Wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) have been used for monitoring animals but when their habitats have difficult access and are areas of a large expanse, remote monitoring by classic techniques becomes a difficult task. The use of traditional WSNs requires a restrictive number of hops in a multi-hoping routing scheme, traveling long distances to the sink node where data is stored by nodes and UAVs are used to collect data by visiting each node. However, the use of UAVs is not straightforward since the energy balance between the WSN and UAV has to be carefully calibrated. Building on this, we propose two data collection schemes in clustered based WSNs: (1) WSN oriented and (2) UAV oriented. In the former, nodes within each cluster member (CM), send information to their cluster head (CH) and for recollection, the UAV visits all CHs. As the UAV visits many CHs the flight time is increased. In the latter, all CHs send data from their CMs to a sink node, hence, the UAV only visits this node, reducing the flying time but with a higher system energy cost. To find the most suitable scheme for different monitoring conditions in terms of the average energy consumption and the buffer capacity of the system, we develop a mathematical model that considers both the dynamics of the WSN along with the UAV.

This paper studies delay-aware formation control for (unmanned aerial vehicle) UAV swarms operating under realistic air-to-air communication latency. An attractive–repulsive distance-based potential-field (ARD-PF) controller is used as an expert to generate demonstrations for imitation learning in multi-UAV cooperative systems. By augmenting the training data with communication delay, the learned policy implicitly compensates for outdated neighbor information and improves swarm coordination during autonomous flight. Extensive simulations across different swarm sizes, formation spacings, and delay levels show that delay-robust imitation learning significantly enlarges the probabilistic stability region compared with classical ARD-PF control and non-robust learning baselines. Formation control performance is evaluated using internal geometric error, global offset, and multi-run stability metrics. In addition, a predictive delay–stability model is introduced, linking the maximum admissible communication delay to swarm size and inter-agent spacing, with low fitting error against simulated stability boundaries. The results provide quantitative insights for designing communication-aware UAV swarm systems under latency constraints.

Recent research on web accessibility has explored the use of artificial intelligence (AI), particularly large language models (LLMs), to support accessibility remediation. However, the field lacks a theoretical perspective explaining how LLMs can be integrated to systematically support this process. This study proposes a theory of LLM-assisted web accessibility remediation. It is built through the integration of qualitative evidence, prior literature, accessibility standards, and empirical studies on LLM-based remediation. The resulting theory provides an explanatory framework describing how LLMs can assist web accessibility remediation through iterative cycles of analysis, transformation, and validation, and identifies key factors including prompting strategies, input representations, and validation mechanisms. This work provides a conceptual foundation for understanding and systematically studying LLM-assisted accessibility remediation, and supports both research and practice by guiding future studies and informing the design of models, methods, tools, and accessibility engineering practices.

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency.

Animal monitoring systems often rely on expensive and challenging GPS-based systems to obtain accurate trajectories. However, an alternative approach is to generate synthetic trajectories that exhibit similar statistical properties to real trajectories. These synthetic trajectories can be used effectively in the design of surveillance systems such as wireless sensor networks and drone-based techniques, which aid in data collection and the delineation of areas for animal conservation and reintroduction efforts. In this study, we propose a data generation method that utilizes simple phase-type distributions to produce synthetic animal trajectories. By employing probability distribution functions based on the exponential distribution, we achieve highly accurate approximations of the movement patterns of four distinct animal species. This approach significantly reduces processing time and complexity. The research primarily focuses on generating animal trajectories for four endangered species, comprising two terrestrial and two flying species, in order to demonstrate the efficacy of the proposed method.