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Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama

Yıl 2025, Cilt: 15 Sayı: 3, 55 - 67, 29.09.2025

Öz

Enerji farkında veri toplama, robotik ve kablosuz sensör ağları için büyük önem taşır. Statik havuz destekli küme tabanlı protokoller enerji açısından verimli çözümler sunsa da, insansız hava aracı (İHA) destekli yaklaşımlar, statik havuzlara kıyasla veri toplama sırasında enerji tüketimini azaltmak için daha iyi alternatifler olarak düşünülebilir. Mevcut İHA odaklı çözümlerin çoğu, pratik bir şekilde dikkate alınması gereken İHA'nın pil kapasitesi üzerinde bir sınır dikkate almamıştır. Bu makale, robot ağı kümelerinde enerji farkında veri toplamayı incelemektedir. Her kümede, bir küme başı (KB) robotu, her küme üyesi (KÜ) robotuna bir işbirlikçi görev atar ve KÜ'lerden veri toplarken, bir İHA, pil sınırlaması nedeniyle KB robotlarının bir alt kümesini ziyaret ederek onlardan veri toplar. Son durumu tamamlamak için, İHA'nın KB alt kümesini ziyaret etme kararı, artık pil kapasitesi ve tüm KB robotlarının konumları ve veri kaliteleri dahil olmak üzere birden fazla faktörle sınırlıdır. Ziyaret edilmeyen KB robotları, veri iletimi için röle düğümleri olarak KB robotlarını kullanır. Bunu takiben, bu makale, veri atlama kısıtlamaları altındaki problemi ele alarak, farklı topolojilerde ve farklı sayıda KB robotu ile bir hassasiyet analizi de sunmaktadır. Simülasyonlar, önerilen politikanın sıfır toplam ortak maliyete ulaştığını, en son yaklaşımların ise önemli ölçüde yüksek toplam ortak maliyetlere yol açtığını göstermektedir. Dahası, önerilen politika, toplam ortak maliyeti geleneksel yaklaşımlara kıyasla %50'ye kadar azaltmaktadır.

