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Wireless Sensor networks survey and research challenges

Wireless Sensor networks survey and research challenges. University of Tehran Dept. Electrical and Computer Engineering. Presented by Hosein Sabaghian-Bidgoli hsabaghianb@gmail.com January 11, 2009. Outlines. Main references Introduction Definition Communication Architecture

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Wireless Sensor networks survey and research challenges

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  1. Wireless Sensor networkssurvey and research challenges University of Tehran Dept. Electrical and Computer Engineering Presented by Hosein Sabaghian-Bidgoli hsabaghianb@gmail.com January 11, 2009

  2. Outlines • Main references • Introduction • Definition • Communication Architecture • Protocol stack • WSN Characteristics • WSN Design factors • WSANs • WSN Structures • WSN Constraints • WSN Applications • WSN types • .

  3. Outlines (cont.) • Task classification • Internal sensor system • Standard • Storage • Testbed • Diagnostic and debugging support • Network services • Localization • Synchronization • Coverage • Compression and aggregation • Security • Communication protocol • Transport • Network • Data link • Physical • Cross-layer • Conclusion

  4. Main references • Ian F. Akyildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci, A Survey on Sensor Networks, IEEE Communications Magazine, August 2002 • Ian F. Akyildiz, Ismail H. Kasimoglu, Wireless sensor and actor networks research challenges, Elsevier Ad Hoc Networks 2 (2004) 351–367 • Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal, Wireless sensor network survey, Elsevier Computer Networks 52 (2008) 2292–2330

  5. [1]

  6. [2]

  7. [3]

  8. IntroductionWSN Definition A sensor network is composed of a large number of sensor nodes that are densely deployed inside or very close to the phenomenon • random deployment • self-organizing capabilities [1]

  9. IntroductionWSN communication Architecture [1]

  10. IntroductionComponents of Sensor Node [1]

  11. IntroductionProtocol Stack • Protocols should be • Power aware • Location aware • Application aware [1]

  12. IntroductionWSN Characteristics • Major differences between sensor and ad-hoc network • Number of nodes is higher • Densely deployment • Sensor nodes are prone to failure. • Frequent topology changes • Broadcast communication paradigm • Limited processing and power capabilities. • Possible absence of unique global ID [1]

  13. IntroductionWSN Design Factors • Fault Tolerance • Scalability • Production Costs • Hardware Constraints • Sensor Network Topology • Environment • Transmission Media • Power Consumption [1]

  14. WSN Design Factors Fault Tolerance • Each Nodes are prone to unexpected failure (more than other network) • Fault tolerance is the ability to sustain sensor network functionalities without any interruption due to sensor node failures. [1]

  15. WSN Design Factors Scalability • Size: Number of node (100 ~1000) • Density : μ(R)=(NR2)/A • Protocol should • be able to scale to such high degree • take advantage of the high density of such networks [1]

  16. WSN Design Factors Production Costs • The cost of a single node must be low given the amount of functionalities • Much less than $1 [1]

  17. WSN Design Factors Hardware Constraints • All these units combined together must • Extremely low power • Extremely small volume [1]

  18. WSN Design Factors Topology • Must be maintained specially in very high densities • Pre-deployment and deployment phase • Post-deployment phase • Re-deployment of additional nodes phase [1]

  19. WSN Design Factors Environment • May be inaccessible • either because of hostile environment • or because they are embedded in a structure • Impact of environment condition • Temperature • Humidity • Movement • Underwater • Underground [1]

  20. WSN Design Factors Transmission Media • RF • Infrared • Optical • Acoustic [3] [1]

  21. WSN Design Factors Power Consumption • Power conservation • Sensing • Communication • Data processing [1]

  22. wireless sensor and actornetworks (WSANs) • WSAN Capabilities • Observing the physical world • Processing the data • Making decisions • Performing appropriate actions • WSAN applications: • battlefield surveillance • microclimate control in buildings • nuclear, biological and chemical attack detection • Home automation • environmental monitoring [2]

  23. WSANs unique characteristics • Real-time requirement • Coordination: • Sensor-Actor Coordination • Actor-Actor Coordination [2]

