Abstract
Recent studies have revealed the benefit of capitalising on the interplay between Unmanned Aerial Vehicles (UAVs) and ground sensors, for the efficient data muling from locations of interest to back-end infrastructures where, it is analysed and processed for further decision making. However, such studies have not considered minimizing the energy spent by a UAV for moving from one location to another; a requirement that can help maximize the lifetime of the resulting hybrid network infrastructure before recharging. This paper proposes an optimal clustering model for a case where, an Unmanned Aerial Vehicle (UAV) is to monitor an area of interest, to collect data captured by a terrestrial sensor network. The proposed clustering algorithm minimises a combination of the energy for routing data in the terrestrial network and the energy used by the UAV to collect data from cluster heads and report to a back-end infrastructure. We formally calculate the optimal number of clusters in a uniformly distributed sensor network, to support existing k-clustering schemes, and for general networks, a general clustering algorithm is proposed. Performance evaluation reveals relevance of accurately modelling the hybrid networks underlying the“Internet-of-Things in Motion”.
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Bagula, A., Ismail, A., Tuyishimire, E.: Generating Dubins path for fixed wing UAVs in search missions. In: International Symposium on Ubiquitous Networking. Springer, Heidelberg (2018)
Tuyishimire, E., Bagula, A., Rekhis, S., Boudriga, N.: Cooperative data muling from ground sensors to base stations using UAVs. In: 2017 IEEE Symposium on Computers and Communications (ISCC), pp. 35–41. IEEE (2017)
Las Fargeas, J., Kabamba, P., Girard, A.: Cooperative surveillance and pursuit using unmanned aerial vehicles and unattended ground sensors. Sensors 15(1), 1365–1388 (2015)
Boudriga, N., Hadj, S.B., Rekhis, S., Bagula, A.: A cloud of UAVs for the delivery of a sink as a service to terrestrial WSNs. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2016)
Bagula, A., Tuyishimire, E., Wadepoel, J., Boudriga, N., Rekhis, S.: Internet-of-things in motion: a cooperative data muling model for public safety. In: 2016 Intl IEEE Conferences Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 17–24. IEEE (2016)
Tuyishimire, E., Adiel, I., Rekhis, S., Bagula, B.A., Boudriga, N.: Internet of things in motion: a cooperative data muling model under revisit constraints. In: 2016 Intl IEEE Conferences Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 1123–1130. IEEE (2016)
Bagula, A., Castelli, L., Zennaro, M.: On the design of smart parking networks in the smart cities: an optimal sensor placement model. Sensors 15(7), 15443–15467 (2015)
Bagula, A., Zennaro, M., Inggs, G., Scott, S., Gascon, D.: Ubiquitous sensor networking for development (USN4D): an application to pollution monitoring. Sensors 12(7), 391–414 (2012). ISSN 1424-8220
Masinde. M.,Bagula, A.: A framework for predicting droughts in developing countries using sensor networks and mobile phones. In: Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists, pp. 390–393. ACM (2010)
Masinde, M., Bagula, A., Mthama, T.N.: The role of ICTs in downscaling and up-scaling integrated weather forecasts for farmers in sub-Saharan Africa. In: In proceedings of ICTD 1202, pp. 122–129. ACM (2012)
Mandava, M., Lubamba, C., Ismail, A., Bagula, H., Bagula, A.: Cyber-healthcare for public healthcare in the developing world. In: Proceedings of the 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 14–19. IEEE (2016)
Bagula, A., Lubamba, C., Mandava, M., Bagula, H., Zennaro, M., Pietrosemoli, E.: Cloud based patient prioritization as service in public health care. In: Proceedings of the ITU Kaleidoscope 2016, 14–16 November, Bangkok, Thailand. IEEE (2016)
Wang, L.-C., Wang, C.-W., Liu, C.-M.: Optimal number of clusters in dense wireless sensor networks: a cross-layer approach. IEEE Trans. Veh. Technol. 58(2), 966–976 (2009)
Duarte-Melo, E.J., Liu, M.: Energy efficiency of many-to-one communications in wireless networks. In: The 2002 45th Midwest Symposium on Circuits and Systems, MWSCAS-2002, vol. 1, pp. I–615. IEEE (2002)
Chen, G., Nocetti, F.B., Gonzalez, J.S., Stojmenovic, I.: Connectivity based k-hop clustering in wireless networks. In: 2002 Proceedings of the 35th Annual Hawaii International Conference on System Sciences, HICSS, pp. 2450–2459. IEEE (2002)
Gu, Y., Wu, Q., Rao, N.S.V.: Optimizing cluster heads for energy efficiency in large-scale heterogeneous wireless sensor networks. Int. J. Distrib. Sens. Netw. 6, 961591 (2010)
Yang, H., Sikdar, B.: Optimal cluster head selection in the leach architecture. In: 2007 IEEE International Performance, Computing, and Communications Conference, IPCCC 2007, pp. 93–100. IEEE (2007)
Zang, C., Zang, S.: Mobility prediction clustering algorithm for UAV networking. In: 2011 IEEE GLOBECOM Workshops (GC Wkshps), pp. 1158–1161. IEEE (2011)
Shi, N., Luo, X.: A novel cluster-based location-aided routing protocol for UAV fleet networks. Int. J. Digit. Content Technol. Appl. 6(18), 376 (2012)
Okcu, H., Soyturk, M.: Distributed clustering approach for UAV integrated wireless sensor networks. Int. J. Ad Hoc Ubiquitous Comput. 15(1–3), 106–120 (2014)
Aurenhammer, F., Klein, R., Lee, D.-T.: Voronoi Diagrams and Delaunay Triangulations, vol. 8. World Scientific, Singapore (2013)
Skiena, S.: Dijkstra’s algorithm. In: Implementing Discrete Mathematics: Combinatorics and Graph Theory with Mathematica, Reading, MA, pp. 225–227. Addison-Wesley (1990)
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Tuyishimire, E., Bagula, B.A., Ismail, A. (2018). Optimal Clustering for Efficient Data Muling in the Internet-of-Things in Motion. In: Boudriga, N., Alouini, MS., Rekhis, S., Sabir, E., Pollin, S. (eds) Ubiquitous Networking. UNet 2018. Lecture Notes in Computer Science(), vol 11277. Springer, Cham. https://doi.org/10.1007/978-3-030-02849-7_32
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DOI: https://doi.org/10.1007/978-3-030-02849-7_32
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