Sensor node deployment strategy based on chaotically optimized bacteria foraging algorithm

Abstract

The invention discloses a sensor node deployment strategy based on a chaotically optimized bacteria foraging algorithm. The strategy comprises the following steps of initializing, setting a cyclic variable, performing chemotaxis cycle, performing multiplication cycle, migrating, judging a condition by which an algorithm cycle ends, if the condition is met, ending the algorithm and outputting an optimal bacterium combination, and if the condition is not met, returning to set the cyclic variable. The strategy provided by the invention has the advantages that WSN nodes are uniformly distributed in a monitoring area in a node coverage scheme acquired by using the chaotically optimized bacteria foraging algorithm, less node redundancy is generated, hardly no coverage blank exists; compared with a random node deployment strategy, the sensor node deployment strategy is improved in node deployment strategy network coverage rate, the nodes are more uniformly distributed in the monitoring area, fewer coverage areas are repeated, redundancy of the nodes is extremely low, the purpose of WSN optimized coverage is achieved, and the optimized algorithm can effectively cover the monitoring area by using the fewer nodes, deployment costs are saved, and meanwhile monitoring time of the WSN is also greatly prolonged.

Description

Sensor Node Deployment Strategy Based on Chaos Optimal Bacteria Foraging Algorithm technical field The invention relates to building a swarm intelligence algorithm to optimize sensor node deployment models and simulation implementation, and belongs to the field of wireless sensor network optimization coverage monitoring areas. Background technique The self-organizing multi-hop wireless sensor network (WirelessSensor Network, WSN) widely used in geological monitoring, environmental protection and other fields has the advantages of flexible deployment, low cost, and wide coverage, but the large-scale random deployment of the monitoring area will bring node distribution. uneven problem. Aiming at this problem, many scholars use swarm intelligence bionic algorithm to optimize the processing. For example, the artificial fish swarm algorithm is used to construct the network coverage model, and the network coverage is optimized by solving the model; the probability perception mo...

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Problems solved by technology

The above-mentioned swarm intelligence algorithm has achieved great results in WSN network coverage optimization, but there are also problems such as high solution complexity, slow convergence speed, low convergence accuracy, and high computing cost.

Therefore, those skilled in the art turned their attention to the Chaos optimization bacterial foraging algorithm (COBFO), and based on this algorithm, simulated the deployment of nodes in the monitoring area. The optimization ability is poor, the search speed and search accuracy need to be improved, and it is easy to fall into local optimum and cause premature

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