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Article
Computer Science, Information Systems
Xiaoheng Deng et al.
Summary: The development of Industrial Internet of Things (IIoT) and Industry 4.0 has transformed the traditional manufacturing industry. With the mobile-edge computing (MEC) system, computation-intensive tasks can be offloaded from resource-constrained IIoT devices to nearby MEC servers, resulting in lower delay and energy consumption for better Quality of Service (QoS).
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Jun Liu et al.
Summary: This paper proposes an innovative score-based attack model to solve the problem of important word selection in textual attack models. The model uses semantically adversarial examples to mislead a text classification model and combines self-attention mechanism and confidence probabilities to select important words.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Guowen Wu et al.
Summary: Social Internet of Things (SIoT) is a fusion of Internet of Things and social networks, which has attracted attention as a target for hackers to spread viruses and breach data confidentiality. To address these problems, a novel virus spread model (STSIR) based on epidemic theory and game theory is proposed, which considers people behavior and the characteristics of SIoTs. The model STSIR introduces an individual-group game to establish the attack and defense model between infected and susceptible SIoT nodes, and differential equations to represent the model. Simulation results show that the model STSIR is more effective in curbing virus spread compared to traditional SIS and SIR models.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Guowen Wu et al.
Summary: To meet the computing requirements of industrial production, EIIoT that combines mobile edge computing with IIoT has emerged. An optimization model based on queuing theory is proposed to solve the task offloading problem in EIIoT. The improved MAQDRL algorithm achieves optimal offloading strategy in dynamic and random multiuser offloading environments.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2023)
Article
Computer Science, Information Systems
Chao Wang et al.
Summary: This article proposes a blockchain-enabled resource orchestration scheme for IoT using deep reinforcement learning. The scheme allows the IoT edge server and end user to reach a consensus on network resource allocation based on blockchain theory. By utilizing a policy network, the intelligent agent can perceive changes in the network's state and make dynamic resource allocation decisions. Simulation results demonstrate that the proposed scheme performs better than other security resource allocation algorithms, with average revenue, user request acceptance rate, and profitability increased by 8.5%, 1.8%, and 11.9%, respectively.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Shigen Shen et al.
Summary: This study proposes a signaling game approach for privacy preservation in edge-computing-based IoT networks. It addresses the issue of malicious IoT nodes requesting private data from an IoT cloud storage system across edge nodes. The optimal privacy preservation strategies for edge nodes are derived and a signaling Q-learning algorithm is designed to achieve convergent equilibrium and game parameters. Simulation results show that the proposed algorithm effectively decreases the optimal probability of malicious requests, enhancing privacy preservation in edge-computing-based IoT cloud storage systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Telecommunications
Yizhou Shen et al.
Summary: This article presents evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme, addressing the issue of malicious requests and proposing a new algorithm for optimal learning strategy.
DIGITAL COMMUNICATIONS AND NETWORKS
(2023)
Article
Computer Science, Hardware & Architecture
Mian Guo et al.
Summary: This paper investigates the problem of green computation offloading in mobile edge computing (MEC) using energy harvesting (EH) devices and proposes a solution through game theory modeling and effective algorithm design.
Article
Engineering, Electrical & Electronic
Chenchen Han et al.
Summary: Delay-tolerant network (DTN) is designed to operate effectively in networks with intermittent connectivity. This article introduces a social-based mechanism for DTN routing design and proposes a collaborative multi-agent reinforcement learning aided routing algorithm.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Jun Cai et al.
Summary: The paper introduces a deep reinforcement learning-based multiuser multitask hybrid computing offloading model to reduce long-term overall system delay. The model makes global offloading decisions for multiple computation-intensive tasks simultaneously, improving system stability and efficiency. The experimental results show that this model outperforms other methods regarding long-term overall system delay and device energy consumption.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Miaojiang Chen et al.
Summary: This paper proposes a polling callback energy-saving offloading strategy, and simulation results show that the proposed algorithm performs better than DDQN, DQN, and BCD-based optimal methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Syed Danial Ali Shah et al.
