subsystem with 19 configurable I/O pins offering up to 6 analog inputs, up to 2 analog outputs as well as serial interface.

Key Features

Ultra Low Power design

TI MSP430 microcontroller

Fast wake-up from sleep (<6us)

Semtech radio transceiver XE1205

European 868MHz ISM band operation

Adjustable datarates up to 153kbit/s

Range up to 2km

Compact 30 x 40mm

On-board temperature sensor

On-board 512kB flash chip

30 pin board-to-board connector

Analog, digital and serial interfaces

On-board or external antenna options

Out-of-the-box TinyOS support

TinyNode 18

The TinyNode 184 is a state of the art ultra-low power OEM module that

provides a simple and reliable way to add wireless communication to sensors,

actuators, and controllers.

Indeed the 184 serie is the first wireless sensor consuming only 3mA in receive mode

TinyNode 184 is optimized to run TinyOS and packaged as a complete wireless subsystem with 19 configurable I/O pins offering up to 6 analog inputs, up to 2 analog outputs as well as serial interface.

Key features

	Ultra Low Power 2.1 V design: > 10 years battery life on 2/3AA Lithium batteries (using 2uA sleep modes)
	16MHz Texas Instruments MSP430 microcontroller (MSP430F2417)
	868 / 915MHz Semtech SX1211 ultra-low power wireless transceiver (3mA in receive mode, 25mA in transmit mode at +10dBm)
	512kB ST m25p40 external flash
	On-board 8-pin expansion connector with power supply, 2 ADC channels, interrupt and UART pins
	On-board battery connector
	Small: 30x40 mm
	RF Receiver sensitivity down to -107 dBm
	Transmit output power programmable up to +10 dBm, on-board SAW filter Bit rates up to 200kbit/s, NRZ coding
	On-board chip antenna, footprint for SMA/MMBX connector
	Fast wakeup from sleep (<1µs)
	Pin compatible to TinyNode584

Mobile Sensor Networks

2008-10-22T21:14:00.000-07:00

Cooperative mapping & location.

Multisensor fusion for distributed fields.

Wireless & underwater networking.

Deployment experiments are carried out in our lab using a large network of integrated mobile sensor platforms.

Energy harvesting from multiple sources (vibration, light and temperature) augment the available on-board mobile node power.

Objective:

Development of algorithms and prototype vehicles for wide-area surveillance and reconnaissance using mobile sensor networks (MWSN). Monitoring on land, water and air using large numbers of mobile sensor nodes is demonstrated at our Distributed Intelligence and Autonomy Lab (DIAL).


Land-based mobile robot fleet at DIAL consisting of inexpensive mobile robots carrying wireless sensors is used to study deployment algorithms.	Bandwidth Maximization using Potential Fields: A potential field is used to reposition sensor nodes to maximize network bandwidth.

Approach:

Deployment of large numbers of sensors using heterogeneous robotic platforms including inexpensive ARRI-Bots.
Use of a Potential Fields (PF) approach to achieve mobility subject to communication bandwidth, energy and navigation/collision.
Use of Extended Kalman Filter (EKF) for information gathering, localization and navigation.
Use of a Discrete Event Controller (DEC) for resource allocation and mission planning.
Data logging and supervisory control using Labview for managing and visualization of sensor networks.
Platform independent algorithms: Land, Aerial, Underwater.
Adaptive Sampling (AS): Efficient, information-driven sensor deployment.

Adaptive Sampling (AS)aims to reconstruct a distributed sensor field
from multiple measurements.

AS is much more efficient than conventional Raster Scan Sampling.

types of Deployment Algorithms:

Communication-Aware deployment consisting of dynamic sensor repositioning in the presence of network communication constraints.
Information-Aware deployment consisting of optimal placement of mobile sensors to gather the most information.
Mission-Aware deployment consisting of resource coordination to accomplish a common mission.
Energy-Aware deployment consisting of conservation and harvesting of energy for sensor network sustainability.

Publications:

[1]	V. Giordano, P. Ballal, F.L. Lewis, B. Turchiano, J.B. Zhang, “Supervisory control of mobile sensor networks: math formulation, simulation, implementation,” IEEE Trans. Systems, Man, Cybernetics Part B, 2006.
[2]	V. Giordano, F.L. Lewis, P. Ballal, and B. Turchiano, “Supervisory controller for task management and resource dispatching in mobile wireless sensor networks,” in Cutting Edge Robotics, ed. V.
[3]	V. Giordano, F.L. Lewis, B. Turchaino, P. Ballal, V. Yeshala, “Matrix computational framework for discrete event control of wireless sensor networks with some mobile agents,” Proc. Mediterranean Conf. Control & Automation, Limassol, Cyprus, June 2005. This paper won an award at MED 05.
[4]	Ankit Tiwari, Prasanna Ballal, Frank L. Lewis, "Energy-Efficient Wireless Sensor Network Design & Implementation for Condition Based Maintenance," Submitted to ACM Trans. on Sensor Networks, Aug 2005.
[5]	Prasanna Ballal, Vincenzo Giordano, Frank Lewis, "Deadlock free dynamic resource assignment in multi-robot systems with multiple missions: a matrix-based approach", submitted to the Mediterranean Conference on Control, 2006.
[6]	Prasanna Ballal, Vincenzo Giordano, Pritpal Dang, Sankar Gorthi, Frank Lewis, "A LabView based test-bed with off-the-shelf components for research in mobile sensor networks," submitted to ISIC 06 Munich, Commuri special session, 2006.
[7]	D.O. Popa and F.L. Lewis, “Algorithms for robotic deployment of WSN in adaptive sampling applications,” in Wireless Sensor Networks and Applications, ed. Y. Li, M. Thai, and W. Wu, Springer-Verlag, Berlin, 2006, to appear.
[8]	P. Dang, F. L. Lewis, D. O. Popa, “Dynamic Localization of Air-Ground Wireless Sensor Networks,” in Advances in Unmanned Aerial Vehicles, ed. Kimon Valavanis, Springer Verlag, Berlin, 2007, to appear.
[9]	K. Sreenath, F. L. Lewis, D. O. Popa, “Simultaneous Adaptive Localization of a Wireless Sensor Network,” in ACM SIGMOBILE Mobile Computing and Communications Review, 2007, to appear.
[10]	Das A.N., Popa D.O., Ballal P., Lewis F.L., “Data-logging and Supervisory Control in Wireless Sensor Networks”, in ACIS Int’l Journal of Wireless and Mobile Computing, 2007, to appear.
[11]	D.O. Popa, M.F. Mysorewala, F.L. Lewis, "Deployment Algorithms and In-Door Experimental Vehicles for Studying Mobile Wireless Sensor Networks", in ACIS Int’l Journal of Wireless and Mobile Computing, 2007, to appear.
[12]	D. O. Popa, M. F. Mysorewala, F. L. Lewis, "EKF-based Adaptive Sampling with Mobile Robotic Sensor Nodes", to appear in Proceedings of International Conference on Intelligent Robots and Systems (IROS), Beijing, China, Oct 2006.
[13]	Priya, S.; Popa, D. O.; Lewis, F.L.; "Energy efficient Mobile Wireless Sensor Networks," to Appear in Proc. of 2006 ASME International Mechanical Engineering Congress & Exposition November 5 – 10, Chicago, Illinois.
[14]	Das A.N., Lewis F.L., Popa D.O., "Data-logging and Supervisory Control in Wireless Sensor Networks" in proceedings of the 2nd ACIS Workshop on Self-Assembly Wireless Networks (SAWN), Las Vegas, USA, 19-20 June 2006, pp.330-338.
[15]	M.F. Mysorewala, D.O. Popa, V. Giordano, F.L. Lewis, "Deployment Algorithms and In-Door Experimental Vehicles for Studying Mobile Wireless Sensor Networks", in Proceedings of 2nd ACIS International Workshop on Self-Assembling Wireless Networks (SAWN), Las Vegas, Nevada, USA, June 2006.
[16]	Popa, D.O., Sanderson A., Hombal, V. et. al., “Optimal Sampling using Singular Value Decomposition of the Parameter Variance Space”, in Proc. of IEEE IROS ’05, Edmonton, CA, August 2005.
[17]	D.O. Popa, K. Sreenath, and F.L. Lewis, “Robotic deployment for environmental sampling applications,” Proc. Int. Conf. Control and Applics., pp. 197-202, Budapest, June 2005.
[18]	Popa, D.O., A. Sanderson, R. Komerska, S. Mupparapu, R. Blidberg, S. Chappel, “Adaptive Sampling Algorithms for Multiple Autonomous Underwater Vehicles”, in Proc. of 2004 Workshop on Underwater Vehicles, Sebasco Estates, ME, June 2004.
[19]	Popa, D.O., A. Sanderson, R. Komerska, S. Mupparapu, R. Blidberg, S. Chappel, “Autonomous Monitoring and Control (ASMAC) - An AUV Fleet Controller”, in Proc. of 2004 Workshop on Underwater Vehicles, Sebasco Estates, ME, June 2004.
[20]	Popa, D.O., Helm, C., Stephanou, H.E., Sanderson, A,“Robotic Deployment of Sensor Networks using Potential Fields”, in Proc. Of International Robotics and Automation Conference, April 2004.
[21]	D. O. Popa, M. F. Mysorewala, and F. L. Lewis, "Adaptive Sampling using Nonlinear EKF with Mobile Robotic Wireless Sensor Nodes", to appear in Proceedings of the Ninth International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, Dec 2006.
[22]	D. O. Popa, F. Lewis, "Robotic Deployment of Sensors in Environmental Monitoring Applications", invited presentation at 2006 US Navy Workshop on Transduction Materials and Devices, State College, PA, May 2006., Texas A&M, September 2004.
[23]	Priya S, Chen CT, Fye D, et al. Piezoelectric windmill: A novel solution to remote sensing Japanese Journal of Applied Physics Part 2-Letters & Express Letters 44 (1-7): L104-L107 2005.

