A survey on combined applications of Meta-heuristic Algorithms with Internet of Things
الموضوعات :Morteza Salamat Mostaghim 1 , Azam Andalib 2 , Hossein Azgomi 3
1 - Department of Computer Engineering, Ra.C., Islamic Azad University, Rasht, Iran
2 - گروه مهندسی کامپیوتر، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران
3 - Department of Computer Engineering, Ra.C., Islamic Azad University, Rasht, Iran
الکلمات المفتاحية: Internet of Things, IoT, Optimization, Meta-heuristics algorithm, Applications of Meta-heuristic Algorithms in IoT,
ملخص المقالة :
Systems based on Internet of Things are dynamic and complex sets of intelligent objects. Today, the global field of IoT is expanding and is opening up new opportunities for technology. Meta-heuristic algorithms are provided to solve complex problems in an acceptable time. These algorithms select the best solution to the problem in order to maximize profit or minimize cost. So far, meta-heuristic algorithms have been used to solve many problems in the Internet of Things.
In this paper, some practical aspects of the Internet of Things are reviewed. In addition, some of the most popular meta-heuristic optimization methods are analyzed. Then, a classification of IoT-based systems that have used meta-heuristic algorithms is presented. This classification includes four levels: discrete or continuous, type of meta-heuristic algorithms, single or multi objectives, and aim or application of the research. This review provides a way for future studies to easily select meta-heuristic algorithms for Internet of Things systems.
المصادر:
[1] Shah, S. H., & Yaqoob, I. (2016). A survey: Internet of Things (IOT) technologies, applications and challenges. 2016 IEEE Smart Energy Grid Engineering (SEGE), 381-385.
[2] Alaba, F. A., Othman, M., Hashem, I. A. T., & Alotaibi, F. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications, 88, 10-28.
[3] Mukadam, Z., & Logeswaran, R. (2020). A cloud-based smart parking system based on IoT technologies. Journal of critical reviews, 7(3), 105-109.
[4] Ghayvat, H., Mukhopadhyay, S., Gui, X., & Suryadevara, N. (2015). WSN-and IOT-based smart homes and their extension to smart buildings. sensors, 15(5), 10350-10379.
[5] Ali, U., & Calis, C. (2019, October). Centralized smart governance framework based on iot smart city using ttg-classified technique. In 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT) (pp. 157-160). IEEE.
[6] Umair, M., Cheema, M. A., Cheema, O., Li, H., & Lu, H. (2021). Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Sensors, 21(11), 3838.
[7] Meng, Z., Li, G., Wang, X., Sait, S. M., & Yıldız, A. R. (2021). A comparative study of metaheuristic algorithms for reliability-based design optimization problems. Archives of Computational Methods in Engineering, 28, 1853-1869.
[8] Donoso, Y., & Fabregat, R. (2016). Multi-objective optimization in computer networks using metaheuristics. Auerbach Publications.
[9] Soler-Dominguez, A., Juan, A. A., & Kizys, R. (2017). A survey on financial applications of metaheuristics. ACM Computing Surveys (CSUR), 50(1), 1-23.
[10] Lessmann, S., Caserta, M., & Arango, I. M. (2011). Tuning metaheuristics: A data mining based approach for particle swarm optimization. Expert Systems with Applications, 38(10), 12826-12838.
[11] Nakib, A., & Talbi, E. G. (Eds.). (2017). Metaheuristics for medicine and biology (Vol. 704). Berlin Heidelberg: Springer.
[12] Zedadra, O., Guerrieri, A., Jouandeau, N., Spezzano, G., Seridi, H., & Fortino, G. (2018). Swarm intelligence-based algorithms within IoT-based systems: A review. Journal of Parallel and Distributed Computing, 122, 173-187.
[13] Villa-Henriksen, A., Edwards, G. T., Pesonen, L. A., Green, O., & Sørensen, C. A. G. (2020). Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems engineering, 191, 60-84.
[14] Marietta, J., & Chandra Mohan, B. (2020). A review on routing in internet of things. Wireless Personal Communications, 111(1), 209-233.
[15] M Singh, M., & Baranwal, G. (2018). Quality of service (qos) in internet of things. In 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU) (pp. 1-6). IEEE.
[16] Alaa, M., Zaidan, A. A., Zaidan, B. B., Talal, M., & Kiah, M. L. M. (2017). A review of smart home applications based on Internet of Things. Journal of network and computer applications, 97, 48-65.
[17] Priyan, M. K., & Devi, G. U. (2019). A survey on internet of vehicles: applications, technologies, challenges and opportunities. International Journal of Advanced Intelligence Paradigms, 12(1-2), 98-119.
[18] Kalarthi, Z. M. (2016). A review paper on smart health care system using internet of things. International Journal of Research in Engineering and Technology, 5(03), 8084.
[19] Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer networks, 151, 211-223.
[20] Sakthivel, S., &Vidhya, G. (2021). A Trust-Based Access Control Mechanism for Intra-Sensor Network Communication in Internet of Things.Arabian Journal for Science and Engineering, 46(4), 3147-3153.
[21] Sahlmann, K., Scheffler, T., &Schnor, B. (2017). Managing IoT device capabilities based on oneM2M ontology descriptions. Proceedings of the 16. GI/ITG KuVSFachgesprächSensornetze, ser. Technical Reports. Hamburg, Germany: HAW Hamburg, 23-26.
[22] Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017). A survey of intrusion detection in Internet of Things.Journal of Network and Computer Applications, 84, 25-37.
[23] Zhang, K., Liang, X., Lu, R., & Shen, X. (2014). Sybil attacks and their defenses in the internet of things.IEEE Internet of Things Journal, 1(5), 372-383.
[24] Reddy, M. P. K., & Babu, M. R. (2019). Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things. Cluster Computing, 22, 1361-1372.
[25] Torre, I., Adorni, G., Koceva, F., & Sanchez, O. (2016). Preventing disclosure of personal data in IoT networks. In 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 389-396). IEEE.
[26] Bangui, H., Ge, M., & Buhnova, B. (2018). Exploring Big Data Clustering Algorithms for Internet of Things Applications.In IoTBDS (pp. 269-276).
