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Maximizing Network Lifetime via 3G Gateway Assignment in Dual-Radio Sensor Networks

Maximizing Network Lifetime via 3G Gateway Assignment in Dual-Radio Sensor Networks. LCN 2012, 10/24/2012 Cisco Systems : Jaein Jeong Australian Nat’l Univ : Xu Xu , Weifa Liang CSIRO: Tim Wark. Introduction A Remote Monitoring Scenario.

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Maximizing Network Lifetime via 3G Gateway Assignment in Dual-Radio Sensor Networks

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  1. Maximizing Network Lifetimevia 3G Gateway Assignment in Dual-Radio Sensor Networks LCN 2012, 10/24/2012 Cisco Systems: JaeinJeongAustralian Nat’l Univ: XuXu, Weifa Liang CSIRO: Tim Wark

  2. IntroductionA Remote Monitoring Scenario • Deployed far away from the monitoring center • Network Model: Dual-Radio • Goal: Maximize Network Lifetime Sensor network Sensor Gateway Monitoring Center Third party network IEEE 802.15.4 link 3G link Base station , 3G) (Low Power

  3. IntroductionChallenges • Explore main components of energy cons. • For gateways • For slave nodes • Identify gateways among all deployed sensors • m gateways • Network lifetime maximized • Route data to m gateways energy-efficiently • Throughput requirement • Delay requirement

  4. Organization • Modeling • Energy consumption • Network lifetime • Heuristics • Establish routing trees • Determine network lifetime • Performance Evaluation • Related Work

  5. 1. ModelingSystem Parameters

  6. 1. ModelingEnergy Cost Flash memorybuffer 3G radio 802.15.4 radio MCU

  7. 1. ModelingNetwork Lifetime • Time before the base station is no longer able to receive data from α percentage of sensors Network Lifetime: L τ τ τ τ τ‘(<= τ) Round1 Round2 Roundr RoundR RoundR+1

  8. 1. ModelingNetwork Lifetime τ τ τ τ τ‘ 2 r R 1 R+1

  9. 1. ModelingProblem Definition • Periodic assignment of gateways • Identify m gateways • Selecting nodes to send data to these gateways • Route data from these nodes to gateways τ τ τ τ τ‘ 2 r R 1 R+1

  10. Organization • Modeling • Energy consumption • Network lifetime • Heuristics • Establish routing trees • Determine network lifetime • Performance Evaluation • Related Work

  11. 2. HeuristicEstablishing the Routing Forest • Routing trees should span at least α *N nodes. • Identify the smallest set of active nodes • Partition the active nodes into m subsets • Find the routing tree 1 1 3 3 7 7 5 5 8 8 11 11 10 10 9 9 4 4 2 2 6 6 1 3 7 5 8 11 10 9 4 2 6

  12. 2. Heuristic(1) Identifying Active Nodes • Choose sensors with high er(v) • m-component constraint • CC(G[V’]) <= m • Or, some nodes may not reach a gateway. high low

  13. 2. Heuristic(1) Identifying Active Nodes 1 3 7 5 8 11 10 9 4 2 6

  14. 2. Heuristic(2) Partitioning active nodes into m subsets 1 1 3 3 7 7 5 5 8 8 11 11 10 10 9 9 4 4 2 Partition G[V’] into G[V] 2 6 6 CC(G[V])=m CC(G[V’])=m’m’<= m

  15. 2. Heuristic(2) Partitioning active nodes into m subsets • F = {S1, S2, …, Sm’} collection of vertex sets. • Select a set with the largest #-vertices, Sl • Partition Sl into Sl1, Sl2s.t. ||Sl1|-||Sl2|| is minimized. • Repeat until m’ = m. 1 Sl1 3 7 5 8 11 10 Sl 9 4 2 Sl2 6

  16. 2. Heuristic(3) Finding routing tree – max-min tree • Find max-min tree Ti(v) for each connected graph Gi and given root v. • The tree Ti rooted at a node with the longest lifetime is selected [9]. 1 3 7 5 8 11 10 Sl1 9 4 2 6 Sl2 [9] W. Liang and Y. Liu. On-line data gathering for maximinizing network lifetime in sensor networks. IEEE Trans. on Mobile Computing, 6:2–11, 2007.

  17. 2. HeuristicDetermining the Network Lifetime • For each tree Ti, evaluate lmin at round r. • If Imin > τ • L = L + τ • er(v) = er(v) – τ*ec(v) – edelta • If lmin <= τ • τ’ = lmin • L = L + τ’ • Terminate the loop 1 3 7 5 8 11 10 τ τ τ τ τ‘ 9 2 r R 1 R+1 4 2 6

  18. 2. HeuristicComplexity • O(MN2) for N = |V|, M = |E| • Proof • Finding #-connected components:O(M) using BFS or DFS • Partitioning active nodes:O(N3logN) [8] • Building a max-min tree rooted at a given node:O(MN2) [9] • For any G(V, E):M=O(N2) [8] D. R. Karger and C. Stein. A new approach to the minimum cut problem.Journal of the ACM, 43:601–640, 1996. [9] W. Liang and Y. Liu. On-line data gathering for maximinizing network lifetime in sensor networks.IEEE Trans. on Mobile Computing, 6:2–11, 2007.

