Vol. 2 No. 2 (2022): Advances in Deep Learning Techniques
Articles

Quantum-Inspired Optimization Techniques for IoT Networks: Focusing on Resource Allocation and Network Efficiency Enhancement for Improved IoT Functionality

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  • Mohan Raparthi,
  • Bhavani Prasad Kasaraneni,
  • Krishna Kanth Kondapaka,
  • Swaroop Reddy Gayam,
  • Sudharshan Putha,
  • Siva Sarana Kuna,
  • Venkata Siva Prakash Nimmagadda,
  • Mohit Kumar Sahu,
  • Sandeep Pushyamitra Pattyam,
  • Praveen Thuniki
Mohan Raparthi
Software Engineer, Google Alphabet (Verily Life Science), Dallas, Texas, USA
Bhavani Prasad Kasaraneni
Independent Researcher, USA
Krishna Kanth Kondapaka
Independent Researcher, CA ,USA
Swaroop Reddy Gayam
Independent Researcher and Senior Software Engineer at TJMax, USA
Sudharshan Putha
Independent Researcher and Senior Software Developer, USA
Siva Sarana Kuna
Independent Researcher and Software Developer, USA
Venkata Siva Prakash Nimmagadda
Independent Researcher, USA
Mohit Kumar Sahu
Independent Researcher and Senior Software Engineer, CA, USA
Sandeep Pushyamitra Pattyam
Independent Researcher and Data Engineer, USA
Praveen Thuniki
Independent Research, Sr Program Analyst, Georgia, USA
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Published 07-07-2022

Keywords

  • Quantum-Inspired Optimization,
  • IoT Networks,
  • Resource Allocation,
  • Network Efficiency,
  • Quantum Annealing,
  • Genetic Algorithms,
  • Particle Swarm Optimization,
  • Scalability,
  • Security,
  • Implementation
    • ...More
    Less

How to Cite

[1]
M. Raparthi, “Quantum-Inspired Optimization Techniques for IoT Networks: Focusing on Resource Allocation and Network Efficiency Enhancement for Improved IoT Functionality”, Adv. in Deep Learning Techniques, vol. 2, no. 2, pp. 1–9, Jul. 2022.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Internet of Things (IoT) networks are characterized by a vast number of interconnected devices that require efficient resource allocation and network management. Traditional optimization techniques may not fully address the complex nature of IoT networks. This paper presents a comprehensive review of quantum-inspired optimization techniques for enhancing resource allocation and network efficiency in IoT environments. We examine how quantum-inspired algorithms such as Quantum Annealing, Quantum Genetic Algorithms, and Quantum Particle Swarm Optimization can be applied to address challenges in resource allocation, network routing, and energy efficiency. By leveraging principles from quantum computing, these techniques offer novel approaches to solving optimization problems in IoT networks. We also discuss the potential benefits and challenges of integrating quantum-inspired optimization techniques into IoT systems, including considerations for scalability, security, and implementation complexity. Overall, this paper provides insights into the promising future of quantum-inspired optimization for enhancing IoT network performance and efficiency.

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