Sensor Node Deployment Method Based on Chaos Optimal Bacteria Foraging Algorithm

Abstract

The invention discloses a sensor node deployment method based on a chaos-optimized bacterial foraging algorithm, which includes the following steps: initialization, setting cycle variables, chemotaxis cycle, reproduction cycle, migration operation, judging the condition of the algorithm to end the cycle, and ending the algorithm if the condition is satisfied And output the optimal bacterial combination, if the condition is not met, return to set the loop variable. Advantages of the present invention: in the node coverage scheme obtained by the bacteria foraging algorithm of chaos optimization, the WSN nodes are evenly distributed in the monitoring area, resulting in very little node redundancy and almost no coverage holes. Compared with the strategy of randomly deploying nodes, The network coverage of the node deployment strategy is improved, the distribution of nodes in the monitoring area is more uniform, the area of ​​repeated coverage is less, and the redundancy of nodes is extremely low, which achieves the purpose of WSN optimal coverage, and the optimized algorithm uses fewer nodes. It can effectively cover the monitoring area, save the deployment cost, and also greatly extend the monitoring time of WSN.

Description

technical field [0001] 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 [0002] 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 model is used to combine the genetic algorithm with the particle swarm ...

AI Technical Summary

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.

[0003] 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. However, studies have shown that the existing bacterial foraging algorithm does not 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 premature

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