[27] Sun, G., Li, J., Dai, J., Song, Z., & Lang, F. (2018). Feature selection for IoT based on maximal information coefficient. Future Generation Computer Systems, 89, 606-616.
[28] Yuan, J., & Li, X. (2018). A reliable and lightweight trust computing mechanism for IoT edge devices based on multi-source feedback information fusion. Ieee Access, 6, 23626-23638.
[29] Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & operations research, 13(5), 533-549.
[30] Yang, X. S. (2020). Nature-inspired optimization algorithms: Challenges and open problems. Journal of Computational Science, 46, 101104.
[31] Bozorg-Haddad, O. (2018). Advanced optimization by nature-inspired algorithms (Vol. 720). Singapore: Springer.
[32] Fausto, F., Reyna-Orta, A., Cuevas, E., Andrade, Á. G., & Perez-Cisneros, M. (2020). From ants to whales: metaheuristics for all tastes. Artificial Intelligence Review, 53, 753-810.
[33] Kaveh, A., & Bakhshpoori, T. (2019). Metaheuristics: Outlines, MATLAB Codes and Examples.doi:10.1007/978-3-030-04067-3
[34] Rani, R., Jain, S., & Garg, H. (2024). A review of nature-inspired algorithms on single-objective optimization problems from 2019 to 2023. Artificial Intelligence Review, 57(5), 1-51.
[35] Vasuki, A. (2020). Nature-inspired optimization algorithms. CRC Press
[36] Yang, X. S. (2020). Nature-inspired optimization algorithms: Challenges and open problems. Journal of Computational Science, 46, 101104.
[37] M Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano.
[38] Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee.
[39] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.
[40] Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.
[41] Yang, X. (2010). S.: A New Metaheuristic Bat-Insspired Algorithm. Nature Inspired Cooperative Strategies for Optimization. Studies in Computational Intelligence (Springer). pp-65-74.
[42] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer Advances in Engineering Software. 69 46–61.
[43] Wang, Y., Geng, X., Zhang, F., & Ruan, J. (2018). An immune genetic algorithm for multi-echelon inventory cost control of IOT based supply chains. IEEE Access, 6, 8547-8555.
[44] Zhang, Y., Li, P., & Wang, X. (2019). Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access, 7, 31711-31722.
[45] Rani, S., Ahmed, S. H., & Rastogi, R. (2020). Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications. Wireless Networks, 26(4), 2307-2316.
[46] Kashyap, N., Kumari, A. C., & Chhikara, R. (2020). Multi-objective Optimization using NSGA II for service composition in IoT. Procedia Computer Science, 167, 1928-1933.
[47] Abbas, S., Javaid, N., Almogren, A., Gulfam, S. M., Ahmed, A., & Radwan, A. (2021). Securing genetic algorithm enabled SDN routing for blockchain based Internet of Things. IEEE Access, 9, 139739-139754.
[48] Khadir, K., Guermouche, N., Guittoum, A., & Monteil, T. (2022). A genetic algorithm-based approach for fluctuating QoS aware selection of IoT services. IEEE Access, 10, 17946-17965.
[49] Lu, Q., Nguyen, K., & Huang, C. (2023). Distributed parallel algorithms for online virtual network embedding applications. International Journal of Communication Systems, 36(1), e4325.
[50] Saiyed, M. F., & Al-Anbagi, I. (2024). A Genetic Algorithm-and t-Test-Based System for DDoS Attack Detection in IoT Networks. IEEE Access, 12, 25623-25641.
[51] Cosido, O., Loucera, C., & Iglesias, A. (2013). Automatic calculation of bicycle routes by combining meta-heuristics and GIS techniques within the framework of smart cities. In 2013 International conference on new concepts in smart cities: Fostering public and private alliances (SMARTMILE) (pp. 1-6). IEEE.
[52] Ebrahimi, M., Shafieibavani, E., Wong, R. K., & Chi, C. H. (2015, June). A new meta-heuristic approach for efficient search in the internet of things. In 2015 IEEE International Conference on Services Computing (pp. 264-270). IEEE.
[53] Yue, S., & Yi, S. (2016). Study of logistics distribution route based on improved genetic algorithm and ant colony optimization algorithm. Internet of Things (IoT) and Engineering Applications, 1(1), 11-17.
[54] Suryani, V., Sulistyo, S., & Widyawan, W. (2016). Trust-based privacy for Internet of Things. International Journal of Electrical and Computer Engineering, 6(5), 2396.
[55] Sabbani, I., Youssfi, M., & Bouattane, O. (2016, September). A multi-agent based on ant colony model for urban traffic management. In 2016 5th International Conference on Multimedia Computing and Systems (ICMCS) (pp. 793-798). IEEE.
[56] Kowshalya, A. M., & Valarmathi, M. L. (2016). Detection of sybil’s across communities over social internet of things. Journal of Applied Engineering Science, 14(1).
[57] Jiang, Y., Ding, Q., & Wang, X. (2016). A recovery model for production scheduling: combination of disruption management and Internet of Things. Scientific Programming, 2016(1), 8264879.
[58] López-Matencio, P., Vales-Alonso, J., & Costa-Montenegro, E. (2017). ANT: Agent Stigmergy‐Based IoT‐Network for Enhanced Tourist Mobility. Mobile Information Systems, 2017(1), 1328127.
[59] Said, O. (2017). Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization. International Journal of Communication Systems, 30(8), e3174.
[60] Mahalaxmi, G., & Rajakumari, K. E. (2017). Multi-agent technology to improve the internet of things routing algorithm using ant colony optimization. Indian Journal of Science & Technology, 10(31), 1-8.
[61] Bellini, V., Di Noia, T., Mongiello, M., Nocera, F., Parchitelli, A., & Di Sciascio, E. (2018). Reflective internet of things middleware-enabled a predictive real-time waste monitoring system. In Web Engineering: 18th International Conference, ICWE 2018, Cáceres, Spain, June 5-8, 2018, Proceedings 18 (pp. 375-383). Springer International Publishing.
[62] Oralhan, Z., Oralhan, B., & Yigit, Y. (2017). Smart city application: internet of things (IoT) technologies based smart waste collection using data mining approach and ant colony optimization. Int. Arab J. Inf. Technol., 14(4), 423-427.
