Presentation on theme: "Energy Aware Routing in Wireless Sensor Networks Jonathan Tate 19 December 2006."— Presentation transcript:
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]
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]
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 1 st broadcast Final state 2 nd broadcast 3 rd broadcast [Ganesan02]
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.Probabilistic (gossiping) 2.Counter-based 3.Distance-based 4.Location-based 5.Cluster-based WSNs require simple, low-resource solution [Ni99]
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]
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]
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 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
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