Kaynakça

  • [1] P. Kamalinejad, C. Mahapatra, Z. Sheng, S. Mirabbasi, V.C.M. Leung, Y.L. Guan, ”Wireless Energy Harvesting for the Internet of Things”, IEEE Communications Magazine, vol. 53, 2015, pp. 102-108.
  • [2] C.W. Tsai, T.P. Hong, G.N. Shiu, ”Metaheuristics for the lifetime of WSN: A review”, IEEE Sensor Journal, vol. 16, 2016, pp. 2812-2831.
  • [3] Y. Zhang, L. Sun, H. Song, X. Cao, ”Ubiquitous WSN for Healthcare: Recent Advances and Future Prospects”, IEEE Internet of Things Journal, vol. 1, no. 4, Aug. 2014.
  • [4] C. Gomez, J. Paradells, ”Urban Automation Networks: Current and Emerging Solutions for Sensed Data Collection and Actuation in Smart Cities”, Sensors 2015, vol. 15, pp. 22874-22898.
  • [5] Ghosh P., Gasparri A., Jin J., Krishnamachari B. (2019) Robotic Wireless Sensor Networks. Mission-Oriented Sensor Networks and Systems: Art and Science. Studies in Systems, Decision and Control, vol 164. Springer.
  • [6] L. Xu, R. Falcon, A. Nayak, I. Stojmenovic, ”Servicing wireless sensor networks by mobile robots”, IEEE Communications Magazine, vol.50, no.7, pp.147-154, July 2012.
  • [7] X. Mkhwanazi, L. Hanh, E. Blake, ”Clustering between Data Mules for Better Message Delivery”, WAINA 2012, 26-29 March 2012, pp.209- 214.
  • [8] X. Liu, T. Wang; W. Jia; A. Liu; K. Chi, ”Quick Convex HullBased Rendezvous Planning for Delay-Harsh Mobile Data Gathering in Disjoint Sensor Networks”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, Dec. 2021, pp. 3844-3854.
  • [9] M. Huang, A. Liu; N.N. Xiong; T. Wang, A. V. Vasilakos ”A Low Latency Communication Scheme for Mobile Wireless Sensor Control Systems”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 2, Feb. 2019.
  • [10] C.-Y.Chang, G.-Jong Yu, TL Wang, and C.-Yu Lin. 2014. ”Path construction and visit scheduling for targets by using data mules.”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44, 10 (2014), 1289–1300.
  • [11] E. L. Lawler, Jan Karel Lenstra, A. H. G. Rinnooy Kan, D. B. Shmoys, ”The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization”, Wiley; 1st edition, September 1, 1985.
  • [12] O. M. Gul,A.M.Erkmen,”Energy-efficient Cluster-Based Data Collection by a UAV with a Limited-Capacity Battery in Robotic Wireless Sensor Networks”, Sensors, vol. 20, 2020.
  • [13] O. M. Gul, A. M. Erkmen and B. Kantarci, ”UAV-Driven Sustainable and Quality-Aware Data Collection in Robotic Wireless Sensor Networks,” in IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25150-25164, 15 Dec.15, 2022, doi: 10.1109/JIOT.2022.3195677.
  • [14] O. M. Gul and A. M. Erkmen, ”Energy-Aware UAV-Driven Data Collection With Priority in Robotic Wireless Sensor Network,” in IEEE Sensors Journal, vol. 23, no. 15, pp. 17667-17675, 1 Aug.1, 2023, doi: 10.1109/JSEN.2023.3286877.
  • [15] O. M. Gul, A. M. Erkmen and B. Kantarci, ”NTN-Aided Quality and Energy-Aware Data Collection in Time-Critical Robotic Wireless Sensor Networks,” in IEEE Internet of Things Magazine, vol. 7, no. 3, pp. 114- 120, May 2024, doi: 10.1109/IOTM.001.2300200.
  • [16] Heinzelman, W., Chandrakasan, A., and Balakrishnan, H., ”EnergyEfficient Communication Protocols for Wireless Microsensor Networks”, Proceedings of the 33rd Hawaaian International Conference on Systems Science (HICSS), January 2000.
  • [17] S. Varshney, R. Kuma, ”Variants of LEACH Routing Protocol in WSN: A Comparative Analysis”, 8th IEEE International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2018, pp. 199–204. doi:10.1109/confluence.2018.8442643
  • [18] C. Eris, O. M. Gul and P. S. Boluk, ”An Energy-Harvesting Aware Cluster Head Selection Policy in Underwater Acoustic Sensor Networks,” 2023 International Balkan Conference on Communications and Networking (BalkanCom),Istanbul,Turkiye, 2023, pp. 1-5.
  • [19] Ç. Eris¸, O. M. Gül, P. S. Bölük, ”A novel reinforcement learning based routing algorithm for energy management in networks”, Journal of Industrial and Management Optimization, vol. 20, no.12, pp. 3678-3696, December 2024. doi: 10.3934/jimo.2024049
  • [20] Ç . Eris¸, O. M. Gül, P. S. Bölük, ”A Novel Medium Access Policy Based on Reinforcement Learning in Energy-Harvesting Underwater Sensor Networks” Sensors 24, no. 17: 5791, September 2024. https://doi.org/10.3390/s24175791
  • [21] Golden, B. L. , Levy, L. , Vohra, R., ”The orienteering problem”, Naval Research Logistics, 34 (3), pp. 307–318, 1987.
  • [22] W. Wen, S. Zhao, C. Shang and C. Y. Chang, ”EAPC: Energy-aware path construction for data collection using mobile sink in wireless sensor networks”, IEEE Sensors Journal, vol. 18, no. 2, pp. 890-901, Jan. 2018.
  • [23] H. Salarian, K.-W. Chin, and F. Naghdy, “An energy-efficient mobilesink path selection strategy for wireless sensor networks,” IEEE Trans. Veh. Technol., vol. 63, no. 5, pp. 2407–2419, Jun. 2014.
  • [24] X. He, X. Fu and Y. Yang, ”Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks,” IEEE Access,vol.7, pp. 176204-176217, 2019.
  • [25] A. Mehto, S. Tapaswi, K. K. Pattanaik, ”PSO-Based Rendezvous Point Selection for Delay Efficient Trajectory Formation for Mobile Sink in Wireless Sensor Networks”, COMNETS 2020, India, 2020, pp. 252-258.
  • [26] J. Zhong, Z. Huang, L. Feng, W. Du and Y. Li, ”A hyper-heuristic framework for lifetime maximization in wireless sensor networks with a mobile sink,” in IEEE/CAA JAS, vol. 7, no. 1, pp. 223-236, January 2020.
  • [27] Vera-Amaro, R.; Rivero-Angeles, M.E.; Luviano-Ju ´ arez, A. ”Data Col- ´ lection Schemes for Animal Monitoring Using WSNs-Assisted by UAVs: WSNs-Oriented or UAV-Oriented”, (MDPI) Sensors, vol. 20, 262, 2020.
  • [28] K. Li, W. Ni, E. Tovar and M. Guizani, ”Joint Flight Cruise Control and Data Collection in UAV-Aided Internet of Things: An Onboard Deep Reinforcement Learning Approach,” in IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9787-9799, 15 June15, 2021, doi: 10.1109/JIOT.2020.3019186.
  • [29] A. G. Hoong, C. Laua and P. Vansteenwegen, ”Orienteering Problem: A survey of recent variants, solution approaches and applications”, European Journal of Operational Research, pp. 315-332, 2016.
  • [30] E. Fountoulakis; G. S. Paschos; N. Pappas, ”UAV Trajectory Optimization for Time Constrained Applications”, IEEE Networking Letters, vol. 2, no. 3, Sept. 2020, pp 136-139.
  • [31] P. Larranaga, C.M.H. Kuıjpers, R.H. Murga, I. Inza and S. Dizdarevic, “Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators”, Artificial Intelligence Review, vol. 13, pp. 129–170, 1999.
  • [32] Yang, Ruixiao, Optimizing Traveling Salesman Problem in Multi-Agent Systems with Practical Constraints, MSc Thesis, MIT, USA, 2024.
  • [33] W. B. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, ”An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. Wir. Com., vol. 1, no. 4, pp. 660-670, Oct. 2002.
  • [34] Montiel, Edgar R., Mario E. Rivero-Angeles, Gerardo Rubino, Heron Molina-Lozano, Rolando Menchaca-Mendez, and Ricardo MenchacaMendez. 2017. ”Performance Analysis of Cluster Formation in Wireless Sensor Networks” Sensors 17, no. 12: 2902.
  • [35] Farahzadi, H.R., Langarizadeh, M., Mirhosseini, M. et al. An improved cluster formation process in wireless sensor network to decrease energy consumption. Wireless Netw 27, 1077–1087 (2021).

