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Embedded Systems for Wireless Sensor Network. Rabi Mahapatra. Background. Advancement of integration between “tiny embedded processors, wireless interfaces, and “micro-sensors” based on MEMS led to emergence of wireless sensor network .

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Embedded Systems for Wireless Sensor Network


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    1. Embedded Systems for Wireless Sensor Network Rabi Mahapatra

    2. Background • Advancement of integration between “tiny embedded processors, wireless interfaces, and “micro-sensors” based on MEMS led to emergence of wireless sensor network. • Characterized by their ability to monitor the physical environment through ad-hoc deployment of numerous tiny, intelligent, wirelessly networked sensor nodes. Mahapatra-Texas A&M-Spring'07

    3. What is Wireless Sensor Networks • Large number of heterogeneous sensor devices • Ad Hoc Network • complex sensor nodes with • communication, processing, storage capabilities Mahapatra-Texas A&M-Spring'07

    4. Emerging applications • Indoor Settings: condition based maintenance of equipment in factory • Outdoor environment: • Monitor natural habitats • Remote ecosystems • Forest fires • Disaster sites • Defense armaments • Spy microsats Mahapatra-Texas A&M-Spring'07

    5. Challenges of WSN • Requirements: small size, large number, tetherless and low cost. Hence constrained by • Energy, computation and communication • Small form factors => prohibits large long lasting batteries • Cost & energy => low power processors, small radios with minimum bandwidth & small transmission ranges. • Ad-hoc deployment => no maintenance and battery replacement • Increase NW lifetime => No raw data to gateway for computation Mahapatra-Texas A&M-Spring'07

    6. Topics to be discussed • Simulation tools on WSN • A simulation architecture overview • Sensor node model & framework of SN • Battery model • Case studies • Bonus points Mahapatra-Texas A&M-Spring'07

    7. Existing Simulators JavaSim: • Pros • Very modular • Easy to use • Cons • Geared for wired inter-networks • No wireless support, not efficient due to overhead SSFNet: a parallel simulator for wireless GlomoSim: • Specific for mobile wireless networks. • Built as a set of libraries. The libraries are built in Parsec( a C-based discrete event simulation language). • Layered architecture with easy plug-in capability. SSFNet and Glomosim are not better than NS-2 in terms of design and extensibility. Mahapatra-Texas A&M-Spring'07

    8. Existing Simulators • NS-2: De facto standard for network simulations • Does support wireless simulations • A primitive energy model is present. • Object oriented design and Lots of documentation. • Uses Tcl to specify the Components, and Otcl to glue them together. Cons: • Difficult to use and learn • Interdependency among modules pose difficult to implement new protocols. • Originally built for wired networks, later extended for wireless. • Supposedly, does not work well for large topologies. Mahapatra-Texas A&M-Spring'07

    9. More Sensor Network Simulator • WSNS • Based on Low Energy Adaptive Clustering Hierarchy (LEACH) protocol developed by Dr. Wendi Heinzelman • Has included Network Preserving Protocol (NPP) for better performance along with LEACH • Not completed for robustness Mahapatra-Texas A&M-Spring'07

    10. Sensor Network Simulator SENSE (www.cs.rpi.edu/~cheng3/sense/) • Component Features: (2004) • Battery Model: Linear Battery, Discharge Rate Dependent and/or Relaxation Battery • Application Layer : Random Neighbor; Constant Bit Rate • Network Layer: Simple Flooding; a simplified verion of ADOV without route repairing, a simplified version of DSR without route repairing • MAC Layer: NullMAC; IEEE 802.11 • Physical Layer: Duplex Transceiver; Wireless Channel • Simulation Engine: CostSimEng (sequential) Mahapatra-Texas A&M-Spring'07

    11. Status of simulators • Other simulator: OpNET • All these tools are not equipped to capture all the aspects of interests in sensor networks. Mahapatra-Texas A&M-Spring'07

    12. Simulator: SensorSim from UCLA • Extension to NS - 2. • Provides battery models, radio propagation models and sensor channel models. • Provides a lightweight protocol stack. • Has support for hybrid simulation. • Must be integrated with NS - 2. Mahapatra-Texas A&M-Spring'07

    13. SensorSim Architecture app monitor and control hybrid network (local or remote) real sensor apps on virtual sensor nodes app GUI app socket comm serial comm ns GUI Interface HS Interface RS232 Ethernet gateway V R V V Gateway Machine V R modified event scheduler Proxies for real sensor nodes Mahapatra-Texas A&M-Spring'07 Simulation Machine

    14. SensorSim Architecture Overview • Sensor NW has three types of nodes: • Sensor nodes: monitor immediate environment, with many transducers • Target nodes: generates various stimuli for sensor nodes • User nodes: client and administration of sensor network • Separate channels: • Sensor channels: communication among sensor nodes and target • Network channels: to user node or gateways and onward transmission to other network. • Concurrent transmission possible • Easier to model complex behavior of sensor nodes, reaction to multiple sensor signals. Mahapatra-Texas A&M-Spring'07

    15. Sensor Network Model architecture Target Sensor channel sensor sensor sensor Wireless channel user Mahapatra-Texas A&M-Spring'07