Mobile robots as gateways into wireless sensor networks

2008-10-13T20:46:00.000-07:00

Overview

It is well known that Intel leads the industry in wireless sensor network research. What may not be quite as well known is Intel's recent work in mobile robotics. In particular, Intel is helping researchers create small, sophisticated mobile robots that can act as gateways into wireless sensor networks.

This is a new venture that is focused on intelligent mobile robots -- robots that are used in flexible environments, not automated toolsets in fixed locations. For example, Intel-based mobile robots will be used at the James Reserve by the Center for Embedded Networked Sensing (CENS) to map terrain and monitor habitats. Intel silicon for robotics applications is also being used by researchers, such as professor Tucker Balch at the Georgia Institute of Technology. Professor Balch is exploring how robots can organize and perform like social insects, such as bees and ants. Future projects may include the possibility of building a ground-based Robonaut, as well as the brains of the 2009 Mars Rover.

Intel's focus is not on the mechanical aspects of robots -- the wheels, motors, grasping arms or physical layout. Instead, this venture is focused on the silicon and software that give a robot its capabilities and intelligence. Intel's role is to assist researchers in putting powerful, sophisticated intelligence into small, standardized packages for mobile robotics. With wireless technologies now practical and available, this is a novel area for research and investigation.

To assist researchers, Intel is offering inexpensive, standards-based hardware, an open-source operating system, and drivers for use in robotics environments. The open-source package lets researchers take advantage of leading-edge Intel XScale microprocessors and Intel Centrino mobile technology, while reducing the overall costs of developing robotics systems.

What is a robot?

Robotics is not a new field. It has been around for decades. In fact, most people have robots in their own home, even if they don't recognize the robots as such. For example, a dishwasher automatically washes and dries your dishes, then grinds up the rinsed-off food so the organic matter doesn't clog your drains. A washing machine soaks, soaps, agitates, and rinses your clothes. Down the street, the car wash-n-wax cleans, brushes, washes, and waxes your car, all for a few dollars. One of the better known home-oriented robots is iRobot's smart vacuum cleaner, called the Roomba, which has already won the Good Housekeeping Award for efficiency and ease of use.

More sophisticated robots are used in manufacturing plants and warehouses. Car makers use automated machines to position car frames, bolt pieces together, and even do welds and priming. In wafer communications, test systems position themselves along grids, take measurements, and then correlate the data into graphs. Robot-assisted heart microsurgery is now performed routinely in the U.S.

To some extent, we have become so used to robots that we no longer pay attention to the automated machines. We look only at the tasks they complete, and we think of them simply as tools. It is easy to think this way: most of today's robots are stationary tools in fixed locations, like a fruit sorter in a cannery, or an alarm sensor that triggers a call to security.

Robots growing in sophistication

Although we are surrounded by robots that we think of as automated tools, there are some sophisticated robots already in use (photo below). A remote telepresence is one of the most common applications that today's mobile, autonomous robots provide. Intelligence for these robots is handled via an embedded microcontroller that manages internal systems, and by a laptop that is attached to the robot. Humans control the robot through wireless communications. In this way, humans can tell the robot to change directions, shift a camera angle, take measurements, grasp objects, and so on. For example, mobile robots can let security personnel stay in a central office and still check out unsupervised areas in a warehouse or other remote site.

Carnegie Mellon University's TagBots use Intel boards

With advances in microchip design, nanotech sciences, software architecture, and mini-power cells, robot systems can be more than just another pair of eyes. They are already being tested and used in a variety of applications. They can traverse different, even dangerous environments and perform complex tasks on their own. For example, mil-spec iRobot Packbots have been used in Afghanistan to detect and map the locations and contents of caves. Another iRobot rover was used in the historic exploration of both the southern and northern shafts that led to the Queen's Chamber in the Great Pyramid at Giza (Egypt). The rover was able to illuminate areas beyond the blocking stones in the shafts, which had last been viewed by human eyes some 4,500 years ago.

Robot mobility issues

Regardless of a robot's design or tasks, there are still three main issues with its mobility:
Localization: How does a robot know where it is in its environment?
Mapping: How does the robot know the details of its environment?
Navigation: How does a robot traverse its environment?
Intel works closely with researchers to identify novel ways for a robot to perform its mobility tasks. Intel is particularly interested in machine-vision libraries that can be used to perform localization and mapping based on monocular- or stereo-vision systems. For example, right now, most robots navigate by using infrared or radio waves to avoid objects in their paths. However, Intel software researchers recently developed several libraries that are very applicable to robotics systems. Intel's computer vision library is already used extensively by vision researchers.

Intel has also released a test version of a technical library for building Bayesian networks to support machine-learning activities. Bayesian networks are a form of probability-based artificial intelligence. Such a network would let a robot navigate by matching sensor data to a map stored in its memory.

Gateways into sensor networks

Two technologies in particular seem to be moving toward an interesting convergence: mobile robotics and wireless sensor networks. The two main questions here are:
Can a mobile robot act as a gateway into a wireless sensor network?
Can sensor networks take advantage of a robot's mobility and intelligence?
One major issue with a mobile robot acting as a gateway is the communication between the robot and the sensor network. Sensor networks typically communicate using 900 MHz radio waves. Mobile robots use laptops that communicate via 802.11, in the 2.4- to 2.483-GHz range. Intel hopes to prove that a sensor net can be equipped with 802.11 capabilities to bridge the gap between robotics and wireless networks.

Intel recently demonstrated how a few motes equipped with 802.11 wireless capabilities can be added to a sensor network to act as wireless hubs. Other motes in the network then use each other as links to reach the 802.11-equipped hubs. The hubs forward the data packets to the main 802.11-capable gateway, which is usually a laptop. Using some motes as hubs cuts down on the number of hops any one data packet has to make to reach the main gateway. It also reduces power consumption across the sensor net.

