Mobile wireless sensor network lifetime maximization by using evolutionary computing methods Abstract   Due to the continuous development and progress of wireless communication technology and sensor network technology, wireless sensor networks (WSNs) have gradually become an attractive technology that facilitates peopleu0027s lives. Due to the extensive use of WSNs, maximizing the lifetime of WSNs to obtain realtime and effective information has become a critical concern. This paper studies the life of mobile wireless sensor networks (MWSNs). MWSNs are a special type of WSN in that the sensor nodes are movable within a certain area. A system model is developed to prolong the network lifetime of MWSNs. This paper uses five evolutionary computing (EC) algorithms to develop the MWSN lifetime optimization model. Numerical simulations are performed to study the advantages and disadvantages of the five algorithms for solving the model. The comparison and discussion can provide advice for using EC algorithms to solve MWSN lifetime maximization problems.

Clusterhead Selection Algorithm Based on Energy and Mobility in Wireless Mobile Sensor Networks Wireless Sensor Networks WSN are selforganizing networks composed of numerous sensor nodes dispersed in an environment communicating by radio technologies. These nodes have limited processing capacity with reduced energy and a mobility dependency. One of the biggest problems in these networks is maximizing the lifetime of the nodes, due to the lack of fixed infrastructure and centralized management. For this purpose, authors proposed the clustering technique to facilitate communication and minimize control traffic. In this paper, we propose a new clusterhead election algorithm in mobile sensor networks based on local information of sensor nodes: residual energy and mobility, supporting network selforganization to avoid isolated node and high blocking probabilities.