    16. SensorSim Model • Sensor node => one wireless NW protocol stack, one or more sensor stack corresponds to as many transducers • Sensor stack detects stimuli, process it and forward them to application layer, which in turn process and send them to user node through wireless channel • A power model corresponding to energy producing-consuming hardware components is also provided. These component can stay at different power saving and performance states. • The algorithm in both the stacks control the mode of power states of hardware components. Also, performance of the algorithm depends on the mode. Mahapatra-Texas A&M-Spring'07

    17. Sensor Node Model in SensorSim Battery Model Node Function Model Sensor Node Applications Power Model (Energy Consumers and Providers) Middleware State Change Sensor Stack 1 Network Protocol Stack Radio Model Sensor Protocol Stack Sensor Layer Network Layer Sensor Layer CPU Model Physical Layer Status Check MAC Layer Physical Layer Sensor #1 Model Physical Layer Sensor #2 Model Wireless Channel Sensor Channel 1 Sensor Channel 2 Mahapatra-Texas A&M-Spring'07

    18. User Node Target Node User Application Target Application Network Protocol Stack Sensor Stack Sensor Layer Network Layer MAC Layer Physical Layer Physical Layer Sensor channel Wireless Channel Mahapatra-Texas A&M-Spring'07

    19. Framework of Sensor Network Simulation • Node Placement & traffic generation • Performance of WSN is affected when topology of node distribution changes • Application requires a typical distribution (uniform for forest fire, Gaussian for perimeter defense) • Three types of traffics: user-to-sensor (command & queries), sensor-to-user (sensor reporting to user) and sensor-to-sensor (collaborative signal processing before reporting) Mahapatra-Texas A&M-Spring'07

    20. Sensor Stack & Channel • Sensor stack is a signal sink that is responsible for triggering the application layer every time a sensing event occurs • Simple sensing scheme to elaborate signal processing can be implemented on sensor stack • Sensor stack acts as a signal source in Target Node and contains signatures unique to the model • Sensor channel model the medium of signal transmission (e.g. ground to carry seismic events). • A good simulation tool should model varieties of mediums and type of sensors ( acoustic, infra red, ultrasonic) Mahapatra-Texas A&M-Spring'07

    21. Battery Model • Goal: increase the battery life time • Need to study how different aspects of real battery behavior can affect the energy efficiency of applications • T = C/I, C is capacity in Ah. I is discharge current • Linear Model: • Linear storage of current. Assumes the maximum capacity is unaffected by discharge rate. • Allows user to see efficiency of user application by providing how much capacity is consumed. The remaining capacity after tdcan be expressed as C = C’ -  I(t)dt integral taken over period t = 0 to td • It assumes that the I(t) will stay same during the period, if operation mode does not change (radio switching from Tx toRx) • Remaining capacity is computed when discharge rate is changed Mahapatra-Texas A&M-Spring'07

    22. Battery Model • Discharge rate dependent model: • Considers the effect of battery discharge rate on maximum capacity • Battery capacity efficiency factor K is introduced. K = Ceff /Cmax • Capacity C = K.C’ – I . Td • K varies with current I and is close to 1 when discharge rate is low and approaches 0 when discharge rate is high. A Popular Battery Model: Dual Foil from UC Berkeley. Mahapatra-Texas A&M-Spring'07

    23. Battery Model • Relaxation Model: • Real-life battery exhibit a phenomenon called “relaxation”.(Fuller 94, Linden 95, Chiasserini 99) • When battery discharge rate is high, diffusion rate of active ingredients through the electrode & electrolyte falls behind. If high discharge is sustained, the battery reaches its end even if the active materials are still available. • However, if discharge current is either cut-off or reduced during the discharge, active materials catches up with depletion of the materials. It gives battery to recover the capacity lost at high discharge rate. • An analytical model has been used for SensorSim Mahapatra-Texas A&M-Spring'07

    24. Case Studies • Low rate/low power vs. high rate/ high power (Fig. 7 in the reference) • Monitoring a moving vehicle in a sensor field • Study the effect of traffic on the sensing and communication traffic and evaluate the power management. Mahapatra-Texas A&M-Spring'07

    25. Power Management Model BZR event Off BZR event BZR event BZR event Off BZR event Transmit transmit Receive Transmit BZR event Receive transmit done receive done Idle transmit transmit receive done Idle timeout receive transmit done timeout(3 sec) Sleep With Power Management Without Power Management Mahapatra-Texas A&M-Spring'07

    26. Some Important Studies • Utility-based decision-making in WSN. • Upper bound on network life-time • Impact of mobility on capacity and life-time • Coverage and Density • Criticality threshold, scalability, integration etc. • Security • Ease of Deployment • Synchronization Mahapatra-Texas A&M-Spring'07

    27. Conclusion We have looked at some of the issues with SN and discussed the Sensor Network model developed at UCLA. • Assignments: • Read the reference papers and look for more titles • Prepare a bibliography on each topic mentioned in the previous page. • Consider one topic as assigned to you in the class and read apex papers on that topic. • Summarize and comment on the contributions and shortcomings. • Due Tuesday morning by e-mail with file name as (your name-sensor.doc) Mahapatra-Texas A&M-Spring'07

    28. Reference • Sung Park, A savvides & M B Srivastava, “Simulating Networks of Wireless Sensors”, Proceedings of the 2001 Winter Simulation Conference. Mahapatra-Texas A&M-Spring'07