We introduce and evaluate a very simple landmark-based network partition technique called Hierarchical Bipartition Routing (HBR) to support routing with delivery guarantee in wireless ad hoc sensor networks. It is a simple routing protocol that can easily be combined with any other greedy routing algorithm to obtain delivery guarantee. The efficiency of HBR increases if the network is sparse and contains obstacles. The space necessary to store the additional routing information at a node u is on average not larger than the size necessary to store the IDs of the neighbors of u. The amount of work to setup the complete data structure is on average proportional to flooding the entire network log(n) times, where n is the total number of sensor nodes. We evaluate the performance of HBR in combination with two simple energy-aware geographic greedy routing algorithms based on physical coordinates and virtual coordinates, respectively. Our simulations show that the difference between using HBR and a weighted shortest path to escape a dead-end is only a few percent in typical cases.
Greedy routing algorithms can easily be extended by taking into account power consumption. Power aware greedy routing in most cases tries to minimize the ratio of the energy consumed by a transmission to the progress made. The progress is the distance reduction towards the destination. This cost over progress (COP) power-aware framework is first introduced in @cite_13 . If the cost is equal for all connections, we obtain the simple greedy algorithm as already discussed above @cite_28 . If the cost of a connection is proportional to the distance between the nodes, the resulting routing is similar to compass routing @cite_33 .
@cite_28 @cite_13 @cite_33
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In this article we propose several new progress based, localized, power, and cost aware algorithms for routing in ad hoc wireless networks. These algorithms attempt to minimize the total power and or cost needed to route a message from source to destination. In localized algorithms, each node makes routing decisions solely on the basis of location of itself, its neighbors and destination. The new algorithms are based on the notion of proportional progress. A node currently holding the packet will forward it to a neighbor, closer to destination than itself, which minimizes the ratio of power and or cost to reach that neighbor, and the progress made, measured as the reduction in distance to destination, or projection along the line to destination. First, we propose Power Progress, Iterative Power Progress, Projection Power Progress, and Iterative Projection Power Progress algorithms, where the proportional progress is defined in terms of power measure. The power metrics are then replaced by cost or power-co... We present a simple framework for designing network layer protocols for sensor networks including localized routing, broadcasting, area coverage, and so on. The framework is general enough and is applicable to a variety of problems, network assumptions, and optimality criteria. Our simple framework is based on optimizing the ratio of the cost of making certain decisions (e.g., selecting a forwarding neighbor for routing) to the progress made in doing so (e.g., reduction in distance to destination). We show how to apply this general guideline for the design of hop count, power awareness, maximal lifetime, beaconless and physical-layer-based routing, minimal energy broadcasting, sensor area coverage, and multicasting protocols. Moreover, we show that in the case of routing, the best known strictly localized position-based techniques are, in almost all cases, special cases of the described general cost-to-progress ratio paradigm We propose a novel localized routing protocol for wireless sensor networks (WSN) that is energy-efficient and guarantees delivery. We prove that it is constant factor of the optimum for dense networks. To forward a packet, a node s in graph G computes the cost of the energy weighted shortest path (SP) between s and each of its neighbors which are closer to the destination than itself. It then selects node x which minimizes the ratio of the cost of the SP to the progress towards the destination. It then sends the message to the first node on the SP from s to x: say node x'. Node x' restarts the same greedy routing process until the destination is reached or the routing fails. To recover from failure, our algorithm invokes face routing that guarantees delivery. This work is the first to optimize energy consumption of face routing. First, we build a connected dominating set from graph G, second we compute its Gabriel graph to obtain the planar graph G'. Face routing is applied on G' only to decide which edges to follow in the recovery process. On each edge, greedy routing is used. This two-phase (greedy-Face) end-to-end routing process (EtE) reiterates until the final destination is reached. Simulation results show that EtE outperforms several existing geographical routing on energy consumption metric.