Intel believes that one of the most interesting technology convergences will be in designing mobile robots that can act as gateways into the wireless sensor networks. For example, Intel recently installed small sensors in a vineyard in Oregon to monitor microclimates. The sensors measured temperature, humidity, and other factors to monitor the growing cycle of the grapes, then transmitted the data from sensor to sensor until the data reached a gateway. There, the data was interpreted and used to help prevent frostbite, mold, and other agricultural problems.

The agricultural example shows just how a sensor network could take advantage of a mobile robot's capabilities. Over time, sensors need to be recalibrated, just like any other measuring equipment. If a robot could act as a gateway to the sensor network, it could automatically perform tasks such as calibration. For example, a robot could periodically collect data along the network, determine which sensors are out of tolerance, move to the appropriate location, and recalibrate each out-of-tolerance device.

To look into using mobile robots as gateways to such wireless sensor networks, Intel is bringing in a Ph.D. candidate from the University of Southern California, under the guidance of professor Gaurav Sukhatme. This person will work with Intel on integrating wireless sensor networks into robotics research for localization techniques. This type of collaboration is just one example of how Intel is promoting the convergence of microelectronics and robotics.

Numerous collaborations on robotics projects

Overall, Intel is working with approximately 20 robotics research groups, including Carnegie Mellon University (CMU), University of Southern California (USC), University of Pennsylvania, Northwestern, and Georgia Tech. Intel is also in discussions with universities and robotics manufacturers, such as Sony, about robotic dogs, and Honda and Samsung on using Intel silicon to build robotic humanoids. Intel is also in discussion with NASA and DARPA (the Defense Advanced Research Projects Agency) on several major projects.

Other pilot projects include professor Sebastian Thrun's CMU research into an aerial mapping helicopter (photo below), which is currently about 4 feet in length and which has been demonstrated in certain DARPA programs. Acroname is also using Intel's open-source robotics package in their latest commercial robot, called Garcia (see photo at beginning).

Sebastian Thrun's aerial mapping helicopter

In other collaborations, professor Balch of Georgia Tech is using Intel technology to develop hundreds of mobile robots in order to model the swarm behavior of insects. Professor Vijay Kumar is using Intel's XScale boards (photo below) and open-source software for off-road robot investigations. Professor Illah Nourbakhsh is teaching mobile robot programming using new robotics systems with Intel XScale boards and the Linux operating system.

Intel boards are being used in a number of robotics projects

Robotics task force

The thrust of Intel's robotics effort is to reduce the cost and engineering required to build small, powerful, sophisticated robots. This thrust, however, requires standards and protocols. Right now, robotics standards and protocols are in their infancy. With technology convergence becoming increasingly important in Intel's areas of interest, Intel is leading industry efforts for the Robotics Engineering Task Force (RETF).

The RETF is modeled after the Internet Engineering Task Force (IETF). RETF allows government and university researchers to work together to establish standard software protocols and interfaces for robotics systems. Currently, government representatives include researchers from NASA, DARPA, and NIST (National Institute of Standards and Technology). All told, approximately 35 government and university researchers are already participating in the RETF.

The most pressing issue for the RETF is devising standards for commanding and controlling the mobile robots. The task force has already defined a charter to develop standards for robotics systems. A working draft of the first framework document is now being reviewed for comments.

The task force has also begun work on standards for bridging networks, on protocols, and on application programming interfaces (APIs). Current issues being discussed include intellectual property rights and copyright. The task force hopes to begin work on full specifications as soon as the framework document is approved. The task force expects to publish its work as open-source code when the work is complete, something it hopes to finish in about two years.

Standardized building blocks

As one of the industry leaders of the RETF, Intel is devising low-cost reference designs for relatively small robots. The reference designs are based on silicon for Intel's XScale microprocessor and Intel Centrino mobile technology, flash memory, and 802.11 wireless networking with built-in support for wireless sensor networks. The designs give researchers an intermediate scale between the embedded microprocessors currently used in internal robotics and the large-scale laptops used for mobile intelligence.

The robotics package also includes the open-source Linux 2.4.19 operating system, as well as a multitude of open-source drivers. Drivers include vision-system drivers for sensing infrared, drivers for ultrasonic devices that measure the distance from a robot to objects in the robot's environment, and so on. The software platform also supports Java applications, and integrates USC's Player device server for robotics systems. All elements in the open-source robotics package are wirelessly connected using 802.11 networks.

With internal robot systems standardized, researchers and developers will not have to redesign the wheel for each robot's brain. Instead, developers can spend more time on mobility, visual recognition systems, and the software for artificial intelligence (AI).

Summary

Having achieved a reputation for leading-edge work in wireless sensor networks, Intel is starting a new venture into wireless, mobile robotics technology. Intel hopes that the two technologies -- mobile robotics and wireless networks -- can be combined efficiently in, for example, ubiquitous computing environments. In the Intel vision, mobile robots act as gateways into wireless sensor networks, such as into the "Smart Dust" networks of wireless motes.

Intel's role in this venture is to assist robotics researchers by providing standardized silicon, an open-source operating system, and a multitude of open-source software drivers for robotics applications. The robotics development package includes silicon for the Intel XScale microprocessor or Intel Centrino mobile technology, the Linux 2.4.19 operating system, and a plethora of robotics software drivers.

Intel has also released a test version of a technical library for building Bayesian networks, which will help advance the ability of robots to navigate their environments. Pilot systems based on Intel's open-source packages are already being deployed in a variety of flexible environments in agricultural, security, and military applications.

With standardized low-cost and open-source building blocks, developers won't have to spend as much time building the brains of their robots. Developers of embedded systems or wireless sensor networks will find that the open-source robotics package makes it much easier to design tomorrow's robotics systems today.

Airmux-200 - Wireless Multiplexer

2008-10-12T00:29:00.000-07:00

Product Summary
RAD's Airmux-200 wireless multiplexer is a carrier-class, high capacity cost effective point-to-point and multi point-to-point transmission system used to connect Ethernet and E1/T1 circuits between two distant sites over broadband wireless.

The Airmux-200 wireless multiplexer combines legacy TDM and Ethernet services for transmission over 2.3 GHz to 2.7 GHz and 4.9 GHz to 5.95 GHz bands. The Airmux-200 wireless multiplexer has a total air data rate of 48 Mbps, with a theoretical maximum transmission distance of up to 80 kilometers (50 miles). Transmission is encrypted to ensure security.

Typical applications include service extension to remote/rural locations, cellular backhaul, emergency services and temporary deployment, wireless backup, voice traffic extension over wireless, and lossless hot spot backhauling.
Benefits
• Combines voice and data over a single link, cutting access costs
• License exempt transmission enables fast and affordable deployment
• Installation and alignment tools enable easy deployment
• Save the costs of leased lines while extending services
• Ideal solution for cellular backhaul applications and remote coverage areas
Quick Specs
Wireless multiplexer combining up to four E1/T1 and up to two Ethernet interfaces
High data rates, up to 48 Mbps
Operates in 2.3 to 2.7GHz and 4.9 to 5.95 GHz bands
Long range: Up to 80 km (50 miles)
Air interface data is encrypted using 128-bit key
Hundreds of links and access devices managed from a single SNMP fault management application
Automatic channel selection
Dry contact alarm relay
Further Product Information
Airmux-200 is a carrier-class, high capacity, affordable wireless multiplexer, connecting E1 or T1 and Ethernet networks point-to-point and multi point-to-point over a wireless link.

Compliant with FCC, CAN/CSA and ETSI regulations for license-exempt transmission, the Airmux-200 operates over 2.3 GHz to 2.7 GHz and 4.9 GHz to 5.95 GHz bands. Wireless transmission enables enterprises to save the cost of leased lines while eliminating the service provider’s need for deploying fiber, thereby enabling rapid deployment of E1 and T1 lines and Ethernet links at a fraction of the cost.