[63] Hong, Y., Chen, L., & Mo, L. (2019). Optimization of cluster resource indexing of Internet of Things based on improved ant colony algorithm. Cluster Computing, 22, 7379-7387.
[64] Thapar, P., & Batra, U. (2018). Implementation of ant colony optimization in routing protocol for internet of things. In Innovations in Computational Intelligence: Best Selected Papers of the Third International Conference on REDSET 2016 (pp. 151-164). Springer Singapore.
[65] Ebadinezhad, S., Dereboylu, Z., & Ever, E. (2019). Clustering-based modified ant colony optimizer for internet of vehicles (CACOIOV). Sustainability, 11(9), 2624.
[66] Azizou, A. Z., Boudries, A., & Amad, M. (2020). Decentralized service discovery and localization in Internet of Things applications based on ant colony algorithm. International Journal of Computing and Digital Systems, 9(5), 941-950
[67] Shi, B., & Zhang, Y. (2021). A novel algorithm to optimize the energy consumption using IoT and based on Ant Colony Algorithm. Energies, 14(6), 1709.
[68] Sung, W. T., & Chiang, Y. C. (2012). Improved particle swarm optimization algorithm for android medical care IOT using modified parameters. Journal of medical systems, 36, 3755-3763.
[69] Sung, W. T., & Hsu, C. C. (2013). IOT system environmental monitoring using IPSO weight factor estimation. Sensor Review, 33(3), 246-256.
[70] Tsai, C. W., Lai, C. F., & Vasilakos, A. V. (2014). Future internet of things: open issues and challenges. Wireless Networks, 20, 2201-2217.
[71] Luo, S., Cheng, L., & Ren, B. (2014). Practical swarm optimization based fault-tolerance algorithm for the internet of things. KSII Transactions on Internet and Information Systems (TIIS), 8(3), 735-748.
[72] Hurtado, L. A., Nguyen, P. H., & Kling, W. L. (2015). Smart grid and smart building inter-operation using agent-based particle swarm optimization. Sustainable Energy, Grids and Networks, 2, 32-40.
[73] Fang, C., Liu, X., Pardalos, P. M., & Pei, J. (2016). Optimization for a three-stage production system in the Internet of Things: procurement, production and product recovery, and acquisition. The International Journal of Advanced Manufacturing Technology, 83, 689-710.
[74] Jinyi, W., Qin, Y., Shrestha, A. P., & Yoo, S. J. (2017, October). Optimization of cognitive radio secondary base station positioning and operating channel selection for IoT sensor networks. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 397-399). IEEE.
[75] Kumrai, T., Ota, K., Dong, M., Kishigami, J., & Sung, D. K. (2016). Multiobjective optimization in cloud brokering systems for connected Internet of Things. IEEE Internet of Things Journal, 4(2), 404-413.
[76] Dai, S., Liwang, M., Liu, Y., Gao, Z., Huang, L., & Du, X. (2018). Hybrid quantum-behaved particle swarm optimization for mobile-edge computation offloading in internet of things. In Mobile Ad-hoc and Sensor Networks: 13th International Conference, MSN 2017, Beijing, China, December 17-20, 2017, Revised Selected Papers 13 (pp. 350-364). Springer Singapore.
[77] Sangeetha, A. L., Bharathi, N., Ganesh, A. B., & Radhakrishnan, T. K. (2018). Particle swarm optimization tuned cascade control system in an Internet of Things (IoT) environment. Measurement, 117, 80-89.
[78] Kashyap, N., Kumari, A. C., & Chhikara, R. (2020). Service Composition in IoT using Genetic algorithm and Particle swarm optimization. Open Computer Science, 10(1), 56-64.
[79] Liu, J., Yang, D., Lian, M., & Li, M. (2021). Research on intrusion detection based on particle swarm optimization in IoT. IEEE Access, 9, 38254-38268.
[80] Subramani, S., & Selvi, M. (2023). Multi-objective PSO based feature selection for intrusion detection in IoT based wireless sensor networks. Optik, 273, 170419.
[81] Bey, M., Kuila, P., Naik, B. B., & Ghosh, S. (2024). Quantum-inspired particle swarm optimization for efficient IoT service placement in edge computing systems. Expert Systems with Applications, 236, 121270.
[82] Tuba, M., & Bacanin, N. (2015, May). Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning. In 2015 IEEE congress on evolutionary computation (CEC) (pp. 499-506). IEEE.
[83] Huo, L., & Wang, Z. (2016). Service composition instantiation based on cross-modified artificial Bee Colony algorithm. China Communications, 13(10), 233-244.
[84] Xu, X., Liu, Z., Wang, Z., Sheng, Q. Z., Yu, J., & Wang, X. (2017). S-ABC: A paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition. Future Generation Computer Systems, 68, 304-319.
[85] Khan, M., Din, S., Gohar, M., Ahmad, A., Cuomo, S., Piccialli, F., & Jeon, G. (2017). Enabling multimedia aware vertical handover Management in Internet of Things based heterogeneous wireless networks. Multimedia Tools and Applications, 76, 25919-25941.
[86] Muhammad, Z., Saxena, N., Qureshi, I. M., & Ahn, C. W. (2017). Hybrid artificial bee colony algorithm for an energy efficient internet of things based on wireless sensor network. IETE Technical Review, 34(sup1), 39-51.
[87] Praveen Kumar Reddy, M., & Rajasekhara Babu, M. (2017). Energy efficient cluster head selection for internet of things. New Review of Information Networking, 22(1), 54-70.
[88] Ahmad, A., Khan, M., Paul, A., Din, S., Rathore, M. M., Jeon, G., & Choi, G. S. (2018). Toward modeling and optimization of features selection in Big Data based social Internet of Things. Future Generation Computer Systems, 82, 715-726.
[89] Santhosh, G., & Prasad, K. V. (2023). Energy optimization routing for hierarchical cluster based WSN using artificial bee colony. Measurement: Sensors, 29, 100848.
[90] Moila, R. L., Velempini, M., & Madiba, M. S. (2020, September). Optimisation of cuckoo search algorithm to improve quality of service routing in cognitive radio ad hoc networks. In 2020 6th IEEE International Energy Conference (ENERGYCon) (pp. 951-957). IEEE.
