Secrecy Offloading Analysis of NOMA-based UAV-aided MEC in IoT

Introduction

In the ever-evolving landscape of the Internet of Things (IoT), the integration of unmanned aerial vehicles (UAVs) and mobile-edge computing (MEC) has become a game-changer. This convergence has paved the way for efficient and secure offloading of computational tasks from resource-constrained IoT devices. However, the inherent risks associated with the Line-of-Sight (LoS) of UAV transmissions pose a significant challenge, as they make the system susceptible to information eavesdropping.

To address this issue, the employment of non-orthogonal multiple access (NOMA) techniques in UAV-aided MEC networks has emerged as a promising solution. NOMA allows multiple devices to share the same time-frequency resources, thereby enhancing spectrum efficiency and enabling convenient offloading services for edge devices (EDs). Yet, the combination of NOMA and UAV-MEC introduces a new set of security concerns, as the eavesdropper (EAV) can potentially intercept the transmitted data.

In this article, we delve into the intricacies of a secure offloading model for a NOMA-based UAV-aided MEC system in IoT networks, considering the presence of an aerial EAV. We analyze the secrecy successful computation probability (SSCP) across the entire system and provide a formulation of an optimization problem to enhance the SSCP. By optimizing the UAV’s altitude and location, as well as the offloading ratio, we aim to mitigate the impact of the EAV and ensure the overall security of the NOMA-based UAV-aided MEC in IoT networks.

NOMA-based UAV-aided MEC in IoT Networks

The integration of NOMA, UAV, and MEC technologies in IoT networks presents a powerful synergy that can significantly improve the performance and security of offloading services. Let’s delve into the key aspects of this system:

Non-Orthogonal Multiple Access (NOMA)

NOMA is a multiple access technique that allows multiple devices to simultaneously access the same time-frequency resource, thereby enhancing spectrum efficiency. This is achieved by allocating different power levels to each device, enabling the receiver to perform successive interference cancellation (SIC) to recover the intended signal.

UAV-aided MEC

In UAV-aided MEC, a UAV equipped with a MEC server acts as a central hub, providing offloading services to resource-constrained IoT devices. The UAV’s mobility and LoS communication links offer several advantages, such as improved coverage, reduced latency, and efficient task offloading.

Security Challenges

However, the LoS communication of the UAV transmission makes the NOMA-based UAV-aided MEC system susceptible to information eavesdropping. The presence of an aerial EAV poses a significant threat, as it can potentially intercept the transmitted data, compromising the overall security of the system.

Secrecy Offloading Analysis

To address the security concerns in the NOMA-based UAV-aided MEC system, we analyze the secrecy performance of the offloading process. The key focus is on deriving the expression of the secrecy successful computation probability (SSCP) and optimizing it to mitigate the impact of the EAV.

Secrecy Successful Computation Probability (SSCP)

The SSCP represents the probability that the offloading task is successfully computed at the MEC server without being intercepted by the EAV. This metric is crucial in evaluating the overall security of the system and guiding the optimization process.

Optimization Problem Formulation

We formulate an optimization problem that aims to maximize the SSCP by optimizing the following parameters:

1. UAV’s Location: Determining the optimal position of the UAV to enhance the secrecy of the offloading process.
2. UAV’s Altitude: Adjusting the UAV’s altitude to improve the secrecy performance.
3. Offloading Ratio: Optimizing the ratio of offloaded tasks to maximize the SSCP.

By solving this optimization problem, we can derive the optimal values for these parameters and enhance the overall secrecy of the NOMA-based UAV-aided MEC system in IoT networks.

Optimization Approach and Simulation Results

To address the optimization problem, we employ a genetic algorithm (GA)-based approach. The GA is a metaheuristic optimization technique that can effectively explore the solution space and converge to the optimal or near-optimal values of the system parameters.

The results of our study are validated through Monte Carlo simulations, which assess the system performance by considering various parameters, including the UAV’s location and altitude, average transmit signal-to-noise ratio (SNR), and offloading ratio. These simulations provide a comprehensive evaluation of the NOMA-based UAV-aided MEC system’s secrecy performance and the effectiveness of the optimization approach.

Practical Implications and Recommendations

The insights gained from this research have several practical implications for the deployment and management of NOMA-based UAV-aided MEC systems in IoT networks:

1. Secure Task Offloading: By optimizing the system parameters, organizations can enhance the security of their IoT device offloading processes, ensuring the confidentiality of sensitive data and reducing the risk of eavesdropping attacks.

2. Efficient Resource Utilization: The NOMA technique, combined with the flexibility of UAV-aided MEC, enables efficient utilization of network resources, leading to improved overall system performance and cost-effectiveness.

3. Adaptive Deployment Strategies: The ability to optimize the UAV’s location and altitude based on the evolving security landscape allows for more agile and responsive deployment strategies, ensuring the system’s resilience against emerging threats.

4. Scalable IoT Infrastructure: The NOMA-based UAV-aided MEC approach can be seamlessly integrated into larger IoT ecosystems, providing a scalable and secure solution for offloading computational tasks from resource-constrained devices.

To leverage these benefits, IT professionals and IoT system architects should consider the following recommendations:

• Conduct Thorough Risk Assessments: Regularly evaluate the security threats and vulnerabilities in the NOMA-based UAV-aided MEC system to inform optimization strategies and deployment decisions.
• Implement Adaptive Optimization Algorithms: Explore advanced optimization techniques, such as the GA-based approach, to dynamically adjust system parameters and maintain optimal secrecy performance.
• Integrate Robust Security Protocols: Combine the NOMA-based UAV-aided MEC system with state-of-the-art encryption, authentication, and intrusion detection mechanisms to create a comprehensive security framework.
• Foster Cross-Disciplinary Collaboration: Encourage collaborations between IoT, cybersecurity, and UAV experts to develop holistic solutions that address the unique security challenges of NOMA-based UAV-aided MEC in IoT networks.

By following these recommendations, IT professionals can leverage the power of NOMA-based UAV-aided MEC to build secure, efficient, and scalable IoT infrastructures that meet the evolving demands of the modern digital landscape.

Conclusion

The integration of NOMA, UAV, and MEC technologies in IoT networks holds significant potential for enhancing the performance and security of offloading services. However, the inherent security risks posed by aerial eavesdroppers require a comprehensive approach to address the challenges.

In this article, we have delved into the secrecy offloading analysis of NOMA-based UAV-aided MEC in IoT networks. By deriving the expression of the SSCP and formulating an optimization problem, we have demonstrated a practical approach to mitigating the impact of the EAV and ensuring the overall security of the system.

Through the implementation of a GA-based optimization technique and comprehensive simulation analysis, we have provided valuable insights and recommendations for IT professionals and IoT system architects. By adopting these strategies, organizations can build secure, efficient, and scalable IoT infrastructures that leverage the synergistic benefits of NOMA, UAV, and MEC technologies.

As the IoT landscape continues to evolve, the security and performance of offloading services will remain a critical concern. The insights and solutions presented in this article serve as a roadmap for IT professionals to navigate the complexities of NOMA-based UAV-aided MEC and deliver cutting-edge, secure IoT solutions for the future.

References

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