## 1 Introduction  
Wireless Sensor Networks (WSNs) are multi-hop self-organizing networks consisting of a set of sensor nodes, small devices, etc., whose task is to monitor events in a specific region and transfer the collected data to a base station (BS) for centralized processing [1, 2]. Based on the limitations of real-world scenarios, the deployment of static nodes alone cannot achieve the desired coverage effect, while the deployment of mobile nodes only has the problem of high cost. The hybrid wireless sensor networks composed of static nodes and mobile nodes are more relevant in practical applications such as environmental monitoring, military surveillance, and disaster recovery [3, 4]. Through collaborative work between nodes, various environmental monitoring and sensing information can be transmitted to the BS in real-time. If a large quantity of nodes is thrown randomly in reasonable locations, they can cover the target area more precisely [5, 6]. However, in real target scenarios, it is often required to throw nodes randomly in a high-density, large-scale manner in sensor networks, but this large-scale random node-throwing approach makes it difficult to deploy nodes in optimal locations at once, resulting in redundant and unperceived regions due to either the over-sparse or dense distribution of nodes in the target region. This node coverage overlap can lead to redundancy in transmitted and received data and channel interference, thus wasting limited energy resources [7]. The nodes rely on their own limited battery power supply, which cannot be replenished in real time, and the secondary replacement of the power supply is not very feasible. As a result, the life cycle of hybrid WSNs is significantly limited. When sensor nodes terminate working because of uneven distribution or exhaustion of energy, malfunction, etc., there are inevitable coverage holes in the network. A coverage blind zone is one which is not perceived by any sensor node in the monitored region. The emergence of blind zones can change the network's topology, which affects normal communication between nodes and the reliability of data. As sensor nodes may stop working at any time, coverage holes can appear anywhere in the network [8]. In addition, if the coverage holes formed are not repaired, the number and size of coverage holes will gradually increase, disrupt the transmission of data, disconnect from the network, and make certain regions of the network non-monitored [9]. Coverage issues are a fundamental indicator of the quality of service (QoS) of hybrid WSNs [10] that reflect how well the sensor network nodes supervise the monitored area. The detection and repair of coverage holes are becoming increasingly important. With the complexity of the actual environment, the full range of environmental characteristics also needs to be studied [11; 12]. In underground infrastructure monitoring, it is often used to monitor storage tanks, power lines, and pipelines. Any faults or leaks can be located and repaired in time by using data shared among sensor nodes [13]. Precision agriculture is a field that is constantly evolving and the need for effective crop production and resource management is improved by monitoring conditions, such as soil mineral composition, moisture, and water content [14]. In medical environment applications, the efficiency and safety of medical devices and the life quality of patients can be effectively improved through patient vital sign monitoring, ward monitoring, medical device monitoring, and drug tracking [15]. In environmental applications, temperature-sensitive sensor nodes in forests are deployed to track the occurrence of fires [16]. Therefore, in this paper, we use the environmental perception importance level indicator as an important factor for consideration when restoring a hole in the node restoration phase.