Multiagent path finding (MAPF) is a critical problem in real-world multiagent systems, where agents reach their respective destinations without colliding with one another. Most MAPF solvers assume agents move at constant speeds with no delays and stop upon reaching their goal. However, real-world nonholonomic agents face delays like deceleration, lane changes, and acceleration, which impact efficiency. Moreover, tasks are continuously generated, making it challenging to meet production requirements. To address this issue, we propose a novel approach, the space-level path segment (SLPS) finding algorithm, which bridges the gap between traditional MAPF methods and the real-world requirements of nonholonomic agents. SLPS defines necessary and optional constraints to construct the collision detection graph (CDG) and formulates an optimization problem to minimize task completion time, resulting in the simplified CDG. It then computes a single SLPS, allowing nonholonomic agents to operate at varying speeds without synchronization. In addition, SLPS supports persistent MAPF tasks through four event-triggered methods, enabling flexible replanning in response to dynamic changes. Experimental results demonstrate that SLPS reduces average task completion time compared to the closest state-of-the-art solvers, proving its efficiency for real-world applications.

Defect detection in multimedia data plays a pivotal role in industrial manufacturing. However, existing methods are primarily designed for closed-world scenarios and can only identify defect classes in the training data, limiting their ability to effectively detect unknown class defects that arise during production. To address this critical limitation, we propose a novel approach by introducing industrial defect open set recognition (IDOSR), which overcomes the challenge of recognizing unknown defect classes. Furthermore, to tackle the issues of limited training samples and subtle inter-class differences in IDOSR, we present a high-frequency feature enhancement open set recognition (HFFE-OSR) method. Specifically, HFFE-OSR employs a high-frequency structural feature fusion enhancement strategy to meticulously extract and fuse defect-related high-frequency structural features. This enables the network to comprehensively learn defect target representations even under limited training samples, resulting in robust feature extraction for known classes, thereby improving the discriminability between known and unknown classes and addressing the difficulty of distinguishing between them. Additionally, a class mutual information constraint strategy is introduced to measure and reduce the mutual information among defect features from different classes. This ensures the independence of defect features across known classes, further enhancing their discriminability and significantly improving recognition performance for known classes. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art OSR methods on ID-OSD and MVTec datasets, achieving improvements of at least 7% in accuracy (ACC), 22% in F1 score, and 10% in AUROC, highlighting the effectiveness of our approach in industrial defect detection.

The multi-agent pickup and delivery problem is central to coordinating multiple agents in real-world applications such as warehouse automation, urban logistics, and robotic delivery networks, where efficient task assignment and pathfinding are vital for maximizing production efficiency. However, existing approaches often struggle to seamlessly integrate task allocation with path planning while also failing to address the demands of continuous pickup and delivery tasks, resulting in suboptimal performance and limited scalability in dynamic environments. To address these problems, we first introduce a novel task allocation approach, which constructs a cost matrix to satisfy pickup and delivery timing constraints for tasks and employs a Mixed-Integer Linear Programming (MILP) model to compute a task assignment matrix queue. Next, the CBS-TAPF framework is proposed, which constructs search forests for tasks and paths to address the joint optimization of task allocation and path planning. This framework is further extended to Continuous Multi-Agent Pickup and Delivery (CMAPD) tasks by dynamically updating the task allocation matrix queue, enhancing robustness and adaptability for real-world, sustained scenarios. Finally, through simulation and real-world experiments, we validated the effectiveness of the proposed methods. The experimental results demonstrate its superiority across diverse environments, ensuring robust performance in various operational scenarios.

Efficient scheduling of multi-shuttle is crucial for optimizing the performance of Automated Storage/Retrieval Systems (AS/RS). Multi-Agent Path Finding (MAPF) techniques play a pivotal role in addressing scheduling challenges by guaranteeing collision-free paths for multiple agents, however the assumptions of MAPF cannot meet the practical applications, such as 1) agents move at constant speeds and change directions instantaneously, 2) agents remain stationary at their destinations, 3) agents have different destinations. In this paper, we propose a novel lifelong multi-shuttle scheduling framework (LMSSF) to fill this gap based on the AS/RS system implemented in Hunan, China. LMSSF can perform re-planning and execution occur simultaneously, ensuring robust performance even in dynamic and uncertain environment. To apply MAPF to practical scenarios, we propose task conflict resolution, an improved single-shot MAPF and a path constraint mechanism to ensure collision-free movement of shuttles and improve the throughput performance of AS/RS system. Empirical evaluations on throughput and occupancy rate of outbound stations demonstrate the superiority of our proposed algorithm. Finally, LMSSF is applied to a real-world system and the experimental results show a 31.8% improvement in throughput compared to conventional strategies.Note to Practitioners—The motivation of this article stems from the challenges posed by the assumptions inherent in most MAPF algorithms when applied to practical AS/RS environments. Traditional MAPF algorithms typically compute discrete collision-free paths based on predefined start and goal locations. However, in real-world scenarios, tasks are continually generated, shuttles are highly dynamic, and various environmental constraints exist. To address these challenges, we propose the LMSSF as a means to adapt existing MAPF algorithms for practical AS/RS systems. This framework incorporates task conflict resolution, path planning optimization, and efficient shuttle control mechanisms. The paths obtained from the MAPF solver can be seamlessly integrated into our framework with minor format conversions. Through comprehensive testing, our algorithm demonstrates significantly improved throughput performance compared to traditional strategies.

