Energy Efficient Geographical Forwarding Algorithm For Wireless Ad Hoc And Sensor Networks

1. Energy-Efficient Geographical Forwarding Algorithm for Wireless Ad Hoc and Sensor Networks Presenter: ZhendongLun 1

3. Conclusion

4. Reference2

5. Energy-efficient routing Goal: to achieve power efficient, multi-hop communication in ad hoc and sensor networks. Types: Topology Control: dynamically chooses the transmit range of each node in such a way that energy consumption is reduced. Power Aware Routing: using some power-aware metrics for determining routesto save energy for multi-hop packet delivery. Sleep Scheduling: chooses some sensors to sleep in order to reduce the energy wasted in an idle state. Globalized Approach: integrates different states of the network(i.e., transmission/reception/idle) into a joint optimization problem, in order to minimize energy consumption. 3

6. Location-based (position-based) routing Goal: make routing decision to the destination based on node geographic position and the position of its one-hop neighbors. Types: Basic distance, progress, and direction based methods Partial flooding and multi-path based path strategies Depth first search based routing with guaranteed delivery Nearly stateless routing with guaranteed delivery Power and cost aware routing 4

7. Energy-efficient location-based routing Goal: makes local routing decisions in order to build a near-optimal power-efficient end-to-end path. Extra information needed: i.e., energy cost for each path, node residual energy 5

8. Network Model One-hop topology N(x) is the set of one-hop neighbors of x Graph G=(V,E) V: set of nodes, E: set of links connecting nodes : residual energy for node x V(G) 6

9. Algorithm Design The network lifetime of a WSN is basically determined by two factors: The energy consumed for per packet end-to-end delivery The energy draining rates at individual nodes Minimize the energy loss at nodes for packet delivery (min-power routing issue) Select the paths with the maximal residual energy Network lifetime highly depends on how these two measurements can be compromised with the assistance of the limited local state information kept at nodes. 7

10. Algorithm Design(cont.) Routing Algorithm Simple mechanisms for energy criticality determining Select the paths with the maximal residual energy 2) Next hop selection using localized Dijkstra’s algorithm Minimize the energy loss at nodes for packet delivery 3) Integration of energy criticality avoidance and localized Dijkstra’s algorithm 8

11. Energy Criticality Determining Each node can independently determine if it is currently an energy-critical node in the network. This procedure has a little communication overhead. 1) the full energy space is divided into L equally- space intervals 2) 3) a node floods its energy index value across the network during the following conditions: a) when the network is initially deployed or b) when its energy index changes(drops) into the energy-critical region. ( ) 9

12. Next Hop Selection Using Localized Dijkstra’s Algorithm Procedure for a packet holder (either an intermediate node or the source node) to select its next hop. Each packet holder applies Dijkstra’s algorithm to its local topology built as follows: P(u,v)= 10

13. Next Hop Selection Using Localized Dijkstra’sAlgorithm (cont.) Implement Dijkstra’s algorithm on , in order to find the next hop of x. Upon receiving the packet, the next hop will repeat the same operations. This behavior repeats until the destination t is reached. Based on the localized Dijkstras’s algorithm, the chosen path is: x u v  t, total weight is 12.5 11

14. Integration of Energy Criticality Avoidance and Localized Dijkstra’s Algorithm Define a set of energy criticality ratios as{r1, r2, …, rk}, sorted in a decreasing order. For an node x to choose its next hop, these ratios will be enforced sequentially. First round, only consider the neighbor nodes whose residual energy above the energy criticality level determined by r1. If no next hop is found using localized Dijkstra’s algorithm, r2 is then enforced. This process continues until all neighbor nodes of x are considered as next hop candidates. However, if no next hop that makes positive progress can be found, one-hop local flooding of the packet is used for a rescue to overcome the local maxima issue. 12

15. Simulation Results Compare the average network lifetime between this proposed algorithm (DECA) and the power-cost2 algorithm. The network lifetime is measured as the time when the first node runs out of its energy. 13

16. Simulation Results (cont.) Single-sink WSNS 14

17. Simulation Results (cont.) Four-sink WSNS 15

18. Conclusion To achieve prolonged network lifetime, the proposed algorithm design assumes network nodes keep their respective one-hop neighborhood view and employs the strategies of localized implementation of Dijkstra’s algorithm and energy-criticality avoidance in next hop selection for packet forwarding. Simulation results demonstrate that this designed algorithm can prolong the network lifetime as compared with related work. 16

19. Reference Q. Yu, B. Zhang, C. Liu, and H.T. Mouftah, “Energy-Efficient Geographical Forwarding Algorithm for Wireless Sensor Networks,” Proceedings IEEE Wireless Communications and Networking Conference WCNC2008 (Networking Track), Las Vegas, Nevada, April 2008, pp. NET16.1.1-NET16.1.6 17

20. Questions/Comments??? 18