Construction of Data Aggregation Tree for Multi-objectives in Wireless Sensor Networks through Jump Particle Swarm Optimization

As a typical data aggregation technique in wireless sensor networks, the spanning tree has the ability of reducing the data redundancy and therefore decreasing the energy consumption. However, the tree construction normally ignores some other practical application requirements, such as network lifetime, convergence time and communication interference. In this case, the way how to design a tree structure subjected to multi-objectives becomes a crucial task, which is called as multi-objective steiner tree problem (MOSTP). In view of this kind of situation, a multi-objective optimization framework is proposed, and a heuristic algorithm based on jump particle swarm optimization (JPSO) with a specific double layer encoding scheme is introduced to discover Pareto optimal solution. Furthermore, the simulation results validate the feasibility and high efficiency of the novel approach by comparison with other approaches.