IoT Network Security with Quantum PSO-Based Optimization

The Internet of Things (IoT) is transforming industries and daily life. Still, its increasing demand for secure and efficient communication has led to innovative solutions like Intelligent Reflecting Surfaces (IRS) assisted wireless powered IoT networks. Researchers from the University of Ulsan and Sejong University have proposed a novel scheme that combines energy harvesting, friendly jamming, and quantum PSO-based optimization to maximize sum secrecy throughput.

The proposed system consists of two stages: wireless energy transfer on the downlink and wireless information transmission on the uplink. During wireless energy transfer, the hybrid access point sends energy to users and a friendly jammer, which then harvests energy with the help of IRS. Subsequently, during wireless information transmission, the user transmits information to the hybrid access point while the jammer emits signals to confuse eavesdroppers without interfering with legitimate transmission.

The phaseshift matrix of IRS and time allocation for downlink and uplink are jointly optimized to maximize sum secrecy throughput of the network. To tackle the non-convex problem, an alternating optimization method is employed, reformulating the problem into two subproblems. First, the IRS phase shift is solved using quantum particle swarm optimization (QPSO), then the optimization of time allocation for downlink and uplink using the bisection method.

The proposed scheme relies on several key components to achieve its goals: Intelligent Reflecting Surface (IRS), friendly jammer, energy harvesting, and quantum PSO-based optimization. Simulation results demonstrate the effectiveness of the proposed method in improving network security and overall performance, achieving significant gains in throughput and secrecy rate compared to other baseline schemes.

While there are challenges and limitations that need to be addressed, such as non-convex problem, complexity of IRS phase shift optimization, and time allocation for downlink and uplink, the proposed scheme has the potential to revolutionize IoT network security and efficiency. Future directions include developing advanced optimization techniques, improving IRS phase shift optimization, and enhancing time allocation strategies.

Quantum PSOBased Optimization of Secured IRSAssisted Wireless Powered IoT Networks

The article “Quantum PSOBased Optimization of Secured IRSAssisted Wireless Powered IoT Networks” by Abid Afridi, Iqra Hameed, and Insoo Koo explores the concept of intelligent reflecting surface (IRS) assisted physical layer security (PLS) in a wireless powered Internet of Things (IoT) network. The authors propose a novel scheme that combines IRS, friendly jammer, and energy harvesting to maximize sum secrecy throughput.

Intelligent Reflecting Surface-Assisted Physical Layer Security

The proposed scheme consists of two stages: wireless energy transfer (WET) on the downlink (DL) and wireless information transmission (WIT) on the uplink (UL). In the first phase, the hybrid access point (HAP) sends energy to users and a jammer, which then harvest energy with the help of IRS. During WIT, the user transmits information to the HAP while the jammer emits signals to confuse eavesdroppers without interfering with legitimate transmission.

The phaseshift matrix of IRS and time allocation for DL and UL are jointly optimized to maximize sum secrecy throughput. To tackle the nonconvex problem, an alternating optimization method is employed, reformulating the problem into two subproblems. First, the IRS phase shift is solved using quantum particle swarm optimization (QPSO), followed by optimizing time allocation for DL and UL using the bisection method.

Simulation Results and Performance Comparison

Simulation results demonstrate that the proposed method achieves significant performance improvements compared to other baseline schemes. For IRS elements N = 35, the proposed scheme achieves a throughput of 194 bps/Hz, which is 85% higher than the standard PSO approach and 143% higher than the fixed time random phase (8 bps/Hz) approach.

These results validate the proposed approach’s effectiveness in improving network security and overall performance. The authors’ scheme demonstrates the potential of IRS-assisted PLS to enhance wireless-powered IoT networks, making them more secure and efficient.

Key Concepts and Techniques

The article employs several key concepts and techniques, including:

1. Intelligent Reflecting Surface (IRS): A technology that uses a surface to reflect and focus electromagnetic waves, enhancing signal quality and security.
2. Physical Layer Security (PLS): A technique that utilizes the physical properties of wireless communication channels to secure data transmission.
3. Quantum Particle Swarm Optimization (QPSO): An optimization algorithm inspired by quantum mechanics and particle swarm optimization, used to solve complex problems.
4. Bisection Method: A numerical method for finding roots of equations, employed in this article to optimize time allocation.

Research Implications and Future Directions

The proposed scheme has significant implications for the development of secure wireless powered IoT networks. The authors’ work demonstrates the potential of IRS-assisted PLS to enhance network security and performance, making them more suitable for various applications, such as smart cities, industrial automation, and healthcare.