Quantum Learning-Enabled Green Communication for Next-Generation Wireless Systems

Jaiswal, Ajay; Kumar, Sushil; Kaiwartya, Omprakash; Kashyap, Pankaj Kumar; Kanjo, Eiman; Kumar, Neeraj; Song, Houbing

doi:10.1109/tgcn.2021.3067918

Cited by 24 publications

(5 citation statements)

References 30 publications

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“…In [18], the authors compared the performance of PSO and differential evolution (DE) algorithms for resource allocation in ad hoc networks of quantum-enabled IoT devices. The simulation results showed that the DE algorithm outperformed the PSO algorithm in terms of energy consumption and data transmission rate.…”

Section: Comparison Of Previous Optimization Algorithmsmentioning

confidence: 99%

Improved Quantum-inspired Hybrid Particle Swarm Optimization Based Resource Allocation for Quantum- Enabled IoT Devices in Ad Hoc Networks

2024

Preprint

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Ad hoc networks of quantum-enabled IoT devices require efficient resource allocation techniques to optimize energy consumption and data transmission rates. This paper proposes an enhanced version of quantum-inspired hybrid particle swarm optimization called the Improved quantum-inspired hybrid particle swarm optimization (IQHPSO) algorithm for resource allocation in ad hoc networks of quantum-enabled IoT devices. The enhanced QHPSO algorithm incorporates a novel innovation technique based on adaptive parameter tuning and a global best selection strategy that further improves the optimization performance of the QHPSO approach. We evaluate the proposed approach through simulations and compare its performance with the original QHPSO algorithm and other state-of-the-art optimization techniques regarding objective functions such as energy consumption, data transmission rate, and network lifetime. Our results show that the IQHPSO algorithm outperforms the original QHPSO algorithm by 10-15% in energy consumption, 5-10% in data transmission rate, and 3-7% in network lifetime. Furthermore, our results demonstrate that the IQHPSO algorithm outperforms other state-of-the-art optimization techniques regarding these objective functions. We also discuss the practical implementation of the IQHPSO algorithm in a real-world ad hoc network of quantum-enabled IoT devices. This research contributes to the ongoing development of quantum-inspired optimization techniques for wireless networking applications. Furthermore, it demonstrates the benefits of incorporating innovative approaches, such as adaptive parameter tuning and global best selection strategy, to improve the performance of existing optimization algorithms.

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Section: Comparison Of Previous Optimization Algorithmsmentioning

confidence: 99%

Improved Quantum-inspired Hybrid Particle Swarm Optimization Based Resource Allocation for Quantum- Enabled IoT Devices in Ad Hoc Networks

2024

Preprint

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“…It includes a group of search agents that explore the most feasible output depending on randomization and few specific rules. These rules are nature-inspired [26,27]. Lee and Teng proposed an improved version for LEACH clustering protocol to reduce packet loss rate as well as to prolong the network lifetime using fuzzy inference systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

et al. 2021

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Congestion in wireless sensor networks (WSNs) is an unavoidable issue in today’s scenario, where data traffic increased to its aggregated capacity of the channel. The consequence of this turns in to overflowing of the buffer at each receiving sensor nodes which ultimately drops the packets, reduces the packet delivery ratio, and degrades throughput of the network, since retransmission of every unacknowledged packet is not an optimized solution in terms of energy for resource-restricted sensor nodes. Routing is one of the most preferred approaches for minimizing the energy consumption of nodes and enhancing the throughput in WSNs, since the routing problem has been proved to be an NP-hard and it has been realized that a heuristic-based approach provides better performance than their traditional counterparts. To tackle all the mentioned issues, this paper proposes an efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization (ECA-HA) to improve the network performance. This approach is a combination of traffic-oriented and resource-oriented optimization. Specially, ant colony optimization has been employed to find multiple congestion-free alternate paths. The forward ant constructs multiple congestion-free paths from source to sink node, and backward ant ensures about the successful creation of paths moving from sink to source node, considering energy of the link, packet loss rate, and congestion level. Huffman coding considers the packet loss rate on different alternate paths discovered by ant colony optimization for selection of an optimal path. Finally, the simulation result presents that the proposed approach outperforms the state of the art approaches in terms of average energy consumption, delay, and throughput and packet delivery ratio.

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“… 5 , 6 However, it is difficult for most classical cryptographic algorithms to ensure long-term security of the data stored in the remote cloud due to upcoming quantum computers and fast quantum algorithms. 7 , 8 , 9 Especially, quantum supremacy (or quantum advantage) has been demonstrated, 10 , 11 so classical cryptography will certainly face lots of threats and challenges.…”

Section: Introductionmentioning

confidence: 99%

Edge-assisted quantum protocol for secure multiparty logical AND its applications

Shi

Fang

2023

iScience

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Quantum Learning-Enabled Green Communication for Next-Generation Wireless Systems

Cited by 24 publications

References 30 publications

Improved Quantum-inspired Hybrid Particle Swarm Optimization Based Resource Allocation for Quantum- Enabled IoT Devices in Ad Hoc Networks

Improved Quantum-inspired Hybrid Particle Swarm Optimization Based Resource Allocation for Quantum- Enabled IoT Devices in Ad Hoc Networks

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

Edge-assisted quantum protocol for secure multiparty logical AND its applications

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