###### Abstract  
Directional sensor networks are a widely used architecture in the sensing layer of the Internet of Things (IoT), which has excellent data collection and transmission capabilities. The coverage hole caused by random deployment of sensors is the main factor restricting the quality of data collection in the IoT sensing layer. Determining how to enhance coverage performance by repairing coverage holes is a very challenging task. To this end, we propose a node deployment optimization method to enhance the coverage performance of the IoT sensing layer. Firstly, with the goal of maximizing the effective coverage area, an improved particle swarm optimization (IPSO) algorithm is used to solve and obtain the optimal set of sensing directions. Secondly, we propose a repair path search method based on the improved sparrow search algorithm (ISSA), using the minimum exposure path (MEP) found as the repair path. Finally, a node scheduling algorithm is designed based on MEP to determine the optimal deployment location of mobile nodes and achieve coverage enhancement. The simulation results show that compared with existing algorithms, the proposed node deployment optimization method can significantly improve the coverage rate of the IoT sensing layer and reduce energy consumption during the redeployment process. deployment optimization; coverage enhancement; node scheduling; directional sensor; IoT sensing layer +
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necessary to repair coverage holes and achieve coverage enhancement by adjusting the work angle and position of sensors. There are two optimization methods for node deployment to achieve coverage enhancement in DSNs: adjusting the work angle and redeploying mobile nodes. The deployment optimization algorithm based on adjusting the work angle assumes that nodes do not move, and expands the coverage range by rotating sensors to achieve coverage enhancement. Intelligent optimization algorithms are the most effective method for adjusting sensing direction. Particle swarm optimization, ant colony optimization, and bat algorithms all have the characteristics of fewer parameters and faster convergence speed [8]. In addition, methods based on geometric (Voronoi diagram [9], Delaunay triangulation [10]) and virtual force [11] are also classic sensing direction adjustment methods. The deployment optimization method based on adjusting the sensing direction can effectively improve network coverage, but this method relies on higher sensor density. In a random deployment area, only adjusting the sensing direction cannot achieve full coverage, and there are still coverage holes that cannot be repaired. Mobile node redeployment is currently a hot topic in coverage enhancement research. Mobile node redeployment focuses more on the mobility of nodes, and achieves coverage enhancement by scheduling nodes to repair coverage holes. The virtual force algorithm and swarm intelligence are practical algorithms for solving deployment optimization problems. These algorithms have received widespread attention in the redeployment of mobile nodes [12; 13]. Recently, the method based on the minimum exposure path (MEP) has been favored by researchers [14]. The principle of this type of method is to repair coverage holes by scheduling sensors to the searched MEP. The characteristic of MEP-based methods is to prioritize repairing the areas with the worst coverage, which can achieve the maximum coverage enhancement effect under the same conditions. Because the MEP is the path with the worst coverage in the monitoring area, scheduling sensors to the MEP or adjusting the sensing direction of sensors on the MEP can achieve maximum coverage performance improvement. The coverage enhancement method based on mobile node redeployment performs well in repairing coverage holes. However, this method requires a lot of energy, and deploying a large number of mobile sensors will also increase the construction cost of the IoT sensing layer.