Energy-aware Georouting with Guaranteed Delivery in Wireless Sensor Networks with Obstacles

International audienceWe propose, EtE, a novel end-to-end localized routing protocol for wireless sensor networks that is energy-efficient and guarantees delivery. To forward a packet, a node s in graph G computes the cost of the energy weighted shortest path between s and each of its neighbors in the forward direction towards the destination which minimizes the ratio of the cost of the shortest path to the progress (reduction in distance towards the destination). It then sends the message to the first node on the shortest path from s to x: say node x′. Node x′ restarts the same greedy rout- ing process until the destination is reached or an obstacle is encountered and the routing fails. To recover from the latter scenario, local minima trap, our algorithm invokes an energy-aware Face routing that guarantees delivery. Our work is the first to optimize energy consumption of Face routing. It works as follows. First, it builds a connected dominating set from graph G, second it computes its Gabriel graph to obtain the planar graph G′. Face routing is invoked and applied to G′ only to determine which edges to follow in the recovery process. On each edge, greedy rout- ing is applied. 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 en- ergy consumption metric and delivery rate. Moreover, we prove that the computed path length and the total energy of the path are constant factors of the optimal for dense networks

Energy-aware Georouting with Guaranteed Delivery in Wireless Sensor Networks with Obstacles

International audienceWe propose, EtE, a novel end-to-end localized routing protocol for wireless sensor networks that is energy-efficient and guarantees delivery. To forward a packet, a node s in graph G computes the cost of the energy weighted shortest path between s and each of its neighbors in the forward direction towards the destination which minimizes the ratio of the cost of the shortest path to the progress (reduction in distance towards the destination). It then sends the message to the first node on the shortest path from s to x: say node x′. Node x′ restarts the same greedy rout- ing process until the destination is reached or an obstacle is encountered and the routing fails. To recover from the latter scenario, local minima trap, our algorithm invokes an energy-aware Face routing that guarantees delivery. Our work is the first to optimize energy consumption of Face routing. It works as follows. First, it builds a connected dominating set from graph G, second it computes its Gabriel graph to obtain the planar graph G′. Face routing is invoked and applied to G′ only to determine which edges to follow in the recovery process. On each edge, greedy rout- ing is applied. 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 en- ergy consumption metric and delivery rate. Moreover, we prove that the computed path length and the total energy of the path are constant factors of the optimal for dense networks

An Integrated Approach For Energy Efficient Routing Over Ad-Hoc Network Using Soft Computing

In the past few years, wireless communication has grown rapidly. Through these greatest feature is provided by the network without wires. Handheld devices and any user can take their place. They benefit from a small device, the long-lasting battery. The new communication standard for high-bandwidth services. For communication, the fixed network infrastructure through such is not necessary. These self-organizing network (Ad hoc networks) has received a massive interest in recent times. The most common application of wireless networks is set standard for mobile communications (GSM) and wireless local area network (WLAN). Node is not arranged in any particular manner in such a network. Therefore, in order to ensure communication between the nodes, a number of routing protocols have been developed for such a network. In this proposed system we design the network on adoc network those have some node and we will use the artificial neural network for the training and testing the network. In this system we will give the input neuron value and set a bias value for the purpose of training of network to improve the efficiency of network. After the number of training session in network we get the less energy consumption in that particular network. The objective of this paper work, the process of the minimum number of hops through the use of physical layer information instead of the default distance vector algorithm proposed amendments after AODV routing protocol version based routing discovery