Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio

Improvement of cluster head selection in leach protocol of wireless sensor network

WSNs suffer from many issues such as coverage, security, energy-efficiency and localization. Among these issues, energy-efficiency is the most critical, as sensor nodes are battery operated, hence the need to optimize energy. Clustering technique has shown to be more suitable for energy efficiency, which is why LEACH protocol is considered. Despite that, the LEACH algorithm shows some drawbacks in the area of energy efficiency that needs to be enhanced in order to improve its performance. Therefore, since routing, communication and broadcasting between the nodes are done by the cluster heads, a modification in the LEACH algorithm was proposed where the improvement was done in cluster head selection to reduce the energy consumption. In this thesis, unlike the LEACH which uses the residual energy of the entire network, the cluster head selection takes into account the residual energy of each node to calculate the threshold value for next round. The cluster head selection uses a modified stochastic algorithm- the deterministic algorithm which calculates the residual energy of each node after each round to select the node with the highest residual energy as the cluster head. This approach makes the clustering algorithm adaptive to network dynamics as each node is considered to be a cluster head at a point. For simulation, the approach is implemented with LEACH in OMNET++ with Castalia and the results show that there is 3% increase in network lifetime

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next‐Generation Wireless Sensor Networks

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