Wireless Sensor Networks Minimumenergy communication Mario agalj supervised

Wireless Sensor Networks: Minimum-energy communication Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario. cagalj@epfl. ch Wireless Sensor Networks: Minimum-energy communication

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 2

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 3

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

Unicast communication model § Link-based model each link weighed § how to chose a weight? § § Power-Aware Metric [Chang 00] § Maximize nodes’ lifetimes include remaining battery energy (Ei) n Wireless Sensor Networks: Minimum-energy communication 5

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 onehop neighbour of the sink node § minimum costs at node A are total costs aggregated along minimum cost paths § D C § Minimum cost topology Minimum Energy Networks [Rodoplu 99] § optimal spanning tree rooted at one-hop neighbors of the sink node § each node considers only its closest neighbors - minimum neighborhood § Wireless Sensor Networks: Minimum-energy communication B A

7 Building minimum cost topology § Minimum neighborhood notation: - minimum neighborhood of node § P 1: minimum number of nodes enough to ensure connectivity § P 2: 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 Wireless Sensor Networks: Minimum-energy communication B

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

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 minimum cost to it neighbors § upon receiving min. cost update forwarding table § Eventually the minimum cost topology is built Wireless Sensor Networks: Minimum-energy communication 9

An example of data routing § Different routing policies § different packet priorities § Properties § energy efficiency § nuglets [Butt 01] § scalability § packets flow toward nodes with § increased fault-tolerance lower costs Wireless Sensor Networks: Minimum-energy communication 10

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

Broadcast communication model b Eac a § § c § 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. ] Trade-off between the spent energy and the number of newly reached nodes Power-aware metric § § § Ebc § § 12 include remaining battery energy (Ei) embed WMA (ej/Nj) Every node j is assigned a broadcast cost Wireless Sensor Networks: Minimum-energy communication

Broadcast cover problem (BCP) § Set cover problem Example: C 1={S 1, S 2, S 3} C 2={S 3, S 4, S 5} C*= § BCP Greedy algorithm: at each iteration add the set Sj that minimizes ratio cost(Sj)/(#newly covered nodes) Wireless Sensor Networks: Minimum-energy communication 13

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 14

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

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

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

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 18

Minimum-energy broadcast b Pab a § § c if (Pac – Pab < Pbc) then transmit at Pac 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? § § § Pac Propagation model: Omnidirectional antennas Wireless Multicast Advantage (WMA) [Wieselthier et al. ] Minimum-energy broadcast: Challenges: § § Pbc § § 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 19