دو الگوریتم نیروی مجازی فازی برای بهبود چیدمان حسگرها در شبکه‌های حسگر بی‌سیم
الموضوعات :
1 - استادیار گروه مهندسی کامپیوتر، دانشگاه بجنورد، بجنورد، ایران
الکلمات المفتاحية: پوشش حداکثری, جایابی حسگر, الگوریتم نیروی مجازی, سیستم فازی,
ملخص المقالة :
پوشش حداکثری منطقه یک هدف مهم در چیدمان حسگرهای شبکه حسگر بی‌سیم است که تحقق آن به افزایش توان نظارتی شبکه کمک می‌کند. در بسیاری از کاربردها حسگرها ابتدا به صورت تصادفی در منطقه تحت نظارت توزیع می‌شوند، سپس چیدمان آن‌ها باید طوری اصلاح شود که پوشش شبکه حداکثر گردد. الگوریتم نیروی مجازی (VFA) سعی می‌کند تا با در نظر گرفتن نیروهای دافعه و جاذبه بین حسگرها از یک چیدمان اولیه به یک چیدمان مطلوب‌تر برسد. در این مقاله از ترکیب سیستم فازی تاکاشی-سوگنو با الگوریتم نیروی مجازی برای دستیابی به چیدمان مجدد بهتری از حسگرها استفاده می‌شود. برای تنظیم وفقی پارامتر فاصله بهینه حسگرها در این مقاله دو روش فازی مطرح و اثر هر یک از آن‌ها بر افزایش کارآمدی الگوریتم نیروی مجازی بررسی خواهد شد. مقایسه عملکرد روش‌های پیشنهادی با روش‌های رقیب نشان می‌دهد که تنظیم هوشمندانه و وفقی فاصله بهینه به کمک سیستم فازی باعث دستیابی به نرخ پوشش بالاتر نسبت به الگوریتم نیروی مجازی سنتی (VFA)، الگوریتم نیروی مجازی بهبودیافته (IVFA)، الگوریتم توزیع مجدد فازی (FRED)، و روش‌های متاهیورستیک GA و PSO خواهد شد. همچنین، روش‌های پیشنهادی مبتنی بر نیروی مجازی نسبت به GA و PSO به زمان بسیار کمتری نیز برای حل مسئله نیاز دارند.
المصادر:
S. Fattah, A. Gani, I. Ahmedy, M. Y. I. Idris, and I. A. Targio Hashem, “A Survey on Underwater Wireless Sensor Networks: Requirements, Taxonomy, Recent Advances, and Open Research Challenges,” Sensors, vol. 20, no. 18, Art. no. 18, Jan. 2020, doi: 10.3390/s20185393.
D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, “Applications of Wireless Sensor Networks: An Up-to-Date Survey,” Appl. Syst. Innov., vol. 3, no. 1, 2020, doi: 10.3390/asi3010014.
S. Loganathan and J. Arumugam, “Clustering Algorithms for Wireless Sensor Networks Survey,” Sens. Lett., vol. 18, no. 2, pp. 143–149, Feb. 2020, doi: 10.1166/sl.2020.4193.
A. Singh, S. Sharma, and J. Singh, “Nature-inspired algorithms for Wireless Sensor Networks: A comprehensive survey,” Comput. Sci. Rev., vol. 39, p. 100342, Feb. 2021, doi: 10.1016/j.cosrev.2020.100342.
A. Boulmaiz, N. Doghmane, S. Harize, N. Kouadria, and D. Messadeg, “Chapter 9 - The use of WSN (wireless sensor network) in the surveillance of endangered bird species,” in Advances in Ubiquitous Computing, A. Neustein, Ed. Academic Press, 2020, pp. 261–306. doi: 10.1016/B978-0-12-816801-1.00009-8.
J.-A. Jiang et al., “A WSN-based automatic monitoring system for the foraging behavior of honey bees and environmental factors of beehives,” Comput. Electron. Agric., vol. 123, pp. 304–318, Apr. 2016, doi: 10.1016/j.compag.2016.03.003.
