Abstract
The paper put forward to an algorithm based on hybrid mutation particle optimization swarm strategy (HMPOA), it can solve the position coordinates of the unknown nodes. The algorithm uses static sampling to determine the performance index values of particles, then the arc grouping method is used to divide the particle swarm into several subgroups. Finally, the hybrid mutation strategy is used to improve the convergence speed and positioning accuracy of the algorithm, which can overcome the location accuracy of unknown node that overly dependent on the RSSI physical measurement value. Numerical experiments show that the algorithm has fast convergence speed and high positioning accuracy for unknown nodes, and it is feasible for RSSI positioning.
Highlights
Wireless sensor network (WSN) is a network composed of a large number of wireless sensor nodes with cheap, small volume, low energy and communication capabilities
Location information is an important information contained in sensor node monitoring messages
Aiming at the node location problem of wireless sensor networks, this paper establishes a random optimization model with noise interference, and proposes a Hybrid Mutation-based particle swarm Optimization Algorithm (HMPOA) to solve the location coordinates of the unknown nodes, the algorithm uses the static sampling to determine the performance index of the particle, uses the arc grouping method to divide the particle swarm into several subgroups, and uses the mixed mutation strategy to improve the convergence speed and positioning accuracy, and can overcome the problem that the location accuracy of the unknown node is overly dependent on the RSSI physical measurement value
Summary
Wireless sensor network (WSN) is a network composed of a large number of wireless sensor nodes with cheap, small volume, low energy and communication capabilities It has been widely used in the fields of national defense, environmental monitoring, target tracking and positioning, production safety and other fields [1]. Aiming at the node location problem of wireless sensor networks, this paper establishes a random optimization model with noise interference, and proposes a Hybrid Mutation-based particle swarm Optimization Algorithm (HMPOA) to solve the location coordinates of the unknown nodes, the algorithm uses the static sampling to determine the performance index of the particle, uses the arc grouping method to divide the particle swarm into several subgroups, and uses the mixed mutation strategy to improve the convergence speed and positioning accuracy, and can overcome the problem that the location accuracy of the unknown node is overly dependent on the RSSI physical measurement value
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