Efficiency and Energy-Aware Data Collection via a UAV with Limited Capacity Battery in Robotic Wireless Sensor Network under Various Topologies

Yıl 2025, Cilt: 15 Sayı: 3, 55 - 67, 29.09.2025

Öz

Energy-aware data collection is of paramount importance for robotic and wireless sensor networks. Although static sink-aided cluster-based protocols provide energy-efficient solutions, unmanned aerial vehicle (UAV)-aided approaches can be considered as better alternatives to reduce energy consumption while data acquisition compared with static sinks. Most of the existing UAV-driven solutions have not considered a limit on the battery capacity of the UAV, which needs to be considered in a practical manner. This article investigates energy-aware data collection in robot network clusters. In each cluster, a cluster head (CH) robot allocates one collaborative task to each cluster member (CM) robot and collects data from CMs whereas a UAV collects data from CH robots by visiting a subset of them due to its battery limitation. To complement the state-of-theart, UAV decision for visiting the subset of CHs is constrained to multiple factors including residual battery capacity, as well as locations and data qualities of all CH robots. Nonvisited CH robots use CH robots as relay nodes for data forwarding. Following upon this, by considering the problem under data hopping constraints, this article also presents a sensitivity analysis under different topologies with various number of CH robots. Simulations show that the proposed policy achieves zero total joint cost whereas the state-of-the-art approaches result in significantly high total joint costs. Furthermore, the proposed policy reduces the total joint cost by up to 50% with respect to the conventional approaches.

