An integrated MCDM-based charging scheduling in a WRSN with multiple MCs

Abstract

Recently, a few Multi-Criteria Decision Making (MCDM)-based charging scheduling schemes have been proposed. However, these schemes have still connoted the problems from the viewpoint of assigning weights to multi-criteria and exploiting redundant capability of a Mobile Charger (MC). In this paper, we propose an efficient charging scheduling scheme using an integrated FCNP-TOPSIS to solve the above-mentioned problems. The proposed scheme firstly divides the whole network into sub-areas by using the Fuzzy C-Means (FCM) algorithm so as to evenly distribute charging request load into multiple MCs and assign a MC to each sub-area. Next, each MC draws up a charging schedule into on-demand or semi-on-demand charging scheduling scheme according to the MC’s charging capability and the number of charging Request Nodes (cRNs). In charging scheduling, first the Fuzzy Cognitive Network Process (FCNP) assigns the relative weights to multi-criteria to characterize the cRNs and predict the potential-to-be-Bottlenecked Nodes (pBNs). Then the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) selects the most suitable next charging location for on-demand charging scheduling and the proactive charging nodes among the predicted pBNs for semi-on-demand charging scheduling. While drawing up the on-demand charging schedule, the partial charging time at each charging location is calculated considering the weights of multi-criteria by FCNP. Extensive simulation experiments have been conducted to show that the proposed scheme greatly improves the charging and network performance at various performance metrics compared to existing schemes. In special, if the number of nodes is 650, the network lifetime of the proposed scheme is 129.4%, 239.8%, 282.5%, 283.2% and 293.6% longer compared to the FAHP-VWA-TOPSIS, FLCSD, AHP-TOPSIS, OPPC, and NJNP schemes, respectively.