  24. WSN structure • A WSN typically has little or no infrastructure • There are two types of WSNs • Structured model • Unstructured model [3]

  25. Unstructured model • Densely deployed (many node) • Randomly Deployed • Can have uncovered regions • Left unattended to perform the task • Maintenance is difficult • managing connectivity • detecting failures [3]

  26. Structured model • Deployed in a pre-planned manner • Fewer nodes • Lower network maintenance • Lower cost • No uncovered regions [3]

  27. WSN constraints • Resource constraints • limited energy • short communication range • low bandwidth • limited processing • limited storage • Design constraints • application dependent • environment dependent • size of the network / number of node • deployment scheme • network topology (obstacle) [3]

  28. Available sensors in the market • Generic nodes (take measurements) • Light, Temperature, Humidity, barometric pressure, velocity, Acceleration, Acoustics, magnetic field • Gateway (bridge) node • gather data from generic sensors and relay them to the base station • higher processing capability • higher battery power • higher transmission (radio) range [3]

  29. Types of sensor network • Depending on the environment • terrestrial WSN • Ad Hoc (unstructured) • Preplanned (structured) • underground WSN • Preplanned • more expensive equipment, deployment, maintenance • underwater WSN • fewer sensor nodes( sparse deployment) • more expensive than terrestrial • acoustic wave communication • Limited bandwidth • long propagation delay • signal fading

  30. Types of sensor network (cont.) • Depending on the environment • multi-media WSN • sensor nodes equipped with cameras and microphones • pre-planned to guarantee coverage • High bandwidth/low energy, QoS, filtering, data processing and compressing techniques • mobile WSN • ability to reposition and organize itself in the network • Start with Initial deployment and spread out to gather information • deployment, localization, self-organization, navigation and control, coverage, energy, maintenance, data process

  31. WSN applications [3]

  32. WSN applications (Open research issues) • application-specific characteristics and requirements of • environmental monitoring • health monitoring • industrial monitoring • Military tracking • Coupled with today’s technology • Lead to different hardware platforms and software development • more experimental work is necessary to make these applications more reliable and robust in the real world • Applying sensor technology to industrial applications will improve business

  33. Tasks Classification • Systems • Each sensor node is an individual system • Development of new platforms, operating systems, and storage schemes • Communication protocols • Between sensors • In different layer(app, trspt, net, DLink, phy) • services • which are developed • to enhance the application • to improve system performance • and network efficiency [3]

  34. Internal sensor system • sensor platform • radio components • processors • Storage • sensors (multiple) • OS • OS must support these sensor platforms researches: • Designing platforms that support • automatic management • optimizing network longevity, • distributed programming [3]

  35. Platform Sample 1(Bluetooth-based sensor networks) • WSN typically uses single freq (Share channel) • BTnodes use spread-spectrum transmission • A special version of TinyOS is used • Two radio communication • Master (up to 7 connection) • Slave • Note: • Bluetooth is connection oriented • New node enables its slave radio • Topology: connected tree • high throughput, high energy consumption [3]

  36. Platform Sample 2:VigilNet(Detection-and-classification system) • detection and classification • vehicles • persons • persons carrying ferrous objects • 200 sensor nodes with • Magnetometer • motion sensor, • and a microphone • deployed in a preplanned manner • four tiers hierarchical architecture • sensor-level, • node-level, • group-level, • and base-level [3]

  37. Internal sensor system Standards • IEEE 802.15.4: • standard for low rate wireless personal area networks (LR-WPAN) • low cost deployment • low complexity • low power consumption • topology :star and peer-to-peer • physical layer: 868/915 MHz ~2.4 GHz • MAC layer: CSMA-CA mechanism [3]

  38. Internal sensor system Standards • ZigBee • higher layer communication protocols built on the IEEE 802.15.4 standards for LR-PANs. • simple, low cost, and low power • embedded applications • can form mesh networks connecting hundreds to thousands of devices together. • types of ZigBee devices: • ZigBee coordinator: stores information, bridge • ZigBee router: link groups of devices • ZigBee end device: sensors, actuators communicate only to routers [3]

