Informations générales
Grade
Doctorant
Biographie courte
Titulaire d’un diplôme d’ingénieur en informatique obtenu en 2001 à l’ENSI (Tunisie). Technologue informatique à l’Institut supérieur d’études technologiques de Zaghouan et membre du laboratoire SMART (Tunisie) depuis 2022. Ses recherches actuelles portent sur l’optimisation de l’énergie dans l’Internet des objets.
Axes de recherche
Publications
-
2024Moez Elarfaoui, Nadia Ben Azzouna
CLUSTERING BASED ON HYBRIDIZATION OF GENETIC ALGORITHM AND IMPROVED K-MEANS (GA-IKM) IN AN IOT NETWORK
International Journal of Wireless & Mobile Networks (IJWMN), Vol.16, No.6, December 2024, 2024
Résumé
The continuous development of Internet infrastructures and the evolution of digital electronics, particularly Nano-computers, are making the Internet of Things (IoT) emergent. Despite the progress, these IoT objects suffer from a crucial problem which is their limited power supply. IoT objects are often deployed as an ad-hoc network. To minimize their consumption of electrical energy, clustering techniques are used. In this paper, a centralized clustering algorithm with single-hop routing based on a genetic algorithm and Improved k-means is proposed. The proposed approach is compared with the LEACH, K-means and OK-means algorithms. Simulation results show that the proposed algorithm performs well in terms of network lifetime and energy consumption.
BibTeX
- Rani, S., Ahmed, S.H. & Rastogi, R. Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications. Wireless Netw26, 2307–2316 (2020).
- HUSSAIN, Muhammad Zunnurain et HANAPI, Zurina Mohd. Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review. Electronics, 2023, vol. 12, no 3, p. 482.
- Abdulzahra, Ali Mohammed Kadhim, Ali Kadhum M. Al-Qurabat, and Suha Abdulhussein Abdulzahra. « Optimizing energy consumption in WSN-based IoT using unequal clustering and sleep scheduling methods. » Internet of Things 22 (2023): 100765.
- DEL-VALLE-SOTO, Carolina, RODRÍGUEZ, Alma, et ASCENCIO-PIÑA, Cesar Rodolfo. A survey of energy-efficient clustering routing protocols for wireless sensor networks based on metaheuristic approaches. Artificial Intelligence Review, 2023, p. 1-72.
- Del-Valle-Soto, C., Rodríguez, A. & Ascencio-Piña, C.R. A survey of energy-efficient clustering routing protocols for wireless sensor networks based on metaheuristic approaches. Artif Intell Rev 56, 9699–9770 (2023). https://doi.org/10.1007/s10462-023-10402-w.
- Yuste-Delgado, Antonio-Jesus, Juan-Carlos Cuevas-Martinez, and Alicia Triviño-Cabrera. « A distributed clustering algorithm guided by the base station to extend the lifetime of wireless sensor networks. » Sensors 20.8 (2020): 2312.
- Shahraki, Amin, et al. « A survey and future directions on clustering: From WSNs to IoT and modern networking paradigms. » IEEE Transactions on Network and Service Management 18.2 (2020): 2242-2274.
- Wohwe Sambo, Damien, et al. « Optimized clustering algorithms for large wireless sensor networks: A review. » Sensors 19.2 (2019): 322.
- 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.
- Rostami, Ali Shokouhi, et al. Survey on clustering in heterogeneous and homogeneous wireless sensor networks. The Journal of Supercomputing 74 (2018): 277-323.
- MERAH, Malha, ALIOUAT, Zibouda, HARBI, Yasmine, et al. Machine learning‐based clustering protocols for Internet of Things networks: An overview. International Journal of Communication Systems, 2023, p. e5487.
- Singh, Santar Pal, and S. C. Sharma. « Genetic-algorithm-based energy-efficient clustering (GAEEC) for homogenous wireless sensor networks. » IETE journal of research 64.5 (2018): 648-659.
- Heidari, Ehsan, et al. A novel approach for clustering and routing in WSN using genetic algorithm and equilibrium optimizer. International Journal of Communication Systems 35.10 (2022): e5148.
- Razzaq, Madiha, Devarani Devi Ningombam, and Seokjoo Shin. Energy efficient K-means clustering-based routing protocol for WSN using optimal packet size. 2018 International Conference on Information Networking (ICOIN). IEEE, 2018.
- El Khediri, Salim, et al. « Improved node localization using K-means clustering for Wireless Sensor Networks. » Computer Science Review 37 (2020): 100284.
