No abstract

To address the problem of insufficient coverage of WSN and poor network coverage in obstacle environments, the study proposes an improved particle swarm optimization (PSO) combined with a hybrid grey wolf algorithm. The speed and position of the PSO particle's search for superiority are enhanced through the guiding nature of the superior wolf in the grey wolf optimization (GWO), thus the convergence speed and search precision are improved. Based on this, the study applies the improved PSO to a wireless sensor networks (WSO) coverage optimization model and uses model comparison to test the effectiveness and superiority of the algorithm. According to the results, the node network coverage of PSO, genetic algorithm (GA), data envelopment analysis (DEA), GWO, and grey wolf particle swarm optimization (GWPSO) reach 85.97%, 87.24%, 88.76%, 89.31%, and 91.05% respectively in the trapezoidal obstacle environment. And the node network coverage of the research-designed GWPSO algorithm reaches the highest value of its kind. This shows that the research-designed GWPSO has superior performance in the optimization control of sensor coverage deployment compared with similar algorithms. The design provides a new path for optimizing wireless sensor node network coverage.

Unmanned aerial vehicle mobile ad hoc networks (UAVMANETs) formed by multi-UAV self-assembling networks have rapidly developed and been widely used in many industries in recent years. However, UAVMANETs suffer from the problems of complicated key negotiations and the difficult authentication of members’ identities during key negotiations. To address these problems, this paper simplifies the authentication process by introducing a Latin square to improve the process of signature aggregation in the Boneh–Lynn–Shacham (BLS) signature scheme and to aggregate the keys negotiated via the elliptic-curve Diffie–Hellman (ECDH) protocol into new keys. As shown through security analysis and simulations, this scheme improves the efficiency of UAVMANET authentication and key negotiation while satisfying security requirements.

With the continuous improvement and development of wireless sensor network, it have been enriched to a great extent. Monitoring, processing and transmitting all kinds of sensing data is its main function, so their coverage issues have received widespread attention. Among them, the WSN coverage based on DV-Hop node positioning technology has low cost and power consumption with high scalability, and is extremely widely used. However, the current error control and WSN coverage of DV-Hop node positioning are not enough for practical applications. This research innovatively adopts an improved sparrow search pattern to optimise the DV-Hop localisation algorithm. The study introduces a deviation correction factor to adjust the minimum number of node hops and uses the minimum mean squared error criterion to correct the calculation error to reduce error. In addition, the study improved the sparrow search algorithm by means of a GPS optimisation population initialisation. In the algorithm performance comparison, GSSA showed the best convergence efficiency compared to other algorithms. The average error of the GSSADV-Hop localisation constructed in the study is 0.72 m, which is 77.71% less than the traditional DV-Hop error.The study provides a reference idea for the application of DV-Hop in WSN coverage, and offers a novel solution for the optimization of DV-Hop.