Summary: This article focuses on addressing the quality of service challenges in a mobile environment by utilizing the concepts of multiaccess edge computing (MEC) and software-defined networking (SDN).
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Jiwei Huang et al.
Summary: With the development of 5G cellular communication systems and mobile-edge computing, this article proposes an efficient task offloading scheme for multiple devices and base stations. The goal is to minimize the delay in completing device tasks. By analyzing local and offloading delays, the authors formulate a nonlinear and nonconvex optimization problem using game theory and propose a distributed task offloading algorithm called DOLA. Simulation experiments validate the efficacy of DOLA and demonstrate its superiority compared to existing schemes.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Yuwei Li et al.
Summary: This article introduces a vehicular fog-edge computing paradigm and tackles the challenges of offloading computing tasks to vehicles through a multistage Stackelberg game. By using incentive mechanisms and pricing strategies, resource coordination and utility maximization for all parties are achieved.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Bingxin Yao et al.
International Journal of Circuits, Systems and Signal Processing
(2022)
Article
Computer Science, Hardware & Architecture
Qiang Tang et al.
Summary: This paper investigates a mobile edge computing system aided by multiple access points and a UAV. By dividing the computing tasks of IoTDs and jointly optimizing task allocation, power distribution, and UAV trajectory, the goal is to minimize the consumption of communication, calculation, and flight over a finite UAV mission duration. By decomposing the problem and iteratively solving sub-problems through specific methods, the proposed approach outperforms other comparison baselines.
Article
Engineering, Electrical & Electronic
Weiyang Feng et al.
Summary: The paper introduces a reverse offloading framework to optimize the computation resource allocation in CVIS for reduced system latency and improved performance. By designing different strategies and algorithms, the burden on the VEC server is successfully reduced, resulting in enhanced performance outcomes.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Yizhou Shen et al.
Summary: This study addresses the issue of IoT malware dissemination in edge computing-assisted IoT systems. It predicts the optimal dissemination strategy and develops an algorithm to assess IoT availability. The research provides insights into the impact of system features on malware dissemination and availability assessment.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2022)
Article
Computer Science, Software Engineering
Jiashu Wu et al.
Summary: This article proposes a profit and cost-oriented optimization model for edge-cloud computation offloading, taking into account task heterogeneity, load balancing, and profit from computation tasks. An improved Moth-flame optimizer, PECCO-MFI, is introduced to address the complexity and non-differentiability of the optimization objective, and is integrated into the edge-cloud environment. Comprehensive experiments demonstrate the superior performance of the proposed method in optimizing the task offloading model under the edge-cloud environment.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Engineering, Civil
Junwei Liang et al.
Summary: In this paper, a Bayesian Game theory and Deep Q-learning Network-based IDS, called GaDQN-IDS, is proposed for addressing the balance between accuracy and efficiency in Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs). The dynamic intrusion detection game model is used to handle the interaction between IDS and attackers, achieving adaptive adjustment and retraining of IDS, ultimately showing better performance in simulation results.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Peiying Zhang et al.
Summary: Space-air-ground integration is a key trend in the 6G era, and efficient scheduling of multi-dimension network resources is a major challenge. By employing reinforcement learning and a bandwidth-aware virtual network resource allocation algorithm, the proposed approach outperforms conventional algorithms in terms of long-term average reward, acceptance rate, and long-term reward/cost.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2022)
Article
Computer Science, Hardware & Architecture
Yingying Jiang et al.
Summary: With the emergence of mobile edge computing (MEC), the computing and delay requirements of mobile devices can be solved by deploying edge clouds closer to the devices. In dense 5G heterogeneous networks where macro base stations (MBS) and multiple small base stations (SBS) are deployed, the offloading decision needs to consider multiple choices. To address the problem of computing offloading and resource allocation, a collaborative optimization strategy based on multi-agent deep reinforcement learning (MADRL) is proposed. Simulation results demonstrate that this strategy outperforms other baseline schemes in terms of service response delay and energy consumption.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Automation & Control Systems
Panjun Sun et al.
