İHA ağları için uyarlanabilir, dengeli ve enerji verimli bir kümeleme mekanizması

İnsansız hava araçları(İHA), hem sivil hem de askeri olmak üzere birçok alanda yaygın olarak kullanılmaktadır. Özellikle sivil uygulama alanlarında hem ekonomiklik hem de kolay temin edilebilirlikleri sayesinde küçük ölçekli İHA'lar tercih edilmektedir. Ancak bu araçlar bazı uygulamalarda tek başına kullanıldığında yetersiz kalmaktadır. Mini İHA’lardaki bu yetersizlik çoğu durumda kısıtlı enerji ve taşıma kapasitesi olarak karşımıza çıkmaktadır. Bu soruna çözüm olarak birden fazla İHA’ların birleşmesiyle oluşturulan sürü İHA ağları önerilmiştir. Böylelikle bu ağlarda bulunan İHA’lara farklı görevler verilerek bu yetersizliğe çözüm önerisi sunulmuştur. Bu ağların birçok avantajları olmasıyla birlikte zorlukları da bulunmaktadır. Bu zorluklar sırasıyla enerji kısıtı, düşük güçlü ve kayıplı kablosuz haberleşme arayüzü ve düşük faydalı yük taşıma kapasitesi olarak karşımıza çıkar. Bu çalışmada, insansız hava aracı sürüleri için uyarlanabilir, dengeli ve enerji verimli yeni bir kümeleme mekanizması önerilmiştir.

An adaptive, balanced and energy efficient clustering mechanism for UAV networks

Unmanned aerial vehicles (UAVs) are widely used in many fields, both civilian and military. Mostly mini UAVs are used in civilian applications which are preferred both in terms of affordability and availability. However, these vehicles are insufficient for some applications when they are used alone. This inadequacy is often observed in mini-UAVs having limited capacity in terms of energy storage and payloads. As a solution to this challenge, swarms of networked mini UAVs have been proposed. Thus, the UAVs in such networks are assigned with different tasks to accomplish the overall mission. While such UAV networks have many advantages, they also come with challenges. These challenges include limited on-board energy storage, low power and lossy wireless communication interface, and limited useful payload carrying capability. In this study, a new adaptive, balanced and energy efficient clustering mechanism has been proposed for such UAV networks.

Kaynakça

[1] Mcdonald AB. A Mobility-Based Framework for Adaptive Dynamic Cluster-Based Hybrid Routing in Wireless Ad Hoc Networks. Ph.D. Thesis, University of Pittsburgh, USA, 2000.

[2] Ramanathan R, Martha S. "Hierarchically‐organized, multihop mobile wireless networks for quality‐of‐service support". Mobile Networks and Applications, 3(1), 101-119, 1998.

[3] Sahingoz OK. "Mobile networking with UAVs: Opportunities and challenges". 2013 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, 28-31 May 2013.

[4] Li X, Zhang T, Li J. "A particle swarm mobility model for flying ad hoc networks". 2017 IEEE Global Communications Conference, Singapore, 4-8 January 2017.

[5] Newcome LR. Unmanned Aviation: A Brief History of Unmanned Aerial Vehicles. 1nd ed. Reston, VA, USA, American Institute of Aeronautics and Astronautics Inc., 2004.

[6] Valavanis KP, Vachtsevanos GJ. Handbook of Unmanned Aerial Vehicles. 1st ed. Netherlands, Springer, 2015.

[7] Scherer J, Yahyanejad S, Hayat S, Yanmaz E, Vukadinovic V, Andre T, Bettstetter C, Rinner B, Khan A, Hellwagner H. "An autonomous multi-UAV system for search and rescue". 2015 Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use, Florence, Italy, 18 May 2015.

[8] Saska M, Chudoba J, Přeučil L, Thomas J, Loianno G, Třešňák A, Vonásek V, Kumar V. "Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance". 2014 International Conference on Unmanned Aircraft Systems, Orlando, FL, USA, 27-30 May 2014.

[9] Saha HN, Das NK, Pal SK, Basu S, Auddy S, Dey R, Nandy A, Pal D, Roy N, Mitra D, Biswas S, Maity T. "A cloud based autonomous multipurpose system with self-communicating bots and swarm of drones". 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 8-10 January 2018.

[10] Ruan L, Wang J, Chen J, Xu Y, Yang Y, Jiang H, Zhang Y, Xu Y. "Energy-efficient multi-UAV coverage deployment in UAV networks: A game-theoretic framework". China Communications, 15(10), 194-209, 2018.

[11] Palan NG, Barbadekar BV, Patil S. "Low energy adaptive clustering hierarchy (LEACH) protocol: A retrospective analysis". 2017 International Conference On Inventive Systems and Control, Coimbatore, India, 19-20 January 2017.

[12] Sahingoz OK. "Networking models in flying ad-hoc networks (FANETs): Concepts and challenges". Journal of Intelligent & Robotic Systems, 74(1-2), 513-527, 2014.

[13] Zang C, Zang S. "Mobility prediction clustering algorithm for UAV networking". 2011 IEEE GLOBECOM Workshops, Houston, TX, USA, 5-9 December 2011.

[14] Luo F, Jiang C, Du J, Yuan J, Ren Y, Yu S, Guizani M. "A distributed gateway selection algorithm for UAV networks". IEEE Transactions on Emerging Topics in Computing, 3(1), 22-33, 2014.

