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Energy Aware Routing in Wireless Sensor Networks Jonathan Tate 19 December 2006.

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Presentation on theme: "Energy Aware Routing in Wireless Sensor Networks Jonathan Tate 19 December 2006."— Presentation transcript:

1 Energy Aware Routing in Wireless Sensor Networks Jonathan Tate 19 December 2006

2 Outline Wireless Sensor Networks Routing strategies Reducing energy impact of routing Simulation as a design tool

3 Wireless Sensor Networks A type of MANET Every node is a router and a data source Nodes are severely resource-constrained Rapidly changing topology May contain thousands of nodes Resilient to failure of individual nodes Self-organising [Akyildiz02, Culler04]

4 What does a WSN do? Nodes monitor the environment Sensor data has geographical context Identity of individual node is unimportant Hostile environments –Environmental monitoring –Military –Surveillance –Emergency and disaster management [Akyildiz02, Culler04, Szewczyk04]

5 Sensor Nodes Spec chip [Berkley03]Intel mote [Club04] MICA [Polastre03] MICA 2 [Crossbow06]

6 Topology Control No control over physical location of nodes Signal strength modulation to control connectivity Logical structure overlaid on physical topology Inter-cluster routing Node-centric zones of two hops [Royer99, Beijar02, Chen01, Chiang97]

7 Energy-Aware Routing Maximise network lifetime (no accepted definition) Communication is the most expensive activity Possible goals include: –Shortest-hop (fewest nodes involved) –Lowest energy route –Route via highest available energy –Distribute energy burden evenly –Lowest routing overhead Distributed algorithms cost energy Changing component state costs energy [Raghunathan02, Jones01, Singh98, Weiser94, Shah02, Stojmenovic01]

8 Routing Strategies Aim to make communication more efficient Trade-off between routing overhead and data transmission cost Strategies incur differing levels of communication and storage overhead Hybrid approaches are possible [Jones01, Beijar02, Royer99, Broch98]

9 Stateless Routing Nodes maintain no routing information Flooding –Messages rebroadcast to neighbours Gossiping –Messages rebroadcast to neighbours, probability <1 Geographic –Need to know direction to destination Epidemic –Pairwise exchange of messages between carriers –Copes with temporary network partition –No routing state, but message buffering infeasible in WSNs [Vahdat00, Xu01, Karp00, Ko98, Imielinski96]

10 Proactive and Reactive Routing Proactive routing –Routes created and maintained in advance –Low latency, high resource demand –Does not scale to large networks Reactive routing –Routes created and cached as required –High latency, lower resource demand [Johnson96, Perkins94, Perkins97, Das00, Park97]

11 Data-centric Routing Routing application data rather than packets Node identities unknown to users Data naming and labelling Users express interests in named data, protocol sets up data flows Combines routing and distributed data management Data aggregated and summarised in flows Well suited to WSN paradigm [Intanagonwiwat00, Ratnasamy02, Heinzelman99]

12 Flooding Used in data delivery or route discovery Very simple algorithm, implicit multicast Observed results surprisingly complex –Stragglers, Backward Links, Long Links, Clustering Last 5% of nodes take as much time as preceding 95%, independent of radio power Some nodes will never receive the message Redundant communications waste energy [Ni99, Ganesan02]

13 Flooding Behaviour 1 st broadcast Final state 2 nd broadcast 3 rd broadcast [Ganesan02]

14 Broadcast Storm Problem Flooding is appropriate if topology changes rapidly; other approaches cannot keep up Broadcast Storm Problem –Redundancy –Contention –Collisions WSN nodes cannot afford energy or computation cost of wasteful communication [Ni99]

15 Solving the BSP Cannot ignore problem as flooding is needed Nodes attempt to determine how much the network will benefit from rebroadcast Proposed classes of solution: 1.Probabilistic (gossiping) 2.Counter-based 3.Distance-based 4.Location-based 5.Cluster-based WSNs require simple, low-resource solution [Ni99]

16 Gossiping Simple extension of flooding Probability of rebroadcast, p<1 Bimodal behaviour theory –For given p, results are consistent –Very few nodes receive message, or almost all –Critical probability, p c, at which switch occurs –Significant energy savings by setting p just above p c Protocols modified to use gossiping perform better (e.g. AODV+G, DSR+G) [Haas02]

17 Gossiping Bimodal behaviour formalised and analysed p c varies between systems p c cannot be determined analytically Determine p c for a system by simulation –Depends on reliable, accurate simulation Simulations find no evidence of phase transition behaviour at p c, contradicting theory –Is the theory or simulation result correct? [Sasson02]

18 Network Simulation Real-world experiments often infeasible Reproducible conditions Simulated entities may not yet exist No simulation is 100% accurate –Too little detail harms accuracy –Too much detail harms scalability [Heidemann01, Johnson99, Kotz03]

