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Adaptive Protocols for Information Dissemination in Wireless Sensor Networks

Adaptive Protocols for Information Dissemination in Wireless Sensor Networks. The X – Matrix Team. http://www.cs.ucl.ac.uk/students/fshariff/projects/spin. Who, What and How. The X-Matrix Team - Wasif, Fahd, Philip, Muhammad and Kumardev

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Adaptive Protocols for Information Dissemination in Wireless Sensor Networks

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  1. Adaptive Protocols for Information Dissemination in Wireless Sensor Networks The X – Matrix Team http://www.cs.ucl.ac.uk/students/fshariff/projects/spin X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  2. Who, What and How • The X-Matrix Team- Wasif, Fahd, Philip, Muhammad and Kumardev • The paper - Negotiation-based Protocols for Disseminating Information in Wireless Sensor Networks byJoanna Kulik,Wendi Rabiner Heinzelman,and Hari Balakrishnan, Massachusetts Institute of Technology, Cambridge, MA, USA • The broad concepts outlined in the paper • Our Approach • De-construction and Analysis of work • Presentation Structure and Flow X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  3. Fundamental Concepts • Wireless Sensor Networks • Sensors – typical size, weight, power characteristics • Sensor Networks are a subset of Ad Hoc Networks • Fixed / Mobile • Routing in Ad Hoc / Sensor Networks • Traditional protocols – Classic flooding, Gossiping • Adaptive protocols – SPIN, Others • What are these so-called ‘adaptive protocols’? X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  4. Classic Flooding A B C D Sink Node X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  5. (a) (a) A B A • Implosion • Data overlap B C (a) (a) D C r q s (r,s) (q,r) Problems with Classic Flooding • Energy Conservation X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  6. Gossiping • Alternative to Classic Flooding • Randomisation to conserve energy • Avoids implosion A B C D X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  7. B D G C A E F The Ideal Protocol • “Ideal” • Shortest-path routes • No wasted energy • No redundant data X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  8. SPIN: Negotiation and Dissemination Overview of SPIN • Application-Level Control • Meta-Data Negotiation • Spin Messages • ADV – New data advertisement • REQ – Request for data • DATA – The actual data message • SPIN Resource Management ADV A B REQ A B DATA A B X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  9. SPIN family of protocols • Point-to-Point • SPIN-PP: a 3-stage handshake protocol for point-to-point media • SPIN-EC: SPIN-PP with a low-energy threshold • Broadcast • SPIN-BC: a 3-stage handshake protocol for broadcast media • SPIN-RL: SPIN-BC for lossy networks X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  10. SPIN-PP D A B DATA message E ADV message C REQ message X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  11. SPIN-EC • SPIN-PP with simple energy conservation heuristic • When the low-energy threshold is observed, the node reduces its participation in the protocol • Node can still receive • Data messages cannot be transmitted X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  12. Questioning SPIN for Point-to-Point • Why use PP when we already have BC? • Do we need energy conservation or is it application dependent? X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  13. Point-to-Point Media Simulations • Compare SPIN-PP and SPIN-EC with classic flooding, gossiping and the ideal protocol • Parameters of interest include: • Data throughput • Energy usage • Enhanced ns simulator X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  14. Simulation Testbed • 25 nodes, 59 edges • 25 data items • 3 items/node  overlap • Antenna reach: 10 m 16 bytes 500 bytes Meta-data Data No network losses or queuing delays X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  15. Unlimited Energy Simulations --SPIN-PP --Ideal --Flooding • Flooding fastest • SPIN-PP uses 3.5x less energy than flooding X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  16. Limited Energy Simulations --SPIN-PP --SPIN-EC --Ideal --Flooding • SPIN-EC distributes nearly the same amount as the ideal • SPIN uses energy at a much slower rate X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  17. Simulation Issues • Does not take into account for any delay caused by meta-data negotiation • ns constraints: • Memory • CPU time • A simulator model of a real-world system is necessarily a simplification of the real-world system itself X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  18. SPIN-BC Motivations • One-to-many communication is: • 1/n times cheaper in a broadcast network than in a point-to-point network • where n is the number of neighbours for each node • Saves energy • Lets each node overhear all transactions that occur  coordinate better X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  19. SPIN-BC • For lossless broadcast network • Uses a shared channel • Like SPIN-PP, uses ADV, REQ and DATA messages • Three differences: • Messages sent to a broadcast address • When received ADV, sets random timer, sends REQ upon timeout. Other nodes hearing REQ will cancel their timer • Nodes will send data to the broadcast address only once, assuming lossless network X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  20. E E E ADV DATA D D D C ADV REQ SPIN-BC Example E B A D C A Nodes with data A Nodes without data A Nodes waiting