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secure computation offloading Using Enhanced Genetic Algorithm for Ocean IoT

JOURNAL OF GRID COMPUTING 2025年第1期23卷 1-19页

作者： Du, Ruizhong Zhao, Jianwei Gao, Yan Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Hebei Peoples R China Hebei Univ Hebei Prov Key Lab High Confidence Informat Syst Baoding 071000 Peoples R China Tianjin Univ Sch New Media & Commun Tianjin 300000 Peoples R China

The network nodes in the Ocean Internet of Things exhibit strong heterogeneous characteristics, which introduce complex and high-dimensional constraints for the optimization of task offloading in ocean mobile edge computing. To prevent data from being intercepted by malicious attackers during computation offloading and to protect client privacy, an offloading framework named the Enhanced Genetic Algorithm for secure computation offloading is proposed. The population initialization and crossover operators are optimized before the initialization phase to accelerate convergence and avoid the risk of excessive resource consumption due to cyclic scheduling. An elitism strategy is also introduced, along with dynamic mutation rate adjustment, to enhance the search efficiency and global optimization capability of the algorithm. The superiority of the proposed framework is validated through simulation experiments and a comprehensive comparison with other benchmark algorithms. Compared to the Simulated Annealing, Particle Swarm Optimization algorithms, the Min-Min algorithm, Basic Genetic Algorithm and Proximal Policy Optimization, the proposed scheduling algorithm demonstrates a significant enhancement in secure capacity during local computation, with improvements of 47.60%, 42.16%, 15%, 25% and 16.67%, respectively. Additionally, it achieves an increase in security during the computation offloading process by 16.54%, 16.22%, 14.29%, 12.56% and 10.02% respectively.

关键词： Ocean internet of things Mobile edge computing secure computation offloading Enhanced genetic algorithm Privacy protection

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secure computation offloading and Service Caching in Mobile Edge Computing Networks

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IEEE COMMUNICATIONS LETTERS 2024年第2期28卷 432-436页

作者： Wu, Mengru Li, Kexin Qian, Liping Wu, Yuan Lee, Inkyu Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Univ Macau State Key Lab Internet Things Smart City Zhuhai Macau Peoples R China Univ Macau Dept Comp & Informat Sci Zhuhai Macau Peoples R China Korea Univ Sch Elect Engn Seoul 02841 South Korea

Mobile edge computing (MEC) enables the caching of various services close to users, thereby reducing service delay for emerging applications. However, realizing efficient and secure computation offloading is challenging due to the limited storage capacity of MEC servers and the offloading security issue arising from the open nature of wireless channels. In this letter, we investigate a MEC network with an eavesdropper, where the MEC server caches service programs required for task execution. Moreover, we propose a cooperative jamming-assisted scheme to enhance the security of computation offloading, in which a friendly jammer actively transmits signals to disrupt eavesdropping. Considering the limited caching capacity of the MEC server and the security concerns of computation offloading, we formulate a delay minimization problem by jointly optimizing service caching placement, transmit power, and devices' computation offloading decisions. To tackle this problem, we develop an efficient layered algorithm using successive convex approximation and the cross entropy-based technique. Numerical results demonstrate the effectiveness of our proposed scheme in reducing the overall delay in completing devices' computation tasks compared to baseline schemes.

关键词： Task analysis Servers Jamming Delays Security Eavesdropping Wireless communication Mobile edge computing service caching secure computation offloading cooperative jamming

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secure computation offloading for Integrated Communication and Computing Maritime Networks

Secure Computation Offloading for Integrated Communication a...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Zhang, Chaoyue Lin, Bin Han, Yongchun Yang, Lue Duan, Jianli Dalian Maritime Univ Informat Sci & Technol Coll Dalian 116026 Peoples R China Qingdao Univ Technol Sch Sci Qingdao 266520 Peoples R China

ISBN: (纸本)9798350378412

Mobile Edge Computing (MEC) has been regarded as a promising paradigm for provisioning computation resources to process massive computation-intensive tasks. In this paper, we investigate the integrated communication and computing maritime network via the assistance of a High Altitude Platform (HAP) subject to the eavesdropping attack. Specifically, the transmit power and offloading ratio are jointly optimized to minimize the maximal overall time for processing computation tasks, under the secure offloading rate and energy consumption constraints. The iterative algorithm is adopted to obtain the near-optimal solutions by decomposing two variables into subproblems. Specifically, the convex optimization method is exploited to solve the transmit power subproblem and the offloading ratio subproblem. Numerical results demonstrate the effectiveness of our proposed solution in reducing the overall time for processing computation tasks.

