Mobile Charging of Wireless Sensor Networks for Internet of Things: A Multi-Attribute Decision Making Approach

Abstract

The Internet of Things (IoT) has become an emerging and booming area of interest among the researchers and academia people. There is a rich set of IoT applications that include environment monitoring, e-healthcare, industry automation, and so on. Wireless sensor network (WSN) is a predominant alternative to make IoT more realistic as it connects physical devices to the Internet through a gateway. For real-time IoT applications, WSN with an ability to maintain energy efficient communication among sensor nodes for fast service delivery to the users is of utmost importance. However, the energy-limited battery remarkably limits the longer operability of nodes which hinders the continuous flow of sensory data to the Internet. In this regard, energy replenishment of energy-hungry nodes through wireless mobile chargers (MCs) is a promising alternative to alleviate the limited energy problem in the WSNs. To this end, we propose a multi-attribute decision making scheme that incorporates different network attributes (NAs), namely residual energy, distance to MC, neighborhood criticality, and charging significance. First, we determine the relative weights of different NAs by employing the entropy weight method (EWM). Next, the technique for order preference by similarity to ideal solution (TOPSIS) is applied for ranking the nodes in order to determine their charging schedule. Rigorous simulations are carried out to facilitate the quantitative evaluation of the proposed scheme. The comparison results reveal that our scheme outperforms the relevant state-of-the-art methods with respect to charging latency, number of dead nodes, and charging efficiency.