Airmux-200 consists of an indoor and an outdoor unit connected by a Cat-5e outdoor Ethernet cable, allowing a maximum distance of 100 meters (328 feet) between the two units. The outdoor unit can be ordered with an integrated antenna or with a connector for an external antenna.
Long distance, full duplex link
Airmux-200 integrates up to four unframed E1 or T1 ports and up to two Ethernet ports for a total air interface throughput of 48 Mbps. This is equivalent to a net payload throughput of up to 18 Mbps full duplex. The maximum range of the Airmux-200 is 80 km (50 miles). Throughput is a function of the distance and regulation.

An integrated 10/100BaseT Ethernet bridge transparently forwards frames and learns up to 2,000 MAC addresses. The accurate E1/T1 clock recovery, low round-trip delay and high link availability position the Airmux-200 as a carrier-class wireless transmission system.
Advanced encryption and security features
Airmux-200 incorporates safeguards to secure the wireless transmission against possible attack. The advanced encryption standard (AES) and dynamic encryption key change are aimed to prevent unauthorized eavesdropping. These mechanisms, together with a coded time stamp (CCM), prevent false transmission from an intruding terminal. The network management system and the equipment are protected by a password and a challenge/response scheme.

An Airmux-200 link can be managed by a Windows-based application supplied with the device. The RADview SNMP fault management application can be ordered in cases where multiple links are to be deployed and managed from a central platform. All parameter configurations are link-based, simplifying maintenance and installation.

Airmux-200 is a perfect solution for connecting remote enterprise locations, cellular backhaul, broadband Last Mile services and hotspot backhauling.

Configurations: Airmux-200 also functions in a Power over Ethernet mode for Ethernet services and in a co-location (multipoint-to-point) mode.

activate the application

2008-10-11T01:31:00.000-07:00

AdaptivEnergy RLP energy-harvesting beam

Typical effect of decreasing temperature, which reduces the effective storage capacity of many lithium batteries
Efficient energy collection is vital to making energy harvesting a viable technology. To complement the high output of the RLP, AdaptivEnergy engineers have designed electronics to efficiently capture and store the energy produced. While the circuit design is proprietary, the efficiency of the design can be traced to the use of ultra-low-power components. The Joule-Thief offers several options for energy storage, including capacitors and lithium batteries. A version with the latest thin-film lithium-ion battery technology is being evaluated. The goal is to provide appropriate energy storage means for the customer's application.

Paradigm Shifts
It is hard to determine if it is the emergence of new technology or the evolution of application requirements that brings change. In both cases, end users often must alter the way they look at operating requirements and opportunities. For example, powering sensors with energy harvested from ambient sources requires a paradigm shift in how electronic devices are powered. If continuous operation of an electronic device is not necessary, then the average power output of an energy-harvesting device is not the metric of concern. AdaptivEnergy encourages customers to "think joules, not watts" because the important criterion is the amount of energy needed to perform the customer's task, not how much power can be generated. Once this is understood and the amount of energy collected by the device is known, it becomes a simple task to determine how much data can be collected and how often a transmission can be made.

The same technology that allows for power-autonomous sensing devices also ushers in new application opportunities. In this case, wireless sensors are being deployed with increasing frequency where the installation of wiring is not a viable option. A good example of this is the use of sensors to monitor the health of machinery. Wireless sensors allow more machinery parameters to be monitored simply because their installation and maintenance requirements are relatively palatable compared with their hard-wired cousins. In addition, they make it possible to avoid unscheduled downtime, to limit maintenance only to times when it is required, and to reduce operating and maintenance costs.

Another application of energy harvesting and wireless sensors is structural health monitoring, a hot topic after the collapse of the I-35W Mississippi River Bridge in Minneapolis, Minnesota, and the collapse of the construction cranes in New York City. This application genre also includes aircraft. In this industry, when the expense of installation, maintenance, and the fuel required to carry the additional weight of the wiring is taken into consideration, the cost per foot of wire can approach $2000. Furthermore, in many aircraft applications, the sensors are simply too difficult to reach to change the batteries. And even if it were possible to reach the sensors, the cost of the labor to purchase, store, and change a battery far exceeds the cost of the battery itself. The ultimate goal of aircraft manufacturers is to embed sensors in the structures during fabrication. Automobile manufacturers face similar challenges, particularly with the high price of gasoline. The increase in fuel efficiency enabled by reductions in vehicle weight has led auto manufacturers to consider wireless sensors and switches to help reduce wiring harness weight in vehicles, which now weighs up to 500 pounds.

Structural health monitoring applications, such as the collapse of this New York City crane, are well served by energy-harvesting-enabled wireless sensors
Predictions and Trends
These tactical paradigm shifts herald broader changes. For example, perennial futurist Ray Kurzweil predicts a day when "ambient intelligence" will enhance the way people interact with their environment. In Kurzweil's world, sensors will be scattered around like dust to sense everything in the environment and provide feedback to improve our lives and safety. This multitude of sensors will likely not be battery powered. With microcontrollers and sensors in more and more simple electronic devices (e.g., refrigerators, washing machines, and coffeemakers), it's beginning to look as if Kurzweil's vision of the future may be correct. The smart home of the near future will be filled with sensors, and it's anticipated that energy-harvesting devices that can power the sensors will play an important role in their future.

Another fact that Kurzweil likes to point out is that technology improvements tend to follow exponential trends. For example, Moore's law predicts the number of transistors on an integrated circuit will double every two years. This prediction proved accurate, and the trend continues to this day. Similar exponential shifts can be expected in the size and power requirements of electronics, where devices become increasingly smaller and require less and less energy to operate. Paralleling these shifts, the capacity, or energy density, of energy-harvesting technology, such as AdaptivEnergy's Joule-Thief, will increase exponentially. When the impacts of these trends converge, a new world of energy-harvesting-powered electronic devices will become feasible and more and more self-powered electronic applications will become part of our daily lives.

Today's Products
The commercialization of AdaptivEnergy's energy-harvesting technology (see sidebar "Test Drive Power Harvesting") began with a project for In-Q-Tel, the venture capital arm of the U.S. intelligence community, but AdaptivEnergy's customers are now exploring a wide range of applications for the Joule-Thief beyond wireless sensing. In fact, the technology is being evaluated in applications as diverse as machinery and structural health monitoring, vehicular sensing and switching, and asset tracking.

As power requirements for electronics continue to decrease exponentially, and the power output of energy-harvesting devices continues to increase exponentially, more and more self-powered electronic applications will become feasible. A question often asked is "Can you power my cell phone or MP3 player?" Maybe not today, but that day is coming, and it may not be too far in the future. In the meantime, powering wireless sensors appears to be an ideal application for energy harvesting.

Test Drive Power Harvesting
For evaluation purposes, AdaptivEnergy offers a vibration-powered Wireless Sensor Demonstration Kit (Figure 6) in which five sensing devices, a Texas Instruments MSP430 microcontroller, and a Texas Instruments CC2500 wireless radio are powered entirely by a vibration source. All the sensor data can be transmitted approximately once per second with the included vibration source, or every five seconds with vibration amplitudes in the tens of mg. For those unfamiliar with vibration amplitudes, this represents a vibration that is barely perceptible to human touch. If longer durations between transmissions can be tolerated, it's possible to power the device with imperceptible vibrations.

Figure 6. AdaptivEnergy Wireless Sensor Demonstration Kit
To specify energy-harvesting material for a wireless node, you need to know the frequency and amplitude of the source vibration, the energy required to perform a transmission, and how often a transmission must be performed.

RLP and Joule-Thief are registered trademarks and trademarks, respectively, of AdaptivEnergy LLC.

