International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Extending the Energy Efficiency of Nodes in an Internet of Things (IoT) System via Robust Clustering Techniques


Affiliations
1 Department of Computer Science, Applied College, University of Tabuk, Tabuk, Saudi Arabia
 

Wireless sensor networks (WSNs) are useful in many industries due to their capacity to perceive their surroundings and relay that information to base stations. Improving energy efficiency in wireless sensor networks while still meeting Quality of Service (QoS) requirements like low latency and data security is no easy feat. There have been numerous suggestions for making networks more energy efficient without reducing service quality, but only a few have been proven to work. This study recognizes the paucity of prior thorough work in the area and sets out to remedy that. The primary objectives of this research into wireless sensor networks are to optimize energy consumption, reduce latency, and boost service quality. Since these networks are so pervasive in cutting-edge industries like healthcare, defence, and navigation, accurately predicting their energy efficiency and data transfer rates is essential. This study uses a rigorous strategy to isolate and address the underlying causes of energy efficiency and increased delay. Security and the average transmission latency are still taken into account. For this reason, the proposed approach protocol, which enhances the energy efficiency gains by combining the EESAA protocol for effective clustering with the proposed approach Protocol. The proposed algorithm provides high efficiency in terms of energy consumption, which results in increased lifetime of the nodes comprising the wireless sensor network.

Keywords

Wireless Sensor Network (WSN), Lifetime Maximization, Internet of Things (IoT), Survivability, Quality of Service (QoS), Whale Optimization, LEACH, SEP, H-LEACH, MAMC, PEGASIS.
User
Notifications
Font Size

  • Mokabberi, A. Iranmehr, and M. Golsorkhtabaramiri, "A Review of Energy-efficient QoS-aware Composition in the Internet of Things," in 2023 8th International Conference on Technology and Energy Management (ICTEM), 2023: IEEE, pp. 1-6.

  • A. Perera and M. Katz, "Novel Data and Energy Networking for Energy Autonomous Light-based IoT Nodes in WPAN Networks," in 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023: IEEE, pp. 1-6.

  • M. Asif, A. Ihsan, W. U. Khan, A. Ranjha, S. Zhang, and S. X. Wu, "Energy-efficient beamforming and resource optimization for AmBSC-assisted cooperative NOMA IoT networks," IEEE Internet of Things Journal, 2023.

  • K. E. S. Desikan, V. J. Kotagi, and C. S. R. Murthy, "Decoding the Interplay Between Latency, Reliability, Cost, and Energy While Provisioning Resources in Fog-Computing-Enabled IoT Networks," IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2404-2416, 2022.

  • H. Shang, D. Lu, and Q. Zhou, "Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules," Neural Comput. Appl., vol. 33, no. 9, pp. 3901–3909, 2021, doi: 10.1007/s00521-020-05510-5.

  • N. Rathour, V. Kumar, S. S. Kundu, Y. Gehlot, and A. Gurung, "Sigma Home: An IoT-Based Home Automation Using Node MCU," in 2023 2nd International Conference on Edge Computing and Applications (ICECAA), 2023: IEEE, pp. 1317-1322.

  • Y. Alzahrani, J. Shen, and J. Yan, "Energy-Efficient Data Consistency based Sampling Rate Optimization and Aggregation Method for IoT," in 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2023: IEEE, pp. 1348-1353.

  • X. Yang, L. Shu, K. Li, Z. Huo, S. Shu, and E. Nurellari, "Silos: An intelligent fault detection scheme for solar insecticidal lamp iot with improved energy efficiency," IEEE Internet of Things Journal, vol. 10, no. 1, pp. 920-939, 2022.

  • R. Ramkumar and C. Balasubramanian, "A novel cluster head selection scheme based on BCO for Internet of Things," in 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2023: IEEE, pp. 1-6.

  • Z. Ding, L. Shen, H. Chen, F. Yan, and N. Ansari, "Energy-Efficient Topology Control Mechanism for IoT-Oriented Software-Defined WSNs," IEEE Internet of Things Journal, 2023.

  • W.-P. Nwadiugwu, W. Ejaz, M. Kaneko, and A. Anpalagan, "Neural-Network Assisted Packet Accelerators for Internet of Things Network Systems," IEEE Internet of Things Journal, 2023.

  • N. Sivasankari and S. Kamalakannan, "Fuzzy Logic-based Man-in-the-Middle Attack Detection and Improving Routing Efficiency in the IoT Network," in 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), 2023: IEEE, pp. 1-6.

  • M. Majid, "Optimizing Energy Efficiencies of IoT-based Wireless Sensor Network Components for Metaverse Sustainable Development using Carry Resist Adder based Booth Recoder (CRABRA)," in 2023 20th Learning and Technology Conference (L&T), 2023: IEEE, pp. 91-96.

  • S. K. Chaurasiya, S. Mondal, A. Biswas, A. Nayyar, M. A. Shah, and R. Banerjee, "An Energy-Efficient Hybrid Clustering Technique (EEHCT) for IoT-Based Multilevel Heterogeneous Wireless Sensor Networks," IEEE Access, vol. 11, pp. 25941-25958, 2023.

  • M. S. Batta, H. Mabed, Z. Aliouat, and S. Harous, "Battery State-of-Health Prediction-Based Clustering for Lifetime Optimization in IoT Networks," IEEE Internet of Things Journal, vol. 10, no. 1, pp. 81-91, 2022.

