Unmanned aerial vehicle (UAV) swarm networking and collaboration have significant prospects in both civilian and military applications, due to its remarkable properties in cooperative efficiency, reduced risks, and operational cost. Traditional algorithms have challenging issues of high computational complexity and low efficiency in UAV swarm networking and collaboration, while computational intelligence (CI) has attracted increasing attention since it has advantages in solving complex optimization problems. The networking of UAV swarms serves as an essential foundation for collaboration, and intelligent collaboration is a crucial means of enhancing the performance of UAV swarm systems. To date, extensive CI-based algorithms have been proposed to improve the networking and collaboration capabilities of UAV swarms, and several relevant surveys have also been presented. However, existing surveys either review networking or collaboration. To the best of our knowledge, there is no survey that simultaneously concentrates on CI-based UAV swarm networking and collaboration. In this survey, we provide a comprehensive overview of CI-based networking and collaboration algorithms from six typical aspects including channel access, network routing, cooperative task assignment, cooperative path planning, cooperative search, and cooperative jamming. More importantly, to help researchers choose appropriate algorithms to satisfy the requirements of different missions, we classify CI-based algorithms into four categories, namely heuristic behavior search-based algorithms, policy design-based algorithms, policy learning-based algorithms, and hybrid algorithms. Finally, we discuss open issues and future directions that may influence future research on UAV swarm intelligence networking and collaboration. This review may provide new insights and valuable references for researchers in this field.

In a mobile RFID system, a large number of tags move in and out of the system continuously, so that the reader has very limited time to recognize all the tags. As a result, the effective and efficient identification of tags in mobile environments is a more challenging problem compared to conventional static RFID systems. In this paper, we propose an efficient bit-detecting (EBD) protocol to accelerate the reading process of large-scale mobile RFID systems. In these systems, some previously recognized tags, i.e., known tags, may stay in the reader's reading range for two consecutive reading cycles, and some unknown tags may newly participate in the current reading cycle. In the proposed EBD protocol, a new bit monitoring method is proposed to detect the presence of known tags using a small number of slots, and to retrieve their IDs from the back-end database. Next, an $M$ -ary bit-detecting tree recognition method is proposed to rapidly recognize unknown tags without generating any idle slots. This new protocol is shown to perform better than existing methods reported in the literature. Both theoretic and simulation results are present to demonstrate that the proposed protocol is superior to existing protocols in terms of lower time cost.

Being able to provide a relatively easy and inexpensive way to collect data, portable RFID readers have gained increasing popularity in wide-ranging RFID applications. In order to maximize the reader's battery life, efficient tag identification protocols are of paramount importance in large-scale passive RFID systems. This paper proposes a time- and energy-aware protocol based on M-ary collision tree (MCT) for efficient RFID tag identification. Thanks to Manchester encoding, the proposed MCT protocol recursively divides colliding tags into M subsets with at least two non-empty ones according to the information of the first log_2 M colliding bits. Through the new M-ary collision tree recognition method, MCT can effectively identify all the tags within its reading range. Theoretic analysis demonstrates that it takes the proposed MCT protocol fewer number of message bits to identify all the tags, which reduces not only the identification time but also the energy cost. Simulation results are also presented to show that compared with other benchmark works, the proposed MCT protocol is able to reduce the average identification time and energy cost by at least 16.12% and 15.73%, respectively.

Autonomous navigation of multiple unmanned aerial vehicles (UAVs) serves as the foundation for their widespread applications in various fields. However, multi-UAV cooperative navigation is a challenging problem because it is difficult for UAVs to plan efficient paths while avoiding collisions with both neighboring UAVs and other obstacles. In this work, an efficient reinforcement learning (RL)-based cooperative navigation algorithm (RL-CN) is proposed to make optimal decisions in dense and dynamic environments. In RL-CN, the multi-UAV cooperative navigation is formulated as a Markov decision process and an enhanced RL method is proposed for continuous control of multiple UAVs. To solve the sparse reward problem, a group of reward functions are designed in reward shaping. Next, a staged-tuning with two subactor networks strategy is developed to accelerate the training process and improve the navigation performance. Then, an enhanced prioritized experience replay strategy is designed by considering both Temporal Difference (TD) error and connectivity trend. Thus, high-value transitions are more frequently selected to further enhance the training process. Finally, we conduct comprehensive simulation experiments and provide comparative results to substantiate the effectiveness and robustness of RL-CN algorithm.

Unmanned aerial vehicles (UAVs) deployed as a swarm can offer a flexible and cost-efficient solution for surveillance missions in large-scale adversarial environments. UAV swarms possess superior real-time surveillance capabilities in detecting time-sensitive targets with speed and security, surpassing alternative techniques such as satellites and ground-based sensors. However, the area surveillance problem in adversarial scenarios using UAV swarms necessitates careful consideration of factors such as swarm positioning, flight attitudes, antenna directionality, the threat posed by adversary detection systems (ADSs), and swarm network topology. To address this challenge, we propose the Integrated Low Detection Probability (ILDP) deployment method for UAV swarms, which incorporates the Danger Avoidance Distributed Motion control algorithm (DADM) for swarm collaboration and the Low Detection Probability Topology Control algorithm (LDPTC) for swarm networking. The DADM algorithm facilitates swarm cooperation in achieving area coverage and evading ADSs by leveraging neighboring, environmental, and threat information in adversarial scenarios. Furthermore, the LDPTC algorithm establishes a topology optimization model that comprehensively considers swarm distribution and the impact of directional antenna sidelobes to reduce transmitting energy leakage on the ground. Our strategy significantly decreases the detection probability of UAV swarms by ADSs, ensuring the operational effectiveness of UAV swarms in dynamic adversarial scenarios. Extensive simulations validate the superiority of our proposed ILDP method, demonstrating considerably lower detection probabilities compared to other approaches in static and dynamic adversary environments across various swarm scales. Moreover, our method excels in real-time surveillance capabilities with lower computational complexity, freeing up computing resources for UAVs to fulfill additional tasks.

Radio frequency identification (RFID) technology has been employed in wide-raging application domains. In most RFID applications, time-efficient identification of missing tags is one of the most fundamental objectives, especially for asset management and anti-theft purposes. In this paper, we propose a time-efficient pair-wise collision-resolving missing tag identification (PCMTI) protocol for large-scale RFID systems. In the protocol, two novel strategies, i.e., the pair-reply and two-collision slot (i.e., a slot with two exact tag responses) resolving strategies, are proposed. The pair-reply strategy can verify two tags in one short response slot simultaneously, while the two-collision slot resolving strategy further increases the number of tags verified in each frame. Both theoretical analysis and simulated results are presented to demonstrate the superiority of the proposed PCMTI protocol, which is capable of outperforming the state-of-the-art comparative protocols with at least a 30% reduction in average identification time for verifying one tag.