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Energy Aware Routing in Wireless Sensor Networks Jonathan Tate 19 December 2006 Outline • • • • Wireless Sensor Networks Routing strategies Reducing energy impact of routing Simulation as a design tool 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] 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] Sensor Nodes MICA [Polastre03] Spec chip [Berkley03] MICA 2 [Crossbow06] Intel mote [Club04] Topology Control • No control over physical location of nodes • Signal strength modulation to control connectivity • Logical structure overlaid on physical topology Inter-cluster routing [Royer99, Beijar02, Chen01, Chiang97] Node-centric zones of two hops 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] 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] 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] 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] 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] 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] Flooding Behaviour [Ganesan02] 1st broadcast 2nd broadcast 3rd broadcast Final state 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] 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. 2. 3. 4. 5. • [Ni99] Probabilistic (gossiping) Counter-based Distance-based Location-based Cluster-based WSNs require simple, low-resource solution 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, pc, at which switch occurs Significant energy savings by setting p just above pc • Protocols modified to use gossiping perform better (e.g. AODV+G, DSR+G) [Haas02] Gossiping • • • • Bimodal behaviour formalised and analysed pc varies between systems pc cannot be determined analytically Determine pc for a system by simulation – Depends on reliable, accurate simulation • Simulations find no evidence of phase transition behaviour at pc, contradicting theory – Is the theory or simulation result correct? [Sasson02] 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] 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] 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 100-150 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] 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] 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 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 References [Perkins94] C. 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Elliot, “The mistaken axioms of wireless-network research”, Dartmouth College Computer Science Technical Report TR2003-467, July 2003. Questions • Thank you for your attention • Your questions, please…