Ensuring reliable transmission and secure communication remains a critical challenge for next-generation wireless networks, particularly in the context of sixth-generation (6G) and Internet of Things (IoT) systems. This paper investigates the reliability and physical layer security of active reconfigurable intelligent surface (ARIS)-assisted dual-hop relaying in non-orthogonal multiple access (NOMA) networks over Nakagami-mfading channels. We develop a comprehensive system model where a base station communicates with two users via a relay and an ARIS, in the presence of a potential eavesdropper. Closed-form expressions for outage probability (OP) and intercept probability (IP) are derived for both near and far users, capturing the effects of ARIS configuration, power allocation, and channel fading. Extensive Monte Carlo simulations confirm the accuracy of the analytical results and reveal that increasing the number of ARIS elements, optimizing amplification factors, and carefully allocating power can significantly enhance both reliability and secrecy performance. The findings provide valuable insights for the design of robust and secure ARIS-assisted NOMA networks in practical wireless environments.

Efficient fire drill evacuation in high-rise buildings demands real-time perception and optimized path planning. This study presents an autonomous evacuation framework that integrates deep learning-based object detection with multi-objective trajectory planning. Using RGB-D vision, the system detects human traffic and staircases, projecting them into the robot’s workspace. The evacuation task is modeled as a Moving Target Traveling Salesman Problem (MT-TSP) and solved via a modified NSGA-II algorithm, balancing travel time, path length, safety, and accessibility. Experiments show over 90% detection accuracy and significant improvements in navigation efficiency, demonstrating the effectiveness of combining deep learning with evolutionary optimization for robotic fire drill planning.

Planning the energy-efficient and collision-free paths for reconfigurable robots in complex environments is more challenging than conventional fixed-shaped robots due to their flexible degrees of freedom while navigating through tight spaces. This article presents a novel algorithm, energy-efficient batch informed trees* (BIT*) for reconfigurable robots, which incorporates BIT*, an informed, anytime sampling-based planner, with the energy-based objectives that consider the energy cost for robot’s each reconfigurable action. Moreover, it proposes to improve the direct sampling technique of informed RRT* by defining an$L^2$greedy informed setthat shrinks as a function of the state with the maximum admissible estimated cost instead of shrinking as a function of the current solution, thereby improving the convergence rate of the algorithm. Experiments were conducted on a tetromino hinged-based reconfigurable robot as a case study to validate our proposed path planning technique. The outcome of our trials shows that the proposed approach produces energy-efficient solution paths, and outperforms existing techniques on simulated and real-world experiments.