Achieving full-view coverage of dynamic targets in camera sensor networks (CSNs) remains a significant challenge, particularly in large-scale and dynamic environments where traditional optimization-based methods struggle to achieve efficient coordination. This study aims to develop an adaptive and scalable framework that enables coordinated sensing and dynamic reconfiguration of camera agents to maximize full-view coverage in real time. To this end, we propose an Automatic Grouping Algorithm (AGA) that reformulates the full-view coverage problem as a multi-agent cooperativecompetitive learning process. AGA integrates three synergistic mechanisms: (i) the Inter-group Competition Mechanism (IeCmM), which enhances adaptive camera positioning through competitive group evolution; (ii) the Intra-group Collaboration Mechanism (IrClM), which optimizes local sensing parameters to improve the coverage rate; and (iii) the Adaptive Adjustment Mechanism (AAM), which dynamically reconfigures group assignments to maintain optimal coverage under environmental variations. These modules are jointly optimized under a multi-agent reinforcement learning (MARL) framework with coordinated policy updates. Extensive experiments demonstrate that AGA consistently outperforms both traditional and state-of-the-art MARL-based baselines, achieving 16%–44% higher full-view coverage efficiency than traditional methods and beyond 71% improvement over advanced learning-based approaches such as HYGMA, HGAP, and GACG. Moreover, AGA exhibits rapid convergence, reaching near-optimal coverage within 10 episodes, and strong scalability across dynamic large-scale environments, demonstrating its effectiveness in complex CSN deployments.

As the use of ultrahigh frequency (UHF) radio-frequency identification (RFID) increases in various fields requiring the rapid identification of a large number of tags, research has shifted toward improving the access capacity of RFID systems. This article proposes a frequency division multiple access (FDMA) extension of conventional time division multiple access (TDMA) RFID systems by modulating tag signals to different Miller-subcarrier frequencies, achieving a theoretical throughput limit value of 0.9135 average successfully read tags per slot, which is 2.48 times that of the usually referred dynamic frame-slotted Aloha algorithm. The power spectral density (PSD) of Miller-modulated subcarrier (MMS) sequences is derived to uncover the tag interference caused by the modulation signal sidelobe power within each subchannel. After that, a successive interference cancellation (SIC) scheme is employed in a four-tag signal reception situation, enhancing the simultaneous tag replies interference suppression and achieving a small performance deterioration if compared with a conventional TDMA system. An experimental analysis is also described to show the feasibility of the proposed FDMA extension of TDMA-based UHF RFID systems.

The study focuses on achieving full view coverage in a camera sensor network to effectively monitor moving objects from multiple perspectives. Three key issues are addressed: camera direction selection, location selection, and moving object monitoring. There are three steps to maximize coverage of moving targets. The first step involves proposing the Maximum Group Set Coverage (MGSC) algorithm, which selects the camera sensor direction for traditional target coverage. In the second step, a composed target merged from a set of fixed directional targets represents multiple views of a moving object. Building upon the MGSC algorithm, the Maximum Group Set Coverage with Composed Targets (MGSC-CT) algorithm is presented to determine camera sensor directions that cover subsets of fixed directional targets. Additionally, a constraint on the number of cameras is imposed for camera location selection, leading to the study of the Maximum Group Set Coverage with Size Constraint (MGSC-SC) algorithm. Each of these steps formulates a problem on group set coverage and provides an algorithmic solution. Furthermore, improved versions of MGSC-CT and MGSC-SC are developed to enhance the coverage speed. Computer simulations are employed to demonstrate the significant performance of the algorithms.