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• Open Access   Article

  Fast Accessing, Monitoring and Analyzing of Water Resource Management Information using 5G Technology and IoT

  Pratap Singh Soalnki, M.S. Hanumanthappa, N.D. Atkekar

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.2 , pp.1-5, Apr-2023

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  India has rolled-out the 5G wireless technology. The Nation’s 5G services official inaugurated by Prime Minister of India on October 1st 2022. The Fifth-generation (5G) technology having extreme capabilities in terms of high bandwidth, low latency, reliability and ability to connect large number of network devices. This communication technology has the potential to enhance efficiencies, address problems, and create new delivery mechanisms across different functions, services and schemes of the Government. The Government is also intend to established the 5G laboratories for quick prototyping of 5G use cases that cater to the requirements of several sectors. This new technology can be used in Water Resource, Agriculture, Environment, Smart Home, Smart Grid, Smart Transportation, Smart Health, Public Safety, Industries and many more. Water is natural and limited available resource therefore management of Water Resources, protection and supervision is very essential. Presently, Improvement in water resource management is an important and relevant issue. Internet of Things (IOT) is rapidly growing and become an easy way to access the physical objects or devices which are connected through internet. IOT can access any devices which have built in sensors, unique address identifier with the ability to collect and transfer the data without any human intervention. Using IOT we can control the devices from anywhere efficiently and safely [1]. The IOT based on 5G technology is an advance technology which could help tremendously for managing the water resources locally as well as remotely. IoT devices paired with 5G will help transfer, monitor and analyze the water resource data. National Water Informatics Center (NWIC) under Ministry of Jal Shakti (Govt. of India) has setup the Working Group Committee for adoption of 5G technology for identifying the possible 5G based use cases (applications) in Water Resource sector. First Author is the member of Working Group Committee for CWPRS [2]. 5G technology is magnificent opportunity for management of water infrastructures. In this paper we are discussing on possible uses of 5G technology and their benefits towards efficient water resource management along with the literature survey.

  Key-Words / Index Term
  5G Technology, IoT, Wireless Network, Water Resource Management, High Speed Data, Bandwidth

  References
  [1] Preeti R. Ashtikar, Pratap Singh Solanki, P.R. Khatarkar “Water Resource Management Using Internet of Things (IoT) : Literature Survey”, International Journal for Research in Applied Science & Engineering Technology (IJRASET) , Volume 7 Issue IX, Sep 2019, Page 80-83.
  [2] NWIC White Paper: 5G use cases Labs, White Paper on “Opportunity to Design, Develop, Prototype 5G Applications and Use Cases”, Department of Telecommunication, Govt. of India (2022).
  [3]Jia, M., Komeily, A., Wang, Y., Srinivasan, R.S. “Adopting Internet of Things for the development of smart buildings: A review of enabling technologies and applications”. Autom. Constr. 2019, 101, 111–126.
  [4]Jayti Bhatt, JigneshPatoliya “IoT Based Water Quality Monitoring System “ International Journal of Industrial Electronics and Electrical Engineering, ISSN: 2347-6982 Volume-4, Issue-4, Apr.-2016.
  [5] Falguni Jindal, Rishabh Jamar, PrathameshChuri “Future and Challenges and Internet of Things “International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 2, April 2018.
  [6] Wei Zhou, Yuqing Zhang, and PengLiu “ The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved”,, Member, IEEE.
  [7] Kiran M. Dhobale1, Sangmeshwar P. Gorgile1, Pradnya J. Gunjal1, Krushna A. Hirve1, Prof. U. A. Mande1 “IOT based Smart Water Supply management System ” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE)” Vol. 6, Issue 1, January 2017.
  [8] Sonali Deshmukh, Praveen Barapatre “Internet of Things Based System for Water Resource Engineering”, International Conference On Emanations in Modern Technology and Engineering ,Volume 5, Issue 3 , pp 240 – 242.
  [9] Lin Ding, Qilin Wang, Yuan Zheng, Xiushuo Chen, YidanZheng, Jing Zhang “Analysis of Health Assessment of Water Resoucres of Futun River in Rizhao City by 5G Wireless Communication”, Wireless Communication and Mobile Computing Volume 2022, Article ID 3175769, page No. 14.
  [10] Huseien, G.F., Shah, K.W. “Potential Applications of 5G Network Technology for Climate Change Control: A Scoping Review of Singapore” Sustainability 2021, 13, 9720. https://doi.org/10.3390/su13179720
  [11] Lin Ding,Qilin Wang,Yuan Zheng,Xiushuo Chen,YidanZheng,Jing Zhang, and HuiXu “Analysis of Health Assessment of Water Resources of Futuan River in Rizhao City by 5G Wireless Communication” , Hindawi Wireless Communications and Mobile Computing Volume 2022, Article ID 3175769, 14 pages https://doi.org/10.1155/2022/3175769
  [12]Mortada M. Abdulwahab, Hadeel A.Mohamed, Mutseuim A. Alameen, Mohamed A. Mosalam, Faris M.Elsadig “ Wireless Sensor Network of Monitoring Water Distribution Network Service using IoT”, International Journal of Scientific Research in Computer Science and Engineering, Volume 11, Issue I, pp- 55-55, February 2023.
  [13] M. Chitra, D. Sadhihskumar, R. Aravindh, M. Murali, R.Vaittilingame “IoT based Water Flood Detection and Early Warning System” International Journal of Scientific Research in Computer Science and Engineering, Volume 08, Issue 5, pp- 47-53, October 2020.

