Smart sensor networks have a wide range of applications in the fields of environmental monitoring and industrial automation, but their energy efficiency and node coverage optimization problems need to be solved. This paper proposes a dual-strategy improved particle swarm optimization algorithm (CMKPSO) for solving the deployment optimization problem of electronic sensing nodes. Combining the Boolean sensing model and probabilistic sensing model, the coverage probability calculation framework is constructed. The cross-variance and adaptive parameter improvement strategies are designed to enhance the global search capability of the algorithm and accelerate the convergence. Simulation experiments show that the evaluation function value of CMKPSO is finally stabilized at 0.94, which is 13%, 9%, and 4% higher than that of PSO, VF, and EABC. The CMKPSO algorithm reduces the average queue captains to less than 15, so that most of the queue captains are concentrated in less than 10, and significantly reduces the packet loss rate of the network and the node load pressure.