S. Cao and A. Sanchez-Azofeifa, “Modeling seasonal surface temperature variations in secondary tropical dry forests,” Int. J. Appl. Earth Obs. Geoinformation, vol. 62, pp. 122–134, Oct. 2017, doi: 10.1016/j.jag.2017.06.008.
J. Amutha, J. Nagar, and S. Sharma, “A Distributed Border Surveillance (DBS) System for Rectangular and Circular Region of Interest with Wireless Sensor Networks in Shadowed Environments,” Wirel. Pers. Commun., vol. 117, no. 3, pp. 2135–2155, Apr. 2021, doi: 10.1007/s11277-020-07963-2.
N. Jeyakkannan, P. Manimegalai, and G. P. Venkatesan, “Systematic CO2 monitoring using machine learning enabled WSN to develop the anti-hazard strategies for the future,” Int. J. Biomed. Eng. Technol., vol. 34, no. 1, pp. 31–44, 2020.
A. Ali, Y. K. Jadoon, S. A. Changazi, and M. Qasim, “Military Operations: Wireless Sensor Networks based Applications to Reinforce Future Battlefield Command System,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), Nov. 2020, pp. 1–6. doi: 10.1109/INMIC50486.2020.9318168.
S. M. Mohamed, H. S. Hamza, and I. A. Saroit, “Coverage in mobile wireless sensor networks (M-WSN): A survey,” Comput. Commun., vol. 110, pp. 133–150, Sep. 2017, doi: 10.1016/j.comcom.2017.06.010.
R. Priyadarshi, B. Gupta, and A. Anurag, “Deployment techniques in wireless sensor networks: a survey, classification, challenges, and future research issues,” J. Supercomput., vol. 76, no. 9, pp. 7333–7373, Sep. 2020, doi: 10.1007/s11227-020-03166-5.
G. P. Gupta and S. Jha, “Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks,” Wirel. Netw., vol. 25, no. 6, pp. 3167–3177, Aug. 2019, doi: 10.1007/s11276-018-1709-0.
H. T. T. Binh, N. T. Hanh, L. V. Quan, N. D. Nghia, and N. Dey, “Metaheuristics for maximization of obstacles constrained area coverage in heterogeneous wireless sensor networks,” Appl. Soft Comput., vol. 86, p. 105939, 2020, doi: https://doi.org/10.1016/j.asoc.2019.105939.
D. Liang, H. Shen, and L. Chen, “Maximum Target Coverage Problem in Mobile Wireless Sensor Networks,” Sensors, vol. 21, no. 1, p. 184, Dec. 2020, doi: 10.3390/s21010184.
Z. Dong, C. Shang, C.-Y. Chang, and D. S. Roy, “Barrier Coverage Mechanism Using Adaptive Sensing Range for Renewable WSNs,” IEEE Access, vol. 8, pp. 86065–86080, 2020, doi: 10.1109/ACCESS.2020.2992867.
W. Barkhoda and H. Sheikhi, “Immigrant imperialist competitive algorithm to solve the multi-constraint node placement problem in target-based wireless sensor networks,” Ad Hoc Netw., vol. 106, p. 102183, Sep. 2020, doi: 10.1016/j.adhoc.2020.102183.
H. ZainEldin, M. Badawy, M. Elhosseini, H. Arafat, and A. Abraham, “An improved dynamic deployment technique based-on genetic algorithm (IDDT-GA) for maximizing coverage in wireless sensor networks,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 10, pp. 4177–4194, Oct. 2020, doi: 10.1007/s12652-020-01698-5.
M. Abo-Zahhad, S. M. Ahmed, N. Sabor, and S. Sasaki, “Rearrangement of Mobile Wireless Sensor Nodes for Coverage Maximization Based on Immune Node Deployment Algorithm,” Comput Electr Eng, vol. 43, no. C, pp. 76–89, Apr. 2015, doi: 10.1016/j.compeleceng.2015.04.003.
