The performance of heterogeneous sensor networks is enhanced by high‐energy heterogeneous nodes. Determining the number and deployment of heterogeneous nodes is a significant research issue. A heterogeneous node configuration algorithm is presented in this paper, which can be used for overall network planning before the deployment of heterogeneous nodes. Subsequently, factors such as network performance and economic cost are comprehensively considered, and integrated into a single index using the entropy weighting method. The proportion of different indicators is then determined, and a formula for calculating the required number of heterogeneous nodes under various network conditions is derived by considering parameters such as network area size, node communication threshold distance, and the number of common nodes. Experimental results demonstrate that the proposed algorithm not only reduces networks costs but also enhances overall networks performance.

Hybrid sensor networks, which contain mobile nodes and stationary nodes, are being used more and more widely. The second deployment of mobile nodes is a key problem to be solved, and the deployment performance of the network directly affects the monitoring effect of the network. Optimizing the configuration ratio of the two nodes can effectively reduce the network cost. In this paper, under the premise of knowing the coverage of the required monitoring area, the impact of sensor devices on node configuration is studied through parameter analysis, and the number and types of sensors that should be deployed in the hybrid sensor network are deduced, which can be conveniently and accurately used to design the actual hybrid sensor network. At the same time, for the secondary deployment of mobile nodes, this paper proposes a new mobile coverage method BS‐CCP (box search and concentric circle positioning) to improve the coverage of the hybrid sensor network and maximize the coverage of the target area with the specified sensor types and numbers. Compared with existing work, the method in this paper reduces the number of iterations and reduces the number of required nodes. Comparing BS‐CCP with the existing network mobile coverage algorithm, the experimental results show that the coverage obtained by this method is larger and more efficient.

Water quality sensor networks can be used for monitoring water environment, early warning and prevention of water pollution through accurate collection of water quality information. Effective deployment of the network can improve its monitoring efficiency. After the uniform deployment of the network, the sensor nodes need to be deployed in the key areas reasonably, so as to save the hardware cost and improve the monitoring effect. In this paper, the water area characteristic model is established to get the key monitoring area. Besides, the Gaussian plume model is applied to obtain the impact range of the key monitoring areas. The experimental results show that Qianhai is the key monitoring area, and its impact range is 10.36 m. On this basis, we deploy the sensors using particle swarm optimisation. Simulation results show that key area can be monitored better, whereas other regions can still guarantee a maximum coverage with a total coverage rate of 79.09%.