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Energy-Efficient Topology Control for Wireless Ad Hoc Sensor Networks

Energy-Efficient Topology Control for Wireless Ad Hoc Sensor Networks. Authors ： Yu-Chee Tseng, Yen-Ning Chang, and Bour-Hour Tzeng ICPADS 2002. Outline. Introduction Problem Definition Topology Control under Fixed Powers

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Energy-Efficient Topology Control for Wireless Ad Hoc Sensor Networks

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  1. Energy-Efficient Topology Control for Wireless Ad Hoc Sensor Networks Authors：Yu-Chee Tseng, Yen-Ning Chang, and Bour-Hour Tzeng ICPADS 2002

  2. Outline • Introduction • Problem Definition • Topology Control under Fixed Powers • Topology Control under Variable Powers • Simulation Results • Conclusions

  3. Introduction • Smart sensors • are created by combining tiny sensing materials with electrical circuits • have been proposed recently for various applications

  4. Introduction (cont.) • How to manage the limited battery resource that constrains the life of the network • [4,16] : using power control to reduce interference and improve throughput • [3,9,14] : topology control achieved by tuning transmission powers • [1,10,11,12] : power-aware routing for ad hoc networks • [2,15] : both IEEE 802.11 and Bluetooth support low- power modes • [13] : to design low-power modes on 802.11-based multi-hop networks

  5. Introduction (cont.) • The topology control problem in an ad hoc sensor network • Topology in ad hoc networks is not static since it changes as one changes the transmission powers of hosts • To maximize the lifetime of the network • Hosts’ powers can be fixed or variable throughout the lifetime of the network • We show that optimal lifetimes can be obtained by using a simple minimum spanning tree construction under the fixed power assumption

  6. Introduction (cont.) • Our work is most related to [9], • “Topology control of multihop wireless networks using transmit power adjustment”, IEEE INFOCOM 2000 • Topology control algorithms to form 1-vertex and 2-vertex-connected graphs were presented • To minimize the maximal transmission power of each host in the network • Employs a minimum spanning tree construction • The result is optimal • However, the initial energies of all hosts were assumed to be the same • Our contribution is to extend the applicability of [9] to an environment where hosts’ initial energies are not necessarily equal

  7. Problem Definition • Definition • The network topology is a function of time • G(t)＝(V,E(t)), • where E(t) is the link set induced by the power setting at time t • Our goal is to maintain a certain property of G(t) while keeping its lifetime as long as possible • V : a set of hosts

  8. Problem Definition (cont.) • dist(x,y) : the distance between two hosts • λ(x,y) : the least transmission power needed for x and y to communicate correctly λ(x,y) ＝c × dist(x,y)d where c is a constant, and d is an environment-dependent constant • Bx(t) : the energy level of host x, where t≧0 represents time • Bx(0) is representing x’sinitial energy

  9. Problem Definition (cont.) • For example： • At time t, to connect two hosts, x and y, together using the least power λ(x,y) • Then this link can be sustained for the following length of time

  10. Problem Definition (cont.) • Suppose x had energy Bx(t) at time t and uses power Ps to send. Then after Δt, its remaining energy becomes Bx(t＋Δt)＝ Bx(t)－(Ps × αx × Δt＋ Pr × Δt) where αx is the fraction of time that x transmits during Δt (traffic ratio) • Pris the power consumed in data reception (is a constant) • Ps can be a variable

  11. Problem Definition (cont.) • To consider topology control by tuning transmission powers • we can control the network topology • Ps,x(t) : the transmission power of x • For example： • At instant t, two hosts x and y both Ps,x(t) and Ps,y(t) ≧λ(x,y) • Then there is a communication link between x and y • αx : traffic ratio of each host x • Assume x’s traffic ratio αx remains constant at all times • Different hosts’ traffic ratios are not necessarily the same

  12. Problem Definition (cont.) • PAv(k) : power adjustment problem • to determine the transmission power of each host such that the induced network G(t) remains k-vertex-connected during the time interval [0,T], and such that T is maximized • PAe(k) : power adjustment problem • to determine the transmission power of each host such that the induced network G(t) remains k-edge-connected during the time interval [0,T], and such that T is maximized

  13. Problem Definition (cont.) • Three remarks： • A graph is k-edge(vertex)-connected if the deletion of any k-1 links(vertices) in the network does not partition the network • 1-edge-connected is equivalent to 1-vertex-connected, but this is not true when k≧2 • Vertex-connected is stronger than edge-connected • If all the hosts have the same initial energy, then this problem degenerates to the case considered in [9] • Unidirectional links may exist in the network since hosts may have different transmission powers • However, only bi-directional links are included G(t) since in practice, unidirectional links are difficult to use

  14. Topology Control under Fixed Powers • “Fixed power”：the power function Ps,x(t) remains unchanged throughout the lifetime of the network • The topology G(t) remains unchanged, too • An optimal solution for PAe(1) and PAv(1) • An optimal solution for PAe(2) and PAv(2)

