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] MICA 2 [Crossbow06] Spec chip [Berkley03] 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

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

[Ni99]

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

Solving the BSP

[Ni99] • • • • 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

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 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. 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 234-244, 1994.

[Perkins97] C. Perkins and E. Royer, “Ad-hoc On-Demand Distance Vector Routing”, In MILCOM '97 panel on Ad Hoc Networks, Nov. 1997.

[Johnson96] D. Johnson and D. Maltz, “Dynamic Source Routing in Ad Hoc Wireless Networks”, Mobile Computing, vol. 353, 1996.

[Vahdat01] A. Vahdat and D. Becker. “Epidemic Routing for Partially Connected Ad Hoc Networks”. Technical Report CS-200006, Duke University, April 2000.

[Ko98] Y. Ko and N. Vaidya, “Location-Aided Routing (LAR) in Mobile Ad Hoc Networks”, Mobile Computing and Networking, pages 66-75, 1998.

[Karp00] B. Karp and H. Kung, “GPSR: Greedy Perimeter Stateless Routing for Wireless Networks”, Mobile Computing and Networking, pages 243-254, 2000.

[Xu01] Y. Xu, J. Heidemann and D. Estrin, “Geography-informed Energy Conservation for Ad Hoc Routing”, Mobile Computing and Networking, pages 70-84, 2001.

[Imielinski96] T. Imielinski and J. Navas, GPS-Based Addressing and Routing, Computer Science, Rutgers University, March 1996.

[Park97] V. Park and M. Corson, “A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks”, INFOCOM 3, pages 1405-1413, 1997.

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, 2006.

[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 2006.

[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. 1999.

[Zimmerman80] H. Zimmerman, “OSI Reference Model – The ISO Model of Architecture for Open Systems Interconnection”, IEEE Transactions on Communications, vol. 28, no.4, pages 425-432, April 1980. [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 2002.

[Akyildiz02] I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor networks: a survey”, Computer Networks, no. 38, pages 393-422, 2002.

[Culler04] D. Culler, D. Estrin and M. Srivastava, “Overview of Sensor Networks”, IEEE Computer, vol. 37, no. 8, pages 41-49, August 2004.

[Heinzelman99] W. Heinzelman, J. Kulik and H. Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor networks”, In Proceedings of MOBICOM 1999, Seattle, 174-185, 1999.

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 151-162, Aug 1999.

[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). 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 02-0013, UCLA, February 2002.

[Hall99] E. Hall. “Internet Core Protocols”. O’Reilly, Sebastopol, CA, 2000.

[Club04] Intel Editor’s Day 2004, http://www.clubedohardware.com.br/artigos/119/2 [Polastre03] Wireless Sensor Networks for Habitat Monitoring (abstract),

http://www.eecs.berkeley.edu/IPRO/Summary/03abstracts/chapter6.html

[Crossbow06] Crossbow MICA2 900MHz, http://www.xbow.com/Products/productdetails.aspx?sid=174 [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.

References

[Berkeley03] ForeFront Fall 2003, http://www.coe.berkeley.edu/forefront/fall2003/breakthroughs.html [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 343-358, 2001.

[Singh98] S. Singh, M. Woo, and C. Raghavendra, “Power-Aware Routing in Mobile Ad Hoc Networks”, Mobile Computing and Networking, pages 181-190, 1998.

[Weiser94] M. Weiser, B. Welch, A. Demers, and S. Shenker, “Scheduling for Reduced CPU Energy”, Operating Systems Design and Implementation, pages 13-23, 1994.

[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 2002.

[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 1122-1133, 2001.

[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 85 97, 1998.

[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 197-211, 1997.

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, 2000.

[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, 2000.

[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 2002.

[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 2004.

[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 2001.

[Naoumov03] V. Naoumov and T. Gross. “Simulation of large ad hoc networks”. In Proceedings of MSWIM'03, pages 50-57. ACM Press, 2003.

[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 154-161, 1998

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 1999.

[Kotz03] D. Kotz, C. Newport and C. Elliot, “The mistaken axioms of wireless-network research”, Dartmouth College Computer Science Technical Report TR2003-467, July 2003.

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