Mobile cloud-deployed mobile sensing networks are a growing area of technology where attaining energy utilization is a challenging task during data transmission from mobile sensor devices to the cellular base station. Data offload can address drawbacks like network delay, poor performance, and high-energy consumption. Such a setup requires an efficient scheme that focuses on energy efficiency in a better way reducing the faster death of nodes. In this paper, an energy-aware approach named modified partitioning around medoids with cluster head selection (MPAM-CHS) is proposed, that aims for better clustering of mobile devices and the fairer selection of group head to minimize the energy utilization of the nodes. The proposed scheme consists of four phases like initialization, clustering, cluster head formation, and transmission phase. Initially, the nodes are randomly deployed in the network field and then clustering is performed on them using a modified PAM algorithm to determine the actual cluster points for partitioning the nodes into small groups. Next, the cluster head (CH) or the group head is determined based on the criteria such as residual energy, signal-to-noise ratio (SNR), path loss, and average path loss between the sensor and the sink. Finally, the sensed information collected from the nodes is offloaded to the group head, aggregated, and then sent to the sink. The experimental analysis shows that the proposed algorithm has a significant gain in energy consumption in terms of network utilization and lifetime metrics.
Energy efficiency Mobile cloud Mobile sensing Node clustering Cluster head Network lifetime Threshold distance
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