Abstract
Swarms of unmanned aerial vehicles are increasingly being utilized for a variety of operations. However, extremely variable environmental circumstances alter their intra-UAV minimum safe distance, resulting in collision, and those near swarm’s edge become increasingly vulnerable to connectivity loss. Context-awareness as a strategy for developing pervasive computing in UAVs is gaining popularity to tackle these difficulties. A context awareness-based pervasive computing system model is proposed in this research to improve the safety and connectivity of individual UAVs in a swarm with their neighboring UAVs. To acquire the contexts of different environments the following systems were utilized: For physical, light intensity from real-time picture taken using camera; for human, facial recognition algorithm; for UAV local ICT, the UAV’s built-in CPU utilization percentage; for network ICT, wireless network signal strength using received signal strength analysis. Following simulation, we evaluated the accuracy, reaction time, and significant limits that must be considered. Most situations were recognized with great accuracy, ranging from 84.85% to 100%. On a machine with 16 GB of RAM and a 64-bit operating system, the total system performance had an average reaction time of 2.15 s in a scenario where all contexts were used in a prioritized manner. The environments under consideration, as well as the kind of UAV and its internal hardware system processing capacity, were determined to be key limits on the system’s performance. Analyzing the proposed system’s application, a UAV swarm can complete tasks without colliding while retaining intra-UAV connectivity by transmitting information across a reliable communication network.
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Tegicho, B.E., Bogale, T.E., Graves, C. (2023). Pervasive Computing for Efficient Intra-UAV Connectivity: Based on Context-Awareness. In: Sabir, E., Elbiaze, H., Falcone, F., Ajib, W., Sadik, M. (eds) Ubiquitous Networking. UNet 2022. Lecture Notes in Computer Science, vol 13853. Springer, Cham. https://doi.org/10.1007/978-3-031-29419-8_12
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