Multidimensional time‐series prediction is significant in various fields, such as human production and life, weather forecasting, and artificial intelligence. However, a single model can only focus on specific features of time‐series data, making it unable to consider both linear and nonlinear components simultaneously. In this study, we propose a fusion network that combines the advantages of deep and broad networks for multidimensional time‐series prediction tasks. The complex multidimensional time‐series data are divided into nonlinear and time‐series data. Restricted Boltzmann machine and mapping functions are used for feature learning and generating mapping nodes at the mapping layer. The echo state network and gate recurrent unit are applied in the enhancement layer. The proposed model has been validated on PM2.5 and wind turbine power datasets, proving superior performance in multistep prediction tasks compared to the baseline models.

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.