Kaynakça

  • [1] P. Kamalinejad, C. Mahapatra, Z. Sheng, S. Mirabbasi, V.C.M. Leung, Y.L. Guan, ”Wireless Energy Harvesting for the Internet of Things”, IEEE Communications Magazine, vol. 53, 2015, pp. 102-108.
  • [2] C.W. Tsai, T.P. Hong, G.N. Shiu, ”Metaheuristics for the lifetime of WSN: A review”, IEEE Sensor Journal, vol. 16, 2016, pp. 2812-2831.
  • [3] Y. Zhang, L. Sun, H. Song, X. Cao, ”Ubiquitous WSN for Healthcare: Recent Advances and Future Prospects”, IEEE Internet of Things Journal, vol. 1, no. 4, Aug. 2014.
  • [4] C. Gomez, J. Paradells, ”Urban Automation Networks: Current and Emerging Solutions for Sensed Data Collection and Actuation in Smart Cities”, Sensors 2015, vol. 15, pp. 22874-22898.
  • [5] Ghosh P., Gasparri A., Jin J., Krishnamachari B. (2019) Robotic Wireless Sensor Networks. Mission-Oriented Sensor Networks and Systems: Art and Science. Studies in Systems, Decision and Control, vol 164. Springer.
  • [6] L. Xu, R. Falcon, A. Nayak, I. Stojmenovic, ”Servicing wireless sensor networks by mobile robots”, IEEE Communications Magazine, vol.50, no.7, pp.147-154, July 2012.
  • [7] X. Mkhwanazi, L. Hanh, E. Blake, ”Clustering between Data Mules for Better Message Delivery”, WAINA 2012, 26-29 March 2012, pp.209- 214.
  • [8] X. Liu, T. Wang; W. Jia; A. Liu; K. Chi, ”Quick Convex HullBased Rendezvous Planning for Delay-Harsh Mobile Data Gathering in Disjoint Sensor Networks”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, Dec. 2021, pp. 3844-3854.
  • [9] M. Huang, A. Liu; N.N. Xiong; T. Wang, A. V. Vasilakos ”A Low Latency Communication Scheme for Mobile Wireless Sensor Control Systems”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 2, Feb. 2019.
  • [10] C.-Y.Chang, G.-Jong Yu, TL Wang, and C.-Yu Lin. 2014. ”Path construction and visit scheduling for targets by using data mules.”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44, 10 (2014), 1289–1300.
  • [11] E. L. Lawler, Jan Karel Lenstra, A. H. G. Rinnooy Kan, D. B. Shmoys, ”The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization”, Wiley; 1st edition, September 1, 1985.
  • [12] O. M. Gul,A.M.Erkmen,”Energy-efficient Cluster-Based Data Collection by a UAV with a Limited-Capacity Battery in Robotic Wireless Sensor Networks”, Sensors, vol. 20, 2020.
  • [13] O. M. Gul, A. M. Erkmen and B. Kantarci, ”UAV-Driven Sustainable and Quality-Aware Data Collection in Robotic Wireless Sensor Networks,” in IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25150-25164, 15 Dec.15, 2022, doi: 10.1109/JIOT.2022.3195677.
  • [14] O. M. Gul and A. M. Erkmen, ”Energy-Aware UAV-Driven Data Collection With Priority in Robotic Wireless Sensor Network,” in IEEE Sensors Journal, vol. 23, no. 15, pp. 17667-17675, 1 Aug.1, 2023, doi: 10.1109/JSEN.2023.3286877.
  • [15] O. M. Gul, A. M. Erkmen and B. Kantarci, ”NTN-Aided Quality and Energy-Aware Data Collection in Time-Critical Robotic Wireless Sensor Networks,” in IEEE Internet of Things Magazine, vol. 7, no. 3, pp. 114- 120, May 2024, doi: 10.1109/IOTM.001.2300200.
  • [16] Heinzelman, W., Chandrakasan, A., and Balakrishnan, H., ”EnergyEfficient Communication Protocols for Wireless Microsensor Networks”, Proceedings of the 33rd Hawaaian International Conference on Systems Science (HICSS), January 2000.
  • [17] S. Varshney, R. Kuma, ”Variants of LEACH Routing Protocol in WSN: A Comparative Analysis”, 8th IEEE International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2018, pp. 199–204. doi:10.1109/confluence.2018.8442643
  • [18] C. Eris, O. M. Gul and P. S. Boluk, ”An Energy-Harvesting Aware Cluster Head Selection Policy in Underwater Acoustic Sensor Networks,” 2023 International Balkan Conference on Communications and Networking (BalkanCom),Istanbul,Turkiye, 2023, pp. 1-5.
