[Paper Review] Joint Beamforming and Location Optimization for Secure Data Collection in Wireless Sensor Networks with UAV-Carried Intelligent Reflecting Surface

Christantus O. Nnamani, Muhammad R. A. Khandaker+1 more|arXiv (Cornell University)|Jan 16, 2021

Advanced Wireless Communication Technologies18 citations

TL;DR

This paper proposes a joint optimization framework for UAV-carried intelligent reflecting surfaces (IRS) in wireless sensor networks to maximize secrecy rate by jointly designing beamforming weights, UAV trajectory, and IRS reflection coefficients. It achieves a non-iterative sub-optimal solution that significantly enhances physical layer security, especially under eavesdropper uncertainty and random sensor node distributions.

ABSTRACT

This paper considers unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) for secure data collection in wireless sensor networks. An eavesdropper (Eve) lurks within the vicinity of the main receiver (Bob) while several randomly placed sensor nodes beamform collaboratively to the UAV-carried IRS that reflects the signal to the main receiver (Bob). The design objective is to maximise the achievable secrecy rate in the noisy communication channel by jointly optimizing the collaborative beamforming weights of the sensor nodes, the trajectory of the UAV and the reflection coefficients of the IRS elements. By designing the IRS reflection coefficients with and without the knowledge of the eavesdropper's channel, we develop a non-iterative sub-optimal solution for the secrecy rate maximization problem. It has been shown analytically that the UAV flight time and the randomness in the distribution of the sensor nodes, obtained by varying the sensor distribution area, can greatly affect secrecy performance. In addition, the maximum allowable number of IRS elements as well as a bound on the attainable average secrecy rate of the IRS aided noisy communication channel have also been derived. Extensive simulation results demonstrate the superior performance of the proposed algorithms compared to the existing schemes.

Motivation & Objective

• Address the challenge of secure data collection in wireless sensor networks (WSNs) with high mobility and potential eavesdropping.
• Overcome limitations of conventional IRS and UAV systems by integrating UAV-carried IRS for dynamic, adaptive channel control.
• Maximize the achievable secrecy rate in noisy, multi-sensor environments with random node distributions and unknown eavesdropper channels.
• Develop a non-iterative sub-optimal solution to reduce computational complexity compared to iterative methods.
• Analyze the impact of UAV flight time, sensor distribution randomness, and IRS element count on secrecy performance.

Proposed method

• Formulate a secrecy rate maximization problem involving collaborative beamforming at sensor nodes, UAV trajectory control, and IRS reflection coefficient optimization.
• Use reverse triangle inequality and variable substitution to derive lower bounds on distance products, transforming the non-convex problem into a solvable form.
• Apply Karush-Kuhn-Tucker (KKT) conditions to the transformed problem, reducing it to solving a cubic equation with a single real root.
• Derive the optimal UAV location at each time step via the real root of the cubic equation, enabling non-iterative computation.
• Incorporate constraints on UAV mobility and reflection coefficient design, both with and without eavesdropper CSI knowledge.
• Scale the optimization across time steps by solving for each time instance independently and summing the optimal values.

Experimental results

Research questions

• RQ1 How does the joint optimization of beamforming, UAV trajectory, and IRS reflection coefficients improve secrecy rate in WSNs with UAV-mounted IRS?
• RQ2 What is the impact of UAV flight time and sensor node distribution randomness on the achievable secrecy rate?
• RQ3 How does the number of IRS elements affect the maximum attainable average secrecy rate?
• RQ4 Can a non-iterative sub-optimal solution outperform existing iterative schemes in terms of secrecy rate and computational efficiency?
• RQ5 How does the absence of eavesdropper CSI affect the system's secrecy performance and design robustness?

Key findings

• The proposed non-iterative algorithm achieves superior secrecy rate performance compared to existing schemes, with significant reduction in computational complexity.
• UAV trajectory optimization enables sustained secrecy performance by dynamically positioning the IRS for favorable reflection conditions.
• Randomness in sensor node distribution degrades secrecy rate, but the proposed design mitigates this through adaptive beamforming and IRS control.
• The maximum number of IRS elements is bounded by system hardware and channel conditions, with a derived upper limit that ensures optimal secrecy rate.
• The average secrecy rate of the IRS-aided system is bounded, and the proposed design achieves a high fraction of this theoretical limit.
• Phase disparity at the eavesdropper is increased via IRS beamforming, enhancing constructive reception at the legitimate receiver and destructive interference at the eavesdropper.