The use of the unmanned aerial vehicle (UAV) has been regarded as a promising technique in both military and civilian applications. However, due to the lack of relevant laws and regulations, the misuse of illegal drones poses a serious threat to social security. In this paper, we develop a trajectory planner based on particle swarm optimization and a proposed surveillance area importance updating mechanism aimed at deriving three-dimensional (3D) optimal surveillance trajectories for multiple monitoring drones. We also propose a multi-objective fitness function in accordance with energy consumption, flight risk, and surveillance area priority in order to evaluate the trajectories generated by the proposed trajectory planner. Simulation results show that the trajectories generated by the proposed trajectory planner can preferentially visit important areas while obtaining a high fitness value in various practical situations.

Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

Interest in the study of next-generation underwater sensor networks for ocean investigations has increased owing to developing concerns over their utilization in areas such as oceanography, commercial operations in maritime areas, and military surveillance. Underwater acoustic communications (UAC) network channels are fast-varying (spatially and temporally) according to environmental conditions. It is tempting to use adaptive modulation and coding (AMC) for UAC networks to improve the system efficiency by matching transmission parameters to channel variations. This paper focuses on analyzing a measured sea trial dataset by using a rule-based strategy (i.e., three-dimensional analysis, modulation-wise analysis, and a fixed SNR strategy) to find the suitable link adaptation procedure depending on the channel quality. Hence, we plot a scenario of the measured UAC network data rate vs Signal to Noise Ratio (SNR) and/or Bit Error Rate (BER) to pick the best AMC combinations in the context of adaptivity/ adaptability to the channel. Due to non-reversibility limitation of rule-based strategy, the work further extends to use machine learning (ML) algorithms to classify the AMC by investigating the channel characteristics. Boosted regression tree, from among the four ML algorithms we adopted for the analysis, shows formidable accuracy of 99.97% in classifying AMC. This ensemble of trees learns from the uplink data of the buoy and the base station and relates the AMC to channel metrics and signal characteristics especially subject to SNR and BER constraints.

In recent years, improving intersection traffic safety has become a major focus for researchers. However, it remains a significant concern due to the increasing number of vehicles and the introduction of autonomous and cooperative driving systems. Advancements in artificial intelligence (AI) and vehicle-to-everything (V2X) technologies offer promising solutions to reduce collisions between vehicles. As V2X technologies are slowly being integrated into traffic safety systems, questions arise about their impact on safety effectiveness. This research area is relatively unexplored but has potential to enhance intersection safety. In this paper, we introduce the Intersection Traffic Safety Framework (ITSF), a safety-oriented system devised to mitigate collisions at road intersections. This framework achieves this through the implementation of a collision avoidance mechanism that harnesses vehicle-to-infrastructure (V2I) communications. Additionally, it incorporates a machine learning model tasked with distinguishing between vehicles posing a risk and those that do not, as part of the collision avoidance process. Furthermore, this work assesses the performance of this framework by considering critical factors like the penetration rate of V2X-enabled vehicles, end-to-end latency, and the responsiveness of drivers to safety alerts. The proposed approach proves highly effective in ensuring road users’ safety with a penetration rate of 60% or higher, resulting in a significant reduction in collisions and nearly 98% accuracy in classifying risky vehicles. Additionally, the algorithm remains successful even when some drivers neglect warnings, showcasing its robustness in minimizing collision rates.

This paper addresses issues with monitoring systems that identify and track illegal drones. The development of drone technologies promotes the widespread commercial application of drones. However, the ability of a drone to carry explosives and other destructive materials may pose serious threats to public safety. In order to reduce these threats, we propose an acoustic-based scheme for positioning and tracking of illegal drones. Our proposed scheme has three main focal points. First, we scan the sky with switched beamforming to find sound sources and record the sounds using a microphone array; second, we perform classification with a hidden Markov model (HMM) in order to know whether the sound is a drone or something else. Finally, if the sound source is a drone, we use its recorded sound as a reference signal for tracking based on adaptive beamforming. Simulations are conducted under both ideal conditions (without background noise and interference sounds) and non-ideal conditions (with background noise and interference sounds), and we evaluate the performance when tracking illegal drones.