EFFICIENT MULTIHOP DUAL DATA UPLOAD CLUSTERING BASED MOBILE DATA COLLECTION IN WIRELESS SENSOR NETWORK

The high efficiency of Wireless Sensor Networks (WSNs) purely depends on the data collection scheme. Several data gathering schemes such as multipath, tree, chain, cluster and hybrid topologies are available in literature for gathering data in WSNs. However, the existing data gathering schemes failed to provide a guaranteed reliable network in terms of traffic, mobility, and end-to-end connection. Recent work shows sink mobility can increase the energy efficiency in WSNs. However, data delivery latency often increases owing to the speed limit of Mobile Sink. Most of them utilize the mobility, to address the problem of data gathering in WSNs. In this project, first define WSNs with Mobile Sink and provide an exhaustive taxonomy of their architectures, based on the role of MS. Then, present an overview of load balanced cluster and dual data uploading data gathering process in such a scenario, and recognize the corresponding challenges and issues. On the basis of these issues, Delay Aware Adaptive Multi Hop Routing Protocol called DAMHR is proposed, which is a heuristic method that locates a near-optimal traveling tour that minimizes the energy consumption of sensor nodes and improves the data gathering. Path selection problem is focused in load balanced clustering and delay-guaranteed sensor networks with a path-constrained mobile sink and concentrate on an efficient data gathering scheme, that simultaneously increases the total amount of data and reduces the energy consumption. The optimal path is preferred to meet the necessary on delay as well as minimizes the energy consumption of entire network. Predictable sink mobility is demoralized to improve energy efficiency of sensor networks.