A. K. Paul and T. Sato, “Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges,” J. Sens. Actuator Netw., vol. 6, no. 4, Art. no. 4, Dec. 2017, doi: 10.3390/jsan6040024.
K. Tarnaris, I. Preka, D. Kandris, and A. Alexandridis, “Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study,” Electronics, vol. 9, no. 4, Art. no. 4, Apr. 2020, doi: 10.3390/electronics9040675.
B. Al-Fuhaidi, A. M. Mohsen, A. Ghazi, and W. M. Yousef, “An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm,” J. Sens., vol. 2020, p. 8818826, Nov. 2020, doi: 10.1155/2020/8818826.
Y. Zou and K. Chakrabarty, “Sensor Deployment and Target Localization in Distributed Sensor Networks,” ACM Trans Embed Comput Syst, vol. 3, no. 1, pp. 61–91, Feb. 2004, doi: 10.1145/972627.972631.
C.-C. Yang and J.-H. Wen, “A Hybrid Local Virtual Force Algorithm for Sensing Deployment in Wireless Sensor Network,” in 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Jul. 2013, pp. 617–621. doi: 10.1109/IMIS.2013.109.
Y. Li, B. Zhang, and S. Chai, “An energy balanced-virtual force algorithm for Mobile-WSNs,” in 2015 IEEE International Conference on Mechatronics and Automation (ICMA), Aug. 2015, pp. 1779–1784. doi: 10.1109/ICMA.2015.7237755.
X. Deng, Z. Yu, R. Tang, X. Qian, K. Yuan, and S. Liu, “An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications,” Sensors, vol. 19, no. 8, p. 1817, Apr. 2019, doi: 10.3390/s19081817.
J. Xie, D. Wei, S. Huang, and X. Bu, “A Sensor Deployment Approach Using Improved Virtual Force Algorithm Based on Area Intensity for Multisensor Networks,” Math. Probl. Eng., vol. 2019, p. 8015309, Feb. 2019, doi: 10.1155/2019/8015309.
S. Liu, R. Zhang, and Y. Shi, “Design of coverage algorithm for mobile sensor networks based on virtual molecular force,” Comput. Commun., vol. 150, pp. 269–277, Jan. 2020, doi: 10.1016/j.comcom.2019.11.001.
W. Jun and G. Haoyang, “Virtual force field coverage algorithms for wireless sensor networks in water environments,” Int. J. Sens. Netw., vol. 32, no. 3, pp. 174–181, Jan. 2020, doi: 10.1504/IJSNET.2020.105564.
X. Wang, S. Wang, and D. Bi, “Virtual Force-Directed Particle Swarm Optimization for Dynamic Deployment in Wireless Sensor Networks,” in Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, Berlin, Heidelberg, 2007, pp. 292–303. doi: 10.1007/978-3-540-74171-8_29.
M. Song, L. Yang, W. Li, and T. A. Gulliver, “Improving wireless sensor network coverage using the VF-BBO algorithm,” in 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), Aug. 2013, pp. 318–321. doi: 10.1109/PACRIM.2013.6625496.
S. Wang, X. Yang, X. Wang, and Z. Qian, “A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization,” Sensors, vol. 19, no. 12, Art. no. 12, Jan. 2019, doi: 10.3390/s19122735.
M. Li, J. Hu, and X. Cao, “A Two-Phase Coverage Control Algorithm for Self-Orienting Heterogeneous Directional Sensor Networks,” IEEE Access, vol. 8, pp. 88215–88226, 2020, doi: 10.1109/ACCESS.2020.2993554.
X. Qi, Z. Li, C. Chen, and L. Liu, “A wireless sensor node deployment scheme based on embedded virtual force resampling particle swarm optimization algorithm,” Appl. Intell., Sep. 2021, doi: 10.1007/s10489-021-02745-0.