[91] Sun, G., Liu, Y., Yang, M., Wang, A., Liang, S., & Zhang, Y. (2017). Coverage optimization of VLC in smart homes based on improved cuckoo search algorithm. Computer Networks, 116, 63-78.
[92] Kaur, M., Kaur, G., Sharma, P. K., Jolfaei, A., & Singh, D. (2020). Binary cuckoo search metaheuristic-based supercomputing framework for human behavior analysis in smart home. The Journal of Supercomputing, 76, 2479-2502.
[93] Liu, C., Wang, J., Zhou, L., & Rezaeipanah, A. (2022). Solving the multi-objective problem of IoT service placement in fog computing using cuckoo search algorithm. Neural Processing Letters, 54(3), 1823-1854.
[94] Hassan, M. Y., Najim, A. H., Al-Sharhanee, K. A., Kadhim, M. N., Soliman, N. F., & Algarni, A. D. (2024). A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things.
[95] Cui, Z., Cao, Y., Cai, X., Cai, J., & Chen, J. (2019). Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things. Journal of Parallel and Distributed Computing, 132, 217-229.
[96] Balamurugan, K., & Mahalakshmi, R. (2018). Mathematical analysis of BLDC motor parameter estimation using internet of things (IoT).
[97] Alharbi, A., Alosaimi, W., Alyami, H., Rauf, H. T., & Damaševičius, R. (2021). Botnet attack detection using local global best bat algorithm for industrial internet of things. Electronics, 10(11), 1341.
[98] Malek, M. R. A., Aziz, N. A. A., Alelyani, S., Mohana, M., Baharudin, F. N. A., & Ibrahim, Z. (2022). Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight. Journal of Building Engineering, 47, 103848.
[99] Jagadeesh, K., Kumar, A. S. P., Kumar, A., Srikar, J. P., Babu, P. S., & Aravinth, S. S. (2024). Efficient Path Planning for Wireless Sensor Networks: Minimizing Exposure with a Modified Bat Algorithm. In 2024 International Conference on Inventive Computation Technologies (ICICT) (pp. 1454-1459). IEEE.
[100] Anwar ul Hassan, C. H., Khan, M. S., Ghafar, A., Aimal, S., Asif, S., & Javaid, N. (2017). Energy optimization in smart grid using grey wolf optimization algorithm and bacterial foraging algorithm, in: Advances in Intelligent Networking and Collaborative Systems, Springer International Publishing, 166-177.
[101] Farshin, A., & Sharifian, S. (2017). A chaotic grey wolf controller allocator for Software Defined Mobile Network (SDMN) for 5th generation of cloud-based cellular systems (5G). Computer Communications, 108, 94-109.
[102] Ojha, A., & Chanak, P. (2021). Multiobjective gray-wolf-optimization-based data routing scheme for wireless sensor networks. IEEE Internet of Things Journal, 9(6), 4615-4623.
[103] Seyyedabbasi, A., Kiani, F., Allahviranloo, T., Fernandez-Gamiz, U., & Noeiaghdam, S. (2023). Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms. Alexandria Engineering Journal, 63, 339-357.
[104] Alqahtany, S. S., Shaikh, A., & Alqazzaz, A. (2025). Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks. Scientific Reports, 15(1), 1916.
[105] Reddy, P. K., & Babu, R. (2017). An Evolutionary Secure Energy Efficient Routing Protocol in Internet of Things. International Journal of Intelligent Engineering & Systems, 10(3).
[106] Khalid, A., Javaid, N., Guizani, M., Alhussein, M., Aurangzeb, K., & Ilahi, M. (2018). Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE access, 6, 19509-19529.
[107] Horng, G. J. (2015). The adaptive recommendation mechanism for distributed parking service in smart city. Wireless Personal Communications, 80, 395-413.
[108] Fadel, E., Faheem, M., Gungor, V. C., Nassef, L., Akkari, N., Malik, M. G. A., ... & Akyildiz, I. F. (2017). Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications. Computer Communications, 101, 106-120.
[109] Garofalo, G., Giordano, A., Piro, P., Spezzano, G., & Vinci, A. (2017). A distributed real-time approach for mitigating CSO and flooding in urban drainage systems. Journal of Network and Computer Applications, 78, 30-42.
[110] Reda, H. T., Mahmood, A., Diro, A., Chilamkurti, N., & Kallam, S. (2020). Firefly-inspired stochastic resonance for spectrum sensing in CR-based IoT communications. Neural Computing and Applications, 32(20), 16011-16023.
[111] Quoc, H. D., The, L. N., Doan, C. N., Thanh, T. P., & Xiong, N. N. (2020). Intelligent Differential Evolution Scheme for Network Resources in IoT. Scientific Programming, 2020(1), 8860384.
[112] Aadil, F., Ahsan, W., Rehman, Z. U., Shah, P. A., Rho, S., & Mehmood, I. (2018). Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). The Journal of Supercomputing, 74, 4542-4567.
[113] Abdel-Basset, M., Mohamed, R., Elhoseny, M., Bashir, A. K., Jolfaei, A., & Kumar, N. (2020). Energy-aware marine predators algorithm for task scheduling in IoT-based fog computing applications. IEEE Transactions on Industrial Informatics, 17(7), 5068-5076.
[114] Tripathi, A. K., Sharma, K., Bala, M., Kumar, A., Menon, V. G., & Bashir, A. K. (2020). A parallel military-dog-based algorithm for clustering big data in cognitive industrial internet of things. IEEE Transactions on Industrial Informatics, 17(3), 2134-2142.
[115] Mohamed, A., Saber, W., Elnahry, I., & Hassanien, A. E. (2020). Coyote optimization based on a fuzzy logic algorithm for energy-efficiency in wireless sensor networks. IEEE Access, 8, 185816-185829.
[116] Iwendi, C., Maddikunta, P. K. R., Gadekallu, T. R., Lakshmanna, K., Bashir, A. K., & Piran, M. J. (2021). A metaheuristic optimization approach for energy efficiency in the IoT networks. Software: Practice and Experience, 51(12), 2558-2571.