to transmit REQ X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  21. SPIN-RL • For lossy broadcast network • Two modifications • Firstly, if a node does not receive data within a period of time, it sends REQ again • Secondly, when a data item is repeatedly requested, the node will wait for a predetermined amount of time before responding to any requests. X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  22. SPIN - BC and RL : best option? • Open questions: • Bandwidth-saving, how about utilising IP Multicast? • Reliable multicast? • Need further research • Our opinion: if yes, a trimmed-down version of multicasting is needed. X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  23. Broadcast Media Simulations • Simulation Testbed same as the one used in SPIN-PP with following variations: • Single shared-media channel • Nodes use 802.11 MAC layer protocol • Delay and packet losses taken into account • Simulation Setup • monarch – extension of ns X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  24. Simulations with No Packet Losses • SPIN-BC • Converges quicker than flooding • Dissipates 50% less energy as compared to flooding ---SPIN-BC ---Ideal ---Flooding X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  25. Simulations with Packet Losses • SPIN-RL • Only ideal and SPIN-RL converge because of their ability to recover from packet loss, rest do not converge • This is closer to reality scenario. • Expends more energy as compared to BC and the ideal ---SPIN-BC ---SPIN-RL ---Ideal --Flooding- X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  26. Data Distributed Per Unit Energy • SPIN-RL delivers twice as much data per unit energy than flooding (100% more) ---SPIN-BC ---SPIN-RL ---Ideal ---Flooding X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  27. Validity/Relevance of results • Simulation environment selected in SPIN-RL is a better representation of real world scenario • Channel interference and collision which were ignored in SPIN-BC, PP and EC have been taken into account • SPIN-RL: Theoretical integrity consistent X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  28. Major Short-comings • Simulation Environment does not closely model Wireless Sensor Networks environment • False assumption: the infinite supply of energy in SPIN-RL • Results fall short of supporting a convincing argument in favour of SPIN protocols X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  29. Summary of relevant/similar work What is similar and/or relevant? SPIN and NNTP – comparable? SPIN and Energy-Conservation based routing SPIN and other Flat Multi-hop routing protocols Spin and Others – AIDA, LEACH X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  30. SPIN vs Directed Diffusion • What is directed diffusion? • Similarities: • Optimized for disseminating application-specific information in a sensor network, specifically between source and sink nodes • Use of data naming allows negotiation between nodes prior to data forwarding to eliminate redundancy • Interest (REQ) and data (DATA) caches maintained at each node • Node-local decision making X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  31. SPIN vs Directed Diffusion - 2 • Dissimilarities: • SPIN uses a push model for disseminating information to all nodes, while DD uses a pull model for obtaining information • Data is sent to all nodes in SPIN while data is NOT sent to all nodes in Directed diffusion. X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  32. Sensor Network Applications and SPIN Applications make the Networks SPIN around Typical Sensor Network Applications Application/Network type – Time Critical Application/Network type – Reliable & Re-Usable What kind of Protocols are optimal ? X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  33. Applications and SPIN • Application/Network type – Time Critical • Characteristics • Typical example – Seismic Activity Detection • SPIN – is it optimal for this type of apps? • Application/Network type – Reliable & Re-usable • Characteristics • Typical example – MARS Habitat Monitoring • SPIN – is it optimal for this type of apps? X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  34. Summary and Crystal Ball • The Potential of Wireless Sensor Networks • The Future of Wireless Sensor Networks • The Potential of SPIN • The Limitations of SPIN • The Future of SPIN X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  35. Ask us! We asked Joanna Kulik, one of the SPIN authors..X-Matrix: “Could you address any SPIN protocol weaknesses (if any?)” Joanna: “I haven't thought about SPIN in many years.  I'm sure that there are many weaknesses, and that they would be easy to find.  With SPIN we were just trying to lay some initial groundwork in the field.  With anyinitial work, there are hundreds of ways that the workcould be improved.” X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

  36. References • D. Estrin, R. Govindan, J. Heidemann, S. Kumar, Next century challenges: Scalable coordination in sensor networks, Proc. MOBICOM, 1999, Seattle, 263-270. • C. Intanagonwiwat, R. Govindan, , and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In MobiCOM, Boston, MA, August 2000. • Wireless Networks of Devices (WIND) [http://wind.lcs.mit.edu] • Praveen Rentala, Ravi Musunnuri, Shashidhar Gandham, Udit Saxena, Survey on Sensor Networks • LEACH [http://nms.lcs.mit.edu/projects/leach] X-Matrix TeamMSc Data Communications, Networks and Distributed Systems; Computer Science Department, UCL

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