In this article, we consider an active reconfigurable intelligent surface (ARIS)-aided nonorthogonal multiple access (NOMA) and rate-splitting (RS)-enabled communication system, in which a base station applies RS and NOMA to the downlink transmission of a hidden user and a public user with the assistance of an ARIS in the presence of an illegal user. More specifically, the closed expressions for the outage probability (OP), covert rate, and detection error probability (DEP) are derived. Also, we present the analysis for the secrecy OP (SOP), and analyse the effects of detection threshold, total power consumption, reconfigurable intelligent surface (RIS) deployment distance, and power allocation factors on the system performance. The numerical results show that under the same total power consumption, the ARIS has a lower OP value than the passive RIS (PRIS), which can effectively overcome the “multiplicative fading” effect. Moreover, applying the ARIS can result in a larger covert rate and a higher minimum DEP value at the optimal detection threshold, and can reduce the SOP values of hidden and public users by reducing the power allocation factors. In addition, at the same minimum DEP value, the RS-NOMA scheme has a higher covert rate than the NOMA scheme.

Automated guided vehicle (AGV) is widely used in transportation and distribution of materials. In the development of multi-AGV systems, how to handle deadlocks is a core issue. Most existing researches focus on open systems. Thus those results are not applicable in real-world systems. A few works have paid attention to closed systems recently, but the results are not flexible and generic enough. Therefore, an improved deadlock avoidance algorithm in closed guide-path system is proposed in this paper. The proposed algorithm is divided into an offline stage and an online stage. At the offline stage, the guide-path graph is processed to obtain useful information for the online stage. At the online stage, the algorithm assesses the safety of each resource allocation to ensure a deadlock-free operation. The computational complexity of the algorithm is shown to be$O(|E|)$, where$E$is the set of the edges in the guide-path graph. Based on the algorithm, an overall control strategy is also developed, and then implemented and tested in real systems. It is also compared with Banker’s algorithm and its variants, the results of which show that the proposed algorithm has a better performance.Note to Practitioners—Deadlock is a core issue in guide-path based multi-AGV systems. Once deadlock occurs, the whole system will collapse. This problem is more serious in closed systems, which is paid less attention to in literature. Current deadlock solutions are either too restrictive, resulting in inefficient resource utilization and poor flexibility, or computationally intensive and thus are unable to scale to large systems. To solve this problem, an improved deadlock avoidance algorithm with polynomial computational complexity is proposed in this paper, and it is more flexible than the Banker’s algorithm and its variants. Furthermore, an overall control framework (i.e., including task assignment, path planning and real-time scheduling) is developed based on the deadlock avoidance algorithm, which keeps persistent operation of the considered system while still guaranteeing deadlock-free. However, in this overall control framework, the choice of path is not optimized with respect to travel distance and makespan, which is the direction of future research.

In this article, we develop an analytical framework to study the impact of location-based relay selection strategy on the reliability of cognitive relay networks. By utilizing the tool of stochastic geometry, we first derive a closed-form expression for the reliability-enhanced region (RER), where relaying transmission can achieve higher transmission reliability than direct transmission. Then, we adopt the normalized reliability gain (NRG) to quantify the reliability benefit obtained by using relaying transmission compared to direct transmission, and we obtain the spatial distribution of NRG in the RER. Subsequently, by taking the spatial random nature of relays’ distribution into account, we investigate the reliability benefit obtained by secondary networks with the optimal location-based relay selection (OLB-RS) strategy. To reduce the feedback overhead during relay selection, we propose a region-aware relay selection (RA-RS) strategy and obtain the achievable reliability benefit. The results indicate that the reliability is highly dependent on the location of relay, and the OLB-RS strategy is to select the relay closest to the midpoint between the corresponding secondary source and destination.

Wi-Fi deployed inside a building can be used for positioning indoor users. A commonly used technology is weighted K-nearest neighbor (WKNN) fingerprint which positions a user based on K nearest reference points measured beforehand. The challenge lies in how to configure the value of K to obtain the best positioning accuracy. In this paper, we propose a self-adaptive WKNN (SAWKNN) algorithm with a dynamic K. By adjusting the value of K based on the signal strength, SAWKNN can obtain a better positioning accuracy than traditional WKNN. In particular, a significant percentage of the SAWKNN positioning makes use of a value K = 1. The performance of the proposed algorithm has been evaluated in real-world experiments.

