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

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

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  • The notion of progress is the key concept of several GPS based methods proposed in 1984-86 . Given a transmitting node S, the progress of a node A is defined as the projection onto the lineconnecting S and the final destination.of the distance between S and the receiving node A neighboris in forward direction if the progress is positive (for example, for transmitting node S andreceiving nodes A, C and F in Fig. 1); otherwise it is said to be in backward direction (e.g. nodes Band E in Fig. 1). Basic Distance, Progress,
  • For thispurpose, we design here a simple but efficient method fordisseminating the residual energy status at nodes with lightcommunication overhead.the main effort focuses on discouragingthose energy-starving nodes from energy draining.
  • Energy Efficient Geographical Forwarding Algorithm For Wireless Ad Hoc And Sensor Networks

    1. 1. Energy-Efficient Geographical Forwarding Algorithm for Wireless Ad Hoc and Sensor Networks<br />Presenter: ZhendongLun<br />1<br />
    2. 2. Contents<br /><ul><li>Introduction</li></ul> 1) Energy-efficient routing<br /> 2) Location-based (position-based) routing<br /> 3) Energy-efficient location-based routing<br /><ul><li>Proposed Algorithm</li></ul> 1) Network model<br /> 2) Algorithm design<br /><ul><li>Simulation Results
    3. 3. Conclusion
    4. 4. Reference</li></ul>2<br />
    5. 5. Energy-efficient routing<br />Goal: to achieve power efficient, multi-hop communication <br /> in ad hoc and sensor networks.<br />Types:<br />Topology Control: dynamically chooses the transmit range of each node in such a way that energy consumption is reduced.<br />Power Aware Routing: using some power-aware metrics for determining routesto save energy for multi-hop packet delivery.<br />Sleep Scheduling: chooses some sensors to sleep in order to reduce the energy wasted in an idle state. <br />Globalized Approach: integrates different states of the network(i.e., transmission/reception/idle) into a joint optimization problem, in order to minimize energy consumption.<br />3<br />
    6. 6. Location-based (position-based) routing<br />Goal: make routing decision to the destination based on <br /> node geographic position and the position of its<br /> one-hop neighbors.<br />Types:<br />Basic distance, progress, and direction based methods<br />Partial flooding and multi-path based path strategies<br />Depth first search based routing with guaranteed delivery<br />Nearly stateless routing with guaranteed delivery<br />Power and cost aware routing<br />4<br />
    7. 7. Energy-efficient location-based routing <br />Goal: makes local routing decisions in order to build a <br /> near-optimal power-efficient end-to-end path.<br />Extra information needed:<br />i.e., energy cost for each path, <br /> node residual energy <br />5<br />
    8. 8. Network Model<br />One-hop topology<br />N(x) is the set of one-hop neighbors of x<br />Graph G=(V,E)<br />V: set of nodes, <br />E: set of links connecting nodes<br /> : residual energy for node x V(G)<br />6<br />
    9. 9. Algorithm Design<br />The network lifetime of a WSN is basically determined <br />by two factors:<br />The energy consumed for per packet end-to-end delivery<br />The energy draining rates at individual nodes<br />Minimize the energy loss at nodes for packet delivery<br />(min-power routing issue)<br />Select the paths with the maximal residual energy<br />Network lifetime highly depends on how these two measurements can <br />be compromised with the assistance of the limited local state information <br />kept at nodes.<br />7<br />
    10. 10. Algorithm Design(cont.)<br />Routing Algorithm<br />Simple mechanisms for energy criticality determining<br /> Select the paths with the maximal residual energy<br />2) Next hop selection using localized Dijkstra’s algorithm<br /> Minimize the energy loss at nodes for packet delivery<br />3) Integration of energy criticality avoidance and localized <br />Dijkstra’s algorithm<br />8<br />
    11. 11. Energy Criticality Determining<br />Each node can independently determine if it is currently an <br />energy-critical node in the network.<br />This procedure has a little communication overhead.<br />1) the full energy space is divided into L equally- <br /> space intervals<br /> 2) <br /> 3) a node floods its energy index value across the<br /> network during the following conditions:<br /> a) when the network is initially deployed<br /> or<br /> b) when its energy index changes(drops) into<br /> the energy-critical region.<br /> ( )<br />9<br />
    12. 12. Next Hop Selection Using Localized Dijkstra’s Algorithm<br />Procedure for a packet holder (either an intermediate node or the <br />source node) to select its next hop.<br />Each packet holder applies Dijkstra’s algorithm to its local topology <br />built as follows:<br /> P(u,v)=<br />10<br />
    13. 13. Next Hop Selection Using Localized Dijkstra’sAlgorithm (cont.)<br />Implement Dijkstra’s algorithm on , in order to find the next hop of x.<br />Upon receiving the packet, the next hop will repeat the same operations.<br />This behavior repeats until the destination t is reached. <br />Based on the localized Dijkstras’s algorithm, the chosen path is:<br /> x u v  t, total weight is 12.5 <br />11<br />
    14. 14. Integration of Energy Criticality Avoidance and Localized Dijkstra’s Algorithm<br />Define a set of energy criticality ratios as{r1, r2, …, rk}, sorted in a decreasing <br /> order.<br />For an node x to choose its next hop, these ratios will be enforced sequentially.<br />First round, only consider the neighbor nodes whose residual energy above the <br /> energy criticality level determined by r1.<br />If no next hop is found using localized Dijkstra’s algorithm, r2 is then <br /> enforced.<br />This process continues until all neighbor nodes of x are considered as next hop <br /> candidates.<br />However, if no next hop that makes positive progress can be found, one-hop local <br />flooding of the packet is used for a rescue to overcome the local maxima issue.<br />12<br />
    15. 15. Simulation Results<br />Compare the average network lifetime between this proposed <br />algorithm (DECA) and the power-cost2 algorithm.<br />The network lifetime is measured as the time when the first node <br />runs out of its energy.<br />13<br />
    16. 16. Simulation Results (cont.)<br />Single-sink WSNS<br />14<br />
    17. 17. Simulation Results (cont.)<br />Four-sink WSNS<br />15<br />
    18. 18. Conclusion<br />To achieve prolonged network lifetime, the proposed algorithm design<br />assumes network nodes keep their respective one-hop neighborhood view <br />and employs the strategies of localized implementation of Dijkstra’s<br />algorithm and energy-criticality avoidance in next hop selection for <br />packet forwarding.<br />Simulation results demonstrate that this designed algorithm can prolong <br />the network lifetime as compared with related work.<br />16<br />
    19. 19. Reference<br />Q. Yu, B. Zhang, C. Liu, and H.T. Mouftah, “Energy-Efficient Geographical <br />Forwarding Algorithm for Wireless Sensor Networks,” Proceedings IEEE <br />Wireless Communications and Networking Conference WCNC2008 <br />(Networking Track), Las Vegas, Nevada, April 2008, pp. NET16.1.1-NET16.1.6<br />17<br />
    20. 20. Questions/Comments???<br />18<br />

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