Sensor Networks

2008-10-01T23:09:00.000-07:00

Links to sensor networks related research web-sites:
1. Deborah Estrin's (UCLA) Home Page
2. Tiny OS - An Operating System for Networked Sensors
3. Hari Balakrishnan's (MIT) Home Page
4. University of Wisconsin Sensor Networks Research Group
5 Wireless Integrated Sensor Networks - WINS
6 Extreme Scale Wireless Sensor Networking (Ohio State University)

Links to sensor networks based applications:

1. Traffic Pulse Technology
2. Distributed Surveillance Sensor Network
3. Cougar: The Sensor Network is the Database
4. Eyes - Energy Efficient Sensor Netowrks
5. Reactive Sensor Networks
6. Wireless Sensing Networks
7. Smart Buildings Admit Their Faults
8. Smart Sensor Networks
9. Neural Network Based Sensor Systems for Manufacturing Applications

Distributed Surveillance Sensor Network

2008-08-18T04:29:00.001-07:00

Concept Overview

The purpose of the Distributed Surveillance Sensor Network (DSSN) program is to investigate the applicability of small, inexpensive undersea vehicles to surveillance applications and submarine connectivity. It is based on the concept of a fleet of autonomous undersea vehicles which gather surveillance data and communicate acoustically. Each occasionally docks at an underwater station to dump its data, recharge its batteries, receive any new mission instructions and perhaps remain dormant until its next deployment. The docking station is self powered and is not connected to shore or ship by communications cable. The massive quantity of accumulated data is retrieved at a later time by means of a Flying Plug, a remotely controlled vehicle guided to the docking station by means of a fiber optic microcable (FOMC). The FOMC is the high-bandwidth channel by which the data is recovered and instructions are downloaded to be disseminated to the surveillance fleet. The DSSN program combines the Flying Plug and FOMC, which were developed at SSC San Diego, with other concepts and technology funded by the Office of Naval Research (ONR).

Autonomous Ocean Sampling Network

The conceptual basis for a distributed array of autonomous sensors is provided by the Massachusetts Institute of Technology's Autonomous Ocean Sampling Network (AOSN). AOSN is a distributed, highly mobile, adaptive sensor network composed of a mix of autonomous underwater vehicles (AUV's) which exhibit complementary capabilities. It is being developed for oceanographic characterization. The architecture is very general, hence the mix of AUV's and their payloads can be optimized for specific mission scenarios, making the concept both highly flexible and very powerful.

The AOSN concept is predicated upon the assumption that the geometric growth in signal processing power we are experiencing at the present continues into the future. Besides increasing capabilities and driving costs down, this trend ultimately permits a single hardware device to support multiple applications. For example, digital signal processing (DSP) chips are used to compensate for multipath propagation in the current generation of acoustic modems developed for AOSN. As DSP's become more capable they will be able to support higher reliable data transfer rates. More importantly, as increased processor speed becomes commercially available enough signal processing capability will eventually exist to permit the extraction of information from the multipath signals themselves (which are currently only discriminated against). This capability configures the AUV communications network into a huge multi-static active sonar capable of detecting and localizing anomalies within the volume of seawater supporting the acoustic propagation paths. In time the same basic hardware which was originally employed for data communications can simultaneously detect mines and submarines in the water volume --- with only an upgrade in the silicon! This is a striking, but realistic, example of the efficacy of selecting a system's architecture to take maximum advantage of expected technological evolution.

The Odyssey Vehicle

Odyssey is a low-cost AUV specifically developed by the Massachusetts Institute of Technology, SeaGrant Office for the AOSN Program. Constructed to operate at full ocean depth, Odyssey was designed from the beginning to be both highly capable and inexpensive to mass-produce. At less than two meters in length and not requiring any special handling equipment for launch and recovery, Odyssey can transit at several knots for up to 20 hours due to its ultra-low hydrodynamic drag profile and efficient propulsion system, yielding a very respectable range and oceanographic mission profile. An integral part of the Odyssey is its powerful onboard computer, which is based upon a commercial 68030 processor board. This computer executes a control program based upon a flexible high-level behavioral language developed at MIT, and supports vehicle control in a wide range of conditions and mission profiles. New mission profiles are quickly configured, tested (via a simulator developed by the Charles Stark Draper Laboratory) and entered into the computer's library. A sophisticated acoustic modem (developed by the Woods Hole Oceanographic Institute) is an integral part of the system and is used to support reliable two-way digital communications. A large fraction of Odyssey's internal volume is available for mission sensors. Odyssey is a mature technology which has been successfully deployed and operated in many types of ocean environments, including the arctic.

Concept Demonstration

In FY-95, under ONR sponsorship, SSC San Diego developed an optical docking sensor with processor for installation on SSC San Diego's dedicated Odyssey vehicle (provided by SeaGrant). In March-April 1996 the system was employed in month-long AOSN vehicle docking experiments at Buzzard's Bay, MA, to demonstrate autonomous underwater docking. The ability to reliably mate with an underwater docking station provides the capability for Odyssey to recharge its internal silver-zinc batteries, giving the vehicle greatly extended endurance without requiring its return to the surface. Docking also provides an opportunity for mass data transfer to occur, permitting the vehicle to carry sensors which generate megabytes or gigabytes of data which would be impractical to transmit acoustically.

SSC San Diego was one of three participating groups bringing Odyssey vehicles outfitted with docking sensors to the test. The DSSN (SSC San Diego) approach was based upon optical guidance, the EDC (North Carolina State) system upon magnetic guidance and the Wood's Hole system employed acoustic guidance. The SSC San Diego docking system performed well in the Buzzard's Bay experiment.

Conclusion

Autonomous surveillance systems have the potential to go well beyond the capabilities of existing and even planned surveillance systems if the current paradigm can be superseded. By employing a swarm of small AUV's communicating with each other it is possible to form a distributed sensor (and possibly an effector) network. In this manner it is possible to introduce mobility, dynamic adaptability, redundancy and mutual assistance into the paradigm. Additionally, the mix of AUV's and their payloads becomes a system parameter which can be optimized for prosecution of specific missions. Finally, with cost/performance-ratio anticipated to be the overriding figure of merit for future Navy systems, numerous small, mass-produced AUV's have an inherent advantage over a few large, expensive (and, per unit, more capable) AUV's in many, if not most, mission scenarios. This is particularly true when the large AUV has become so capable (and therefore so expensive) that it is too valuable an asset to be put at-risk, and therefore may not be used in many missions.

The Navy will benefit from surveillance system architectures which intelligently exploit what are expected to remain the US primary commercial technology thrusts for the next decade or more: microelectronics, networking (both signal processing and data communications), robotics and automated mass-production. This is so because such systems are likely to be cost-effective to build and operate. The DSSN architecture is optimally positioned to take full advantage of commercial trends because of its distributed, modular nature and its adaptability to new technologies and economy of scale.

Agile-Link™ Wireless Data Acquisition System

2008-07-27T20:54:00.000-07:00

Description

This system allows for simultaneous data collection from multiple sensing nodes, including MicroStrain's G-Link®, V-Link®, and SG-Link® wireless sensors.

MicroStrain® announces the Agile-Link™ product family. Agile-Link™ is a wireless data acquisition system capable of simultaneous, high-speed data acquisition, for use with wireless strain gauges, accelerometers, temperature, and millivolt level inputs.

Applications

Structural monitoring, smart structures and smart materials

Vibration and acoustic noise testing

Assembly line testing with "smart packaging"

Sports performance and sports medicine analysis

Distributed security and machine health monitoring networks

How it works

MicroStrain, Inc. has developed "frequency agile" sensor transceiver nodes and base stations, which can use a wide range of RF communications frequencies through software configuration. This technique, termed frequency division multiplexing (FDM), allows multiple wireless sensing nodes to communicate simultaneously without RF interference between them.