O. Khatib, “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” in Autonomous Robot Vehicles, I. J. Cox and G. T. Wilfong, Eds. New York, NY: Springer, 1990, pp. 396–404. doi: 10.1007/978-1-4613-8997-2_29.
L.-X. Wang, A course in fuzzy systems and control. Prentice-Hall, Inc., 1996.
A. Osmani, M. Dehghan, H. Pourakbar, and P. Emdadi, “Fuzzy-Based Movement-Assisted Sensor Deployment Method in Wireless Sensor Networks,” in 2009 First International Conference on Computational Intelligence, Communication Systems and Networks, Jul. 2009, pp. 90–95. doi: 10.1109/CICSYN.2009.97.
D. Izadi, J. Abawajy, and S. Ghanavati, “An Alternative Node Deployment Scheme for WSNs,” IEEE Sens. J., vol. 15, no. 2, pp. 667–675, Feb. 2015, doi: 10.1109/JSEN.2014.2351405.
D. Šoštarić and G. Mester, “Drone localization using ultrasonic TDOA and RSS signal: Integration of the inverse method of a particle filter,” FME Trans., vol. 48, no. 1, pp. 21–30, 2020, doi: 10.5937/fmet2001021S.
S. Fattah, A. Gani, I. Ahmedy, M. Y. I. Idris, and I. A. Targio Hashem, “A Survey on Underwater Wireless Sensor Networks: Requirements, Taxonomy, Recent Advances, and Open Research Challenges,” Sensors, vol. 20, no. 18, Art. no. 18, Jan. 2020, doi: 10.3390/s20185393.
D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, “Applications of Wireless Sensor Networks: An Up-to-Date Survey,” Appl. Syst. Innov., vol. 3, no. 1, 2020, doi: 10.3390/asi3010014.
S. Loganathan and J. Arumugam, “Clustering Algorithms for Wireless Sensor Networks Survey,” Sens. Lett., vol. 18, no. 2, pp. 143–149, Feb. 2020, doi: 10.1166/sl.2020.4193.
A. Singh, S. Sharma, and J. Singh, “Nature-inspired algorithms for Wireless Sensor Networks: A comprehensive survey,” Comput. Sci. Rev., vol. 39, p. 100342, Feb. 2021, doi: 10.1016/j.cosrev.2020.100342.
A. Boulmaiz, N. Doghmane, S. Harize, N. Kouadria, and D. Messadeg, “Chapter 9 - The use of WSN (wireless sensor network) in the surveillance of endangered bird species,” in Advances in Ubiquitous Computing, A. Neustein, Ed. Academic Press, 2020, pp. 261–306. doi: 10.1016/B978-0-12-816801-1.00009-8.
J.-A. Jiang et al., “A WSN-based automatic monitoring system for the foraging behavior of honey bees and environmental factors of beehives,” Comput. Electron. Agric., vol. 123, pp. 304–318, Apr. 2016, doi: 10.1016/j.compag.2016.03.003.
S. Cao and A. Sanchez-Azofeifa, “Modeling seasonal surface temperature variations in secondary tropical dry forests,” Int. J. Appl. Earth Obs. Geoinformation, vol. 62, pp. 122–134, Oct. 2017, doi: 10.1016/j.jag.2017.06.008.
J. Amutha, J. Nagar, and S. Sharma, “A Distributed Border Surveillance (DBS) System for Rectangular and Circular Region of Interest with Wireless Sensor Networks in Shadowed Environments,” Wirel. Pers. Commun., vol. 117, no. 3, pp. 2135–2155, Apr. 2021, doi: 10.1007/s11277-020-07963-2.
N. Jeyakkannan, P. Manimegalai, and G. P. Venkatesan, “Systematic CO2 monitoring using machine learning enabled WSN to develop the anti-hazard strategies for the future,” Int. J. Biomed. Eng. Technol., vol. 34, no. 1, pp. 31–44, 2020.