  19. 3. Performance EvaluationAssumptions

  20. 3. Performance EvaluationResidual Energy over Time • N = 100, m = 5, τ = 2 hr 25τ 50τ 75τ 100τ 125τ 150τ 175τ 186τ

  21. 3. Performance EvaluationResidual Energy over Time • N = 100, m = 5, τ = 2 hr • In the first 75 rounds:For all, E > 0.5Einit • In the 175th round:44 nodes, E < 0.2Einit • In the last round:37 nodes, E = 0Throughput req isn’t met 25τ 50τ 75τ 100τ 125τ 150τ 175τ 186τ

  22. 3. Performance EvaluationLifetime over Constraint Parameters • Network throughputα: steadily decreases, then rapidly falls • #-nodesN: decreases more with smaller α • Duration of roundτ: first increases, then decreases • #-gatewaysm: first increases, then decreases • Delivery delayD: increases • Vary τ from 1 hr to 10 hr • m = 5 • Vary α from 0.3 to 1 • N = 100, m = 5, τ = 2 hr • Vary N from 100 to 300 • N = 100, m = 5, τ = 2 hr • Vary D to 10, 20, 30, 60 and 120 min • m = 5, τ = 2 hr • Vary m from 2 to 20 • τ = 2 hr

  23. 3. Performance EvaluationThree Algorithms [7] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy efficient communication protocol for wireless microsensor networks.Proc. of HICSS. IEEE, 2000.

  24. 3. Performance EvaluationThree Algorithms and Lifetime Delivered • In general, Dynamic > LEACH > Static • Superiority of Dynamic and LEACH over Static • More balanced energy consumption • Advantages of Dynamic over LEACH • More efficient gateway identification • More advanced routing forest establishment

  25. 4. Related Work [6] J. Gummeson, D. Ganesan, M. D. Corner, and P. Shenoy. An adaptive link layer for heterogeneous multi-radio mobile sensor networks.IEEE Journal on Selected Areas in Communications, 28:1094–1104, 2010. [10] D. Lymberopoulos, N. B. Priyantha, M. Goraczko, and F. Zhao. Towards efficient design of multi-radio platforms for wireless sensor networks. Proc. of IPSN. IEEE, 2008. [12] C. Sengul, M. Bakht, A. F. Harris, T. Abdelzaher, and R. Kravets. Improving energy conservation using bulk transmission over high-power radios in sensor networks. Proc. of ICDCS. IEEE, 2008. [13] T. Stathopoulos, M. Lukac, D. Mclntire, J. Heidemann, D. Estrin, and W. J. Kaiser. End-to-end routing for dual-radio sensor networks.Proc. of INFOCOM. IEEE, 2007.

  26. 4. Related Work [7] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy efficient communication protocol for wireless microsensor networks.Proc. of HICSS. IEEE, 2000.

  27. Conclusion • Maximizing lifetime of a dual-radio sensor network. • Proposed a model for energy cons and lifetime. • Proposed heuristics that maximizes lifetime • Identifies gateways • Finds the data routing structure • Experiment Results • Our heuristic outperforms other cluster methods. • Future works • Distributed algorithm • Experiments with real energy consumption

  28. Backup Slides

  29. 3. Performance EvaluationLifetime over Throughput Threshold α • Lifetime (L) steadily falls down before α = 0.6, rapidly falls after that. • For α <= 0.6 • Additional nodes are used for connected components. • For α > 0.6 • Additional nodes increase required active nodes and energy cons. • Vary α from 0.3 to 1 • N = 100, m = 5, τ = 2 hr

  30. 3. Performance EvaluationLifetime of #-Nodes N • L starts to drop from a smaller α as N gets larger. • With higher node density, • Smaller # of required extra nodes for m-component • The more distinct impact of α on lifetime. • Higher traffic cause shorter lifetime. • Vary N from 100 to 300 • N = 100, m = 5, τ = 2 hr

  31. 3. Performance EvaluationLifetime over Duration of Period τ • Generally, the larger τ, the shorter L. • Frequent identification balances energy better. • From 1 to 2-3hr, L slightly increases as τ increases. • Too frequent routing • With fixed τ, L gets smaller as N increases. • Vary τ from 1 hr to 10 hr • m = 5

  32. 3. Performance EvaluationLifetime over #-gateways m • Lifetime first increases and then decreases. • Before a turning point:Better energy balancing • After passing the point:More energy cons on 3G • Vary m from 2 to 20 • τ = 2 hr

  33. 3. Performance EvaluationLifetime over Delivery Delay D • A smaller value of D leads to a shorter L • Frequent on-and-off switching of the 3G radios results in energy overheads • Vary D to 10, 20, 30, 60 and 120 min • m = 5, τ = 2 hr

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