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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:

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Presentation on theme: "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:"— Presentation transcript:

1 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

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

3 Introduction Challenges 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 3

4 Organization 1.Modeling – Energy consumption – Network lifetime 2.Heuristics – Establish routing trees – Determine network lifetime 3.Performance Evaluation 4.Related Work 4

5 1. Modeling System Parameters ParameterValue 5 ParameterValue Graph G(V, E)VSet of sensor nodes (N = |V|) ESet of links between sensors (M = |E|) rsrs Data generation rate Location of sesnors ParameterValue Graph G(V, E)VSet of sensor nodes (N = |V|) ESet of links between sensors (M = |E|) rsrs Data generation rate Location of sesnors Gateway / Slaves GatewayIEEE and 3G radios Slave nodesIEEE radio mNumber of gateways ParameterValue Graph G(V, E)VSet of sensor nodes (N = |V|) ESet of links between sensors (M = |E|) rsrs Data generation rate Location of sesnors Gateway / Slaves GatewayIEEE and 3G radios Slave nodesIEEE radio mNumber of gateways QoS metricsαNetwork throughput DDelivery delay

6 1. Modeling Energy Cost Flash memory buffer 3G radio radio MCU ParamDesc PnPn Pwr by radio, MCU P 3G Pwr by 3G P buf Pwr by buffering d(v) #-descendants EoEo Overhead for sync 6

7 1. Modeling Network Lifetime Time before the base station is no longer able to receive data from α percentage of sensors Round 1 τ Network Lifetime: L Round 2 τ Round r τ Round R τ Round R+1 τ‘ (<= τ) 7

8 1. Modeling Network Lifetime 1 τ 2 τ r τ R τ R+1 τ‘ 8

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

10 Organization 1.Modeling – Energy consumption – Network lifetime 2.Heuristics – Establish routing trees – Determine network lifetime 3.Performance Evaluation 4.Related Work 10

11 2. Heuristic Establishing the Routing Forest Routing trees should span at least α *N nodes. 1)Identify the smallest set of active nodes 2)Partition the active nodes into m subsets 3)Find the routing tree

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

13 2. Heuristic (1) Identifying Active Nodes

14 2. Heuristic (2) Partitioning active nodes into m subsets CC(G[V’])=m’ m’<= m CC(G[V])=m Partition G[V’] into G[V] 14

15 2. Heuristic (2) Partitioning active nodes into m subsets F = {S 1, S 2, …, S m’ } collection of vertex sets. Select a set with the largest #-vertices, S l Partition S l into S l1, S l2 s.t. ||S l1 |-||S l2 || is minimized. Repeat until m’ = m SlSl S l2 S l1 15

16 2. Heuristic (3) Finding routing tree – max-min tree Find max-min tree T i (v) for each connected graph G i and given root v. The tree T i rooted at a node with the longest lifetime is selected [9] S l2 S l1 [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,

17 2. Heuristic Determining the Network Lifetime For each tree T i, evaluate l min at round r. If I min > τ – L = L + τ – e r (v) = e r (v) – τ*e c (v) – e delta If l min <= τ – τ’ = l min – L = L + τ’ – Terminate the loop τ 2 τ r τ R τ R+1 τ‘ 17

18 2. Heuristic Complexity O(MN 2 ) for N = |V|, M = |E| Proof 1)Finding #-connected components: O(M) using BFS or DFS 2)Partitioning active nodes: O(N 3 logN) [8] 3)Building a max-min tree rooted at a given node: O(MN 2 ) [9] 4)For any G(V, E): M=O(N 2 ) [8] D. R. Karger and C. Stein. A new approach to the minimum cut problem. Journal of the ACM, 43:601–640, [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,

19 3. Performance Evaluation Assumptions ParametersValues #-Sensors100 – 300 Tx Range100 m Initial Energy Cap200 J Energy Consumption ParamCC2420 radio for IEEE radio MO6012 radio for 3G radio NAND flash memory Data Generation Rate (r s )1 bit / s Data Delivery Latency (D)1 hr Network Throughput Threshold (α)

20 3. Performance Evaluation Residual Energy over Time N = 100, m = 5, τ = 2 hr 25τ50τ175τ186τ75τ100τ125τ150τ 20

21 3. Performance Evaluation Residual Energy over Time N = 100, m = 5, τ = 2 hr ‒In the first 75 rounds: For all, E > 0.5E init ‒In the 175 th round: 44 nodes, E < 0.2E init ‒In the last round: 37 nodes, E = 0 Throughput req isn’t met 25τ50τ175τ186τ75τ100τ125τ150τ 21

22 3. Performance Evaluation Lifetime over Constraint Parameters Network throughput α: steadily decreases, then rapidly falls #-nodes N: decreases more with smaller α Duration of round τ: first increases, then decreases #-gateways m: first increases, then decreases Delivery delay D: increases 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 τ from 1 hr to 10 hr m = 5 Vary m from 2 to 20 τ = 2 hr Vary D to 10, 20, 30, 60 and 120 min m = 5, τ = 2 hr 22

23 3. Performance Evaluation Three Algorithms Algorithm Gateway Selection Time Gateway Selection Criteria StaticAlg LEACH [7] DynamicAlg Algorithm Gateway Selection Time Gateway Selection Criteria StaticAlgOnce in lifetime LEACH [7]Each round DynamicAlgEach round Algorithm Gateway Selection Time Gateway Selection Criteria StaticAlgOnce in lifetimeRandom LEACH [7]Each round Random & Time spent as gateways in previous rounds DynamicAlgEach roundResidual energy 23 [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 Evaluation Three 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 24

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, [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, [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, [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, Hierarchical Power Management Ours Assumptions Methods Examples Hierarchical Power Management Ours Assumptions Use both high low BW radios Optimize their use Sensornet within the network 3G only for remote data Methods Examples Hierarchical Power Management Ours Assumptions Use both high low BW radios Optimize their use Sensornet within the network 3G only for remote data Methods Minimize time for high BW radio while low BW radio is always on. Schedule gateways to achieve max lifetime Examples Hierarchical Power Management Ours Assumptions Use both high low BW radios Optimize their use Sensornet within the network 3G only for remote data Methods Minimize time for high BW radio while low BW radio is always on. Schedule gateways to achieve max lifetime Examples[6, 10, 12, 13] 25

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, LEACHOurs Goals Gateway Selection Energy-aware Routing LEACHOurs Goals Maximizes lifetime by perio- dically changing the set of GWs Gateway Selection Energy-aware Routing LEACHOurs Goals Maximizes lifetime by perio- dically changing the set of GWs Gateway Selection Random & Time spent as GWs in previous rounds Residual energy. Energy-aware Routing LEACHOurs Goals Maximizes lifetime by perio- dically changing the set of GWs Gateway Selection Random & Time spent as GWs in previous rounds Residual energy. Energy-aware Routing Total distance minimization in LEACH does not necessarily lead to the min energy cons. Data routing is designed to balance the energy cons. 26

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 27

28 Backup Slides 28

29 3. Performance Evaluation Lifetime 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 29

30 3. Performance Evaluation Lifetime 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 30

31 3. Performance Evaluation Lifetime 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 31

32 3. Performance Evaluation Lifetime 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 32

33 3. Performance Evaluation Lifetime 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 33


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