Clustered heterogeneous wireless sensor network infrastructure for reliable and efficient path planning of mobile nodes in remote area

Path planning decision by mobile node deployed in a remote environment depends on information gathered from surrounding. Since the decisions are made locally there is no global awareness of path trajectory among nodes, resulting in path overlapping and uneven load balancing due to long distance traversals. This proposal presents a path planning approach that divides the target environment into clusters of equal coverage hole densities using K-mean++ algorithm. Thereafter, mobile nodes are assigned to these clusters and are confined to detect and cover holes within their cluster. The effectiveness of cluster formation is measured using multiple co-relational analyses, and then the variance of effectiveness is performed over different cluster to select the optimum cluster count. Restriction of the mobile nodes in different cluster avoids overlapping of path trajectories and provides load balancing. Comparison with previous state-of-the-art techniques demonstrates that the proposed work provides high coverage with limited movement.