1. 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

2. 2  Large number of heterogeneous sensor devices  Ad Hoc Network  Sophisticated sensor devices  communication, processing, memory capabilities Wireless Sensor Networks

3. 3 Wireless Sensor Networks: Minimum-energy communication 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)

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

5. 5 Wireless Sensor Networks: Minimum-energy communication 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)

6. 6 Wireless Sensor Networks: Minimum-energy communication 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 A B C D

7. 7 Wireless Sensor Networks: Minimum-energy communication 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 A B C

8. 8 Wireless Sensor Networks: Minimum-energy communication Finding minimum neighborhood  We apply shortest path algorithm to 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 cost routes are contained in the minimum neighborhood  Each node considers just its min. neighborhood subgraph H

9. 9 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

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

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

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

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

14. 14 Wireless Sensor Networks: Minimum-energy communication 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

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

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

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

18. 18 Wireless Sensor Networks: Minimum-energy communication 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?

19. 19 Wireless Sensor Networks: Minimum-energy communication Minimum-energy broadcast  Propagation model:  Omnidirectional antennas  Wireless Multicast Advantage (WMA) [Wieselthier et al.] a c b P ab P ac P bc if (P ac – P ab < P bc ) then transmit at P ac  Minimum-energy broadcast:  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