- NEDHAM, Wisal Bassim et AL-QURABAT, Ali Kadhum M. An improved energy efficient clustering protocol for wireless sensor networks. In: 2022 International Conference for Natural and Applied Sciences (ICNAS). IEEE, 2022. p. 23-28.
- Ahmad, Waseem, et al. « Optimizing Energy Efficiency in Wireless Sensor Networks using Enhanced K-Means Cluster Head Selection. » International Journal of Communication Networks and Information Security 16.3 (2024): 565-573.
- Bhushan, Shashi, Raju Pal, and Svetlana G. Antoshchuk. « Energy efficient clustering protocol for heterogeneous wireless sensor network: a hybrid approach using GA and K-means. » 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018.
- Bhola, Jyoti, Surender Soni, and Gagandeep Kaur Cheema. « Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. » Journal of Ambient Intelligence and Humanized Computing 11 (2020): 1281-1288.
- Hassan, A. A. H., et al. « Clustering approach in wireless sensor networks based on K-means: Limitations and recommendations. » Int. J. Recent Technol. Eng 7.6 (2019): 119-126.
- Obeid, Abdulfattah Mohammad, et al. « A survey on efficient power consumption in adaptive wireless sensor networks. » Wireless Personal Communications 101 (2018): 101-117.
- Singh, Jaspreet, Ranjit Kaur, and Damanpreet Singh. « A survey and taxonomy on energy management schemes in wireless sensor networks. » Journal of Systems Architecture 111 (2020): 101782.
- SINGH, Shashank et ANAND, Veena. Load balancing clustering and routing for IoT‐enabled wireless sensor networks. International Journal of Network Management, 2023, vol. 33, no 5, p. e2244.
- Ray, Anindita, and Debashis De. « Energy efficient clustering protocol based on K‐means (EECPK‐means)‐midpoint algorithm for enhanced network lifetime in wireless sensor network. » IET Wireless Sensor Systems 6.6 (2016): 181-191.
- Marutho, Dhendra, Sunarna Hendra Handaka, and Ekaprana Wijaya. « The determination of cluster number at k-mean using elbow method and purity evaluation on headline news. » 2018 international seminar on application for technology of information and communication. IEEE, 2018.
- Kodinariya, Trupti M., and Prashant R. Makwana. « Review on determining number of Cluster in K-Means Clustering. » International Journal 1.6 (2013): 90-95.
- Kramer, Oliver, and Oliver Kramer. Genetic algorithms. Springer International Publishing, 2017.
- Seo, Hyun-Sik, Se-Jin Oh, and Chae-Woo Lee. « Evolutionary genetic algorithm for efficient clustering of wireless sensor networks. » 2009 6th IEEE Consumer Communications and Networking Conference. IEEE, 2009.
- Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.
- BAQERI, Javad. Increase the Lifetime of Wireless Sensor Networks Using Hierarchical Clustering with Cluster Topology Preservation. International Journal of Wireless & Mobile Networks (IJWMN) Vol, 2021, vol. 8.
- RAJ, Bryan, AHMEDY, Ismail, IDRIS, Mohd Yamani Idna, et al. A survey on cluster head selection and cluster formation methods in wireless sensor networks. Wireless Communications and Mobile Computing, 2022, vol. 2022, p. 1-53.
- AL-SULAIFANIE, Adnan Ismail, AL-SULAIFANIE, Bayez Khorsheed, et BISWAS, Subir. Recent trends in clustering algorithms for wireless sensor networks: A comprehensive review. Computer Communications, 2022, vol. 191, p. 395-424.
- DEL-VALLE-SOTO, Carolina, RODRÍGUEZ, Alma, et ASCENCIO-PIÑA, Cesar Rodolfo. A survey of energy-efficient clustering routing protocols for wireless sensor networks based on metaheuristic approaches. Artificial Intelligence Review, 2023, p. 1-72.
- ROY, Nihar Ranjan et CHANDRA, Pravin. Energy dissipation model for wireless sensor networks: a survey. International Journal of Information Technology, 2020, vol. 12, p. 1343-1353.
- Gülbaş, Gülşah, and Gürcan Çetin. « Lifetime Optimization of the LEACH Protocol in WSNs with Simulated Annealing Algorithm. » Wireless Personal Communications4 (2023): 2857-2883.
- Alhijawi, B., Awajan, A. Algorithmes génétiques : théorie, opérateurs génétiques, solutions et applications. Évol. Intel. (2023). https://doi.org/10.1007/s12065-023-00822-6