A. Ali, Y. K. Jadoon, S. A. Changazi, and M. Qasim, “Military Operations: Wireless Sensor Networks based Applications to Reinforce Future Battlefield Command System,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), Nov. 2020, pp. 1–6. doi: 10.1109/INMIC50486.2020.9318168.
S. M. Mohamed, H. S. Hamza, and I. A. Saroit, “Coverage in mobile wireless sensor networks (M-WSN): A survey,” Comput. Commun., vol. 110, pp. 133–150, Sep. 2017, doi: 10.1016/j.comcom.2017.06.010.
R. Priyadarshi, B. Gupta, and A. Anurag, “Deployment techniques in wireless sensor networks: a survey, classification, challenges, and future research issues,” J. Supercomput., vol. 76, no. 9, pp. 7333–7373, Sep. 2020, doi: 10.1007/s11227-020-03166-5.
G. P. Gupta and S. Jha, “Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks,” Wirel. Netw., vol. 25, no. 6, pp. 3167–3177, Aug. 2019, doi: 10.1007/s11276-018-1709-0.
H. T. T. Binh, N. T. Hanh, L. V. Quan, N. D. Nghia, and N. Dey, “Metaheuristics for maximization of obstacles constrained area coverage in heterogeneous wireless sensor networks,” Appl. Soft Comput., vol. 86, p. 105939, 2020, doi: https://doi.org/10.1016/j.asoc.2019.105939.
D. Liang, H. Shen, and L. Chen, “Maximum Target Coverage Problem in Mobile Wireless Sensor Networks,” Sensors, vol. 21, no. 1, p. 184, Dec. 2020, doi: 10.3390/s21010184.
Z. Dong, C. Shang, C.-Y. Chang, and D. S. Roy, “Barrier Coverage Mechanism Using Adaptive Sensing Range for Renewable WSNs,” IEEE Access, vol. 8, pp. 86065–86080, 2020, doi: 10.1109/ACCESS.2020.2992867.
W. Barkhoda and H. Sheikhi, “Immigrant imperialist competitive algorithm to solve the multi-constraint node placement problem in target-based wireless sensor networks,” Ad Hoc Netw., vol. 106, p. 102183, Sep. 2020, doi: 10.1016/j.adhoc.2020.102183.
H. ZainEldin, M. Badawy, M. Elhosseini, H. Arafat, and A. Abraham, “An improved dynamic deployment technique based-on genetic algorithm (IDDT-GA) for maximizing coverage in wireless sensor networks,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 10, pp. 4177–4194, Oct. 2020, doi: 10.1007/s12652-020-01698-5.
M. Abo-Zahhad, S. M. Ahmed, N. Sabor, and S. Sasaki, “Rearrangement of Mobile Wireless Sensor Nodes for Coverage Maximization Based on Immune Node Deployment Algorithm,” Comput Electr Eng, vol. 43, no. C, pp. 76–89, Apr. 2015, doi: 10.1016/j.compeleceng.2015.04.003.
A. K. Paul and T. Sato, “Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges,” J. Sens. Actuator Netw., vol. 6, no. 4, Art. no. 4, Dec. 2017, doi: 10.3390/jsan6040024.
K. Tarnaris, I. Preka, D. Kandris, and A. Alexandridis, “Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study,” Electronics, vol. 9, no. 4, Art. no. 4, Apr. 2020, doi: 10.3390/electronics9040675.
B. Al-Fuhaidi, A. M. Mohsen, A. Ghazi, and W. M. Yousef, “An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm,” J. Sens., vol. 2020, p. 8818826, Nov. 2020, doi: 10.1155/2020/8818826.
Y. Zou and K. Chakrabarty, “Sensor Deployment and Target Localization in Distributed Sensor Networks,” ACM Trans Embed Comput Syst, vol. 3, no. 1, pp. 61–91, Feb. 2004, doi: 10.1145/972627.972631.
C.-C. Yang and J.-H. Wen, “A Hybrid Local Virtual Force Algorithm for Sensing Deployment in Wireless Sensor Network,” in 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Jul. 2013, pp. 617–621. doi: 10.1109/IMIS.2013.109.
