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Wireless Sensor Networks

Wireless Sensor Networks. MOBICOM 2002 Tutorial (Deborah Estrin, Mani Srivastava, Akbar Sayeed). 2006.11.01 Young Myoung,Kang (INC lab) (ymkang@popeye.snu.ac.kr). Contents. Part I : Introduction Part II : Sensor Node Platforms & Energy Issues

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Wireless Sensor Networks

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  1. Wireless Sensor Networks MOBICOM 2002 Tutorial (Deborah Estrin, Mani Srivastava, Akbar Sayeed) 2006.11.01 Young Myoung,Kang (INC lab) (ymkang@popeye.snu.ac.kr)

  2. Contents • Part I : Introduction • Part II : Sensor Node Platforms & Energy Issues • Part III: Time & Space Problems in Sensor Networks • Part IV: Sensor Network Protocols • Part V : Collaborative Signal Processing SNU INC lab.

  3. Part IVSensor Network Protocols SNU INC lab.

  4. Introduction • WSN protocols • Primary theme • long-lived • massively-distributed • Minimize duty cycle and communication • Adaptive MAC • Adaptive Topology • Routing SNU INC lab.

  5. MAC in Sensor Nets • Important attributes of MAC protocols • Energy efficiency • Collision avoidance • Scalability in node density • Latency • Fairness • Throughput • Bandwidth utilization SNU INC lab.

  6. Identifying the Energy Consumers • Major source of energy waste • Idle listening when no sensing events • Collisions • Control overhead • Overhearing SNU INC lab.

  7. sleep listen listen sleep Sensor-MAC(SMAC) • Major components of S-MAC • Periodic listen and sleep • Collision avoidance • Overhearing avoidance • Message passing • Periodic listen and sleep • Turn off radio when sleeping • Reduce duty cycle to ~10% (200 ms on/2s off) • Increased latency for reduced energy SNU INC lab.

  8. SMAC - Collision Avoidance • Collision Avoidance • Problem: • Multiple senders want to talk • Solution: Similar to IEEE 802.11 ad hoc mode (DCF) • Physical and virtual carrier sense • Randomized backoff time • RTS/CTS for hidden terminal problem • RTS/CTS/DATA/ACK sequence SNU INC lab.

  9. Adaptive Topology • Goal: • Exploit high density (over) deployment to extend system lifetime • Provide topology that adapts to the application needs • Self-configuring system that adapts to environment • How many nodes to activate? SNU INC lab.

  10. Neighbor Announcements Messages Help Messages Data Message Data Message Source Source Sink Sink Sink Source Passive Neighbor Active Neighbor (a) Communication Hole (b) Self-configuration transition (c) Final State ASCENT : Adaptive Self-Configuring sEnsor Networks Topologies • The nodes can be in activeorpassivestate. • Active nodes • forward data packets • Passive nodes • do notforward any packets but may sleep or collect network measurements. SNU INC lab.

  11. Wakeup plane: f1 Data plane: f2 STEM : Sparse Topology and Energy Management • Major Concept • Need to separate Wakeup and Data Forwarding Planes • Chosen two separate radios for the two planes • Use separate radio for the paging channel to avoid interference with regular data forwarding • Trades off energy savings for path setup latency SNU INC lab.

  12. Routing • Goal • To disseminate data from sensor nodes to the sink node in energy-awareness manner, hence, maximize the lifetime of the sensor networks. • Problem Description • Given a topology, how to route data? • Traditional Ad hoc routing protocols doesn’t fit • Classification of Routing Protocols • Data Centric Protocols • SPIN , Directed Diffusion • Hierarchical Protocols • LEACH , TEEN • Location Based Protocols • GAF , GEAR SNU INC lab.

  13. Data Centric Routing • The ability to query a set of sensor nodes • Attribute-based naming • Data aggregation during relaying SNU INC lab.

  14. Directed Diffusion • Sink node floods named “interest” with larger update interval • Sensor node sends back data via “gradients” • Sink node then sends the same “interest” with smaller update interval • Query-driven SNU INC lab.

  15. Energy Efficient Routing • Possible Route •Route 1: Sink-A-B-T, total PA = 4, total α = 3 •Route 2: Sink-A-B-C-T, total PA = 6, total α = 6 •Route 3: Sink-D-T, total PA = 3, total α = 4 •Route 4: Sink-E-F-T, total PA = 5, total α = 6 Maximum PA route: 4 Minimum hop route: 3 Minimum energy route: 1 SNU INC lab.