  Citation
  Pratap Singh Soalnki, M.S. Hanumanthappa, N.D. Atkekar, "Fast Accessing, Monitoring and Analyzing of Water Resource Management Information using 5G Technology and IoT," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.2, pp.1-5, 2023

• Open Access   Article

  The Application of Computer in Motor Vehicle Registration over Manual System

  Atianashie Miracle A.

  Survey Paper | Conference-Paper (IJSRCSE)

  Vol.11 , Issue.2 , pp.6-13, Apr-2023

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  Abstract
  the research looks at the limitations of manual motor vehicle registration and brings out a clearer need for the application of computers in motor vehicle registration. The paper equally exposes the challenges with the application of computer motor licensing registration, conceivably. The sample of the study was drawn from the staff of the Board of Internal Revenue in Calabar Cross River State, Nigeria, that are carrying out motor vehicle registration through the use of computer systems. The findings showed that vehicle users and owners faked registration information since there is no standard online registration authentication system. The study shows that the application of computers in motor vehicle registration will increase efficiency and help in fast-tracking stolen vehicles. However, the study recommends that Motor vehicle registration officers should organize seminars and workshops to orientate the public on the use of computer systems in vehicle registration.

  Key-Words / Index Term
  computer, registration, motor licensing

  References
  [1] S. Chauhan, M. A. Khan, and W. Sintunavarat, “Common fixed point theorems in fuzzy metric spaces satisfying ? -contractive condition with common limit range property,” Abstract and Applied Analysis, vol. 2013, 2013, doi: 10.1155/2013/735217.
  [2] R. Apau, F. N. Koranteng, and S. A. Gyamfi, “Cyber-Crime and its Effects on E-Commerce Technologies,” Journal of Information, vol. 5, no. 1, pp. 39–59, 2019, doi: 10.18488/journal.104.2019.51.39.59.
  [3] K. S. Al-Saleh, “Productivity improvement of a motor vehicle inspection station using motion and time study techniques,” Journal of King Saud University - Engineering Sciences, vol. 23, no. 1, pp. 33–41, Jan. 2011, doi: 10.1016/j.jksues.2010.01.001.
  [4] A. Krishnan, P. A. Shyjila, and J. J. Kizhakethottam, “Electronic-secure Vehicle Authorization Mechanism (e-SVAM),” Procedia Technology, vol. 25, pp. 318–325, 2016, doi: 10.1016/j.protcy.2016.08.113.
  [5] B. Andrlík, P. P. Mísa?ová, and J. Vrtalová, “Preparedness of the Czech Republic for Taxation of Road Motor Vehicles based on CO2,” Procedia Economics and Finance, vol. 12, pp. 10–19, 2014, doi: 10.1016/s2212-5671(14)00315-3.
  [6] J. Duda, “The Requirements of University Students in the Employee Benefits by a Prospective Employer,” Procedia Economics and Finance, vol. 12, pp. 130–137, 2014, doi: 10.1016/s2212-5671(14)00328-1.
  [7] H.-G. Barth, “Evaluation of Impact of Government Program for Final Car Market on Car Dealers,” Procedia Economics and Finance, vol. 12, pp. 20–26, 2014, doi: 10.1016/s2212-5671(14)00316-5.
  [8] H. Bohušová, P. Svoboda, and V. Blašková, “The Uncertainty Associated with the Estimated Lease Term and its Impact on Financial Statements Items,” Procedia Economics and Finance, vol. 12, pp. 76–84, 2014, doi: 10.1016/s2212-5671(14)00322-0.
  [9] K. A. Owusu, K. A. Monney, J. Y. Appiah, and E. M. Wilmot, “Effects of computer-assisted instruction on performance of senior high school biology students in Ghana,” Comput Educ, vol. 55, no. 2, pp. 904–910, 2010.
  [10] F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User Acceptance of Computer Technology?: a Comparison of Two Theoretical Models *,” vol. 35, no. 8, 1989.
  [11] P. U. Obichukwu et al., “Evaluation of Extent of Computer Appreciation Skills Acquired by Secondary School Students for Economic Empowerment,” Journal of Educational and Social Research, vol. 11, no. 3, p. 263, 2021.
  [12] Atianashie Miracle A., Chukwuma Chinaza Adaobi, Role of Digitalization in Balancing Work during Pandemic: Case of Microsoft, International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.2, pp.30-37, 2022.