Test and measurement applications often require the combination of wireless strain measurement systems to be used alongside hard-wired sensors, all connected to an existing analog data acquisition system. In order to easily support these applications, MicroStrain has released Agile-Link™ analog output base stations that collect analog data from multi-channel Agile-Link™ sensor nodes and then reconstructs the analog waveforms on the base station's outputs.

To facilitate the use of wireless strain gauges, MicroStrain® has released a PC based Agile-Link™ software package for Windows 95/98/2000/XP machines. In addition, a new Strain Wizard® plug-in for Agile-Link™ supports wireless automatic offset balancing, wireless gain adjustment, and wireless shunt calibration. The Strain Wizard® is important for stress analysis because it allows the end user to convert from bits out to physical units of strain.

Specifications for Agile-Link™ wireless strain sensing nodes used with 1000 ohm foil strain gauges.

Data Storage Capacity

2 megabytes (approximately 1,000,000 data points)

Data Logging Mode

Log up to 1,000,000 data points (from 100 to 65,500 samples or continuous) at 32 Hz to 2048 Hz

Sensor event driven trigger

Commence datalogging when threshold exceeded

Real-time streaming mode

Transmit real time data from node to PC - rate depends on number of active channels: 1 channel - 4 KHz, 2 channels - 2 KHz, 3 channels - 1.33 KHz, 4 channels - 1 KHz, 5 channels - 800 Hz, 6 channels - 666 Hz, 7 channels - 570 Hz, 8 channels - 500 Hz

Low duty-cycle mode

Supports multiple nodes on single RF channel, total update bandwidth of 500 Hz divided by number of nodes

Radio Frequency (RF) Transceiver Carrier

2.4 GHz, direct sequence spread spectrum, license free worldwide (2.450 to 2.490 GHz -16 channels)

RF Data Packet Standard

IEEE 802.15.4, open communication architecture

RF Programming & Downloading

8 minutes to download full memory

Range for Bi-directional RF Link

70 m line-of-sight, up to 300 m with optional high gain antenna

Operating System

Windows XP® Compatible

PC Comm

Serial port, 115.2 kBaud

Sensor Specifications

SG-Link®, G-Link®, V-Link®

Sensor nerwork Simulator and Emulator

2008-07-21T03:57:00.000-07:00

The Necessity of Network Simulation

The emergence of wireless sensor networks brought many open issues to network designers. Traditionally, the three main techniques for analyzing the performance of wired and wireless networks are analytical methods, computer simulation, and physical measurement. However, because of many constraints imposed on sensor networks, such as energy limitation, decentralized collaboration and fault tolerance, algorithms for sensor networks tend to be quite complex and usually defy analytical methods that have been proved to be fairly effective for traditional networks. Furthermore, few sensor networks have come into existence, for there are still many unsolved research problems, so measurement is virtually impossible. It appears that simulation is the only feasible approach to the quantitative analysis of sensor networks.

Why a New Simulator

ns2, perhaps the most widely used network simulator, has been extended to include some basic facilities to simulate sensor networks. However, one of the problems of ns2 is its object-oriented design that introduces much unnecessary interdependency between modules. Such interdependency sometimes makes the addition of new protocol models extremely difficult, only mastered by those who have intimate familiarity with the simulator. Being difficult to extend is not a major problem for simulators targeted at traditional networks, for there the set of popular protocols is relatively small. For example, Ethernet is widely used for wired LAN, IEEE 802.11 for wireless LAN, TCP for reliable transmission over unreliable media. For sensor networks, however, the situation is quite different. There are no such dominant protocols or algorithms and there will unlikely be any, because a sensor network is often tailored for a particular application with specific features, and it is unlikely that a single algorithm can always be the optimal one under various circumstances.

Many other publicly available network simulators, such as JavaSim, SSFNet, Glomosim and its descendant Qualnet, attempted to address problems that were left unsolved by ns2. Among them, JavaSim developers realized the drawback of object-oriented design and tried to attack this problem by building a component-oriented architecture. However, they chose Java as the simulation language, inevitably sacrificing the efficiency of the simulation. SSFNet and Glomosim designers were more concerned about parallel simulation, with the latter more focused on wireless networks. They are not superior to ns2 in terms of design and extensibility.

Features of SENSE

SENSE is designed to be an efficient and powerful sensor network simulator that is also easy of use. We identify the three most critical factors as:

Extensibility: The enabling force behind the fully extensibility network simulation architecture is our progress on component-based simulation. We introduced a component-port model that frees simulation models from interdependency usually found in an object-oriented architecture, and then proposed a simulation component classification that naturally solves the problem of handling simulated time. The component-port model makes simulation models extensible: a new component can replace an old one if they have compatible interfaces, and inheritance is not required. The simulation component classification makes simulation engines extensible: advanced users have the freedom to develop new simulation engines that meet their needs.
Reusability: The removal of interdependency between models also promotes reusability. A component developed for one simulation can be used in another if it satisfies the latter's requirements on the interface and semantics. There is another level of reusability made possible by the extensive use of C++ template: a component is usually declared as a template class so that it can handle different type of data.
Scalability: Unlike many parallel network simulators, especially SSFNet and Glomosim, parallelization is provided as an option to the users of SENSE. The reflects our belief that completely automated parallelization of sequential discrete event models, however tempting it may seem, is impossible, just as automated parallelization of sequential programs. Even if it possible, it is doomed to be inefficient. Therefore, parallelizable models require more effort than sequential models, but a good portion of users are not interested in parallel simulation at all. In SENSE, a parallel simulation engine can only execute components of compatible components. If a user is content with the default sequential simulation engine, then every component in the model repository can be reused.

Currently Available Components and Simulation Engines (as of Oct 21, 2006)

Battery Model:
- Linear Battery
- Discharge Rate Dependent and/or Relaxation Battery
Application Layer:
- Random Neighbor
- Constant Bit Rate
Network Layer:
- Simple Flooding
- A simplified version of ADOV without route repairing
- A simplified version of DSR without route repairing
- Self Selective Routing (SSR)
- Self Healing Routing (SHR)
MAC Layer:
- NullMAC
- IEEE 802.11 with DCF
Physical Layer: Duplex Transceiver
Wireless Channel:
- Free Space
- Adjacency Matrix
Simulation Engine: CostSimEng (sequential)

Sensor Networks

2008-07-08T19:48:00.001-07:00

Media Access Control

Media access in sensor networks should be energy efficient and should also allocate bandwidth fairly to the infrastructure of all the nodes. They have little or no dedicated carrier sensing or collision detection and they have no specific protocol stacks which could specify the design of their media access protocol.

1 Alec Woo, David E. Culler A transmission control scheme for media access in sensor networks, Proceedings of the seventh annual international conference on Mobile computing and networking, July 2001

In this, the authors have proposed a solution to achieve fair allocation of bandwidth by controlling the originating data at a node when the traffic being routed through the node is high and controlling route-thru traffic when the originating data at a node is high. They alsopropose desynchronizing neighbouring nodes so as to avoid collisions.

2. Wei Ye, John Heidemann and Deborah Estrin An Energy-Efficient MAC Protocol for Wireless Sensor Networks, In Proceedings of the 21st International Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2002), New York, NY, USA, June, 2002.

The authors look at overcoming major sources of energy wastage namely collisions, overhearing, control packet overhead and idle listening. For this, they propose synchronized listen and sleep periods to avoid idle listening and heavy control overhead and a contention based scheme to avoid collisions and overhearing.

Multipath Routing

The resilience of a protocol is measured by the likelihood that an alternate path exists between a source and a sink when the primary path fails. This can be increased by having multiple paths between the source and the sink but energy is consumed while keeping these alternate paths alive by sending periodic messages.So the resileince of the the network should be increased while keeping the maintenance overhead ofthese paths low.

