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Optimizing Multihop Wireless Networks for Power Efficiency and Connectivity Control

This chapter explores methods to manage dense wireless sensor networks by adjusting transmission power, controlling network topology, and optimizing node activity. Topics include hierarchical and flat network topologies, power control strategies, k-hop connected dominating sets, and adaptive node behavior. Emphasis is placed on improving energy conservation, reducing interference, and enhancing network performance.

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Optimizing Multihop Wireless Networks for Power Efficiency and Connectivity Control

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  1. Chapter 5Topology Control

  2. Outline • 5.1. Motivations and Goals • 5.2. Power Control and Energy Conservation • 5.3. Tree Topology • 5.4. k-hop Connected Dominating Set • 5.5. Adaptive node activity • 5.6. Conclusions

  3. Outline • 5.1. Motivations and Goals • 5.2. Power Control and Energy Conservation • 5.3. Tree Topology • 5.4. k-hop Connected Dominating Set • 5.5. Adaptive node activity • 5.6. Conclusions

  4. Motivations • A typical characteristic of wireless sensor networks • deploying many nodes in a small area • ensure sufficient coverage of an area, or • protect against node failures • Networks can be too dense: too many nodes in close (radio) vicinity

  5. Motivations • In a very dense networks, too many nodes • Too many collisions • Too complex operation for a MAC protocol • Too many paths to be chosen from for a routing protocol, …

  6. Goals • This chapter looks at methods to deal with such networks by • Reducing/controlling transmission power • Deciding which links to use • Turning some nodes off

  7. Topology Control • Topology control: Make topology less complex • Topology: • Which node is able/allowed to communicate with which other nodes • Topology control needs to maintain invariants, e.g., connectivity

  8. Options for topology control Topology control Flat network All nodes have essentially same role Hierarchical network Assign different roles to nodes and then control node/link activity Power control Hybrid Tree Clustering Adaptive node activity Dominating sets

  9. Outline • 5.1. Motivation and Goals • 5.2. Power Control and Energy Conservation • 5.3. Tree Topology • 5.4. k-hop Connected Dominating Set • 5.5. Adaptive node activity • 5.6. Conclusions

  10. Introduction of Power Control • Power control • The transmitter’s power can be adjusted dynamically over a wide range • Typical radio adjusts their transmitter’s power based on received signal strength B A A C D Connected • Controls the transmission power • Topology control for desired connectivity • Compensate topology changes incurred by mobility and dead nodes Disconnected

  11. Introduction of Power Control • Interactions Power control Large Battery makes Longer Lifetime Battery drain

  12. Introduction of Power Control • Interactions B A C Interference Large Power makes Performance Degradation Source D Destination Power control Large Battery makes Longer Lifetime Battery drain

  13. Introduction of Power Control • Interactions Interference C B A Large Power makes Performance Degradation Source D Destination Power control Different Power makes Load Unbalancing Large Battery makes Longer Lifetime D Destination C B Adjusting power can balance the power consumption A Source A consumes much more power than C Battery drain

  14. Introduction of Power Control • Interactions Adjusting the power of A can improve the spatial reuse C is forbid to communication with B B C D Interference B C A A A C E Large Power makes Performance Degradation Small Power creates more Spatial Reuse Opportunities Source D Destination Power control Different Power makes Load Unbalancing Large Battery makes Longer Lifetime D Destination B Source A consumes much more power than C Battery drain

  15. Error performance Introduction of Power Control • Interactions Adjusting the power of A can improve the spatial reuse B C D Interference B C A A A C E Large Power makes Performance Degradation Small Power creates more Spatial Reuse Opportunities Source D Destination Power control Different Power makes Load Unbalancing Small Power causes More Retransmissions Large Battery makes Longer Lifetime Error rate D Destination B Large power, small error rate Source A consumes much more power than C Battery drain dB

  16. Introduction of Power Control • Targets and Issues • Improve network throughput • Improve transmission range • Improve fairness • Improve connectivity • Power control helps in scheduling • Reduce the interference and energy consumption • Partial combination of above targets • etc.