  Citation
  Atianashie Miracle A., "The Application of Computer in Motor Vehicle Registration over Manual System," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.2, pp.6-13, 2023

• Open Access   Article

  Effective Machine Learning Classifiers for Intrusion Detection in Computer Network

  Kayode A. Okewale, Ifedotun R. Idowu, Bamidele S. Alobalorun, Falilat A. Alabi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.2 , pp.14-22, Apr-2023

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  Cyber security has finally become inevitable due to the increase in use of internet and computer networks. This has given access to cyber-attacks in the network services. However, Intrusion detection systems (IDSs) have been incorporated into networks so as to overcome these huge challenges. IDSs are capable of identifying malicious or abnormal activity in the network and draw the attention of the network administrator to it. Furthermore, approaches based on machine learning (ML) are able to increase IDS effectiveness. In this study, the NSL-KDD CUP dataset was used to develop and validate three individual models using three supervised machine learning algorithms: Classification and Regression Tree (CART), Multinomial Logistics Regression (MLR), and K-Nearest Neighbor (KNN).Data preprocessing and Feature Selection was initiated in order to remove outliers and imbalance in the dataset and optimally select the best feature to avoid data redundancy. Performance evaluation was conducted on each of the model developed with the following metrics as training time, precision accuracy, sensitivity, , f-Score and specificity, The evaluation`s final findings indicate that KNN is the most effective classifier, with a classification accuracy of 99.38% and a training time of 13.64 seconds with the lowest error rate (0.006161), making it the best model and encouraging further study.

  Key-Words / Index Term
  CART, Chi-Square, Computer Networks, IDS, KNN, MLR, NSL KDD CUP