19 Existing Simulators Numerous simulators have been used in WSN and MANET research ns2, SeaWind, MaRS, PowerTOSSIM, TOSSF, Tython, SensorSim, Aeon, EmStar, SENS, Avrora, Atemu, SWAN, GloMoSim, … Few simulators scale to large networks –Hard to partition problem for parallel simulation as any given pair of nodes could interact at any time –Cannot manage level of simulation detail appropriately [Biaz01, Zeng98]

20 The ns-2 and ns-3 Simulators ns-2 widely used in network research Does not directly execute mote code Exponential execution time in the number of nodes Impractical to model networks larger than nodes ns-3 proposed, but not yet implemented ns-3 uses parallelisation for scalability, but still won’t scale to very large networks –Using multiple processors increases capacity, perhaps to ~1000 nodes at best due to coordination overhead –Still nowhere near a million node network [Henderson06, Das02, Naoumov03]

21 Simulation as a Design Tool GP used to evolve cluster head election algorithm in [Weise06] Candidate algorithms evaluated for fitness in a simulated network Offline tuning of algorithm to a network Simulation time restricts feasible exploration of search space [Weise06]

22 Possible Future Directions Design for analysis Logical structures with specialist nodes Online evolution through GP in-network Hierarchical simulation Application-level protocols Distributed scheduling Distributed knowledge management

23 Conclusions WSNs monitor hostile environments using resource-constrained nodes Communications activity is expensive Network lifetime depends on energy management policy Algorithms must suit the target network Large-scale simulation is vital in design, tuning and evaluation of WSN algorithms

24 References [Perkins94]C. Perkins and P. Bhagwat, “Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers”, ACM SIGCOMM'94 Conference on Communications Architectures, Protocols and Applications, pages , [Perkins97]C. Perkins and E. Royer, “Ad-hoc On-Demand Distance Vector Routing”, In MILCOM '97 panel on Ad Hoc Networks, Nov [Johnson96]D. Johnson and D. Maltz, “Dynamic Source Routing in Ad Hoc Wireless Networks”, Mobile Computing, vol. 353, [Vahdat01]A. Vahdat and D. Becker. “Epidemic Routing for Partially Connected Ad Hoc Networks”. Technical Report CS , Duke University, April [Ko98]Y. Ko and N. Vaidya, “Location-Aided Routing (LAR) in Mobile Ad Hoc Networks”, Mobile Computing and Networking, pages 66-75, [Karp00]B. Karp and H. Kung, “GPSR: Greedy Perimeter Stateless Routing for Wireless Networks”, Mobile Computing and Networking, pages , [Xu01]Y. Xu, J. Heidemann and D. Estrin, “Geography-informed Energy Conservation for Ad Hoc Routing”, Mobile Computing and Networking, pages 70-84, [Imielinski96]T. Imielinski and J. Navas, GPS-Based Addressing and Routing, Computer Science, Rutgers University, March [Park97]V. Park and M. Corson, “A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks”, INFOCOM 3, pages , 1997.

25 References [Weise06]T. Weise and K. Geihs, “Genetic Programming Techniques for Sensor Networks”. Proceedings of 5. GI/ITG KuVS Fachgesprach Drahtlose Sensornetze, pages 21-25, [Henderson06]T. Henderson, S. Roy, S. Floyd, and G. Riley, “NS-3 Project Goals”. To appear in WNS2 (Workshop on ns-2: the IP Network Simulator) October [Beijar02]N. Beijar, “Zone Routing Protocol (ZRP)”, unpublished. [Royer99]E. Royer and C. Toh, “A Review of Current Routing Protocols for Ad-Hoc Mobile Wireless Networks”. IEEE Personal Communications, Apr [Zimmerman80]H. Zimmerman, “OSI Reference Model – The ISO Model of Architecture for Open Systems Interconnection”, IEEE Transactions on Communications, vol. 28, no.4, pages , April [Raghunathan02]V. Raghunathan, C. Schurgers, S. Park and M. Srivastava, “Energy-Aware Wireless Microsensor Networks”, IEEE Signal Processing Magazine, vol. 19, no. 2, pages 40-50, March [Akyildiz02]I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor networks: a survey”, Computer Networks, no. 38, pages , [Culler04]D. Culler, D. Estrin and M. Srivastava, “Overview of Sensor Networks”, IEEE Computer, vol. 37, no. 8, pages 41-49, August [Heinzelman99]W. Heinzelman, J. Kulik and H. Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor networks”, In Proceedings of MOBICOM 1999, Seattle, , 1999.