  17. Power Control and Energy Conservation Topology Control of Multihop Wireless Networks using Transmit Power Adjustment R. Ramanathan and R. Rosales-Hain IEEE INFOCOM 2000

  18. Introduction • Topology • The set of communication links between node pairs used by routing mechanism • Uncontrollable factor: mobility, weather, interference, noise • Controllable factor: transmission power, antenna direction

  19. Introduction • A graph is called connected if every pair of distinct vertices in the graph can be connected through some path • A bi-connected graph is a connected graph that is not broken into disconnected pieces by deleting any single vertex (and its incident edges) Connected Bi-connected

  20. Motivation • Drawbacks of wrong topology • Reduce network capacity • Increase interference • Increase end-to-end packet delay • Sparse network • A danger of network partitioning • High end to end delays • Dense network • Many nodes interfere with each other

  21. Static Networks: Min-Max Power Algorithm • Goal • Find a per-node minimal assignment of transmitted power p such that (1) the induced graph is connected and (2) max p is minimum

  22. Min-Max Power Algorithm- Connected Networks • Phase I: CONNECTION • Construct a Minimum cost spanning tree Successful transmit power between i and j 4 E F 3 3 : path loss between i and j s : the receiver sensitivity 2 C D 3 3 1 1 1 1 1 1 3 2 3 : the location of node i A 2 B 2

  23. Min-Max Power Algorithm-Connected Networks • Phase II : Per Node Minimizing Power side-effect-edge: The edge of (C, D) is automatically connected F 4 E A has a path to B via C with smaller power →A adjusts the transmitted power from 2 to 1. 3 3 2 B has a path to A via D with smaller power →B adjusts the transmitted power from 2 to 1. C D 1 1 1 3 3 1 1 3 1 1 1 3 2 2 A 2 B The edge (A, B) can be disconnected to save more energy

  24. Min-Max Power Algorithm- Bi-Connectivity Augmentation • Phase I: BICONN-AUGMENT • Construct a Connected Minimum cost spanning tree Successful transmit power between i and j 4 F E 3 3 : path loss between i and j s : the receiver sensitivity 2 C D 3 3 1 1 1 1 1 3 1 3 : the location of node i A B 2 2 2

  25. Min-Max Power Algorithm- Bi-Connectivity Augmentation • Phase I: BICONN-AUGMENT • Add (u, v) to graph Guntil the network is bi-connected Bi-Connected component of C Bi-Connected component of D F E 4 E F 3 3 C D 2 C D A 1 1 1 3 3 1 3 1 3 1 2 2 B C Bi-Conn. Comp. of C Bi-Conn. Comp. of D D A 2 B => Add (C, D)

  26. Min-Max Power Algorithm- Bi-Connectivity Augmentation • Phase I: BICONN-AUGMENT • Add (u, v) to graph Guntil the network is bi-connected Bi-Connected component of E Bi-Connected component of F F 4 E E F 3 3 2 C D 1 1 1 3 3 1 3 1 3 1 2 2 C Bi-Conn. Comp. of E Bi-Conn. Comp. of F D A 2 B => Add (E, F)

  27. Min-Max Power Algorithm- Bi-Connectivity Augmentation • Phase II: Per Node Minimizing Power • No side-effect-edge →Finish 4 F E 3 3 2 C D 1 1 3 2 4 2 3 4 1 1 1 1 2 A B

  28. Min-Max Power Algorithm- Bi-Connectivity Augmentation • Phase II: Per Node Minimizing Power • An other example has side-effect-edge side-effect-edge: 2 3 The edge of (A, D) is automatically connected 1 B A 3 3 3 2 3 3 2 2 3 1 1 D C 2 Disconnect the edge (A, C) and still Bi-Connectivity →C adjusts the transmitted power from 3 to 2

  29. Min-Max Power Algorithm-Bi-Connectivity Augmentation • Phase II: Per Node Minimizing Power • An other example has side-effect-edge 3 2 Disconnect the edge (B, D) and still Bi-Connectivity →B adjusts the transmitted power from 3 to 2 A 1 B 2 3 3 3 3 2 2 1 3 1 D C 2

  30. Min-Max Power Algorithm-Bi-Connectivity Augmentation • Phase II: Per Node Minimizing Power • Finish 2 3 A 1 B 2 3 3 3 3 2 1 1 3 2 D C 2

  31. Outline • 5.1. Motivation and Goals • 5.2. Power Control and Energy Conservation • 5.3. Tree Topology • 5.4. k-hop Connected Dominating Set • 5.5. Adaptive node activity • 5.6. Conclusions

  32. Retransmission node Retransmission node Introduction of Tree Topology Control • Example: • MPR (Multi-Point Relay) election (b) (a) (b) is better than (a)

  33. a b c c b a d f e f d e Introduction of Tree Topology Control • Example: g g h h (a) (b) a to d needs 2 hops a to d needs 7 hops (a) is better than (b)