  References
  [1.] Kizza, Joseph. Guide to Computer Network Security. 10.1007/978-3-319-55606-2,2017.
  [2] Jyothsna, V., & Munivara Prasad, K. (2020). Anomaly-Based Intrusion Detection System. Computer and Network Security. doi: 10.5772/intechopen.8228, 2017
  [3.] Abduvaliyev, A. S. K. Pathan, J. Zhou, R. Roman, and W. C. Wong,(2021) “On the vital areas of intrusion detection systems in wireless sensor networks,” IEEE Communications Surveys Tutorials, vol. 15, no. 3, pp. 1223–1237, 2017
  [4.] Olofintuyi, S.S. & Omotehinwa, T.O. Performance Evaluation of Supervised Ensemble Cyber Situation Perception Models for Computer Network. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 11 No 2, Pp 1-.14. DOI Affix - https://doi.org/ 10.22624/AIMS/CISDI/V12N1P1. Available online at www.computing-infosystemsjournal.info, 2021
  [5] Ahanger, A.S., Khan, S.M., & Masoodi, F. An Effective Intrusion Detection System using Supervised Machine Learning Techniques. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 1639-1644, 2021
  [6] Afzal Ahmad, Mohammad Asif, Shaikh Rohan Ali "Review Paper on Shallow Learning and Deep Learning Methods for Network security." International Journal of Scientific Research in Computer Science and Engineering 6.5 (2018): 45-54.
  [7] Maimuna Yusuf Ma`aji and Muhammad Sirajo Ali,Models Comparison Based On Intrusion Detection Using Machine Learning,Sule Lamido University Journal of Science &Technology Vol. 6No. 1&2, pp. 74-86https://doi.org/10.56471/slujst.v6i.358.Online ISSN: 2736-0903 PrintISSN: 2736-089Xwww.slujst.edu.ng.,2023
  [8].Iram Abrar, Zahrah Ayub, Faheem Masoodi and Kashmir, Jammu & Kashmir ‘‘A Machine Learning Approach for Intrusion Detection System on NSL-KDD Dataset’’ Proceedings of the International Conference on Smart Electronics and Communication (ICOSEC 2020) IEEE Xplore Part Number: CFP20V90-ART; ISBN: 978-1-7281-5461-9, 2020
  [9] Soe,Y.N; Santosa P.I.; and Hartanto,R. ( "DDoS Attack Detection Based on Simple ANN with SMOTE for IoT Environment," 2019 Fourth International Conference on Informatics and Computing (ICIC), Semarang, Indonesia, pp. 1-5, doi: 10.1109/ICIC47613.2019.8985853,2019
  [10.] Diro AA, Chilamkurti N. Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems 82:761–768 DOI 10.1016/j.future.2017.08.043.
  [11] J. Dhiviya Rose, Isha Mittal, Ramya Mihir "Efficient and Simple Machine Learning-based Malware and Trojan Identification Tool." International Journal of Scientific Research in Computer Science and Engineering 10.2 (2022): 64-68.
  [12]Elbasiony M., Reda, et al. “A hybrid network intrusion detection framework based on random forests and weighted k-means,” Ain Shams Engineering Journal, Vol. 4, No. 4, pp.753-762, 2020
  [13.] Roopak, M., Tian G.Y and Chambers J."An Intrusion Detection System Against DDoS Attacks in IoT Networks," 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2020, pp. 0562-0567, doi: 10.1109/CCWC47524.2020.9031206, 2020
  [14] Eduardo K. Viegas, Altair O. Santin and Luiz S. Oliveira. “Toward a reliable anomaly-based intrusion detection in real-world environments,” Computer Networks, Vol. 127, pp. 200-216,2017
  [15] T. Parvin, T. Nasrin, J. Khatun, M. Chatterjee "Predictive Machine Learning Model for Detection and Classification of Diabetes." International Journal of Scientific Research in Multidisciplinary Studies 7.9 (2021):11-17.
  [16] Dhakne, A.R., Chatur, P.N. A Comprehensive Survey on Intrusion Detection Systems in Wireless Sensor Network. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_65,2016
  [17.] Mehmood, T., &Rais, H. B. M. “Machine learning algorithms in context of intrusion detection.” International Conference on Computer and Information Sciences (ICCOINS): 369-373, 2016
  [18.] Idowu, I. R., Adeniji, O. D., Okewale, K., &Alabi, F. A. Detection of Cyberbullying in the Social Media Space using Maximum Entropy and Convolutional Neural Network. Journal of Engineering Research and Reports, 23(2), 40-48. https://doi.org/10.9734/jerr/2022/v23i217595, 2022
  [19] Alqahtani, H., Sarker, I. H., Kalim, A., Minhaz Hossain, S. M., Ikhlaq, S., & Hossain, S. Cyber intrusion detection using machine learning classification techniques. In N. Chaubey, S. Parikh, & K. Amin (Eds.), Computing Science, Communication and Security: First International Conference, COMS2 2020 Gujarat, India, March 26–27, , Revised Selected Papers (pp. 121-131). (Communications in Computer and Information Science; Vol. 1235 CCIS). Springer, Springer Nature. https://doi.org/10.1007/978-981-15-6648-6_10., 2020
  [20] OOthman, S. M., Ba-Alwi, F. M., Alsohybe, N. T., & Al-Hashida, A. Y. “Intrusion detection model using machine learning algorithm on Big Data environment. ”Journal of Big Data 5(1): 34.201
  [21] Awotunde J.B.,Ajagbe S.A, IdowuI.R., Ndunagu J.N. .An Enhanced Cloud-IoMT-based and Machine Learing for effective COVID-19 Diagniosis System.In :Al-TurjmanF.;Nayyar A.., Devi A., Shukla P.K.(eds) Intelligence of Things: AI-IoT Based Critical-Applications and Innovations. Springer,Cham. https://doi.org/10.1007/978-3-030-82800-4_3, 2021.