[117] Krishna, E. P., & Thangavelu, A. (2021). Attack detection in IoT devices using hybrid metaheuristic lion optimization algorithm and firefly optimization algorithm. International Journal of System Assurance Engineering and Management, 1-14.
[118] Mir, M., Yaghoobi, M., & Khairabadi, M. (2023). A new approach to energy-aware routing in the Internet of Things using improved Grasshopper Metaheuristic Algorithm with Chaos theory and Fuzzy Logic. Multimedia Tools and Applications, 82(4), 5133-5159.
[119] Yang, X. S., Lees, J. M., & Morley, C. T. (2006). Application of virtual ant algorithms in the optimization of cfrp shear strengthened precracked structures. In International Conference on Computational Science (pp. 834-837). Berlin, Heidelberg: Springer Berlin Heidelberg.
[120] Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.
[1] Shah, S. H., & Yaqoob, I. (2016). A survey: Internet of Things (IOT) technologies, applications and challenges. 2016 IEEE Smart Energy Grid Engineering (SEGE), 381-385.
[2] Alaba, F. A., Othman, M., Hashem, I. A. T., & Alotaibi, F. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications, 88, 10-28.
[3] Mukadam, Z., & Logeswaran, R. (2020). A cloud-based smart parking system based on IoT technologies. Journal of critical reviews, 7(3), 105-109.
[4] Ghayvat, H., Mukhopadhyay, S., Gui, X., & Suryadevara, N. (2015). WSN-and IOT-based smart homes and their extension to smart buildings. sensors, 15(5), 10350-10379.
[5] Ali, U., & Calis, C. (2019, October). Centralized smart governance framework based on iot smart city using ttg-classified technique. In 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT) (pp. 157-160). IEEE.
[6] Umair, M., Cheema, M. A., Cheema, O., Li, H., & Lu, H. (2021). Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Sensors, 21(11), 3838.
[7] Meng, Z., Li, G., Wang, X., Sait, S. M., & Yıldız, A. R. (2021). A comparative study of metaheuristic algorithms for reliability-based design optimization problems. Archives of Computational Methods in Engineering, 28, 1853-1869.
[8] Donoso, Y., & Fabregat, R. (2016). Multi-objective optimization in computer networks using metaheuristics. Auerbach Publications.
[9] Soler-Dominguez, A., Juan, A. A., & Kizys, R. (2017). A survey on financial applications of metaheuristics. ACM Computing Surveys (CSUR), 50(1), 1-23.
[10] Lessmann, S., Caserta, M., & Arango, I. M. (2011). Tuning metaheuristics: A data mining based approach for particle swarm optimization. Expert Systems with Applications, 38(10), 12826-12838.
[11] Nakib, A., & Talbi, E. G. (Eds.). (2017). Metaheuristics for medicine and biology (Vol. 704). Berlin Heidelberg: Springer.
[12] Zedadra, O., Guerrieri, A., Jouandeau, N., Spezzano, G., Seridi, H., & Fortino, G. (2018). Swarm intelligence-based algorithms within IoT-based systems: A review. Journal of Parallel and Distributed Computing, 122, 173-187.
[13] Villa-Henriksen, A., Edwards, G. T., Pesonen, L. A., Green, O., & Sørensen, C. A. G. (2020). Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems engineering, 191, 60-84.
[14] Marietta, J., & Chandra Mohan, B. (2020). A review on routing in internet of things. Wireless Personal Communications, 111(1), 209-233.
[15] M Singh, M., & Baranwal, G. (2018). Quality of service (qos) in internet of things. In 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU) (pp. 1-6). IEEE.
[16] Alaa, M., Zaidan, A. A., Zaidan, B. B., Talal, M., & Kiah, M. L. M. (2017). A review of smart home applications based on Internet of Things. Journal of network and computer applications, 97, 48-65.
[17] Priyan, M. K., & Devi, G. U. (2019). A survey on internet of vehicles: applications, technologies, challenges and opportunities. International Journal of Advanced Intelligence Paradigms, 12(1-2), 98-119.
[18] Kalarthi, Z. M. (2016). A review paper on smart health care system using internet of things. International Journal of Research in Engineering and Technology, 5(03), 8084.
[19] Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer networks, 151, 211-223.
[20] Sakthivel, S., &Vidhya, G. (2021). A Trust-Based Access Control Mechanism for Intra-Sensor Network Communication in Internet of Things.Arabian Journal for Science and Engineering, 46(4), 3147-3153.
[21] Sahlmann, K., Scheffler, T., &Schnor, B. (2017). Managing IoT device capabilities based on oneM2M ontology descriptions. Proceedings of the 16. GI/ITG KuVSFachgesprächSensornetze, ser. Technical Reports. Hamburg, Germany: HAW Hamburg, 23-26.
[22] Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017). A survey of intrusion detection in Internet of Things.Journal of Network and Computer Applications, 84, 25-37.
[23] Zhang, K., Liang, X., Lu, R., & Shen, X. (2014). Sybil attacks and their defenses in the internet of things.IEEE Internet of Things Journal, 1(5), 372-383.
[24] Reddy, M. P. K., & Babu, M. R. (2019). Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things. Cluster Computing, 22, 1361-1372.
[25] Torre, I., Adorni, G., Koceva, F., & Sanchez, O. (2016). Preventing disclosure of personal data in IoT networks. In 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 389-396). IEEE.
[26] Bangui, H., Ge, M., & Buhnova, B. (2018). Exploring Big Data Clustering Algorithms for Internet of Things Applications.In IoTBDS (pp. 269-276).
[27] Sun, G., Li, J., Dai, J., Song, Z., & Lang, F. (2018). Feature selection for IoT based on maximal information coefficient. Future Generation Computer Systems, 89, 606-616.
[28] Yuan, J., & Li, X. (2018). A reliable and lightweight trust computing mechanism for IoT edge devices based on multi-source feedback information fusion. Ieee Access, 6, 23626-23638.
[29] Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & operations research, 13(5), 533-549.
[30] Yang, X. S. (2020). Nature-inspired optimization algorithms: Challenges and open problems. Journal of Computational Science, 46, 101104.
[31] Bozorg-Haddad, O. (2018). Advanced optimization by nature-inspired algorithms (Vol. 720). Singapore: Springer.