1.Deepak Ganesan, Ramesh Govindan, Scott Shenker and Deborah Estrin Highly-Resilient, Energy-Efficient Multipath Routing in Wireless Sensor Networks ACM Mobile Computing and Communications Review, Vol. 5, No. 4, October 2001.

The authors propose use of braided multipaths instead of completely disjoint multipaths so as to keep the cost of maintaining themultipaths low. The costs of such alternate paths are also comparable to the primary path because they tend to be much closer to the primary path.

2. J.-H. Chang and L. Tassiulas, Maximum Lifetime Routing in Wireless Sensor Networks, Proc. Advanced Telecommunications and Information Distribution Research Program (ATIRP2000), College Park, MD, Mar. 2000.

The authors propose an algorithm which will route data through a path whose nodes have the largest residual energy. In this way, the nodes in the primary path will not deplete their energy resources through continual use of the same route thus achieving longer life.

3. Rahul C. Shah and Jan Rabaey, Energy Aware Routing for Low Energy Ad Hoc Sensor Networks IEEE Wireless Communications and Networking Conference (WCNC), March 17-21, 2002, Orlando, FL.

The authors propose use of a set of sub-optimal paths occasionally to increase the lifetime of the network. These paths are chosen by means of a probability which depends on how low the energy consumption of each path is.

4. Qun Li and Javed Aslam and Daniela Rus. Hierarchical Power-aware Routing in Sensor Networks In Proceedings of the DIMACS Workshop on Pervasive Networking, May, 2001

The path with the largest residual energy when used to route data in a network, may be very energy-expensive too. So, there is a tradeoff between minimizing the total power consumed and the residual energy of the network. The authors propose an algorithm in which the residual energy of the route is relaxed a bit to pick a more energy efficient path.

Hierarchy Based Routing

1.Qun Li and Javed Aslam and Daniela Rus. Hierarchical Power-aware Routing in Sensor Networks In Proceedings of the DIMACS Workshop on Pervasive Networking, May, 2001.

Groups of sensors in geographic proximity are clustered together as a zone and each zone is treated as an entity. Each zone is allowed to decide how it will route a message across.

2. Wendi Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan,Energy-Efficient Communication Protocols for Wireless Microsensor Networks, Proc. Hawaaian Intl Conf. on Systems Science, January 2000.

The authors propose LEACH (Low Energy Adaptive Clustering Hierarchy) in which clusters have a moving cluster head so that the energy consumption is distributed more equally among all the nodes of the network and thereby achieve graceful degradation. Different nodes become the cluster head in a cluster in different rounds.

Query based routing

In this, the destination nodes propogate a query for data(sensing task) from a node through the network and a node having this data sends the data which matches the query when it receives the query. All the nodes have tables consisting of the sensing tasks queries that it receives and send data which matches these tasks when they receive it.

1.David Braginsky and Deborah Estrin Rumor Routing Algorithm For Sensor Networks Under submission to International Conference on Distributed Computing Systems (ICDCS-22), November 2001.

2. Chalermek Intanagonwiwat, Ramesh Govindan and Deborah Estrin . Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks In Proceedings of the Sixth Annual International Conference on Mobile Computing and Networks (MobiCOM 2000), August 2000, Boston, Massachusetts.

Negotiation based protocols

These protocols use high level data descriptors for to eliminate redundant data transmissions through negotiation. Communication decisions are also taken based on the resources that are available to them.

1.Wendi Rabiner Heinzelman ,Joanna Kulik , Hari Balakrishnan Adaptive protocols for information dissemination in wireless sensor networks Proceedings of the fifth annual ACM/IEEE international conference on Mobile computing and networking, August 1999

2 Joanna Kulik , Wendi Heinzelman , Hari Balakrishnan . Negotiation-based protocols for disseminating information in wireless sensor networks Wireless Networks March 2002

Surveys

1. Elizabeth M. Royer, Chai-Keong Toh, A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks, IEEE Personal Communications, Vol. 6, No. 2, pp. 46-55, April 1999.

2. Praveen Rentala, Ravi Musunnuri, Shashidhar Gandham, Udit Saxena, Survey on Sensor Networks

Others

1. Suresh Singh , Mike Woo , C. S. Raghavendra Power-aware routing in mobile ad hoc networks Proceedings of the fourth annual ACM/IEEE international conference on Mobile computing and networking, October 1998

2. J.-H. Chang and L. Tassiulas, "Routing for maximum system lifetime in wireless ad-hoc networks," Proceedings of 37-th Annual Allerton Conference on Communication, Control,and Computing, Monticello, IL, Sept. 1999.

Open Loop Current Sensors

2008-07-03T23:12:00.000-07:00

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New Wireless Sensor Network Keeps Tabs On The Environment

2008-06-09T22:20:00.000-07:00

Research in the University of Alberta's Faculty of Science may soon be able to answer that question. The departments of computing science and earth and atmospheric science have been working together to create a Wireless Sensor Network that allows for the clandestine data collection of environmental factors in remote locations and its monitoring from anywhere in the world where the Internet is available.

The research team, including Pawel Gburzynski, Mario Nascimento, and Arturo Sanchez-Azofeifa, recently launched EcoNet, a functional model of a WSN for environmental monitoring in the display house in the University of Alberta's Agriculture/Forestry Centre. The display house hosts a small but feature-rich environment that mimics that of a tropical forest. Using a WSN, a number of sensors can continuously monitor factors like temperature and luminosity and will process, store and transmit data co-operatively and wirelessly with other sensors to generate data that can then be collected and made available to users virtually anywhere on the globe. The sensors represent a technology for researchers to monitor diverse phenomena continuously and inconspicuously.

Having the data continuously monitored by researchers substantially increases the chances of uncovering anomalies early enough to investigate them promptly and thoroughly.

The overall framework of WSN can also be extended for use in other closely related scenarios such as monitoring potentially dangerous situations like hazardous waste disposal, or hard-to-witness phenomena such as ice cap movements in the Arctic.

The opportunities these sensors will provide to scientists are paramount in a global environment that is changing at an ever-increasing pace.

Once the display-house prototype is tested and customized, at least two sites are to be fully deployed in the fall, one likely in the Brazilian rainforest, and the other in a forest in Panama.

The project has been made possible by close collaboration with Olsonet Communications Corporation in Ottawa, which has implemented the WSN nodes and supported the project since its inception. Support of this project has also been provided by the University of Alberta, U of A's Centre of Earth Observation Sciences and National Science Engineering Research Council.

SOURCE: University of Alberta

Wireless Ad Hoc Sensor Networks

2008-06-03T23:28:00.000-07:00

A wireless ad hoc sensor network consists of a number of sensors spread across a geographical area. Each sensor has wireless communication capability and some level of intelligence for signal processing and networking of the data. Some examples of wireless ad hoc sensor networks are the following:

Military sensor networks to detect and gain as much information as possible about enemy movements, explosions, and other phenomena of interest.

Sensor networks to detect and characterize Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) attacks and material.

Sensor networks to detect and monitor environmental changes in plains, forests, oceans, etc.

Wireless traffic sensor networks to monitor vehicle traffic on highways or in congested parts of a city.

Wireless surveillance sensor networks for providing security in shopping malls, parking garages, and other facilities.

Wireless parking lot sensor networks to determine which spots are occupied and which are free.

The above list suggests that wireless ad hoc sensor networks offer certain capabilities and enhancements in operational efficiency in civilian applications as well as assist in the national effort to increase alertness to potential terrorist threats.

Two ways to classify wireless ad hoc sensor networks are whether or not the nodes are individually addressable, and whether the data in the network is aggregated. The sensor nodes in a parking lot network should be individually addressable, so that one can determine the locations of all the free spaces. This application shows that it may be necessary to broadcast a message to all the nodes in the network. If one wants to determine the temperature in a corner of a room, then addressability may not be so important. Any node in the given region can respond. The ability of the sensor network to aggregate the data collected can greatly reduce the number of messages that need to be transmitted across the network. This function of data fusion is discussed more below.

