№1, 2024

Haroon Rashid Hammood Al Dallal

Throughout the development of energy efficient routing protocol for wireless sensor network clustering technique has been widely adopted approach. The Selection of cluster heads is also very important for the energy efficiency of the network. In the past, researchers have proposed multiple routing protocols; however, problems are still alive and need to be resolved because of the diversity of WSN applications. This article presents an energy-efficient routing protocol using the advanced approaches of Artificial Intelligence, the most promising field of computer science currently providing the best solutions. The proposed model uses the Deep Q-network to select the cluster head. Moreover, collected data at the cluster head is generalized as low, moderate, and high values using the fuzzy logic technique. After that, the Predictive coding theory algorithm is used for the data compression, and the lossy compression technique is applied to the data. Its compressed form also gives complete information of the data in small size and is delivered to the base station. Again, the transmitted data is reconstructed into its actual format. In the end, to justify the performance of the newly designed routing protocol, simulations are performed using the Matlab tool, and its results are evaluated in quality of service matrices and compared with well-known routing protocols (pp.18-24).

Keywords:Wireless sensor networks, Quality of service, Energy efficiency, Data compression, Predictive coding theory, Matlab tool
  • Amruta Lipare, Damodar Reddy Edla. (2019). Cluster Head Selection and Cluster Construction using Fuzzy Logic in WSNs. IEEE
  • Chunyao, FU, Zhifang JIANG, Wei WEI and Ang WEI. (2013). An Energy Balanced Algorithm of LEACH Protocol in WSN. IJCSI International Journal of Computer Science Issues, 10(1), 1694-0814 www.IJCSI.org
  • Chen Chen, Limao Zhan, Robert Lee Kong Tiong. (2020). A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding. Wireless Networks. Springers. https://doi.org/10.1007/s11276-020-02425-w.
  • Dionisis, Kandris, Christos Nakas, Dimitrios Vomvas and Grigorios Koulouras (2020). A pplications ofWireless Sensor Networks: An Up-to-Date Survey. Appl. Syst. Innov., 3(1), 14; doi:10.3390/asi3010014
  • Dheyab Salman Ibrahim, Abdullah Farhan Mahdi, Qahtan M. Yas. Challenges and Issues for Wireless Sensor Networks: A Survey (2021). Journal of Global Scientific Research (ISSN: 2523-9376), 6(1), 1079-1097.
  • Fu, X. Lopez-Estrada, L. Kim, J.G. (2021). A Q-learning-based approach for enhancing energy efficiency of Bluetooth Low Energy. IEEE. Access, 9, 21286–21295.
  • I.Adumbabul, and K. Selvakumar. An Improved Lifetime and Energy Consumption with Enhanced Clustering in WSNs. Intelligent Automation & Soft Computing
    doi: 10.32604/iasc.2023.029489.
  • M.S. Ali, A. Alqahtani, A. M. Shah, A. Rajab and M. U. Hassan. (2023). Improved-equalized cluster head election routing protocol for wireless sensor networks, Computer Systems Science and Engineering, 44(1), 845–858.
  • M.R. Reddy, M. L. R. Chandra, P. Venkatramana, and R Dilli (2023). Energy-efficient cluster head selection in wireless sensor networks using an improved grey wolf optimization algorithm, Computers, 12(2), 1-17. doi: 10.3390/computers12020035
  • Maha Salih Abdulridhaa, Ghaihab Hassan Addayb, Imad S. Alshawi. (2020). FSFS: Fast simple flooding strategy in wireless sensor networks. Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University.
  • Mohd Adnan, Liu Yang, Tazeem Ahmad, and Yang Tao. (2021). An Unequally Clustered Multi-hop Routing Protocol Based on Fuzzy Logic for Wireless Sensor Networks. 
    doi: 10.1109/ACCESS.2021.3063097
  • Nurfazrina Mohd Zamry , Anazida Zainal,  Murad A. Rassam. LEACH-CR: Energy Saving Hierarchical Network Protocol Based on Low-Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks. (2021) 3rd International Cyber Resilience Conference (CRC) | IEEE | doi: 10.1109/CRC50527.2021.9392488
  • Padmalaya Nayak. (2016). A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime. IEEE sensors journal, 16(1). 
  • S.A. Nikolidakis, D. Kandris, D. D. Vergados and C. Douligeris. (2013). Energy efficient routing in wireless sensor networks through balanced clustering, Algorithms, 6(4), 29–42.
  • Yao Liang, Yiemi Li. (2014). An Efficient and Robust Data Compression Algorithm in Wireless Sensor Networks. Article in IEEE Communications Letters.
    doi: 10.1109/LCOMM.2014.011214.132319
  • Xing Fu and Jeong Geun Kim. (2023). Deep-Q-Network-Based Packet Scheduling in an IoT Environment. Sensors, 23, 1339. https:/doi.org/10.3390/s23031339