[32] Fausto, F., Reyna-Orta, A., Cuevas, E., Andrade, Á. G., & Perez-Cisneros, M. (2020). From ants to whales: metaheuristics for all tastes. Artificial Intelligence Review, 53, 753-810.
[33] Kaveh, A., & Bakhshpoori, T. (2019). Metaheuristics: Outlines, MATLAB Codes and Examples.doi:10.1007/978-3-030-04067-3
[34] Rani, R., Jain, S., & Garg, H. (2024). A review of nature-inspired algorithms on single-objective optimization problems from 2019 to 2023. Artificial Intelligence Review, 57(5), 1-51.
[35] Vasuki, A. (2020). Nature-inspired optimization algorithms. CRC Press
[36] Yang, X. S. (2020). Nature-inspired optimization algorithms: Challenges and open problems. Journal of Computational Science, 46, 101104.
[37] M Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano.
[38] Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee.
[39] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.
[40] Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.
[41] Yang, X. (2010). S.: A New Metaheuristic Bat-Insspired Algorithm. Nature Inspired Cooperative Strategies for Optimization. Studies in Computational Intelligence (Springer). pp-65-74.
[42] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer Advances in Engineering Software. 69 46–61.
[43] Wang, Y., Geng, X., Zhang, F., & Ruan, J. (2018). An immune genetic algorithm for multi-echelon inventory cost control of IOT based supply chains. IEEE Access, 6, 8547-8555.
[44] Zhang, Y., Li, P., & Wang, X. (2019). Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access, 7, 31711-31722.
[45] Rani, S., Ahmed, S. H., & Rastogi, R. (2020). Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications. Wireless Networks, 26(4), 2307-2316.
[46] Kashyap, N., Kumari, A. C., & Chhikara, R. (2020). Multi-objective Optimization using NSGA II for service composition in IoT. Procedia Computer Science, 167, 1928-1933.
[47] Abbas, S., Javaid, N., Almogren, A., Gulfam, S. M., Ahmed, A., & Radwan, A. (2021). Securing genetic algorithm enabled SDN routing for blockchain based Internet of Things. IEEE Access, 9, 139739-139754.
[48] Khadir, K., Guermouche, N., Guittoum, A., & Monteil, T. (2022). A genetic algorithm-based approach for fluctuating QoS aware selection of IoT services. IEEE Access, 10, 17946-17965.
[49] Lu, Q., Nguyen, K., & Huang, C. (2023). Distributed parallel algorithms for online virtual network embedding applications. International Journal of Communication Systems, 36(1), e4325.
[50] Saiyed, M. F., & Al-Anbagi, I. (2024). A Genetic Algorithm-and t-Test-Based System for DDoS Attack Detection in IoT Networks. IEEE Access, 12, 25623-25641.
[51] Cosido, O., Loucera, C., & Iglesias, A. (2013). Automatic calculation of bicycle routes by combining meta-heuristics and GIS techniques within the framework of smart cities. In 2013 International conference on new concepts in smart cities: Fostering public and private alliances (SMARTMILE) (pp. 1-6). IEEE.
[52] Ebrahimi, M., Shafieibavani, E., Wong, R. K., & Chi, C. H. (2015, June). A new meta-heuristic approach for efficient search in the internet of things. In 2015 IEEE International Conference on Services Computing (pp. 264-270). IEEE.
[53] Yue, S., & Yi, S. (2016). Study of logistics distribution route based on improved genetic algorithm and ant colony optimization algorithm. Internet of Things (IoT) and Engineering Applications, 1(1), 11-17.
[54] Suryani, V., Sulistyo, S., & Widyawan, W. (2016). Trust-based privacy for Internet of Things. International Journal of Electrical and Computer Engineering, 6(5), 2396.
[55] Sabbani, I., Youssfi, M., & Bouattane, O. (2016, September). A multi-agent based on ant colony model for urban traffic management. In 2016 5th International Conference on Multimedia Computing and Systems (ICMCS) (pp. 793-798). IEEE.
[56] Kowshalya, A. M., & Valarmathi, M. L. (2016). Detection of sybil’s across communities over social internet of things. Journal of Applied Engineering Science, 14(1).
[57] Jiang, Y., Ding, Q., & Wang, X. (2016). A recovery model for production scheduling: combination of disruption management and Internet of Things. Scientific Programming, 2016(1), 8264879.
[58] López-Matencio, P., Vales-Alonso, J., & Costa-Montenegro, E. (2017). ANT: Agent Stigmergy‐Based IoT‐Network for Enhanced Tourist Mobility. Mobile Information Systems, 2017(1), 1328127.
[59] Said, O. (2017). Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization. International Journal of Communication Systems, 30(8), e3174.
[60] Mahalaxmi, G., & Rajakumari, K. E. (2017). Multi-agent technology to improve the internet of things routing algorithm using ant colony optimization. Indian Journal of Science & Technology, 10(31), 1-8.
[61] Bellini, V., Di Noia, T., Mongiello, M., Nocera, F., Parchitelli, A., & Di Sciascio, E. (2018). Reflective internet of things middleware-enabled a predictive real-time waste monitoring system. In Web Engineering: 18th International Conference, ICWE 2018, Cáceres, Spain, June 5-8, 2018, Proceedings 18 (pp. 375-383). Springer International Publishing.
[62] Oralhan, Z., Oralhan, B., & Yigit, Y. (2017). Smart city application: internet of things (IoT) technologies based smart waste collection using data mining approach and ant colony optimization. Int. Arab J. Inf. Technol., 14(4), 423-427.
[63] Hong, Y., Chen, L., & Mo, L. (2019). Optimization of cluster resource indexing of Internet of Things based on improved ant colony algorithm. Cluster Computing, 22, 7379-7387.
[64] Thapar, P., & Batra, U. (2018). Implementation of ant colony optimization in routing protocol for internet of things. In Innovations in Computational Intelligence: Best Selected Papers of the Third International Conference on REDSET 2016 (pp. 151-164). Springer Singapore.
[65] Ebadinezhad, S., Dereboylu, Z., & Ever, E. (2019). Clustering-based modified ant colony optimizer for internet of vehicles (CACOIOV). Sustainability, 11(9), 2624.
