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Enhancing smart grid reliability through cross-domain optimization of IoT sensor placement and communication links

Author

Listed:
  • Saket Sarin

    (Panjab University)

  • Sunil K. Singh

    (Panjab University)

  • Sudhakar Kumar

    (Panjab University)

  • Shivam Goyal

    (Panjab University)

  • Brij B. Gupta

    (Asia University, 413 Taichung, Taiwan, & School of Cybersecurity, Korea University, Seoul, South Korea, & Symbiosis Centre for InformationTechnology (SCIT), Symbiosis International University)

  • Varsha Arya

    (Hong Kong Metropolitan University (HKMU), Hong Kong, China, & Lebanese American University, Beirut, Lebanon 1102, & University of Petroleum and Energy Studies (UPES))

  • Razaz Waheeb Attar

    (Princess Nourah bint Abdulrahman University)

  • Shavi Bansal

    (Insights2Techinfo, Jaipur, India, & UCRD, Chandigarh University)

  • Ahmed Alhomoud

    (Northern Border University)

Abstract

The increasing complexity and demands of modern power grids necessitate advanced solutions for real-time monitoring and control. This paper presents a novel cross-domain optimization framework designed to enhance the reliability and cost-efficiency of smart grid monitoring systems through strategic IoT sensor placement and communication network design. Utilizing the IEEE 57-bus system and utilizing MATPOWER for power grid simulations alongside NS-3 for communication network modeling, the framework integrates both domains to achieve optimal deployment configurations. Our approach ensures extensive grid observability and robust data transmission by iteratively refining sensor placement based on communication link costs and customizing routing policies. The proposed framework, Synergistic Optimization Framework for Enhanced Reliability (SOFER), demonstrates significant improvements in key performance metrics. Specifically, we achieve 99.5% system reliability, measured by Mean Time Between Failures (MTBF) exceeding 10,000 h. The system exhibits exceptional robustness, maintaining full functionality with tolerance to single component failures and 80% functionality during multi-component failures. Network performance metrics indicate an average data transmission latency of 50 milliseconds, bandwidth utilization efficiency of 85%, and a packet loss rate of less than 0.5%. The optimization algorithm converges rapidly within 30 iterations, providing high-quality solutions that ensure grid observability with 100% coverage and effective redundancy. Comparative analysis with existing methods highlights a 25% improvement in cost reduction and 20% enhancement in reliability. These results underscore the efficacy of the integrated approach, making it a optimal solution for modern smart grid systems. Compared to traditional approaches, SOFER demonstrates a 20% improvement in system reliability, a 25% reduction in overall deployment costs, and a 28.6% decrease in data latency, positioning it as a high-performance solution for modern smart grids. This framework paves the way for future advancements in smart grid monitoring, emphasizing the critical interplay between IoT sensor networks and communication infrastructure.

Suggested Citation

  • Saket Sarin & Sunil K. Singh & Sudhakar Kumar & Shivam Goyal & Brij B. Gupta & Varsha Arya & Razaz Waheeb Attar & Shavi Bansal & Ahmed Alhomoud, 2025. "Enhancing smart grid reliability through cross-domain optimization of IoT sensor placement and communication links," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(1), pages 1-16, March.
    • Handle: RePEc:spr:telsys:v:88:y:2025:i:1:d:10.1007_s11235-024-01235-1
    DOI: 10.1007/s11235-024-01235-1
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    References listed on IDEAS

    as
    1. Stoyanova, Ivelina & Monti, Antonello, 2019. "Cross-domain Pareto optimization of heterogeneous domains for the operation of smart cities," Applied Energy, Elsevier, vol. 240(C), pages 534-548.
    2. Pooja Chaudhary & Brij Gupta & A. K. Singh, 2022. "Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 81(1), pages 23-39, September.
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