Energy-Efficient Topology to Enhance the Wireless Sensor Network Lifetime Using Connectivity Control

Abstract

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of research has been done to expand their applications and improve their efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two metaheuristic algorithms, namely SA (Simulated Annealing) and VNS (Variable Neighbourhood Search), to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

Improved data clustering using multi-trial vector-based differential evolution with Gaussian crossover

“…In clustering, objects are grouped without prior knowledge, while classification methods use predefined classes in which objects are assigned. The application of clustering can be found in social network analyse [17], robotics [18], biology [35], and networks [16,40,41]. The types of clustering methods can be categorized as hierarchical clustering [20], mixed clustering [23], learning network clustering [36], and partition clustering [30].…”