An image processing inspired mobile sink solution for energy efficient data gathering in wireless sensor networks
This paper presents a gradient-based multi-hop clustering protocol combined with a mobile sink (MS) solution for efficient data gathering in wireless sensor networks. The main insight for the clustering algorithm is drawn from image processing field and namely from the watershed transform, widely used for image segmentation. The proposed algorithm creates multi-hop clusters whose cluster heads (CHs) as well as cluster members near the CHs have high energy reserves. Specifically, the energy of the sensors in a cluster increases progressively as getting closer to the CH. As the nodes closer to the CH are most burdened with relaying of data from other cluster members, the higher levels of available energy at these nodes prolong the network lifetime eventually. After cluster formation, a MS periodically visits each CH and collects the data from cluster members already gathered at the CH. Simulation results show the higher performance of the proposed scheme in comparison to other competent approaches in the literature.
mobile sink data gathering wireless sensor networks watershed transform node clustering