MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

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 (USCT) 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 (TE2BP) 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 (ViSHD) 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.

Research on automatic detection technology for the compensation hole shape and position parameters of a brake master cylinder

Machine vision is a key technology to achieve high detection accuracy for the compensation hole parameters of automobile brake master cylinders, which influence automobile safety and parking reliability. As an important part of the automobile brake master cylinder, the compensation hole can play an important role in regulating the brake fluid in the reservoir tank and brake chamber, and its dimensional accuracy and processing quality are strictly controlled. Therefore, determining how to accurately obtain images of the compensation hole is a primary problem in the detection of compensation hole parameters. In this paper, fully automatic equipment for compensation hole detection in automobile brake master cylinders is designed using an image processing algorithm to realize the automatic positioning of the compensation hole and automatic detection of size parameters. Experiments show that the automatic positioning and detection time for the compensation hole is less than 8 s, the detection accuracy of the compensation hole size is higher than ±0.021 mm, and the position detection accuracy for the compensation hole is higher than ±0.045 mm. The compensation hole detection technology proposed in this paper provides high real-time performance and good robustness while meeting accuracy requirements and detection speed.