  • C. Kathirvel and P. Deepa, "Design and Implementation of IoT based Dual Axis Solar Tracking System," in 2023 3rd International Conference on Smart Data Intelligence (ICSMDI), 2023: IEEE, pp. 542-545.

  • A. Iqbal and T.-J. Lee, "Opportunistic Backscatter Communication Protocol Underlying Energy Harvesting IoT Networks," IEEE Access, 2023.

  • P. Satyanarayana, K. Bhoomika, D. Mukesh, P. Srujana, R. M. Bai, and Y. S. Sriramam, "Implementation of Improved Energy Balanced Routing Protocol to Enlarge Energy Efficiency in MANET for IoT Applications," in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), 2023, vol. 1: IEEE, pp. 380-385.

  • N. Stricker, J. Hora, A. Gomez, and L. Thiele, "Energy-Efficient Bootstrapping in Multi-hop Harvesting-Based Networks," in 2023 18th Wireless On-Demand Network Systems and Services Conference (WONS), 2023: IEEE, pp. 1-8.

  • F. Xu, H.-C. Yang, and M.-S. Alouini, "Ultra-Green Relay Transmission with Wireless Power Transfer for Advanced IoT: Session-Specific Analysis and Optimization," IEEE Internet of Things Journal, 2023.

  • M. González-Palacio, D. Tobón-Vallejo, L. M. Sepúlveda-Cano, S. Rúa, and L. B. Le, "Machine-learning-based combined path loss and shadowing model in LoRaWAN for energy efficiency enhancement," IEEE Internet of Things Journal, 2023.

  • D. Ray, P. Bhale, S. Biswas, P. Mitra, and S. Nandi, "A Novel Energy-efficient Scheme For RPL Attacker Identification In IoT Networks Using Discrete Event Modeling," IEEE Access, 2023.

  • X. Liu, Z. Liu, B. Lai, B. Peng, and T. S. Durrani, "Fair energy-efficient resource optimization for multi-UAV enabled Internet of Things," IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3962-3972, 2022.

  • S. Boehm and H. Koenig, "Radio-in-the-Loop Simulation Modeling for Energy-Efficient and Cognitive IoT in Smart Cities: A Cross-Layer Optimization Case Study," in 2023 18th Wireless On-Demand Network Systems and Services Conference (WONS), 2023: IEEE, pp. 126-133.

  • S. Huang, G. Chuai, W. Gao, and K. Zhang, "Agency Selling Format-Based Incentive Scheme in Cooperative Hybrid VLC/RF IoT System With SLIPT," IEEE Internet of Things Journal, vol. 10, no. 8, pp. 7366-7379, 2022.

  • W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-efficient communication protocol for wireless microsensor networks," in Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000: IEEE, p. 10 pp. vol. 2.

  • A. Razaque, S. Mudigulam, K. Gavini, F. Amsaad, M. Abdulgader, and G. S. Krishna, "H-LEACH: Hybrid-low energy adaptive clustering hierarchy for wireless sensor networks," in 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), 2016: IEEE, pp. 1-4.

  • P. Harichandan, A. Jaiswal, and S. Kumar, "Multiple Aggregator Multiple Chain routing protocol for heterogeneous wireless sensor networks," in 2013 International Conference on Signal Processing and Communication (ICSC), 2013: IEEE, pp. 127-131. How to cite this article:

  • S. Lindsey and C. S. Raghavendra, "PEGASIS: Power-efficient gathering in sensor information systems," in Proceedings, IEEE aerospace conference, 2002, vol. 3: IEEE, pp. 3-3.

  • M. Islam, M. Matin, and T. Mondol, "Extended Stable Election Protocol (SEP) for three-level hierarchical clustered heterogeneous WSN", IET Conference on Wireless Sensor Systems (WSS 2012).


Abstract Views: 146

PDF Views: 1




  • Extending the Energy Efficiency of Nodes in an Internet of Things (IoT) System via Robust Clustering Techniques

Abstract Views: 146  |  PDF Views: 1

Authors

Abdullah A. Al-Atawi
Department of Computer Science, Applied College, University of Tabuk, Tabuk, Saudi Arabia

Abstract


Wireless sensor networks (WSNs) are useful in many industries due to their capacity to perceive their surroundings and relay that information to base stations. Improving energy efficiency in wireless sensor networks while still meeting Quality of Service (QoS) requirements like low latency and data security is no easy feat. There have been numerous suggestions for making networks more energy efficient without reducing service quality, but only a few have been proven to work. This study recognizes the paucity of prior thorough work in the area and sets out to remedy that. The primary objectives of this research into wireless sensor networks are to optimize energy consumption, reduce latency, and boost service quality. Since these networks are so pervasive in cutting-edge industries like healthcare, defence, and navigation, accurately predicting their energy efficiency and data transfer rates is essential. This study uses a rigorous strategy to isolate and address the underlying causes of energy efficiency and increased delay. Security and the average transmission latency are still taken into account. For this reason, the proposed approach protocol, which enhances the energy efficiency gains by combining the EESAA protocol for effective clustering with the proposed approach Protocol. The proposed algorithm provides high efficiency in terms of energy consumption, which results in increased lifetime of the nodes comprising the wireless sensor network.

Keywords


Wireless Sensor Network (WSN), Lifetime Maximization, Internet of Things (IoT), Survivability, Quality of Service (QoS), Whale Optimization, LEACH, SEP, H-LEACH, MAMC, PEGASIS.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F223685