Research On WSN Routing Technology With Natural Computation

Wireless sensor networks(WSN) can sense, process data and has low power, low cost, nodes of small size,which conducive to collecting and processing large amounts of data in the complex environment. But it has some shortcomings, such as limited hardware resources and battery capacity, complex network topology and difficult to replace the battery. To solve this problem, many of energy-efficient data fusion algorithm and network communication protocols have been proposed. Natural Computation(NC) is based on natural mechanism for solving those difficult to establish effective formal model and deal with it. In large-scale optimization of complex systems design, optimal control and other fields, it has broad application prospects.This thesis focuses on WSN modeling, and applying the method of natural computation for simulation and optimization design.The specific contents are as follows:(1)Research on WSN routing model. Firstly, the basic architecture of the WSN and the form of the network coverage are introduced.Secondly, representative WSN routing protocols is elaborated, and the chain and cluster routing model are studied. For the two routing model, from the point of view of energy analysis and modeling, energy optimization formula is established and solutions is proposed.(2) Research on Natural Computing(NC).The swarm intelligence algorithm, and cloud intelligent algorithms are mainly studied and shortcomings of the ant colony algorithm and particle swarm algorithm are improved. For the chain Routing model, an optimal approach for WSN is proposed based on self-adaptive ant colony optimization (SA-ACO) energy optimization,which includes the dynamic probability selection, optimizing pheromone matrix and genetic variation of combining process;for the cluster Routing model, an optimal approach for WSN is proposed, based on the Cloud Adaptive Particle Swarm Optimization (CAPSO) algorithm, which includes network clustering, network modeling, iteration optimization with CAPSO algorithm, and so on.(3) Research on WSN Model with Natural Computation. The routing algorithm based on SA-ACO optimizes pheromone parameters through adaptive approach. The simulation for chain routing model prove that WSN node energy consumption is reduced, the viability of network nodes is increased. And the introduction of a certain variation of the rules is to improve the search ability of the algorithm to prevent the node into a local optimal solution. Compared with the traditional ACO algorithms, the algorithm has a lower energy consumption and node mortality. The routing algorithm based on CAPSO combined with fuzziness and randomness of cloud intelligent algorithms, change the basic PSO algorithm which has a fixed value of inertia weight. According to the fitness of different particles, the algorithm uses different inertia weight, and is optimized by the cloud model. Results show that, CAPSO reduces energy consumption and network delay.