Mobile edge computing (MEC) is one of the key technologies for fifth generation networks and beyond, which brings computation resources in proximity to end users. While enabling computing capability at the network edge, it also introduces extra energy consumption to the network operation. Moreover, the nonuniform traffic distribution of mobile network makes the utilization of MEC platforms unbalanced. In this paper, we propose an online optimization strategy of MEC server (MECS) computation task offloading with sleep control scheme to minimize the long term energy consumption of the MECS network. First, the energy optimization problem under delay constraint is formulated which considers both the radio and computation resources. Then, a Lyapunov-based approach is proposed to convert the long term optimization problem to a per-slot optimization problem which only requires information of current time slot. An online offloading algorithm is proposed to make decisions in each time slot. Finally, the system performance is evaluated and the impacts of several key parameters are analyzed. Simulation results demonstrate that our proposed strategy can significantly reduce the long term average energy consumption of the mobile edge network by 30% to 90% under different workload scenarios, while keeping a relatively low system delay compared with the schemes without MEC cooperation and sleep control.

The dramatic development of Internet of Things (IoT) is not only leading to the spectrum crunch, but is also resulting in exorbitant energy consumption. It is thus desirable to liberate IoT from the constraint of the spectrum scarcity and to rein in the growing energy consumption. Wireless energy harvesting relay-assisted underlay cognitive networks (WEH-CRNs), which combine cognitive radio and wireless energy harvesting techniques to alleviate the spectrum and energy constraints by reusing spare spectrum for data transmissions and harvesting ambient energy for power supplies, are conceived as an efficient solution for massive IoT deployments. However, all existing works about WEH-CRNs did not take into account of the spatial location distribution of nodes, which is very important for energy harvesting and information transmission. Therefore, in this paper, we develop a framework for the design and analysis of WEHCRNs with spatial randomly distributed nodes (WEH-RCRNs). We first propose an efficient relay selection strategy in WEHRCRNs to determine when and which relay should be selected to assist transmission. Then, based on the proposed relay selection strategy, we derive the expression for outage probability to measure the outage performance of WEH-RCRNs. Finally, the impacts of related network parameters on the outage probability is also explored on the basis of our analysis results.

With the ever-increasing spectrum requirements for transmitting explosively growing mobile data, spectrum-efficient solutions need to be integrated into future mobile networks. Spectrum sharing enables the primary system to share licensed spectrum with the secondary system. Thus, it is conceived as an appealing solution for improving spectrum usage to eliminate the spectrum supply-demand gap. In this paper, we develop an efficient transmit power control strategy for underlay spectrum sharing networks with spatially Poisson-distributed primary users. A distinguishing feature of the proposed strategy is that it only requires channel state information and location information of a few primary users close to the secondary transmitter, rather than those for all primary users. Furthermore, we evaluate the outage performance of the secondary system and the interference situation of the primary system under this kind of transmit power control strategy. Numerical results demonstrate that the proposed transmit power control strategy can achieve near-optimal outage performance compared to the perfect power control strategy, while reducing the control complexity and the feedback burden significantly at the same time.

In this paper, we present performance analysis for underlay cognitive decode-and-forward relay networks with the Nth best relay selection scheme over Nakagami-m fading channels. Both the maximum tolerated interference power constraint and the maximum transmit power limit are considered. Specifically, exact and asymptotic closed-form expressions are derived for the outage probability of the secondary system with the Nth best relay selection scheme. The selection probability of the Nth best relay under limited feedback is discussed. In addition, we also obtain the closed-form expression for the ergodic capacity of the secondary system with a single relay. These expressions facilitate in effectively evaluating the network performance in key operation parameters and in optimizing system parameters. The theoretical derivations are extensively validated through Monte Carlo simulations. Both theoretical and simulation results show that the fading severity of the secondary transmission links has more impact on the outage performance and the capacity than that of the interference links does. Through asymptotic analysis, we show that the diversity order for the Nth best relay selection scheme is min(m1, m3) × (M - N) + m3, where M denotes the number of cognitive relays, and m1and m3represent the fading severity parameters of the first-hop transmission link and the second-hop transmission link, respectively.

In this paper, considering a relay assisted hybrid overlay/underlay cognitive radio system with peak interference power constraints for protecting the primary system, the upper bound of outage probability of secondary system with power limit is derived. The results show that the outage performance of secondary system improves with the increase of maximum tolerated interference power before a specific threshold, but it keeps unchanged after the threshold. The results also show that more relays can provide higher diversity gain to get better outage performance in this kind of cognitive radio system. It is also indicated that the outage performance of secondary system is affected by the activity behavior of primary network, and relay cooperation can decrease this kind of affection.

A robust joint multiuser scheduling and precoding design is proposed for uplink multiuser MIMO systems with imperfect channel state information (CSI). The effect of the antenna correlations is involved in the channel model. The multiuser scheduling and the precoding that avail the total mean square error (TMSE) minimization criteria under individual power constraint are firstly deduced and the combination of the two parts is designed in the following. The proposed design is compared with the robust precoding with random scheduling scheme and the non-robust scheme. The simulation results show that the joint design helps achieving larger system throughput and the robust algorithm mitigates the performance loss due to the imperfect CSI.