  • [19] Ç. Eris¸, O. M. Gül, P. S. Bölük, ”A novel reinforcement learning based routing algorithm for energy management in networks”, Journal of Industrial and Management Optimization, vol. 20, no.12, pp. 3678-3696, December 2024. doi: 10.3934/jimo.2024049
  • [20] Ç . Eris¸, O. M. Gül, P. S. Bölük, ”A Novel Medium Access Policy Based on Reinforcement Learning in Energy-Harvesting Underwater Sensor Networks” Sensors 24, no. 17: 5791, September 2024. https://doi.org/10.3390/s24175791
  • [21] Golden, B. L. , Levy, L. , Vohra, R., ”The orienteering problem”, Naval Research Logistics, 34 (3), pp. 307–318, 1987.
  • [22] W. Wen, S. Zhao, C. Shang and C. Y. Chang, ”EAPC: Energy-aware path construction for data collection using mobile sink in wireless sensor networks”, IEEE Sensors Journal, vol. 18, no. 2, pp. 890-901, Jan. 2018.
  • [23] H. Salarian, K.-W. Chin, and F. Naghdy, “An energy-efficient mobilesink path selection strategy for wireless sensor networks,” IEEE Trans. Veh. Technol., vol. 63, no. 5, pp. 2407–2419, Jun. 2014.
  • [24] X. He, X. Fu and Y. Yang, ”Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks,” IEEE Access,vol.7, pp. 176204-176217, 2019.
  • [25] A. Mehto, S. Tapaswi, K. K. Pattanaik, ”PSO-Based Rendezvous Point Selection for Delay Efficient Trajectory Formation for Mobile Sink in Wireless Sensor Networks”, COMNETS 2020, India, 2020, pp. 252-258.
  • [26] J. Zhong, Z. Huang, L. Feng, W. Du and Y. Li, ”A hyper-heuristic framework for lifetime maximization in wireless sensor networks with a mobile sink,” in IEEE/CAA JAS, vol. 7, no. 1, pp. 223-236, January 2020.
  • [27] Vera-Amaro, R.; Rivero-Angeles, M.E.; Luviano-Ju ´ arez, A. ”Data Col- ´ lection Schemes for Animal Monitoring Using WSNs-Assisted by UAVs: WSNs-Oriented or UAV-Oriented”, (MDPI) Sensors, vol. 20, 262, 2020.
  • [28] K. Li, W. Ni, E. Tovar and M. Guizani, ”Joint Flight Cruise Control and Data Collection in UAV-Aided Internet of Things: An Onboard Deep Reinforcement Learning Approach,” in IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9787-9799, 15 June15, 2021, doi: 10.1109/JIOT.2020.3019186.
  • [29] A. G. Hoong, C. Laua and P. Vansteenwegen, ”Orienteering Problem: A survey of recent variants, solution approaches and applications”, European Journal of Operational Research, pp. 315-332, 2016.
  • [30] E. Fountoulakis; G. S. Paschos; N. Pappas, ”UAV Trajectory Optimization for Time Constrained Applications”, IEEE Networking Letters, vol. 2, no. 3, Sept. 2020, pp 136-139.
  • [31] P. Larranaga, C.M.H. Kuıjpers, R.H. Murga, I. Inza and S. Dizdarevic, “Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators”, Artificial Intelligence Review, vol. 13, pp. 129–170, 1999.
  • [32] Yang, Ruixiao, Optimizing Traveling Salesman Problem in Multi-Agent Systems with Practical Constraints, MSc Thesis, MIT, USA, 2024.
  • [33] W. B. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, ”An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. Wir. Com., vol. 1, no. 4, pp. 660-670, Oct. 2002.
  • [34] Montiel, Edgar R., Mario E. Rivero-Angeles, Gerardo Rubino, Heron Molina-Lozano, Rolando Menchaca-Mendez, and Ricardo MenchacaMendez. 2017. ”Performance Analysis of Cluster Formation in Wireless Sensor Networks” Sensors 17, no. 12: 2902.
  • [35] Farahzadi, H.R., Langarizadeh, M., Mirhosseini, M. et al. An improved cluster formation process in wireless sensor network to decrease energy consumption. Wireless Netw 27, 1077–1087 (2021).
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Elektrik Mühendisliği (Diğer)
Bölüm Akademik ve/veya teknolojik bilimsel makale
Yazarlar

