###### Abstract  
Coverage is an important factor for the effective transmission of data in the wireless sensor networks. Normally, the formation of coverage holes in the network deprives its performance and reduces the lifetime of the network. In this paper, a multi-intelligent agent enabled reinforcement learning-based coverage hole detection and recovery (MiA-CODER) is proposed in order to overcome the existing challenges related to coverage of the network. Initially, the formation of coverage holes is prevented by optimizing the energy consumption in the network. This is performed by constructing the unequal Sierpinski cluster-tree topology (UST) and the cluster head is selected by implementing multi-objective black widow optimization (MoBWo) to facilitate the effective transmission of data. Further, the energy consumption of the nodes is minimized by performing dynamic sleep scheduling in which Tsallis entropy enabled Bayesian probability (TE2 BP) is implemented to switch the nodes between active and sleep mode. Then, the coverage hole detection and repair are carried out in which the detection of coverage holes if any, both inside the cluster and between the clusters, is completed by using the virtual sector-based hole detection (VSHD) protocol. Once the detection is over, the BS starts the hole repair process by using a multi-agent SARSA algorithm which selects the optimal mobile node and replaces it to cover the hole. By doing so, the coverage of the network is enhanced and better QoSensing is achieved. The proposed approach is simulated in NS 3.26 and evaluated in terms of coverage rate, number of dead nodes, average energy consumption and throughput. coverage enhancement; clustering; sleep scheduling; hole detection; hole recovery +
Footnote †: journal: Applied  
network increased the coverage but the location of these extra nodes introduced placement issues and also increased the deployment cost [5]. The QoSensing was also affected by the increased consumption of energy by the sensing nodes. The detection of boundary nodes is an important factor to optimize the energy consumption which includes the detection of event boundary nodes and coverage hole boundary nodes [6]. The energy consumption of the sensor nodes was reduced by implementing the process of clustering in which the sensor nodes were clustered, and the cluster head was elected. In order to reduce the energy consumption further the path selection was executed based on optimization [7]. The scheduling of nodes between the active and sleep mode contributed to improved energy efficiency which further increased the lifetime of the network. This method not only solved coverage issues but also improved throughput in the network [8]. The improvement in the coverage was introduced by selecting the optimal coverage area with flexibility in field of view, but inefficient optimization resulted in increased redundancy [9]. The technique optimization was implemented for the efficient placement of sensor nodes in order to improve the coverage in the network [10]. The technology of unmanned aerial vehicles was leveraged in wireless sensor networks in which the sensor nodes offloaded the data to the UAVs which improved the network coverage and reduced the consumption of energy [11]. The combination of coverage optimization and energy regulation processes increased the QoSensing of the transmission and extended the lifetime of the sensor nodes [12]. The implementation of the mobile sink comparatively increased the network coverage, and further, the usage of the long-range wireless interface improved the QoSensing of the network [13]. The deployment of sensors in zone-based regions based on the detection probabilities solved the coverage problems but increased the deployment cost limiting the adoption of this method in real-life scenarios [14].