Summary: This paper constructs a new tripartite game model to enhance trust and cooperation among service participants. By calculating the ideal stable state point, it addresses the trust issues in network edge services, and designs relevant experimental studies to verify and compare the correctness and effectiveness of several factors that affect the convergence stability of the evolutionary games.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Theory & Methods
Peiying Zhang et al.
Summary: This paper proposes a virtual network embedding (VNE) algorithm assisted by resource knowledge description (RKD) and deep reinforcement learning (DRL) to address the requirements of efficient network resource utilization and security in industrial Internet of Things (IIoT) applications. By using social attribute perception to measure the security of physical nodes, standardizing resource constraints with RKD, and using a DRL agent to derive the probability of physical node embedding, the algorithm shows significant advantages in VNE, as demonstrated by simulation experiments.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Md Zahangir Alam et al.
Summary: This paper investigates the computation offloading problem in a high mobility internet of vehicles (IoVs) environment, and proposes a cooperative decentralized VMEC network to solve the task offloading problem in high mobility environments.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Huan Zhou et al.
Summary: This article proposes a game theory-based computation offloading method to improve the Quality of Service (QoS) of applications by encouraging cloud-edge servers to participate in the task offloading process. Numerical simulation results demonstrate that the method outperforms other benchmark schemes in different scenarios and effectively promotes the trade of computational resources between edge servers and cloud servers.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Hyeonseok Seo et al.
Summary: With the development of IoT, there is an increased demand for delay-sensitive applications, leading to the emergence of mobile-edge computing (MEC) as a promising technology. This article proposes a differential pricing scheme to reflect the user's usage of server computational resources and suggests optimal offloading and pricing strategies. Numerical results demonstrate the effectiveness of the proposed scheme in improving the efficiency of edge server's computational resources.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Chenhao Wu et al.
Summary: This paper proposes a hybrid task offloading scheme based on deep reinforcement learning, which achieves vehicle-to-edge and vehicle-to-vehicle offloading by considering delay constraints and resource demand. The approach effectively reduces task delay and energy consumption, achieving high-efficiency resource management.
Article
Telecommunications
Chen Chen et al.
Summary: This article proposes an end-edge-cloud architecture for task computation offloading in the Internet of Vehicles (IoV) and utilizes an A3C-based algorithm to solve the dynamic offloading problem. Experimental results show that the proposed scheme achieves good performance and outperforms other schemes in terms of task completion efficiency and fairness.
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING
(2022)
Proceedings Paper
Computer Science, Software Engineering
Jiaying Yin et al.
Summary: In this paper, a scalable priority-based index policy called PIER is proposed to optimize energy efficiency in fog computing. It has a wide range of applications in the field and is proven superior to benchmark policies through simulations.
ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT I
(2022)
Article
Computer Science, Artificial Intelligence
Shih-Yang Lin et al.
Summary: This paper proposes a PKMR method for vehicle to vehicle to RSU data offloading based on the architecture of SDN controller inside MEC server, which achieves better data offloading performance by using prediction mechanism and quality function for path selection.
IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Computer Science, Artificial Intelligence
Jun Liu et al.
Summary: The study extended the CBoW word vector model and proposed a cross-domain sentiment-aware word embedding learning model, which can capture both the sentiment information and domain relevance of a word. The experimental results demonstrate that the model has higher accuracy and Macro-F1 value when dealing with sentiment information.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Information Systems
Fenghui Zhang et al.
Summary: The study addresses the load balancing issues of multiple independent cloudlets and proposes decentralized learning algorithms to improve service quality and user experience. Application scenarios for static and dynamic users are tested, with experiments showing improved load balancing and service quality.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Analytical
Shuyang Li et al.
Summary: This study focuses on the offloading decision and resource allocation problem in the UAV-assisted MEC environment, optimizing the offloading policy using the SAC algorithm and effectively reducing delay, energy consumption, and the size of discarded tasks.
Article
Computer Science, Information Systems
Guisong Yang et al.
