Security and Reliability-Optimal Offloading for Mobile Edge Computing in Low-latency Industrial IoT

HU Yulin; YU Xinlan; GAO Wei; ZHU Yao

doi:10.11999/JEIT250262

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HU Yulin, YU Xinlan, GAO Wei, ZHU Yao. Security and Reliability-Optimal Offloading for Mobile Edge Computing in Low-latency Industrial IoT[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250262

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HU Yulin, YU Xinlan, GAO Wei, ZHU Yao. Security and Reliability-Optimal Offloading for Mobile Edge Computing in Low-latency Industrial IoT[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250262

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Security and Reliability-Optimal Offloading for Mobile Edge Computing in Low-latency Industrial IoT

doi: 10.11999/JEIT250262 cstr: 32379.14.JEIT250262

HU Yulin^{1
,
,},
YU Xinlan¹,
GAO Wei¹,
ZHU Yao¹

Electronic Information School, Wuhan University, Wuhan 430000, China

Funds: The National Key R&D Plan (2023YFE0206600), The Fundamental Research Funds for the Central Universities (2042024kf1006)

Received Date: 2025-04-11
Rev Recd Date: 2025-08-05

Available Online: 2025-08-27

Abstract

Abstract

Objective With the advancement of Industry 4.0, the increasing demand for mission-critical and latency-sensitive applications is driving the evolution of Industrial Internet of Things (IIoT) networks. Mobile Edge Computing (MEC) has emerged as a key solution to alleviate the computational constraints of User Equipment (UE), thus enhancing IIoT system performance. However, ensuring high reliability and security remains a critical challenge in multi-access MEC networks. To support Ultra-Reliable and Low-Latency Communication (URLLC), short packet transmission is employed, introducing Finite BlockLength (FBL) effects that impacts communication performance. Additionally, queuing delays at the servers present a significant bottleneck in meeting latency requirement. While existing studies predominantly focus on secure transmission under the Infinite BlockLength (IBL) assumption, comprehensive investigations into Physical Layer Security (PLS) and reliability in the FBL regime remain scarce. To address this gap, this paper aims to jointly optimize the security and reliability of MEC networks. Specifically, an analytical expression for end-to-end Leakage Failure Probability (LFP) is derived to assess the security and reliability performance. Furthermore, an efficient two-phase time length allocation algorithm is proposed to enhance overall system performance. Methods This study examines the uplink URLLC scenario in a multi-access MEC network. First, leveraging FBL theory, the communication phase within frame structure is modeled, and the decoding error probabilities of both the server and the eavesdropper are characterized. Subsequently, Extreme Value Theory (EVT) is employed to model the delay violation probability in the computation phase. Based on these formulations, the end-to-end LFP is derived to comprehensively assess the system’s PLS and reliability. Given the stringent end-to-end latency constraints in URLLC, a fundamental trade-off between security and reliability arises. To investigate this trade-off, an optimization problem is formulated to minimize LFP while satisfying the latency constraint, with the time length of the communication and computation phases as the optimization variables. However, due to the presence of multiplicative non-convex Q-functions in the objective function, solving this optimization problem poses significant challenges. To overcome this, a local point expansion technique is employed to approximate the non-convex terms as summations, facilitating problem reformulation. Additionally, auxiliary variables are introduced to enable convex relaxation of the Q-functions, transforming the problem into a convex optimization framework. The reformulated problem is then efficiently solved using the Successive Convex Approximation (SCA) algorithm. In response to the issue of performance degradation in algorithm convergence when the task scale increases, an Accelerated-SCA (A-SCA) algorithm is introduced. By leveraging the problem structure, a closed-form expression for the time allocation strategy corresponding to the system's optimal reliability is derived, which improves the initial point selection mechanism. Finally, simulation results validate the accuracy, convergence, and adaptability of the proposed algorithm, while also revealing the trade-off between security and reliability. Results and Discussions The proposed FBL coding-based time allocation scheme is evaluated through numerical simulations to validate its effectiveness. A comparative analysis with the IBL coding-based scheme is conducted, demonstrating the performance gains achieved through the joint optimization of communication and computation phases, while highlighting the limitations of IBL coding. Furthermore, the quality of the sub-optimal solution obtained via the SCA algorithm is assessed by benchmarking against the exhaustive search method. Additionally, both constrained and unconstrained resource scenarios are considered to explore the trade-offs between security and reliability, as well as between time resource and LFP. First, (Fig. 2) analyzes the convergence behavior of the proposed SCA-based time allocation scheme, demonstrating that the sub-optimal solution obtained closely approximates the global optimum. Furthermore, by optimizing the initial point selection with the A-SCA algorithm, the convergence speed and scalability are significantly enhanced. (Fig. 3) evaluates the variation of LFP with the eavesdropper’s channel gain under different task data sizes, revealing the fundamental trade-off between security and reliability, along with the associated performance compensation mechanism. (Fig. 4) illustrates the LFP trend as a function of the server’s channel gain, comparing the proposed scheme with IBL coding-based scheme. The results indicate that even in scenarios with low secure channel capacity, the proposed scheme achieves a 21.4% performance improvement, thereby validating its superiority in joint optimization across both phases. (Fig. 5) investigates the impact of task workload on LFP, while (Fig. 6) explores an unconstrained resource scenario, analyzing the influence of transmit power on LFP and the corresponding service time requirements. Conclusions This study explores the joint optimization of security and reliability in multi-access MEC networks supporting URLLC. In the FBL regime, the decoding error probability is modeled in the communication phase, while the delay violation probability is characterized using EVT in the computation phase. Based on these models, the LFP is derived as a key metric to quantify the system’s end-to-end PLS and reliability. Given the stringent latency constraints, a two-phase time length allocation optimization problem is formulated to minimize LFP, balancing security and reliability. To address the problem’s non-convexity, this paper first decouples the coupled variables through local linearization and reconstructs the problem. Next, convex upper and lower bounds of the complex Q-function are derived, enabling convex relaxation of the objective function. Subsequently, the SCA algorithm is applied iteratively to obtain a sub-optimal solution for the original problem. To tackle the issue of deteriorating convergence performance and increased complexity as the task scale grows, an A-SCA algorithm is proposed, which optimizes the initial point selection to address this challenge. Finally, simulation results validate the convergence, accuracy, and scalability of the proposed algorithm, demonstrating its performance advantages through comparison with existing approaches. Additionally, the impact of key system parameters on performance is systematically analyzed. The proposed LFP metric exhibits significant extensibility, being applicable to collaborative optimization of physical layer security and network layer security in URLLC systems, reliable task offloading in MEC, and throughput optimization in MIMO systems. Furthermore, the solution framework for LFP optimization problem, including problem decoupling and reconstruction, introduction of auxiliary variables for Q-function convex relaxation, iterative solving via SCA, and optimized initial point selection, provides valuable insights for solving multi-variable non-convex optimization problems in the FBL regime.
- Mobile Edge Computing (MEC),
- Finite BlockLength (FBL),
- Extreme Value Theory (EVT),
- Physical Layer Security (PLS)

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