  Citation
  Kayode A. Okewale, Ifedotun R. Idowu, Bamidele S. Alobalorun, Falilat A. Alabi, "Effective Machine Learning Classifiers for Intrusion Detection in Computer Network," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.2, pp.14-22, 2023

• Open Access   Article

  An Improved Location-Based Genetic Algorithm for Routing in FANETs

  Amrita Yadav

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.2 , pp.23-30, Apr-2023

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  Abstract
  This paper describes a genetic algorithm-based approach which uses location-based routing strategy for flying ad hoc networks (FANETs). The objective of this paper is to enhance transmission between the nodes in FANETs since routing is one of the primary issues in FANETs and see whether genetic algorithms can be a solution for this issue. Many routing protocols have been designed and implemented but there is no such efficient protocol designed yet which improves routing on all parameters. The paper also shows comparison between the standard genetic algorithm and proposed genetic algorithm. The considered parameters for comparison and testing are packet delivery rate, overhead, end-to-end delay, throughput and energy utilization. The outcomes demonstrate that the suggested method outperforms the current genetic algorithm.

  Key-Words / Index Term
  UAV, Routing, protocol, FANET, genetic, location, energy, algorithm

  References
  [1] Cumino, Pedro &Maciel, Kaled& Tavares, Thais & Oliveira, Helder May &Rosário, Denis &Cerqueira, Eduardo. “Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network”. Sensors. 20. 67. 10.3390/s20010067, 2019.
  [2] Yanmaz, Evsen & Yahyanejad, Saeed &Rinner, Bernhard &Hellwagner, Hermann &Bettstetter, Christian. “Drone Networks: Communications, Coordination, and Sensing. Ad Hoc Networks”. 68. 10.1016/j.adhoc.2017.09.001, 2017.
  [3] Bekan Kitaw, “Exploiting Packet Travel Time and Energy factor in delay sensitive application of MANET”, International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), , vol.9, Issue.4, pp.36-43, 2021.
  [4] Hidehiko Okada, “Evolutionary Training of Binary Neural Networks by Genetic Algorithm”, International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), vol.9, Issue.6, pp.63-68, 2021.
  [5] Jebari, Khalid, “Selection Methods for Genetic Algorithms”. International Journal of Emerging Sciences. 3. 333-344, 2013.
  [6] Arpan Kumar Kar, “Bio inspired computing - A review of algorithms and scope of applications”. Expert Syst. Appl. 59, C, pp. 20–32. DOI:https://doi.org/10.1016/j.eswa.2016.04.018, 2016.
  [7] Almufti, Saman &Marqas, Ridwan & Saeed, Vaman, “Taxonomy of bio-inspired optimization algorithms”, Journal of Advanced Computer Science & Technology. 8. 23. 10.14419/jacst.v8i2.29402,2019.
  [8] Panchal, G., Panchal, D., & Patel, P. ,”Solving NP hard Problems using Genetic Algorithm”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, 1824-1827, 2015.
  [9] Kim, Kyung-Joong& Cho, Sung-Bae, “Evolutionary Algorithms for Board Game Players with Domain Knowledge” 10.1007/978-3-540-72705-7_4, 2007.
  [10] Carretero, Javier & Xhafa, Fatos, “Use of genetic algorithms for scheduling jobs in large scale grid applications”, Technological and Economic Development of Economy - TECHNOL ECON DEV ECON. 12. 11-17. 10.1080/13928619.2006.9637716, 2006.
  [11] Bader, Ehab & H. O. Nasereddin, Hebah, “Using Genetic Algorithm in Network Security”. International Journal of Research and Reviews in Applied Sciences. 5. 148-154, 2019.
  [12] Al-Ghazal, Mustafa & El-Sayed, Ayman &Kelash, Hamedy, “ Routing Optimization using Genetic Algorithm in Ad Hoc Networks” 497 - 503. 10.1109/ISSPIT.2007.4458010, 2007.
  [13] Nagib, Gihan., “Optimal Routing In Ad-Hoc Network Using Genetic Algorithm” Int. J. Advanced Networking and Applications. 3. 1323-1328,2012.
