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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References
Context. https://www.merriam-webster.com/dictionary/context. Accessed 20 Mar 2022
Face detection and tracking using CamShift. https://www.mathworks.com/help/vision/ug/face-detection-and-tracking-using-camshift.html. Accessed 26 Jan 2022
Matlab-deep-learning. https://github.com/matlab-deeplearning/mtcnn-face-detection. Accessed 20 Oct 2022
Show CPU cores utilization in Matlab. https://stackoverflow.com/questions/25950727/show-cpu-coresutilization-in-matlab. Accessed 22 Jan 2022
Abdelfattah, A.S., Abdelkader, T., EI-Horbaty, E.S.M.: Reliable web service consumption through mobile cloud computing. In: Mobile Computing-Technology and Applications. IntechOpen (2018)
Al-Muhtadi, J., Saleem, K., Al-Rabiaah, S., Imran, M., Gawanmeh, A., Rodrigues, J.J.: A lightweight cyber security framework with context-awareness for pervasive computing environments. Sustain. Urban Areas 66, 102610 (2021)
Argrow, B., et al.: The NCAR/EOL community workshop on unmanned aircraft systems for atmospheric research. Ph.D. thesis, National Center for Atmospheric Research (2017)
Bekmezci, I., Sahingoz, O.K., Temel, Ş: Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw. 11(3), 1254–1270 (2013)
Daud, M., Khan, Q., Saleem, Y.: A study of key technologies for IoT and associated security challenges. In: 2017 International Symposium on Wireless Systems and Networks (ISWSN), pp. 1–6 (2017). https://doi.org/10.1109/ISWSN.2017.8250042
Han, Y., Liu, L., Duan, L., Zhang, R.: Towards reliable UAV swarm communication in d2d-enhanced cellular networks. IEEE Trans. Wirel. Commun. 20(3), 1567–1581 (2021). https://doi.org/10.1109/TWC.2020.3034457
Henricksen, K., Indulska, J.: A software engineering framework for context-aware pervasive computing. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications, 2004, pp. 77–86 (2004). https://doi.org/10.1109/PERCOM.2004.1276847
Hosseini, N., Jamal, H., Haque, J., Magesacher, T., Matolak, D.W.: UAV command and control, navigation and surveillance: a review of potential 5g and satellite systems. In: 2019 IEEE Aerospace Conference, pp. 1–10 (2019). https://doi.org/10.1109/AERO.2019.8741719
Lin, H., Yan, Z., Fu, Y.: Adaptive security-related data collection with context awareness. J. Netw. Comput. Appl. 126, 88–103 (2019)
Mahama, E., et al.: Testing and evaluating the impact of illumination levels on UAV-assisted bridge inspection. In: 2022 IEEE Aerospace Conference (AERO), pp. 1–8 (2022). https://doi.org/10.1109/AERO53065.2022.9843209
Mahama, E., et al.: Testing and evaluation of radio frequency immunity of unmanned aerial vehicles for bridge inspection. In: 2021 IEEE Aerospace Conference (50100), pp. 1–8 (2021). https://doi.org/10.1109/AERO50100.2021.9438457
Mostefaoui, G., Pasquier-Rocha, J., Brezillon, P.: Context-aware computing: a guide for the pervasive computing community. In: The IEEE/ACS International Conference on Pervasive Services, 2004. ICPS 2004. Proceedings, pp. 39–48 (2004). https://doi.org/10.1109/PERSER.2004.1356763
Mozaffari, M., Saad, W., Bennis, M., Nam, Y.H., Debbah, M.: A tutorial on UAVs for wireless networks: applications, challenges, and open problems. IEEE Commun. Surv. Tutor. 21(3), 2334–2360 (2019). https://doi.org/10.1109/COMST.2019.2902862
Plathottam, S.J., Ranganathan, P.: Next generation distributed and networked autonomous vehicles: review. In: 2018 10th International Conference on Communication Systems Networks (COMSNETS), pp. 577–582 (2018). https://doi.org/10.1109/COMSNETS.2018.8328277
Sahingoz, O.K.: Networking models in flying ad-hoc networks (FANETs): concepts and challenges. J. Intell. Robot. Syst. 74(1), 513–527 (2014)
Shang, F., Su, W., Wang, Q., Gao, H., Fu, Q.: A location estimation algorithm based on RSSI vector similarity degree. Int. J. Distrib. Sens. Netw. 10(8), 371350 (2014)
Silva, C., Sobral, A., Vieira, R.T.: An automatic facial expression recognition system evaluated by different classifiers. In: X Workshop de Visão Computacional, at Uberlândia, Minas Gerais, Brazil, pp. 208–212 (2014)
Tegicho, B.E., Bogale, T.E., Eroglu, A., Edmonson, W.: Connectivity and safety analysis of large scale UAV swarms: based on flight scheduling. In: 2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 1–6 (2021). https://doi.org/10.1109/CAMAD52502.2021.9617780
Tegicho, B.E., Geleta, T.N., Bogale, T.E., Eroglu, A., Edmonson, W., Bitsuamlak, G.: Effect of wind on the connectivity and safety of large scale UAV swarms. In: 2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), pp. 1–6 (2021). https://doi.org/10.1109/BlackSeaCom52164.2021.9527821
Tegicho, B.E., Graves, C.: Automatic emoji insertion based on environment context signals for the demonstration of pervasive computing features. In: SoutheastCon 2021, pp. 1–6 (2021). https://doi.org/10.1109/SoutheastCon45413.2021.9401878
Yılmaz, Ö., Erdur, R.C.: IConAwa-an intelligent context-aware system. Expert Syst. Appl. 39(3), 2907–2918 (2012)
Zeng, T., Semiari, O., Mozaffari, M., Chen, M., Saad, W., Bennis, M.: Federated learning in the sky: joint power allocation and scheduling with uav swarms. In: ICC 2020–2020 IEEE International Conference on Communications (ICC), pp. 1–6 (2020). https://doi.org/10.1109/ICC40277.2020.9148776
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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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