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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

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

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

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

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

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

CS600B Hall-Effect Current Sensor Series Features:Insulation between input and output Low warm-up drift, broad bandwidth Competi... Applications:Overload Protection Current Monitoring of Electric Welders UPS Switch...

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CS600N Hall-Effect Current Sensor Series Features:Interference resistant Convenient PCB installation Small, low energy consumpti... Applications:AC Frequency Conversion, Servo Motors Current Monitoring of Electric We...