关键词： Integrated communication and computing maritime networks secure computation offloading convex optimization

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Non-Orthogonal Multiple Access Assisted secure computation offloading via Cooperative Jamming

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第7期71卷 7751-7768页

作者： Wu, Yuan Ji, Guangyuan Wang, Tianshun Qian, Liping Lin, Bin Shen, Xuemin Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Zhuhai Sci & Technol Res Inst Zhuhai 519031 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Dalian Maritime Univ Dept Commun Engn Dalian 116026 Peoples R China Network Commun Res Ctr Peng Cheng Lab Shenzhen 518052 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure computation offloading under the eavesdropping-attack, in which a malicious node overhears the edge-computing user's (EUs) offloading transmission to the edge-computing server (ES), and NOMA is used for the EU's offloading and meanwhile for providing artificial jamming to the eavesdropper. Since multi-user simultaneous transmission over a same frequency channel can be enabled by NOMA, a wireless user (WU) can form a NOMA pair with the EU to provide cooperative jamming to the eavesdropper while also gaining an opportunity of sending its data. Focusing on the EU-WU pair with the fixed WU's energy-provisioning, we exploit the physical layer security to quantify the EU's offloading throughput with the help of WU's jamming. We then study the joint optimization of the EU's computation offloading and the EU-WU's NOMA transmission for minimizing the EU's total energy consumption subject to its latency-requirement in completing the computation-task. By utilizing the feature of analytical solution of the WU's transmission, we then investigate the WU's optimal energy-provisioning for the EU-WU pair, such that both the EU and WU can benefit from the cooperative jamming in a fairness manner. Specifically, we formulate the EU-WU's cooperation as a Nash bargaining game. By identifying the monotonic feature of Nash bargaining problem, we propose a polyblock approximation based algorithm for determining the WU's optimal energy-provisioning to achieve the win-win solution for the paired EU and WU. Finally, we investigate the scenario of multiple EUs and WUs, and aim at finding the stable pairing between the EUs and WUs, such that no individual EU (or WU) would like to change its partner. An efficient algorithm, which is based on the Gale-Shapley theory while exploiting the quantitative feature of EUs and WUs net-rewards, is proposed to achieve the stable EU-WU pairings. Numerical results are provided

关键词： secure computation offloading non-orthogonal multiple access cooperative jamming fairness and stable pairing

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Joint Service Caching and secure computation offloading for Reconfigurable-Intelligent-Surface-Assisted Edge Computing Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 30469-30482页

作者： Wu, Mengru Chen, Weijin Qian, Liping Guo, Lei Lee, Inkyu Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Korea Univ Sch Elect Engn Seoul 02841 South Korea

Mobile edge computing (MEC) pushes computing and caching resources close to the network edge, which allows devices to offload computation-intensive tasks to MEC servers. Considering that wireless signals may be easily blocked by obstacles, reconfigurable intelligent surface (RIS) has emerged as a promising technique to improve the efficiency of computation offloading. In this article, we consider a RIS-assisted MEC network, where a MEC server caches service programs required for task execution and a RIS helps computation offloading in the presence of eavesdropping. Due to the diversity of services and the broadcast nature of wireless channels, it is challenging to achieve efficient and secure computation offloading in this network. Therefore, we first formulate a task completion delay minimization problem by jointly optimizing service caching (SC), computation offloading decisions, RIS passive beamforming, and transmit power subject to the constraints of secure offloading rate and limited storage space. To address the highly nonconvex nature of the problem, we then develop a dual-layer optimization algorithm via a vertical decomposition on its layered structure. The outer-layer problem, which deals with SC and computation offloading decisions, is solved by a cross-entropy-based caching and offloading learning algorithm. For the inner-layer problem that optimizes RIS passive beamforming and transmit power, we utilize a horizontal decomposition by invoking the block coordinate descent method. Finally, simulation results demonstrate that the proposed scheme exhibits performance improvements compared to several baseline schemes.