  [14] Abdullah, Nibras & Al-wesabi, Ola &Baklizi, Mahmoud &Ramadass, Sureswaran. “Multi-constrained routing using genetic algorithm”, IEEE International Conference on RFID-Technologies and Applications, RFID-TA 2013. 10.1109/RFID-TA.2013.6694537, 2013.
  [15] Hampiholi, Aishwarya & Kumar, Vijaya. , “ Efficient routing protocol in IoT using modified Genetic algorithm and its comparison with existing protocols”. 1-5. 10.1109/CIMCA.2018.8739759, 2018.
  [16] J. Seetaram and P. S. Kumar, "An energy aware Genetic Algorithm Multipath Distance Vector Protocol for efficient routing," International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, pp. 1975-1980, doi: 10.1109/WiSPNET.2016.7566488, 2016.
  [17] D. Pal, D. Shah and S. K. Bhagat, "Design of a Genetic Algorithm Based Shortest Path Routing with different Mutation Techniques," 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, pp. 1-5, doi: 10.1109/ICECCT.2019.8869243.
  [18] Chinnasamy, Rajan&Moorthi, Shanthi. , “ Genetic based optimization for multicast routing algorithm for MANET”. Sadhana. 40. 2341-2352. 10.1007/s12046-015-0437-8, 2016.
  [19] Hassan, Mustafa &Muniyandi, Ravie. “An improved hybrid technique for energy and delay routing in mobile ad-hoc networks”. 12. 134-139,2017.
  [20] Singh, Kuldeep & Verma, Anil. “ A Trust Model for Effective Cooperation in Flying Ad Hoc Networks Using Genetic Algorithm”. 10.1109/ICCSP.2018.8524558, 2017.
  [21] Zhang, Guoan& Wu, Min & Duan, Wei & Huang, Xinming, “Genetic Algorithm Based QoS Perception Routing Protocol for VANETs”, Wireless Communications and Mobile Computing. 2018. 1-10. 10.1155/2018/3897857, 2019.
  [22] Sun, Xiuqiao& Wang, Jian & Wu, Weitiao& Liu, Wenjia. “Genetic Algorithm for Optimizing Routing Design and Fleet Allocation of Freeway Service Overlapping Patrol” Sustainability. 10. 4120. 10.3390/su10114120, 2019.
  [23] Delima, AllemarJhone&Sison, Ariel & Medina, Ruji., “A modified genetic algorithm with a new crossover mating scheme”. 7. 165-181. 10.11591/ijeei.v7i2.1047, 2019.
  [24] Trivedi, V., & Nayak, P., “Modified AODV Using Genetic Algorithm to Minimize Energy Consumption in MANET”, 2019.
  [25] Liu, Jianqiang&Huo, Shuai & Wang, Yi, “Throughput optimization for flying ad hoc network based on position control using genetic algorithm”, International Journal of Metrology and Quality Engineering. 11. 11. 10.1051/ijmqe/2020012, 2020.
  [26] Muruganantham, Naveen & El-Ocla, Hosam, “Routing Using Genetic Algorithm in a Wireless Sensor Network”, Wireless Personal Communications. 111. 10.1007/s11277-019-07011-8, 2022.
  [27] Wei, Xing & Yang, Hua & Huang, Wentao, “A Genetic-Algorithm-Based Optimization Routing for FANETs”, Frontiers in Neurorobotics. 15. 697624. 10.3389/fnbot.2021.697624, 2021.
  [28] Uçan, Ferhat &Altilar, D. Turgay, “Using Genetic Algorithms for Navigation Planning in Dynamic Environments”. Applied Computational Intelligence and Soft Computing. 2012. 10.1155/2012/560184, 2012.
  [29] Sajid, Mohammad & Mittal, Himanshu & Pare, Shreya & Prasad, Mukesh, “Routing and scheduling optimization for UAV assisted delivery system: A hybrid approach”, Applied Soft Computing. 126. 109225. 10.1016/j.asoc.2022.109225, 2022.
  [30] Zhang, Jiankang& Liu, Dong & Chen, Sheng & Ng, Soon & Maunder, Robert &Hanzo, L., “Multiple-Objective Packet Routing Optimization for Aeronautical ad-hoc Networks”. IEEE Transactions on Vehicular Technology.2022.
  [31] Venkataraman, Rishi &Muralirajan, Praveenraj&Devu, Pranith& Prasad, R &Kirthiga, S. “Energy Efficient Clustering and Routing Using GA-EAMMH”. 603-608. 10.1109/ICCES54183.2022.9835936, 2022.
  [32] Xin, Wang &Cuiran, Li & Jianli, Xie, “ Research on Clustering Routing Protocol for Energy-Harvesting WSN”. 1460-1464. 10.1109/ICETCI55101.2022.9832077, 2022.
  [33] Han, Youjia& Hu, Huangshui& Guo, Yuxin, “Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm”. IEEE Access. pp. 1-1. 10.1109/ACCESS.2022.3144015, 2022.

  Citation
  Amrita Yadav, "An Improved Location-Based Genetic Algorithm for Routing in FANETs," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.2, pp.23-30, 2023