Informations générales
Doctorant
PhD supervisor : Nadia Ben Azzouna | SMARTLab
Holder of a computer engineering degree obtained in 2001 from ENSI (Tunisia). Computer technologist at the Higher Institute of Technological Studies of Zaghouan and member of the SMART Laboratory (Tunisia) since 2022. His current research focuses on energy optimization in the Internet of Things (IoT).
Axes de recherche
Publications
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2025Hamdi Ouechtati, Nadia Ben Azzouna
Multi-objective clustering and dynamic multi-hop routing in an IoT network based on Pareto optimality
Papier conf, 2025
Résumé
Routing is essential in computer networks as it
directly impacts performance metrics such as throughput and
transmission delay. Particularly in an Internet of Things (IoT)
network where the nodes are limited in energy (battery power)
and the radio component is high energy-intensive. We are always
looking to optimise this procedure in order to increase the network
lifetime. In this work, we present a multi-objective, multihop
routing solution that extends network lifetime while balancing
multiple objectives, such as energy consumption, delay, and
reliability. Our solution was compared with other approaches,
such as Energy Optimization Routing (EOR) and Multi-Objective
Optimization Routing (MOWR) based on the Weighted Sum
technique, demonstrating significant improvements.Nadia Ben Azzouna, Hamdi OuechtatiMulti-Objective Clustering and Reinforcement-based Routing in IoT Networks
Papier journal, 2025
Résumé
The rapid development of devices on the Internet of Things
(IoT) and the diversity of their applications have made them
ubiquitous. However, deploying these devices in large-scale
networks presents several challenges, including limited energy
capacity, security concerns, unreliable links, and transmission
delays. This paper, proposes a multi-objective optimization
approach for wireless IoT networks based on machine learning
techniques. Specifically, a clustering scheme is developd by
using an improved k-means algorithm. This is combined
with a dynamic routing strategy based on multi-objective
Q-learning using parallel Q-tables. This approach leads to
measurable gains in energy efficiency, transmission latency,
and reliability. Compared to existing approaches in similar
contexts, such as weighted sum, the proposed solution achieves
significant improvements in overall network performance. -
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