Y. Li, B. Zhang, and S. Chai, “An energy balanced-virtual force algorithm for Mobile-WSNs,” in 2015 IEEE International Conference on Mechatronics and Automation (ICMA), Aug. 2015, pp. 1779–1784. doi: 10.1109/ICMA.2015.7237755.
X. Deng, Z. Yu, R. Tang, X. Qian, K. Yuan, and S. Liu, “An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications,” Sensors, vol. 19, no. 8, p. 1817, Apr. 2019, doi: 10.3390/s19081817.
J. Xie, D. Wei, S. Huang, and X. Bu, “A Sensor Deployment Approach Using Improved Virtual Force Algorithm Based on Area Intensity for Multisensor Networks,” Math. Probl. Eng., vol. 2019, p. 8015309, Feb. 2019, doi: 10.1155/2019/8015309.
S. Liu, R. Zhang, and Y. Shi, “Design of coverage algorithm for mobile sensor networks based on virtual molecular force,” Comput. Commun., vol. 150, pp. 269–277, Jan. 2020, doi: 10.1016/j.comcom.2019.11.001.
W. Jun and G. Haoyang, “Virtual force field coverage algorithms for wireless sensor networks in water environments,” Int. J. Sens. Netw., vol. 32, no. 3, pp. 174–181, Jan. 2020, doi: 10.1504/IJSNET.2020.105564.
X. Wang, S. Wang, and D. Bi, “Virtual Force-Directed Particle Swarm Optimization for Dynamic Deployment in Wireless Sensor Networks,” in Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, Berlin, Heidelberg, 2007, pp. 292–303. doi: 10.1007/978-3-540-74171-8_29.
M. Song, L. Yang, W. Li, and T. A. Gulliver, “Improving wireless sensor network coverage using the VF-BBO algorithm,” in 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), Aug. 2013, pp. 318–321. doi: 10.1109/PACRIM.2013.6625496.
S. Wang, X. Yang, X. Wang, and Z. Qian, “A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization,” Sensors, vol. 19, no. 12, Art. no. 12, Jan. 2019, doi: 10.3390/s19122735.
M. Li, J. Hu, and X. Cao, “A Two-Phase Coverage Control Algorithm for Self-Orienting Heterogeneous Directional Sensor Networks,” IEEE Access, vol. 8, pp. 88215–88226, 2020, doi: 10.1109/ACCESS.2020.2993554.
X. Qi, Z. Li, C. Chen, and L. Liu, “A wireless sensor node deployment scheme based on embedded virtual force resampling particle swarm optimization algorithm,” Appl. Intell., Sep. 2021, doi: 10.1007/s10489-021-02745-0.
O. Khatib, “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” in Autonomous Robot Vehicles, I. J. Cox and G. T. Wilfong, Eds. New York, NY: Springer, 1990, pp. 396–404. doi: 10.1007/978-1-4613-8997-2_29.
L.-X. Wang, A course in fuzzy systems and control. Prentice-Hall, Inc., 1996.
A. Osmani, M. Dehghan, H. Pourakbar, and P. Emdadi, “Fuzzy-Based Movement-Assisted Sensor Deployment Method in Wireless Sensor Networks,” in 2009 First International Conference on Computational Intelligence, Communication Systems and Networks, Jul. 2009, pp. 90–95. doi: 10.1109/CICSYN.2009.97.
D. Izadi, J. Abawajy, and S. Ghanavati, “An Alternative Node Deployment Scheme for WSNs,” IEEE Sens. J., vol. 15, no. 2, pp. 667–675, Feb. 2015, doi: 10.1109/JSEN.2014.2351405.
D. Šoštarić and G. Mester, “Drone localization using ultrasonic TDOA and RSS signal: Integration of the inverse method of a particle filter,” FME Trans., vol. 48, no. 1, pp. 21–30, 2020, doi: 10.5937/fmet2001021S.
