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.

SAR (Synthetic Aperture Radar) image semantic segmentation has attracted increasing attention in the remote sensing community recently, due to SAR’s all-time and all-weather imaging capability. However, SAR images are generally more difficult to be segmented than their EO (Electro-Optical) counterparts, since speckle noises and layovers are inevitably involved in SAR images. On the other hand, EO images could only be obtained under cloud-free conditions, which limits their applications. To this end, this letter investigates how to introduce EO features to assist the training of a SAR-segmentation model so that the model could segment SAR images without their EO counterparts in application, and proposes a distilled heterogeneous feature alignment network (DHFA-Net), where a SAR-segmentation student model learns and aligns the features from a pre-trained EO-segmentation teacher model. In the proposed DHFA-Net, both the student and teacher models employ an identical architecture but different parameter configurations, and a heterogeneous feature distillation module is explored for transferring latent EO features from the teacher model to the student model through heterogeneous feature distillation and then supervising the training of the SAR-segmentation model. Moreover, a heterogeneous feature alignment module is designed to aggregate multi-scale features for segmentation by feature alignment approach in each of the student and teacher models. By enabling the multi-scale heterogeneous feature aggregation, the SAR segmentation performance could be boosted. Experimental results on two public datasets demonstrate the superiority of the proposed DHFA-Net.

Various attacks in communication network threaten the security of vehicular system. For cooperative location-sensing system, man-in-the-middle attack and eavesdropping attack would lead to catastrophic collapse of vehicular network localization. In this paper, we propose a federated cryptosystem localization based on optimized constraints. In the localization phase, a message passing algorithm combining belief propagation (BP) and variational message passing (VMP) is derived by defining penalty function, which can detect the distance outliers. The messages generated by each vehicular node or base station are encrypted with Paillier cryptosystem. Because Paillier cryptosystem is featured by homomorphic addition, message aggregation reduces large amount of decryption. The localization messages finally work in a federated transmission scheme for privacy preserving against man-in-the-middle attack and eavesdropping attack. In terms of localization accuracy, algorithm parameters, convergence analysis and efficiency, simulation results show that cybersecurity of cooperative localization is proved to be effective in various scenarios of vehicular network.

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%.