  16. Database Centric Approach • Traditional Approach • Data is extracted from sensors and stored on a front-end server • Query processing takes place on the front-end • Sensor Database System • Distributed query processing over a sensor network Sensor DB Warehouse Sensor DB Sensor DB Sensor DB Sensor DB Front End Front End SNU INC lab.

  17. Sensor DB Architecture SNU INC lab.

  18. Part IICollaborative Signal Processing SNU INC lab.

  19. Introduction • Sensor Network from SP perspective • Provide a virtual map of the physical world: • Monitoring a region in a variety of sensing modalities • (acoustic, seismic, thermal, …) • Two key components: • Networking and routing of information • Collaborative signal processing (CSP) for extracting and processing information from the physical world SNU INC lab.

  20. Space-Time sampling • Sensors sample the spatial signal field in a particular modality (e.g., acoustic,seismic) • Sensor field decomposed into space-time cells to enable distributed signal processing (multiple nodes per cell) Space Space Time Time Uniform space-time cells Non-uniform space-time cells SNU INC lab.

  21. Single Target Tracking • Initialization: Cells A,B,C and D are put on detection alert for a specified period • Five-step procedure: • A track is initiated when a target is detected in a cell (Cell A – Active cell). Detector outputs of active nodes are sent to the manager node • Manager node estimates target location at N successive time instants using outputs of active nodes in Cell A. • Target locations are used to predict target location at M<N future time instants • Predicted positions are used to create new cells that are put on detection alert • Once a new cell detects the target it becomes the active cell SNU INC lab.

  22. Why CSP? • More information about a phenomenon can be gathered from multiple measurements • Multiple sensing modalities (acoustic, seismic, etc.) • Multiple nodes • Limited local information gathered by a single node • Inconsistencies between measurements • malfunctioning nodes • Variability in signal characteristics and environmental conditions • Complementary information from multiple measurements can improve performance SNU INC lab.

  23. Manager node Manager node Various Forms of CSP • Single Node, Multiple Modality (SN, MM) • Simplest form of CSP: no communication burden • Decision fusion • Data fusion (higher computational burden) • Multiple Node, Single Modality (MN, SM) • Higher communication burden • Decision fusion • Data fusion (higher computational burden) • Multiple Node, Multiple Modality (MN, MM) • Highest communication and computational burden • Decision fusion across modalities and nodes • Data fusion across modalities, decision fusion across nodes • Data fusion across modalities and nodes SNU INC lab.

  24. Event Detection • Simple energy detector • Detect a target/event when the output exceeds an adaptive threshold (CFAR) • Detector output: • At any instant is the average energy in a certain window • Is sampled at a certain rate based on a priori estimate of target velocity and signal bandwidth • Output parameters for each event: • max value (CPA – closest point of approach) • time stamps for: onset, max, offset • time series for classification • Multi-node and multi-modality collaboration SNU INC lab.

  25. Constant False Alarm Rate (CFAR) Detection • Energy detector is designed to maintain a CFAR • Detector threshold is adapted to the statistics of the decision variable under noise hypothesis • Let x[n] denote a sensor time series • Energy detector: W is the detector window length • Detector decision: Target present Target absent Target present Target absent SNU INC lab.

  26. Single Measurement Classifier M=3 classes x C(x)=2 Event feature vector Decision (max) Class likelihoods SNU INC lab.

  27. Multiple Measurement ClassifierData Fusion M=3 classes C(x)=3 Concatenated event feature vector Event feature vectors from 2 measurements Class likelihoods Decision (max) SNU INC lab.

  28. Comb. Comb. Comb. Multiple Measurement Classifier – Soft Decision Fusion C(x)=1 Final Decision (max) Component decision combiner Event feature vectors from 2 measurements SNU INC lab.

  29. Multiple Measurement Classifier – Hard Decision Fusion M=3 classes 1 C(x)=1 3 Majority vote 1 Final decision Event feature vectors from 3 measurements Component hard decisions SNU INC lab.

  30. Summary • WSN protocols • MAC • Routing • WSN CSP • Data Fusion • Decision Fusion SNU INC lab.

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