Abstract
Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime.
Highlights
Wireless Sensor Networks (WSNs) are one of the most important technologies changing the world in that such networks can be used in variety of applications, such as environment monitoring, military surveillance and object tracking, disaster area relief, industrial control and seismic monitoring
To meet the principle of soundness, the penalty function combined with the phenotype sharing function is introduced to convert the nonlinear constrained optimization problem to a non-constrained one and applied in the definition of fitness function to ensure that all particles are feasible
We mainly describe the impact of the energy and lifetime in WSNs on the establishment of a reliable network
Summary
Wireless Sensor Networks (WSNs) are one of the most important technologies changing the world in that such networks can be used in variety of applications, such as environment monitoring, military surveillance and object tracking, disaster area relief, industrial control and seismic monitoring. (2) We apply a phenotype sharing function of the objective space in our algorithm for the establishment of reliable routes (similar to the approach in [9]) Since it is a multi-objective optimization, a fitness function based on the phenotype sharing is designed considering both the Pareto dominance and the neighborhood density of the objective space. (3) Different from [9], in order to declare the validity of encoding scheme, we prove that the Prufer sequence can satisfy well the principles of non-redundancy, completeness and soundness It can reduce the redundancy of the search space, and improve the search efficiency, and thereby enhance the performance of the algorithm.
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