Research On Sensor Allocation And Coordination Based On Improved Multi-Objective Artificial Bee Colony Algorithm

Sensors have increasingly broad application prospects in various fields,including military,environmental,medical,and industrial.Because of their unique properties of limited energy,sensing range,and communication range,special requirements are imposed on sensor allocation and cooperative detection methods.That is,under the condition of limited resources,the detection probability of targets in the surveillance area should be improved as much as possible,the communication cost between sensor nodes should be reduced,the tracking accuracy of the sensor node cluster tracking target should be improved and the unnecessary sensor nodes should be reduced to participate in the tracking while maintaining good tracking accuracy.As a key technology for infinite sensor network applications,the assignment of sensor node locations and the selection of which nodes for collaborative tracking are essentially optimization problems,which are multi-objective optimization problems due to multiple conflicting factors that need to be optimized simultaneously.As a new type of optimization algorithm,the swarm intelligence optimization algorithm provides an efficient method for solving the optimal or suboptimal solutions of complex optimization problems.It is of practical significance to apply swarm intelligence optimization algorithm to the study of sensor assignment and collaboration problems.In this paper,with the goal of exploring artificial swarm algorithms in solving problems in sensor assignment and collaboration,the following work was accomplished:An improved multi-objective artificial bee colony algorithm is proposed,and the time complexity of the algorithm is analyzed and its convergence is proved,it is tested on the test function and compared with other multi-objective optimization algorithms to verify its effectiveness.A sensor assignment model with the objectives of sensor node coverage and connectivity is constructed.An improved multi-objective artificial bee colony algorithm is applied to the problem for simulation experiments,and a set of Pareto optimal solutions is obtained,which provides more feasible solutions for decision makers.A multi-objective optimization model that minimizes the covariance of the estimated position of the target and minimizes the number of sensor nodes used is proposed for the problem of dynamic clustering of sensors to track the target using the lower bound of Cramer-Rao as a judgment criterion.A probability-based sensing model is also employed to minimize the joint miss detection probability of the sensors and the selected number of sensor nodes as multi-objective functions.According to the discrete characteristics of the problem,the state vector of each bee is changed,and each bee is a vector consisting of 0 and 1,representing whether the corresponding sensor node joins the cluster or not.The sensor assignment scheme is derived by solving the model in simulation experiments,and the decision maker can select the sensor nodes according to the actual situation.