Wireless Sensor Networks: Minimum-energy communication

1. Wireless Sensor Networks: Minimum-energy communication

2. Wireless Sensor Networks • Large number of heterogeneous sensor devices • Ad Hoc Network • Sophisticated sensor devices • communication, processing, memory capabilities Wireless Sensor Networks: Minimum-energy communication

3. Project Goals • Devise a set communication mechanisms s.t. they • Minimize energy consumption • Maximize network nodes’ lifetimes • Distribute energy load evenly throughout a network • Are scalable (distributed) Wireless Sensor Networks: Minimum-energy communication

4. Minimum-energy unicast Wireless Sensor Networks: Minimum-energy communication

5. Unicast communication model • Link-based model • each link weighed • how to chose a weight? • Power-Aware Metric [Chang00] • Maximize nodes’ lifetimes • include remaining battery energy (Ei) Wireless Sensor Networks: Minimum-energy communication

6. Unicast problem description • Definitions • undirected graph G = (N, L) • links are weighed by costs • the path A-B-C-D is a minimum cost path from node A to node D, which is the one-hop neighbour of the sink node • minimum costs at node A are total costs aggregated along minimum cost paths • Minimum cost topology • Minimum Energy Networks [Rodoplu99] • optimal spanning tree rooted at one-hop neighbors of the sink node • each node considers only its closest neighbors - minimum neighborhood D C B A Wireless Sensor Networks: Minimum-energy communication

7. Building minimum cost topology • Minimum neighborhood • notation: - minimum neighborhood of node • P1: minimum number of nodes enough to ensure connectivity • P2: no node falls into the relay space of any other node • Finding a minimum neighborhood • nodes maintain a matrix of mutual link costs among neighboring nodes (cost matrix) • the cost matrix defines a subgraph H on the network graph G C A B Wireless Sensor Networks: Minimum-energy communication

8. Finding minimum neighborhood • We apply shortest path algorithmto find optimal spanning tree rooted at the given node • Theorem 1: The nodes that immediately follow the root node constitute the minimum neighborhood of the root node • Theorem 2: The minimum costroutes are contained in the minimum neighborhood • Each node considers just its min. neighborhood subgraph H Wireless Sensor Networks: Minimum-energy communication

9. Distributed algorithm • Each node maintains forwarding table • E.g. [originator ¦ next hop ¦ cost ¦ distance] • Phase 1: • find minimum neighborhood • Phase 2: • each node sends its minimumcost to it neighbors • upon receiving min. costupdate forwarding table • Eventually the minimum cost topology is built Wireless Sensor Networks: Minimum-energy communication

10. An example of data routing • Different routing policies • different packet priorities • nuglets [Butt01] • packets flow toward nodes with lower costs • Properties • energy efficiency • scalability • increased fault-tolerance Wireless Sensor Networks: Minimum-energy communication

11. Minimum-energy broadcast Wireless Sensor Networks: Minimum-energy communication

12. Every node j is assigned abroadcast cost Broadcast communication model • Omnidirectional antennas • By transmitting at the power level max{Eab,Eac} node a can reach both node b and node c by a single transmission • Wireless Multicast Advantage (WMA) [Wieselthier et al.] b Eab Ebc Eac a c • Trade-off between the spent energy and the number of newly reached nodes • Power-aware metric • include remaining battery energy (Ei) • embed WMA (ej/Nj) Wireless Sensor Networks: Minimum-energy communication

13. Example: C1={S1, S2, S3} C2={S3, S4, S5} C*= • BCP Greedy algorithm: at each iteration add the set Sj that minimizes ratio cost(Sj)/(#newly covered nodes) Broadcast cover problem (BCP) • Set cover problem Wireless Sensor Networks: Minimum-energy communication

14. Distributed algorithm for BCP • Phase 1: • learn neighborhoods (overlapping sets) • Phase 2:(upon receiving a bcast msg) 1: if neighbors covered HALT 2: recalculate the broadcast cost 3: wait for a random time before re-broadcast 4: if receive duplicate msg in the mean time goto 1: • Random time calculation • random number distributed uniformly between 0 and Wireless Sensor Networks: Minimum-energy communication

15. Simulations • GloMoSim [UCLA] • scalable simulation environment for wireless and wired networks average node degree ~ 6 average node degree ~ 12 Wireless Sensor Networks: Minimum-energy communication

16. Simulation results (1/2) Wireless Sensor Networks: Minimum-energy communication

17. Simulation results (2/2) Wireless Sensor Networks: Minimum-energy communication

18. Conclusion and future work • Power-Aware Metrics • trade-off between residual battery capacity and transmission power are necessary • Scalability • each node executes a simple localized algorithm • Unicast communication • link based model • Broadcast communication • node based model • Can we do better by exploiting WMA properly? Wireless Sensor Networks: Minimum-energy communication

19. Minimum-energy broadcast: if (Pac – Pab < Pbc) thentransmit atPac Minimum-energy broadcast • Propagation model: • Omnidirectional antennas • Wireless Multicast Advantage (WMA) [Wieselthier et al.] b Pab Pbc Pac a c • Challenges: • As the number of destination increases the complexity of this formulation increases rapidly. • Requirement for distributed algorithm. • What are good criteria for selecting forwarding nodes? • Broadcast Incremental Power (BIP) [Wieselthier et al.] • Add a node at minimum additional cost • Centralized • Cost (BIP) <= Cost (MST) • Improvements? • Take MST as a reference • Branch exchange heuristic… • … to embed WMA in MST Wireless Sensor Networks: Minimum-energy communication