  39. Internal sensor system Standards • IEEE 802.15.3: • physical and MAC layer standard high data rate WPAN. • support real-time multi-media streaming • data rates (11 Mbps to 55 Mbps) • time division multiple access (TDMA) =>QoS • synchronous and asynchronous data transfer • wireless speakers, portable video, wireless connectivity for gaming, cordless phones, printers, and televisions [3]

  40. Internal sensor system Standards • WirelessHART (released in September 2007) • Process measurement and control applications • based on IEEE 802.15.4 • supports channel hopping, and time-synchronized messaging • Security with encryption, verification, authentication and key management • support mesh, star, and combined network topologies • manages the routing and network traffic [3]

  41. Internal sensor system Standards • ISA100.11a • defines the specifications for the OSI layer, security, and system management • low energy consumption, scalability, infrastructure, robustness • interoperability with other wireless devices • use only 2.4 GHz radio and channel hopping to minimize interference • provides simple, flexible, and scaleable security functionality. [3]

  42. Internal sensor system Standards • 6LoWPAN • IPv6-based Low power Wireless Personal Area Networks • over an IEEE 802.15.4 based network. • Low power device can communicate directly with IP devices using IPbased protocols • Wibree • designed for low power consumption, short-range communication, and low cost devices • is designed to work with Bluetooth • operates on 2.4 GHz • data rate of 1 Mbps • linking distance is 5–10 m. • was released publicly in October 2006. [3]

  43. Internal sensor system Storage • problems • storage space is limited • Communication is expensive • Solutions • Aggregation and compression • query-and-collect (selective gathering) • a storage model to satisfy storage constraints and query requirements • GEM: Graph Embedding • provides an infrastructure for routing and data-centric storage • choosing a labeled guest graph • embed the guest graph onto the actual sensor topology • Each node has a label encoded with its position • each data item has a name that can be mapped to a label • TSAR: Two-tier sensor storage architecture • Multi-resolution storage: provides storage and long-term querying of the data for data-intensive applications [3]

  44. Internal sensor system Testbeds • Provides researchers a way to test their protocols, algorithms, network issues and applications in real world setting • Controlled environment to deploy, configure, run, and monitoring of sensor remotely • Some testbeds: • ORBIT: Open access research testbed for next generation wireless networks • 64 nodes, 1 GHZ • MoteLab: web-based WSN testbed • central server handles scheduling, reprogramming and data logging of the nodes • Emulab: remotely accessible mobile and wireless sensor (such as a robot) [3]

  45. Internal sensor system Diagnostics and debugging support • Measure and monitor the sensor node performance of the overall network • to guarantee the success of the sensor network in the real environment • Sympathy: • is a diagnosis tool for detecting and debugging failures in sensor networks • designed for data-collection applications • detects failures in a system by selecting metrics such as • Connectivity • data flow • node’s neighbor • can identify three types of failures: self, path and sink • Analysis of data packet delivery: • packet delivery performance at the physical and MAC layers [3]

  46. Internal sensor system Open research issues • optimization of (HW, SW, HW/SW) to make a WSN efficient • more practical platform solution for problems in new applications • data structure • Performance • energy-efficient storage • Performance • communication throughput when network size increases • Scalability issues can degrade system performance • Optimizing protocols at different layers • services to handle node before and after failures [3]

  47. Network services • Localization • Synchronization • Coverage • Compression and aggregation • Security [3]

  48. Network services Localization • Problem: • determining the node’s location (position) • Solutions: • global positioning system (GPS) • Simple • Expensive • outdoor • beacon (or anchor) nodes • does not scale well in large networks • problems may arise due to environmental conditions • proximity-based • Make use of neighbor nodes to determine their position • then act as beacons for other nodes [3]

  49. Network services Localization • Other solutions: • Moore’s algorithm: • distributed algorithm for location estimation without the use of GPS or fixed beacon (anchor) nodes • algorithm has three phases: • cluster localization phase • cluster optimization phase • cluster transformation phase [3]

  50. Network services Localization • Other solutions: • RIPS: Radio Interferometric Positioning System • Two radio transmitters create an interference signal at slightly different frequencies • At least two receivers are needed to measure relative phase of two signal • The relative phase offset is a function of the relative positions [3]

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