The basic goals of a wireless ad hoc sensor network generally depend upon the application, but the following tasks are common to many networks:

Determine the value of some parameter at a given location: In an environmental network, one might one to know the temperature, atmospheric pressure, amount of sunlight, and the relative humidity at a number of locations. This example shows that a given sensor node may be connected to different types of sensors, each with a different sampling rate and range of allowed values.

Detect the occurrence of events of interest and estimate parameters of the detected event or events: In the traffic sensor network, one would like to detect a vehicle moving through an intersection and estimate the speed and direction of the vehicle.

Classify a detected object: Is a vehicle in a traffic sensor network a car, a mini-van, a light truck, a bus, etc.

Track an object: In a military sensor network, track an enemy tank as it moves through the geographic area covered by the network.

In these four tasks, an important requirement of the sensor network is that the required data be disseminated to the proper end users. In some cases, there are fairly strict time requirements on this communication. For example, the detection of an intruder in a surveillance network should be immediately communicated to the police so that action can be taken.

Wireless ad hoc sensor network requirements include the following:

Large number of (mostly stationary) sensors: Aside from the deployment of sensors on the ocean surface or the use of mobile, unmanned, robotic sensors in military operations, most nodes in a smart sensor network are stationary. Networks of 10,000 or even 100,000 nodes are envisioned, so scalability is a major issue.

Low energy use: Since in many applications the sensor nodes will be placed in a remote area, service of a node may not be possible. In this case, the lifetime of a node may be determined by the battery life, thereby requiring the minimization of energy expenditure.

Network self-organization: Given the large number of nodes and their potential placement in hostile locations, it is essential that the network be able to self-organize; manual configuration is not feasible. Moreover, nodes may fail (either from lack of energy or from physical destruction), and new nodes may join the network. Therefore, the network must be able to periodically reconfigure itself so that it can continue to function. Individual nodes may become disconnected from the rest of the network, but a high degree of connectivity must be maintained.

Collaborative signal processing: Yet another factor that distinguishes these networks from MANETs is that the end goal is detection/estimation of some events of interest, and not just communications. To improve the detection/estimation performance, it is often quite useful to fuse data from multiple sensors. This data fusion requires the transmission of data and control messages, and so it may put constraints on the network architecture.

Querying ability: A user may want to query an individual node or a group of nodes for information collected in the region. Depending on the amount of data fusion performed, it may not be feasible to transmit a large amount of the data across the network. Instead, various local sink nodes will collect the data from a given area and create summary messages. A query may be directed to the sink node nearest to the desired location.

Sensor types and system architecture:
With the coming availability of low cost, short range radios along with advances in wireless networking, it is expected that wireless ad hoc sensor networks will become commonly deployed. In these networks, each node may be equipped with a variety of sensors, such as acoustic, seismic, infrared, still/motion videocamera, etc. These nodes may be organized in clusters such that a locally occurring event can be detected by most of, if not all, the nodes in a cluster. Each node may have sufficient processing power to make a decision, and it will be able to broadcast this decision to the other nodes in the cluster. One node may act as the cluster master, and it may also contain a longer range radio using a protocol such as IEEE 802.11 or Bluetooth.

Wireless Sensor Networks

2008-04-25T03:14:00.000-07:00

What are sensor networks?

Wireless Ad hoc and Sensor Networks (WASNs) are most commonly referred to as wireless interconnections of a large number of sensor nodes, communicating without any pre-existing infrastructure. WASN technologies will have huge influence on many civilian and military applications, including for example national security, transportation systems, health care and environmental monitoring.

INTRODUCTION

Smart environments represent the next evolutionary development step in building, utilities, industrial, home, shipboard, and transportation systems automation. Like any sentient organism, the smart environment relies first and foremost on sensory data from the real world. Sensory data comes from multiple sensors of different modalities in distributed locations. The smart environment needs information about its surroundings as well as about its internal workings; this is captured in biological systems by the distinction between exteroceptors and proprioceptors. PDABSC(Base Station Controller, Preprocessing)BSTWirelessSensorMachine MonitoringMedical MonitoringWireless SensorWirelessData Collection NetworksWireless(Wi-Fi 802.11 2.4GHzBlueToothCellular Network, -CDMA, GSM)PrinterWireland(Ethernet WLAN, Optical)Animal MonitoringVehicle MonitoringOnlinemonitoringServertransmitterAny where, any time to accessNotebookCellular PhonePCShip MonitoringWireless Sensor NetworksRovingHumanmonitorData Distribution NetworkManagement Center(Database large storage, analysis)Data Acquisition Network
The challenges in the hierarchy of: detecting the relevant quantities, monitoring and collecting the data, assessing and evaluating the information, formulating meaningful user displays, and performing decision-making and alarm functions are enormous. The information needed by smart environments is provided by Distributed Wireless Sensor Networks, which are responsible for sensing as well as for the first stages of the processing hierarchy. The importance of sensor networks is highlighted by the number of recent funding initiatives, including the DARPA SENSIT program, military programs, and NSF Program Announcements.
The figure shows the complexity of wireless sensor networks, which generally consist of a data acquisition network and a data distribution network, monitored and controlled by a management center. The plethora of available technologies makes even the selection ofcomponents difficult, let alone the design of a consistent, reliable, robust overall system.
The study of wireless sensor networks is challenging in that it requires an enormous breadth of knowledge from an enormous variety of disciplines. In this chapter we outline communication networks, wireless sensor networks and smart sensors, physical transduction principles, commercially available wireless sensor systems, self-organization, signal processing and decision-making, and finally some concepts for home automation.

COMMUNICATION NETWORKS
The study of communication networks can encompass several years at the college or university level. To understand and be able to implement sensor networks, however, several basic primary concepts are sufficient.

Network Topology
The basic issue in communication networks is the transmission of messages to achieve a prescribed message throughput (Quantity of Service) and Quality of Service (QoS). QoS can be specified in terms of message delay, message due dates, bit error rates, packet loss, economic cost of transmission, transmission power, etc. Depending on QoS, the installation environment, economic considerations, and the application, one of several basic network topologies may be used.
A communication network is composed of nodes, each of which has computing power and can transmit and receive messages over communication links, wireless or cabled. The basic network topologies are shown in the figure and include fully connected, mesh, star, ring, tree, bus. A single network may consist of several interconnected subnets of different topologies. Networks are further classified as Local Area Networks (LAN), e.g. inside one building, or Wide Area Networks (WAN), e.g. between buildings.
Bus
Ring
Star
Mesh
Fully Connected
Tree
Basic Network Topologies
Fully connected networks suffer from problems of NP-complexity [Garey 1979]; as additional nodes are added, the number of links increases exponentially. Therefore, for large networks, the routing problem is computationally intractable even with the availability of large amounts of computing power.
Mesh networks are regularly distributed networks that generally allow transmission only to a node’s nearest neighbors. The nodes in these networks are generally identical, so that mesh nets are also referred to as peer-to-peer (see below) nets. Mesh nets can be good models for large-scale networks of wireless sensors that are distributed over a geographic region, e.g. personnel or vehicle security surveillance systems. Note that the regular structure reflects the communications topology; the actual geographic distribution of the nodes need not be a regular mesh. Since there are generally multiple routing paths between nodes, these nets are robust to failure of individual nodes or links. An advantage of mesh nets is that, although all nodes may be identical and have the same computing and transmission capabilities, certain nodes can be designated as ‘group leaders’ that take on additional functions. If a group leader is disabled, another node can then take over these duties.
All nodes of the star topology are connected to a single hub node. The hub requires greater message handling, routing, and decision-making capabilities than the other nodes. If a communication link is cut, it only affects one node. However, if the hub is incapacitated the network is destroyed. In the ring topology all nodes perform the same function and there is no leader node. Messages generally travel around the ring in a single direction.