[66] Azizou, A. Z., Boudries, A., & Amad, M. (2020). Decentralized service discovery and localization in Internet of Things applications based on ant colony algorithm. International Journal of Computing and Digital Systems, 9(5), 941-950
[67] Shi, B., & Zhang, Y. (2021). A novel algorithm to optimize the energy consumption using IoT and based on Ant Colony Algorithm. Energies, 14(6), 1709.
[68] Sung, W. T., & Chiang, Y. C. (2012). Improved particle swarm optimization algorithm for android medical care IOT using modified parameters. Journal of medical systems, 36, 3755-3763.
[69] Sung, W. T., & Hsu, C. C. (2013). IOT system environmental monitoring using IPSO weight factor estimation. Sensor Review, 33(3), 246-256.
[70] Tsai, C. W., Lai, C. F., & Vasilakos, A. V. (2014). Future internet of things: open issues and challenges. Wireless Networks, 20, 2201-2217.
[71] Luo, S., Cheng, L., & Ren, B. (2014). Practical swarm optimization based fault-tolerance algorithm for the internet of things. KSII Transactions on Internet and Information Systems (TIIS), 8(3), 735-748.
[72] Hurtado, L. A., Nguyen, P. H., & Kling, W. L. (2015). Smart grid and smart building inter-operation using agent-based particle swarm optimization. Sustainable Energy, Grids and Networks, 2, 32-40.
[73] Fang, C., Liu, X., Pardalos, P. M., & Pei, J. (2016). Optimization for a three-stage production system in the Internet of Things: procurement, production and product recovery, and acquisition. The International Journal of Advanced Manufacturing Technology, 83, 689-710.
[74] Jinyi, W., Qin, Y., Shrestha, A. P., & Yoo, S. J. (2017, October). Optimization of cognitive radio secondary base station positioning and operating channel selection for IoT sensor networks. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 397-399). IEEE.
[75] Kumrai, T., Ota, K., Dong, M., Kishigami, J., & Sung, D. K. (2016). Multiobjective optimization in cloud brokering systems for connected Internet of Things. IEEE Internet of Things Journal, 4(2), 404-413.
[76] Dai, S., Liwang, M., Liu, Y., Gao, Z., Huang, L., & Du, X. (2018). Hybrid quantum-behaved particle swarm optimization for mobile-edge computation offloading in internet of things. In Mobile Ad-hoc and Sensor Networks: 13th International Conference, MSN 2017, Beijing, China, December 17-20, 2017, Revised Selected Papers 13 (pp. 350-364). Springer Singapore.
[77] Sangeetha, A. L., Bharathi, N., Ganesh, A. B., & Radhakrishnan, T. K. (2018). Particle swarm optimization tuned cascade control system in an Internet of Things (IoT) environment. Measurement, 117, 80-89.
[78] Kashyap, N., Kumari, A. C., & Chhikara, R. (2020). Service Composition in IoT using Genetic algorithm and Particle swarm optimization. Open Computer Science, 10(1), 56-64.
[79] Liu, J., Yang, D., Lian, M., & Li, M. (2021). Research on intrusion detection based on particle swarm optimization in IoT. IEEE Access, 9, 38254-38268.
[80] Subramani, S., & Selvi, M. (2023). Multi-objective PSO based feature selection for intrusion detection in IoT based wireless sensor networks. Optik, 273, 170419.
[81] Bey, M., Kuila, P., Naik, B. B., & Ghosh, S. (2024). Quantum-inspired particle swarm optimization for efficient IoT service placement in edge computing systems. Expert Systems with Applications, 236, 121270.
[82] Tuba, M., & Bacanin, N. (2015, May). Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning. In 2015 IEEE congress on evolutionary computation (CEC) (pp. 499-506). IEEE.
[83] Huo, L., & Wang, Z. (2016). Service composition instantiation based on cross-modified artificial Bee Colony algorithm. China Communications, 13(10), 233-244.
[84] Xu, X., Liu, Z., Wang, Z., Sheng, Q. Z., Yu, J., & Wang, X. (2017). S-ABC: A paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition. Future Generation Computer Systems, 68, 304-319.
[85] Khan, M., Din, S., Gohar, M., Ahmad, A., Cuomo, S., Piccialli, F., & Jeon, G. (2017). Enabling multimedia aware vertical handover Management in Internet of Things based heterogeneous wireless networks. Multimedia Tools and Applications, 76, 25919-25941.
[86] Muhammad, Z., Saxena, N., Qureshi, I. M., & Ahn, C. W. (2017). Hybrid artificial bee colony algorithm for an energy efficient internet of things based on wireless sensor network. IETE Technical Review, 34(sup1), 39-51.
[87] Praveen Kumar Reddy, M., & Rajasekhara Babu, M. (2017). Energy efficient cluster head selection for internet of things. New Review of Information Networking, 22(1), 54-70.
[88] Ahmad, A., Khan, M., Paul, A., Din, S., Rathore, M. M., Jeon, G., & Choi, G. S. (2018). Toward modeling and optimization of features selection in Big Data based social Internet of Things. Future Generation Computer Systems, 82, 715-726.
[89] Santhosh, G., & Prasad, K. V. (2023). Energy optimization routing for hierarchical cluster based WSN using artificial bee colony. Measurement: Sensors, 29, 100848.
[90] Moila, R. L., Velempini, M., & Madiba, M. S. (2020, September). Optimisation of cuckoo search algorithm to improve quality of service routing in cognitive radio ad hoc networks. In 2020 6th IEEE International Energy Conference (ENERGYCon) (pp. 951-957). IEEE.
[91] Sun, G., Liu, Y., Yang, M., Wang, A., Liang, S., & Zhang, Y. (2017). Coverage optimization of VLC in smart homes based on improved cuckoo search algorithm. Computer Networks, 116, 63-78.
[92] Kaur, M., Kaur, G., Sharma, P. K., Jolfaei, A., & Singh, D. (2020). Binary cuckoo search metaheuristic-based supercomputing framework for human behavior analysis in smart home. The Journal of Supercomputing, 76, 2479-2502.
[93] Liu, C., Wang, J., Zhou, L., & Rezaeipanah, A. (2022). Solving the multi-objective problem of IoT service placement in fog computing using cuckoo search algorithm. Neural Processing Letters, 54(3), 1823-1854.