26 References [Ni99]S. Ni, Y. Tseng, Y. Chen, and J. Sheu. “The Broadcast Storm Problem in a Mobile Ad Hoc Network”. Proceedings of the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking, pages , Aug [Sasson03]Y. Sasson, D. Cavin, and A. Schiper. “Probabilistic Broadcast for Flooding in Wireless Mobile Ad Hoc Networks”. Proceedings of IEEE Wireless Communications and Networking Conference (WCNC 2003) [Haas02]L. Li and J. Halpern and Z. Haas. “Gossip-Based Ad Hoc Routing”, unpublished. [Ganesan02]D. Ganesan, B. Krishnamachari, A. Woo, D. Culler, D. Estrin, S. Wicker. “Complex Behavior at Scale: An Experimental Study of Low-Power Wireless Sensor Networks”. Technical Report CSD-TR , UCLA, February [Hall99]E. Hall. “Internet Core Protocols”. O’Reilly, Sebastopol, CA, [Club04]Intel Editor’s Day 2004, [Polastre03]Wireless Sensor Networks for Habitat Monitoring (abstract), [Crossbow06]Crossbow MICA2 900MHz, [Chen01]B. Chen, K. Jamieson, H. Balakrishnan, R. Morris, “Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks”, Mobile Computing and Networking, pages 85-96, 2001.

27 References [Berkeley03]ForeFront Fall 2003, [Jones01]C. Jones, K. Sivalingam, P. Agrawal, and J. Chen, “A Survey of Energy Efficient Network Protocols for Wireless Networks”, Wireless Networks, vol. 7, no. 4, pages , [Singh98]S. Singh, M. Woo, and C. Raghavendra, “Power-Aware Routing in Mobile Ad Hoc Networks”, Mobile Computing and Networking, pages , [Weiser94]M. Weiser, B. Welch, A. Demers, and S. Shenker, “Scheduling for Reduced CPU Energy”, Operating Systems Design and Implementation, pages 13-23, [Shah02]R. Shah, J. Rabaey, “Energy Aware Routing for Low Energy Ad Hoc Sensor Networks”, In Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), Orlando, FL, March [Stojmenovic01]I. Stojmenovic and X. Lin, “Power-aware localized routing in wireless networks”, IEEE Transactions on Parallel and Distributed Systems, vol. 12, no. 11, pages , [Biaz01]S. Biaz, G. Holland, Y. Ko and N. Vaidya, “Evaluation of Protocols for Wireless Networks”, unpublished. [Broch98]J. Broch, D. Maltz, D. Johnson, Y. Hu and J. Jetcheva, “A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols”, Mobile Computing and Networking, pages , [Chiang97]C. Chiang, H. Wu, W. Liu and M. Gerla, “Routing in Clustered Multihop, Mobile Wireless Networks With Fading Channel”, In Proceedings of IEEE SICON'97, pages , 1997.

28 References [Das00]S. Das, C. Perkins and E. Royer, “Performance Comparison of Two On-demand Routing Protocols for Ad Hoc Networks”, INFOCOM 1, pages 3-12, [Intanagonwiwat00]C. Intanagonwiwat, R. Govindan and D. Estrin, “Directed diffusion: a scalable and robust communication paradigm for sensor networks”, Mobile Computing and Networking, pages 56-67, [Ratnasamy02]S. Ratnasamy, B. Karp, L. Yin, F. Yu, D. Estrin, R. Govindan, and S. Shenker, “GHT: A Geographic Hash Table for Data-Centric Storage in SensorNets”, In Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Applications (WSNA), Atlanta, Georgia, September [Szewczyk04]R.Szewczyk, J. Polastre, A. Mainwaring and D. Culler, “Lessons From A Sensor Network Expedition”, In Proceedings of the First European Workshop on Sensor Networks (EWSN), January [Heidemann01]J. Heidemann, N. Bulusu, J. Elson, C. Intanagonwiwat, K. Lan, Y. Xu, W. Ye, D. Estrin, and R. Govindan. “Effects of detail in wireless network simulation”. In Proceedings of the SCS Multiconference on Distributed Simulation, pages 3-11, January [Naoumov03]V. Naoumov and T. Gross. “Simulation of large ad hoc networks”. In Proceedings of MSWIM'03, pages ACM Press, [Zeng98]Xiang Zeng and Rajive Bagrodia and Mario Gerla. “GloMoSim: A Library for Parallel Simulation of Large-Scale Wireless Networks”, Workshop on Parallel and Distributed Simulation, pages , 1998

29 References [Johnson99]D. Johnson. “Validation of wireless and mobile network models and simulation”. In Proceedings of the DARPA/NIST Network Simulation Validation Workshop, Fairfax, Virginia, USA, May [Kotz03]D. Kotz, C. Newport and C. Elliot, “The mistaken axioms of wireless-network research”, Dartmouth College Computer Science Technical Report TR , July 2003.

30 Questions Thank you for your attention Your questions, please…

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