  15. Topology Control under Fixed PowersSolution for PAe(1) andPAv(1) • Is similar to the typical minimum spanning tree construction • How long a link can be sustained as the metric in the construction • The lifetime of link (x,y) is defined as • A link with a longer lifetime implies a lower cost

  16. F A 1 6 5 G E 4 5 6 2 3 B C 4 D Topology Control under Fixed PowersSolution for PAe(1) andPAv(1)(cont.)

  17. Topology Control under Fixed PowersSolution for PAe(1) andPAv(1)(cont.) AlgorithmFPA(1)//Fixed power adjustment for PAe(1) and PAv(1) • From V, construct all possible C2|V| node pairs. Sort these node pairs into a list(PAIR), based on their lifetimes in descending order • Construct |V| clusters of node(s) by placing each node into one separate cluster • Retrieve the first node pair (x,y) from PAIR.If x and y are not in the same cluster, proceed to the next step.Otherwise, repeatedly retrieve more node pairs from PAIR until one (x,y), such that x and y are in different clusters, is found.

  18. Topology Control under Fixed PowersSolution for PAe(1) andPAv(1)(cont.) • Connect link (x,y) by performing the following two steps • If Ps,x(t)＜λ(x,y), set Ps,x(t)＝λ(x,y) • If Ps,y(t)＜λ(x,y), set Ps,y(t)＝λ(x,y) • Merge the two clusters containing x and y into one cluster.If all the nodes in V are already in one cluster, terminate the algorithm; otherwise, go to step 3 and repeat.

  19. Topology Control under Fixed PowersSolution for PAe(1) andPAv(1)(cont.) • FPA(1) employs a greedy approach similar to the standard minimum spanning tree construction by using the lifetimes of links as the costs. • The resulting network is not necessarily a spanning tree — cycles may exist • Whether or not two nodes are connected is not determined by their link lifetime, but by how much power they use

  20. F A 1 6 5 G E 4 5 6 2 3 B C 4 D Topology Control under Fixed PowersSolution for PAe(1) andPAv(1)(cont.)

  21. Topology Control under Fixed PowersSolution for PAe(2) andPAv(2)(cont.) • FPA(2) algorithm • utilizes the resulting network of FPA(1) • Further extends the network to the 2-edge or 2-vertex-connected cases

  22. Topology Control under Fixed PowersSolution for PAe(2) andPAv(2)(cont.) AlgorithmFPA(2) • Run FPA(1) to obtain the transmission power Ps,x(t) of each host x.Identify all 2-edge-/2-vertex-connected components in the resulting network • Again, let PAIR be the sorted list of all C2|v| node pairs • Retrieve the first node pair (x,y) from PAIR.If x and y are not in the same 2-edge-/2-vertex-connected component, then proceed to the next step.Otherwise, retrieve more node pairs from PAIR until one (x,y), such that x and y are in different components, is found.

  23. Topology Control under Fixed PowersSolution for PAe(2) andPAv(2)(cont.) • Connect link (x,y) by performing the following two steps • If Ps,x(t)＜λ(x,y), set Ps,x(t)＝λ(x,y) • If Ps,y(t)＜λ(x,y), set Ps,y(t)＝λ(x,y) • Identify all 2-egde-/2-vertex-connected components in the network.If only one component remains, terminate the algorithm; otherwise, go to step3.

  24. Topology Control under Variable Powers • FPA(1) and FPA(2) are optimal only when the nodes’ transmission powers, once selected, remain unchanged • Under the variable power assumption, • FPA(1) and FPA(2) are not necessarily optimal

  25. Topology Control under Variable Powers (cont.) AlgorithmVPA(k) //Variable power adjustment for PAe(k) and PAv(k), k=1 or2 • Run FPA(k) on the current network to determine hosts’ transmission powers • After a fixed interval Δt, check hosts’ remaining energies.If none of the hosts are dead, go back to step 1;otherwise, terminate the algorithm

  26. Topology Control under Variable Powers (cont.) • This implies that we only need to reevaluate the network topology when the order of links in PAIR changes

  27. Simulation Results • Environment • hosts：are randomly generated • 10 × 10 plane on the real domain • Where each unit is 1 kilometer • The electricity in each host is randomly set between 80 to 120 units with a uniform distribution • A time unit is one hour long • The power-consumption constant c is set to 1 • A pair of hosts with 100 units of electricity separated by 1 km has a lifetime of 100 hours, while such hosts separated by 10 km has a lifetime of 1 hour

  28. Simulation Results 50 randomly generated hosts RH(1) ：28.27, FPA(1) ：34.53, VPA(1) ：38.23 RH(2) ：16.70, FPA(2) ：20.33, VPA(2) ：26.33

  29. Simulation Results 100 randomly generated hosts RH(1) ：36.65, FPA(1) ：42.83, VPA(1) ：49.78 RH(2) ：22.62, FPA(2) ：25.13, VPA(2) ：32.11

  30. Conclusions • To address the ad hoc network topology control problem by taking into account hosts’ remaining energies • The contribution lies in extending the applicability of the work in [9] to the case where hosts’ initial energies can differ

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