[15] Feng T, Fan W, Tang J, Zeng W. "Consensus-based robust clustering and leader election algorithm for homogeneous UAV clusters". Journal of Physics: Conference Series, IOP Publishing, 1168(3), 1-10, 2019.

[16] Gankhuyag G, Shrestha AP, Yoo SJ. "Robust and reliable predictive routing strategy for flying ad-hoc networks". IEEE Access, 5, 643-654, 2017.

[17] Zafar W, Khan BM. "A reliable, delay bounded and less complex communication protocol for multicluster FANETs". Digital Communications and Networks, 3(1), 30-38, 2017.

[18] Khan A, Aftab F, Zhang Z. "BICSF: Bio-inspired clustering scheme for FANETs". IEEE Access, (7), 31446-31456, 2019.

[19] Leonov AV. "Application of bee colony algorithm for FANET routing". 2016 17th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, Erlagol, Russia, 30 June-4 July 2016.

[20] Arafat MY, Moh S. "Localization and clustering based on swarm intelligence in UAV networks for emergency communications". IEEE Internet of Things Journal, 6(5), 8958-8976, 2019.

[21] Maistrenko VA, Alexey LV, Danil VA. "Experimental estimate of using the ant colony optimization algorithm to solve the routing problem in FANET". 2016 International Siberian Conference on Control and Communications, Moscow, Russia, 12-14 May 2016.

[22] Xing N, Zong Q, Tian B, Dou L, Wang Q. "Network Formation Game for Routing in Unmanned Aerial Vehicle Networks". 2018 IEEE/CIC International Conference on Communications in China, Beijing, China, 16-18 August 2018.

[23] Singh K, Verma AK. "Applying OLSR routing in FANETs". 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, India, 8-10 May 2014.

[24] Singh K, Verma AK. "Experimental analysis of AODV, DSDV and OLSR routing protocol for flying adhoc networks (FANETs)". 2015 IEEE International Conference on Electrical, Computer and Communication Technologies, Coimbatore, India, 5-7 March 2015.

[25] Zeng Y, Zhang R, Lim TJ. "Wireless communications with unmanned aerial vehicles: Opportunities and challenges". IEEE Communications Magazine, 54(5), 36-42, 2016.

[26] Gong L, Bai Y, Chen M, Qian D. "Link availability prediction in ad hoc networks". 2008 14th IEEE International Conference on Parallel and Distributed Systems, Melbourne, VIC, Australia, 8-10 December 2008.

[27] Ephremides A, Wieselthier JE, Baker DJ. "A design concept for reliable mobile radio networks with frequency hopping signaling". Proceedings of the IEEE, 75(1), 56-73, 1987.

[28] Chen G, Nocetti FG, Gonzalez JS, Stojmenovic I. "Connectivity based k-hop clustering in wireless networks". The 35th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA, 10-10 Januanry 2002.

[29] Heinzelman WB, Chandrakasan AP, Balakrishnan H. "An application-specific protocol architecture for wireless microsensor networks". IEEE Transactions on wireless communications, 1(4), 660-670, 2002.

Kaynak Göster

Bibtex @araştırma makalesi { pajes908911, journal = {Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi}, issn = {1300-7009}, eissn = {2147-5881}, address = {}, publisher = {Pamukkale Üniversitesi}, year = {2021}, volume = {27}, pages = {220 - 228}, doi = {}, title = {An adaptive, balanced and energy efficient clustering mechanism for UAV networks}, key = {cite}, author = {Gormus, Sedat and Kıran, Harun Emre} }
APA Gormus, S , Kıran, H . (2021). An adaptive, balanced and energy efficient clustering mechanism for UAV networks . Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi , 27 (2) , 220-228 . Retrieved from https://dergipark.org.tr/tr/pub/pajes/issue/61143/908911
MLA Gormus, S , Kıran, H . "An adaptive, balanced and energy efficient clustering mechanism for UAV networks" . Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 27 (2021 ): 220-228 <https://dergipark.org.tr/tr/pub/pajes/issue/61143/908911>
Chicago Gormus, S , Kıran, H . "An adaptive, balanced and energy efficient clustering mechanism for UAV networks". Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 27 (2021 ): 220-228
RIS TY - JOUR T1 - An adaptive, balanced and energy efficient clustering mechanism for UAV networks AU - Sedat Gormus , Harun Emre Kıran Y1 - 2021 PY - 2021 N1 - DO - T2 - Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi JF - Journal JO - JOR SP - 220 EP - 228 VL - 27 IS - 2 SN - 1300-7009-2147-5881 M3 - UR - Y2 - 2021 ER -
EndNote %0 Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi An adaptive, balanced and energy efficient clustering mechanism for UAV networks %A Sedat Gormus , Harun Emre Kıran %T An adaptive, balanced and energy efficient clustering mechanism for UAV networks %D 2021 %J Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi %P 1300-7009-2147-5881 %V 27 %N 2 %R %U
ISNAD Gormus, Sedat , Kıran, Harun Emre . "An adaptive, balanced and energy efficient clustering mechanism for UAV networks". Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 27 / 2 (Nisan 2021): 220-228 .
AMA Gormus S , Kıran H . An adaptive, balanced and energy efficient clustering mechanism for UAV networks. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2021; 27(2): 220-228.
Vancouver Gormus S , Kıran H . An adaptive, balanced and energy efficient clustering mechanism for UAV networks. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2021; 27(2): 220-228.
IEEE S. Gormus ve H. Kıran , "An adaptive, balanced and energy efficient clustering mechanism for UAV networks", Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 27, sayı. 2, ss. 220-228, Nis. 2021