Hexagonal grid-based dynamic routing adjustment for optimising network lifetime in wireless sensor network

Over past few years we have seen a growing interest in the prospect use of wireless sensor networks (WSNs) in enormous domain of applications and it became an interesting research area. The formation of WSNs, which consist of small battery with limited power, make it has a huge shortage of energy. Many researchers proposed various techniques to solve this problem, aiming to prolong the network lifetime. In the scientific literatures, some of the researchers proposed routing protocols to pass the data through the network. These include the division of sensing area into grids or cells as clusters, each cluster select one node as Cluster-Head (CH). CHs task inside the network is to connect with each other in order to receive the data from MNs and send it to S. The other suggested technique assumed that the sink has to be routable and moves around the network and collect data from MNs directly. By doing so, the mobile sink can reduce the energy depletion of the nodes subsequently, will enhance the network lifetime. The major issues while utilising this technique is how to detect the sink while moving and this is still a challenge. This research try to solve the problem that occur while utilizing the clustering technique, which are the CHs that close to the S node will deplete the energy faster than others, as well as, during the sink mobility technique, solving the detection of the mobile sink location process. However, a new protocol has proposed called Hexagonal Grid based Dynamic Routes Adjustment (HGDRA) which divides the network cells into hexagonal clusters and used the mobile sink technique inside the network area in order to prolong the network lifetime. The proposed technique tries to reconstruct a data packet path that detects the mobile sink with less energy consumption, as well as improving the network lifetime. The experimental result that gained via Ns2 simulator demonstrate that the proposed approach produce better quality solutions in terms of network reconstruction cost, number of hops, nodes residual energy and network lifetime when compared with the benchmark approach.

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