Summary: Computation offloading from mobile devices to edge servers is a new paradigm to reduce completion latency of intensive computations in mobile-edge computing. Offloading time is crucial for delay-sensitive computing tasks, and selecting an optimal offloading node is important to minimize the offloading time. This study formulates an optimal offloading node selection strategy based on available bandwidth and device location, demonstrating its effectiveness over classic strategies in terms of offloading time through extensive numerical results.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Wenhao Fan et al.
Summary: This article proposes a game-based collaborative computing offloading scheme to balance the computing delays of tasks on each MEC-BS. By utilizing a distributed iterative algorithm, efficient solution to the tasks is achieved, with simulation results showing fast convergence of the proposed algorithm and the reduction of total computing delay by 45%-50% on average in multiple scenarios, demonstrating the superiority of the scheme through comparisons with reference schemes.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Runhua Wang et al.
Summary: This paper jointly analyzes the computation offloading and resource allocation in vehicular edge computing based on the Walrasian equilibrium, proposing an algorithm with fast convergence rate to find the solution of VEC Walrasian equilibrium, which is shown effective through simulation results.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Tong Liu et al.
Summary: This article introduces an optimized task offloading strategy based on mobile-edge computing in an ultradense network. A double deep Q network (DDQN) approach is proposed using reinforcement learning, along with a context-aware attention mechanism. Extensive simulations demonstrate the effectiveness of the proposed method.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Jianhua Liu et al.
Summary: In this article, interactions between a sensor device-edgeVM pair and a DDoS attacker are investigated using a game-theoretic framework, under the constraints of task time, resource budget, and incomplete knowledge of machine learning tasks processing time. A Bayesian Q-learning game is used to model the strategic resource allocation problem between the sensor device-edgeVM pair and the attacker, and a greedy Q-learning algorithm is proposed for dependable resource allocation against DDoS attacks. Numerical simulation results show the superiority of the proposed mechanism in the sensor edge cloud under incomplete information for DDoS attacks.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Hardware & Architecture
Binbin Huang et al.
Summary: This paper discusses a cost-aware collaborative task-execution scheme in energy harvesting D2D networks, and the experimental results demonstrate that the scheme can effectively improve system efficiency and increase the number of completed tasks, while reducing task latency and dropout rate.
Article
Computer Science, Hardware & Architecture
Bo Li et al.
Summary: This paper introduces a three-layer hierarchy control framework for SDN-IoV based on MEC technology, aiming at enhancing IoV performance. It investigates controller placement and replacement strategies to minimize delay and achieve load balance, showing better performance than two baselines in terms of delay and load balance index.
Article
Engineering, Civil
Xiaolong Xu et al.
Summary: This study introduces a secure service offloading method named SOME, aiming to enhance service utility and privacy security in SDN-enabled EC. It addresses the uncertainty of edge network through SDN controllers and utilizes LSH for utility- and privacy-aware service selection.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Ao Zhou et al.
Summary: The growing demand for mobile services requires efficient task offloading and resource allocation strategies taking into account the impact of container instance startup time. This study proposes a novel approach that addresses these challenges and improves overall performance significantly over existing solutions.
2021 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2021)
(2021)
Article
Computer Science, Information Systems
Shuyang Li et al.
Summary: This research addresses the offloading decision problem in an SDN-driven MEC environment with multiple users and servers to prevent the abuse of computing resources by end-users. By utilizing deep reinforcement learning and game theory, an optimization framework is proposed to maximize the profit of MEC servers. Extensive simulation results demonstrate that our approach outperforms existing solutions in terms of performance.
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Guangxu Zhu et al.
IEEE COMMUNICATIONS MAGAZINE
(2020)
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(2020)
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Abegaz Mohammed et al.
2020 17TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP)
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Junxia Li et al.
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IEEE TRANSACTIONS ON MOBILE COMPUTING
(2019)
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IEEE COMMUNICATIONS MAGAZINE
(2018)
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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
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IEEE CLOUD COMPUTING
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