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ABSTRACT

The energy-efficient routing strategies regulate the network lifetime for wireless sensor networks (WSNs). The researchers have continuously improved the WSN lifetime using their algorithms and strategies since the sensor world. Clustering significantly improves the network lifetime and energy efficiency in WSN. In this paper, the concept of double cluster heads (CHs) is introduced in a single cluster of the WSN and Ant Colony Optimization as the nature-inspired algorithm is applied for a CH selection between double CHs. The Ant Colony Optimization is based on the natural behavior of the ant. The simulation result presents that the proposed algorithm enhances the network lifetime and residual energy because a double cluster head significantly reduces the workload of a single cluster head. The other nature-inspired algorithms can be applied for the energy efficiency of WSN in the future.

The modern smart cities are highly dependent on the performance of the Internet of Things (IoT) based energy-efficient sensor networks. Energy efficiency is a critical and indispensable issue for wireless sensor networks (WSNs). In the collection of sensor nodes, one node is selected to collect data and forward it to the base station. The modern base stations in smart cities are unmanned aerial vehicle (UAV) based. This paper presents a linear regression based model, where the initial residual energy and its corresponding transmission power are submitted to the proposed system, then it generates a prediction model since transmission power depends on the residual energy of the sensor node. Based on this model the transmission power of the sensor node can be calculated for data transmission as a higher residual node is best suitable for data transmission to the base station. As presented in the simulation result, the regression based model gives better performance for energy efficiency in WSN.