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Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Author

Listed:
  • Kuruva Lakshmanna

    (Department of Information Technology, Vellore Institute of Technology, Vellore 632014, India)

  • Neelakandan Subramani

    (Department of Computer Science and Engineering, R.M.K Engineering College, Chennai 601206, India)

  • Youseef Alotaibi

    (Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

  • Saleh Alghamdi

    (Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Osamah Ibrahim Khalafand

    (Department of Computer Engineering, Al-Nahrain Nano Renewable Energy Research Center, Al-Nahrain University, Baghdad 10071, Iraq)

  • Ashok Kumar Nanda

    (Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur 502313, India)

Abstract

The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.

Suggested Citation

  • Kuruva Lakshmanna & Neelakandan Subramani & Youseef Alotaibi & Saleh Alghamdi & Osamah Ibrahim Khalafand & Ashok Kumar Nanda, 2022. "Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7712-:d:846804
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    References listed on IDEAS

    as
    1. Guang Li & Fangfang Liu & Ashutosh Sharma & Osamah Ibrahim Khalaf & Youseef Alotaibi & Abdulmajeed Alsufyani & Saleh Alghamdi, 2021. "Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, May.
    2. Nishant Jha & Deepak Prashar & Osamah Ibrahim Khalaf & Youseef Alotaibi & Abdulmajeed Alsufyani & Saleh Alghamdi, 2021. "Blockchain Based Crop Insurance: A Decentralized Insurance System for Modernization of Indian Farmers," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    3. Hemavathi & Sreenatha Reddy Akhila & Youseef Alotaibi & Osamah Ibrahim Khalaf & Saleh Alghamdi, 2022. "Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning," Energies, MDPI, vol. 15(6), pages 1-27, March.
    4. Salil Bharany & Sandeep Sharma & Sumit Badotra & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol," Energies, MDPI, vol. 14(19), pages 1-20, September.
    5. Satheeshkumar Palanisamy & Balakumaran Thangaraju & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "A Novel Approach of Design and Analysis of a Hexagonal Fractal Antenna Array (HFAA) for Next-Generation Wireless Communication," Energies, MDPI, vol. 14(19), pages 1-18, September.
    6. Sur Singh Rawat & Saleh Alghamdi & Gyanendra Kumar & Youseef Alotaibi & Osamah Ibrahim Khalaf & Lal Pratap Verma, 2022. "Infrared Small Target Detection Based on Partial Sum Minimization and Total Variation," Mathematics, MDPI, vol. 10(4), pages 1-19, February.
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    2. Aoqi Xu & Mehdi Darbandi & Danial Javaheri & Nima Jafari Navimipour & Senay Yalcin & Anas A. Salameh, 2023. "The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods," Sustainability, MDPI, vol. 15(7), pages 1-28, March.
    3. Mesfer Al Duhayyim & Heba G. Mohamed & Mohammed Aljebreen & Mohamed K. Nour & Abdullah Mohamed & Amgad Atta Abdelmageed & Ishfaq Yaseen & Gouse Pasha Mohammed, 2022. "Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management," Sustainability, MDPI, vol. 14(18), pages 1-17, September.

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