| 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.
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Sunday, July 27, 2008
Agile-Link™ Wireless Data Acquisition System
Monday, July 21, 2008
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)
Tuesday, July 8, 2008
Sensor Networks
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.
Thursday, July 3, 2008
Open Loop Current Sensors
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