[94] Hassan, M. Y., Najim, A. H., Al-Sharhanee, K. A., Kadhim, M. N., Soliman, N. F., & Algarni, A. D. (2024). A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things.
[95] Cui, Z., Cao, Y., Cai, X., Cai, J., & Chen, J. (2019). Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things. Journal of Parallel and Distributed Computing, 132, 217-229.
[96] Balamurugan, K., & Mahalakshmi, R. (2018). Mathematical analysis of BLDC motor parameter estimation using internet of things (IoT).
[97] Alharbi, A., Alosaimi, W., Alyami, H., Rauf, H. T., & Damaševičius, R. (2021). Botnet attack detection using local global best bat algorithm for industrial internet of things. Electronics, 10(11), 1341.
[98] Malek, M. R. A., Aziz, N. A. A., Alelyani, S., Mohana, M., Baharudin, F. N. A., & Ibrahim, Z. (2022). Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight. Journal of Building Engineering, 47, 103848.
[99] Jagadeesh, K., Kumar, A. S. P., Kumar, A., Srikar, J. P., Babu, P. S., & Aravinth, S. S. (2024). Efficient Path Planning for Wireless Sensor Networks: Minimizing Exposure with a Modified Bat Algorithm. In 2024 International Conference on Inventive Computation Technologies (ICICT) (pp. 1454-1459). IEEE.
[100] Anwar ul Hassan, C. H., Khan, M. S., Ghafar, A., Aimal, S., Asif, S., & Javaid, N. (2017). Energy optimization in smart grid using grey wolf optimization algorithm and bacterial foraging algorithm, in: Advances in Intelligent Networking and Collaborative Systems, Springer International Publishing, 166-177.
[101] Farshin, A., & Sharifian, S. (2017). A chaotic grey wolf controller allocator for Software Defined Mobile Network (SDMN) for 5th generation of cloud-based cellular systems (5G). Computer Communications, 108, 94-109.
[102] Ojha, A., & Chanak, P. (2021). Multiobjective gray-wolf-optimization-based data routing scheme for wireless sensor networks. IEEE Internet of Things Journal, 9(6), 4615-4623.
[103] Seyyedabbasi, A., Kiani, F., Allahviranloo, T., Fernandez-Gamiz, U., & Noeiaghdam, S. (2023). Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms. Alexandria Engineering Journal, 63, 339-357.
[104] Alqahtany, S. S., Shaikh, A., & Alqazzaz, A. (2025). Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks. Scientific Reports, 15(1), 1916.
[105] Reddy, P. K., & Babu, R. (2017). An Evolutionary Secure Energy Efficient Routing Protocol in Internet of Things. International Journal of Intelligent Engineering & Systems, 10(3).
[106] Khalid, A., Javaid, N., Guizani, M., Alhussein, M., Aurangzeb, K., & Ilahi, M. (2018). Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE access, 6, 19509-19529.
[107] Horng, G. J. (2015). The adaptive recommendation mechanism for distributed parking service in smart city. Wireless Personal Communications, 80, 395-413.
[108] Fadel, E., Faheem, M., Gungor, V. C., Nassef, L., Akkari, N., Malik, M. G. A., ... & Akyildiz, I. F. (2017). Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications. Computer Communications, 101, 106-120.
[109] Garofalo, G., Giordano, A., Piro, P., Spezzano, G., & Vinci, A. (2017). A distributed real-time approach for mitigating CSO and flooding in urban drainage systems. Journal of Network and Computer Applications, 78, 30-42.
[110] Reda, H. T., Mahmood, A., Diro, A., Chilamkurti, N., & Kallam, S. (2020). Firefly-inspired stochastic resonance for spectrum sensing in CR-based IoT communications. Neural Computing and Applications, 32(20), 16011-16023.
[111] Quoc, H. D., The, L. N., Doan, C. N., Thanh, T. P., & Xiong, N. N. (2020). Intelligent Differential Evolution Scheme for Network Resources in IoT. Scientific Programming, 2020(1), 8860384.
[112] Aadil, F., Ahsan, W., Rehman, Z. U., Shah, P. A., Rho, S., & Mehmood, I. (2018). Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). The Journal of Supercomputing, 74, 4542-4567.
[113] Abdel-Basset, M., Mohamed, R., Elhoseny, M., Bashir, A. K., Jolfaei, A., & Kumar, N. (2020). Energy-aware marine predators algorithm for task scheduling in IoT-based fog computing applications. IEEE Transactions on Industrial Informatics, 17(7), 5068-5076.
[114] Tripathi, A. K., Sharma, K., Bala, M., Kumar, A., Menon, V. G., & Bashir, A. K. (2020). A parallel military-dog-based algorithm for clustering big data in cognitive industrial internet of things. IEEE Transactions on Industrial Informatics, 17(3), 2134-2142.
[115] Mohamed, A., Saber, W., Elnahry, I., & Hassanien, A. E. (2020). Coyote optimization based on a fuzzy logic algorithm for energy-efficiency in wireless sensor networks. IEEE Access, 8, 185816-185829.
[116] Iwendi, C., Maddikunta, P. K. R., Gadekallu, T. R., Lakshmanna, K., Bashir, A. K., & Piran, M. J. (2021). A metaheuristic optimization approach for energy efficiency in the IoT networks. Software: Practice and Experience, 51(12), 2558-2571.
[117] Krishna, E. P., & Thangavelu, A. (2021). Attack detection in IoT devices using hybrid metaheuristic lion optimization algorithm and firefly optimization algorithm. International Journal of System Assurance Engineering and Management, 1-14.
[118] Mir, M., Yaghoobi, M., & Khairabadi, M. (2023). A new approach to energy-aware routing in the Internet of Things using improved Grasshopper Metaheuristic Algorithm with Chaos theory and Fuzzy Logic. Multimedia Tools and Applications, 82(4), 5133-5159.
[119] Yang, X. S., Lees, J. M., & Morley, C. T. (2006). Application of virtual ant algorithms in the optimization of cfrp shear strengthened precracked structures. In International Conference on Computational Science (pp. 834-837). Berlin, Heidelberg: Springer Berlin Heidelberg.
[120] Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.