关键词： Task analysis Servers Delays Internet of Things Optimization Array signal processing Security Mobile edge computing (MEC) reconfigurable intelligent surface (RIS) secure computation offloading service caching (SC)

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secure and Multistep computation offloading and Resource Allocation in Ultradense Multitask NOMA-Enabled IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 5347-5361页

作者： Zhou, Tianqing Fu, Yanyan Qin, Dong Nie, Xuefang Jiang, Nan Li, Chunguo East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

Ultradense networks are widely regarded as a promising solution to explosively growing applications of Internet of Things (IoT) mobile devices (IMDs). However, complicated and severe interferences need to be tackled properly in such networks. To this end, both orthogonal multiple access (OMA) and non-OMA (NOMA) are considered under base station (BS) clustering. Then, in order to attain a goal of green and secure computation offloading, under the proportional allocation of computation resources, and the constraints of latency and security cost, joint device association, channel selection, security service assignment, power control, and computation offloading are performed for minimizing the overall energy consumed by all IMDs. It is noteworthy that multistep computation offloading is concentrated to balance the network loads and fully utilize computation resources. Since the finally formulated problem is in a nonlinear mixed-integer form, it may be very difficult to find its closed-form solution. To solve it, an improved whale optimization algorithm (IWOA) is designed. As for this algorithm, the convergence, computation complexity, and parallel implementation are analyzed in detail. Simulation results show that the designed algorithm may achieve lower energy consumption than other existing algorithms under strictly satisfying constraints of latency and security cost.

关键词： Channel selection device association Internet of Things (IoT) power control secure computation offloading ultradense networks whale optimization algorithm (WOA)

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Non-Orthogonal Multiple Access Assisted secure Dual-UAV Mobile Edge Computing Networks 31

Non-Orthogonal Multiple Access Assisted Secure Dual-UAV Mobi...

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31st Wireless and Optical Communications Conference (WOCC)

作者： Ji, Guangyuan Dai, Minghui Wu, Yuan Qian, Liping Su, Zhou Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Zhuhai UM Sci & Technol Res Inst Zhuhai Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Peoples R China

ISBN: (数字)9781665469500

ISBN: (纸本)9781665469500

With the high flexibility and mobility of unmanned aerial vehicles (UAVs), carrying mobile computing servers on UAV is able to provide effectively computation service for the ground users (GUs). However, since UAVs usually adopt lineof-sight transmission, malicious eavesdroppers can eavesdrop on the offloading data of the GUs. Thanks to non-orthogonal multiple access (NOMA), it allows wireless devices to provide cooperative jamming to the eavesdropper during GUs' offloading. In this paper, we propose NOMA assisted secure dual-UAV mobile computation offloading (MEC) networks. Specifically, two UAVs perform as the MEC server and the jamming UAV in turn. When one UAV provides MEC service to the GU, the other UAV sends interference signals to reduce the offloading information overheard by the eavesdropper. We then investigate the joint optimization of the GUs' computation offloading, GUUAV's transmission and UAVs' position to maximize the GUs' benefit in a fairness manner. Due to the non-convexity of the formulated problem, we divide it into three sub-problems and propose algorithms to solve them. Numerical results are provided to validate the efficiency of our proposed algorithms.

关键词： Unmanned aerial vehicle NOMA secure computation offloading cooperative jamming

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SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications

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INTERNET OF THINGS 2019年 7卷

作者： Alli, Adam A. Alam, Muhammad Mahbub Islamic Univ Uganda Mbale Uganda Islamic Univ Technol Gazipur 1704 Bangladesh

computation offloading is one of the important application in Internet of Things (IoT) ecosystem. computational offloading provides assisted means of processing large amounts of data generated by abundant IoT devices, speed up processing of intensive tasks and save battery life. In this paper, we propose a secure computation offloading scheme in Fog-Cloud-IoT environment (SecOFF-FCIoT). Using machine learning strategies, we accomplish efficient, secure offloading in Fog-IoT setting. In particular, we employ Neuro-Fuzzy Model to secure data at the smart gateway, then the IoT device selects an optimal Fog node to which it can offload its workload using Particle Swarm Optimization(PSO) via the smart gateway. If the fog node is not capable of handling the workload, it is forwarded to the cloud after being classified as either sensitive or non-sensitive. Sensitive data is maintained in private cloud. Whereas non-sensitive data is offloaded using dynamic offloading strategy. In PSO, the availability of fog node is computed using two metrics;i) Available Processing Capacity (APC), and ii) Remaining Node Energy (RNE). Selection of cloud is based on Reinforcement Learning. Our proposed approach is implemented for smart city applications using NS-3 simulator with JAVA Programming. We compare our proposed secure computation offloading model with previous approaches which include DTO-SO, FCFS, LOTEC, and CMS-ACO. Simulation results show that our proposed scheme minimizes latency as compared to selected benchmarks. (C) 2019 Elsevier B.V. All rights reserved.

关键词： secure computation offloading Internet of Things Fog-Cloud Neuro-Fuzzy systems Smart city application Reinforcement learning

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