  34. Tree Topology Design and Analysis of an MST-Based Topology Control Algorithm N. Li, J. C. Hou, and L. Sha IEEE INFOCOM 2003

  35. Motivation • The advantage of Topology Control • Minimize the overhearing and then optimize the network spatial reuse • Maintain a connected topology by minimal power • Power-efficient B B I I F F C C A A G G D D H H E E (2) With Topology Control (1) No Topology Control

  36. Goal • Determine the transmission power of each node • Maintain network connectivity • Minimal power consumption

  37. Local Minimum Spanning Tree Algorithm (LMST) • Local Minimum Spanning Tree Algorithm (LMST) • Step 1: Information Collection • Step2: Topology Construction • Step3: Determination of Transmission Power

  38. LMST – Step1: Information Collection • Information Exchange • Each node broadcasts periodically a Hello message using its maximal transmission power. • The Hello message includes the ID and Location of the node. a Maximal Transmission Power b u u‘’s ID and Location d c

  39. LMST – Step1: Information Collection • Information Exchange • Since Hello message includes the node’s ID and Location, after obtaining the Hello message of 1-hop neighbors, node u can construct the local view. a b u d c

  40. LMST – Step2: Topology Construction • The weight of edge between the two nodes is based on Euclidean distance. • The weight of an edge also denotes the transmission power (or distance) between the two nodes c： Coefficient d： distance e 7 a 5 6 7 b 10 5 u 7 6 3 c 4 d

  41. LMST – Step 2: Topology Construction • Each node applies Prim’s algorithm independently to obtain its Local Minimum Spanning Tree. Node uconstructs the Local Minimum Spanning Tree using Prim’s algorithm according to its local view local viewof node u According to the constructed Local Minimum Spanning Tree, node u will use small power to communicate with node a via node binstead of using large power to communicate with node a directly. 7 e a 5 6 7 b 10 5 7 u • Small power: • Creates more spatial reuse opportunity • Decreases energy consumption 6 3 c 4 d

  42. LMST – Step 3: Determination of Transmission Power • By measuring the receiving power of Hello message, each node can determine the specific power levels it needs to reach each of its neighbors. • Two commonly-used propagation models • Free Space • Two-Ray

  43. LMST – Step 3: Determination of Transmission Power • In general, the relation between Pr and Ptis of the following form Where G is a function of • Example • Pth is the required power threshold to successfully receive the message • Pmax is the maximal transmission power e Node b will compute： a b Hello Data Node b transmits data to u： Data with PthG u c d Hello with Pmax

  44. Conclusions • Advantages • Maintain network connectivity by low energy consumption • Reduce the probability of interference • Increase the spatial reuse • Achieve high throughput

  45. Tree Topology On the Construction of Energy-Efficient Broadcast and Multicast Trees in Wireless Networks J. Wieselthier, G. Nguyen, and A. Ephremides IEEE INFOCOM 2000

  46. Introduction • The paper studies the problems of broadcasting and multicasting in wireless networks. • To form a minimum-energy tree • Energy efficiency • Maintain network connectivity

  47. Network Assumptions • The power level of a transmission can be chosen within a given range of values. • The availability of a large number of bandwidth resources. • Sufficient transceiver resources are available at each of the nodes in the network.

  48. Wireless Communications Model • Node-based transmission cost evaluation • Pi,(j,k) = max{Pij, Pik}, • Pij: Transmission power for node i to transmit packets to node j The larger power (Pik ) can cover both of node j and node k Pik > Pij j Pij The smaller power (Pij ) can only cover node j i Pik k

  49. The Broadcast Incremental Power Algorithm • Assume node a is the source node • Step 1: Determining the node that the Source can reach with minimum expenditure of power. 5 g f 4 1.3 1.5 1.2 1.7 3 a b h 0.3 d 0.9 c 1 0.5 2 1.3 0.8 0.7 1.1 j i e 0.3 b 1 a 0.5 a c 0 0 1 2 3 4 5

  50. The Broadcast Incremental Power Algorithm • Step 2: Determine which “new” node can be added to the tree at minimum additional cost. 5 g f 4 ΔPa 1.3 1.5 1.2 1.7 0.5 1 Pbd Pac b a d c 3 0.3 a Pa Pb a b b b h 0.3 d 0.9 • ΔPa= 0.5 – 0.3 = 0.2 c 1 1 0.5 Minimum additional cost 2 1.3 0.8 0.7 1.1 ΔPb j i e 1 • ΔPb= 1 – 0 = 1 0 0 1 2 3 4 5

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