Ömer Melih Gül

Aydan M. Erkmen

Yayımlanma Tarihi 29 Eylül 2025
Gönderilme Tarihi 24 Ağustos 2025
Kabul Tarihi 18 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 3

Kaynak Göster

APA Gül, Ö. M., & Erkmen, A. M. (2025). Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama. EMO Bilimsel Dergi, 15(3), 55-67.
AMA Gül ÖM, Erkmen AM. Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama. EMO Bilimsel Dergi. Eylül 2025;15(3):55-67.
Chicago Gül, Ömer Melih, ve Aydan M. Erkmen. “Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama”. EMO Bilimsel Dergi 15, sy. 3 (Eylül 2025): 55-67.
EndNote Gül ÖM, Erkmen AM (01 Eylül 2025) Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama. EMO Bilimsel Dergi 15 3 55–67.
IEEE Ö. M. Gül ve A. M. Erkmen, “Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama”, EMO Bilimsel Dergi, c. 15, sy. 3, ss. 55–67, 2025.
ISNAD Gül, Ömer Melih - Erkmen, Aydan M. “Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama”. EMO Bilimsel Dergi 15/3 (Eylül2025), 55-67.
JAMA Gül ÖM, Erkmen AM. Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama. EMO Bilimsel Dergi. 2025;15:55–67.
MLA Gül, Ömer Melih ve Aydan M. Erkmen. “Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama”. EMO Bilimsel Dergi, c. 15, sy. 3, 2025, ss. 55-67.
Vancouver Gül ÖM, Erkmen AM. Farklı Topoloji̇ler Altında Robotik Kablosuz Sensör Ağlarında Sınırlı Kapasi̇teli̇ Bataryalı bir İHA Aracılığıyla Veri̇mli̇li̇k ve Enerji Farkında Veri Toplama. EMO Bilimsel Dergi. 2025;15(3):55-67.

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