Game Theory for Distributed IoV Task Offloading With Fuzzy Neural Network in Edge Computing

Xu, Xueqing; Jiang, Qinting; Zhang, Peiming; Cao, Xuefei; Khosravi, Mohammad R.; Alex, Linss T.; Qi, Lianyong; Dou, Wanchun

doi:10.1109/tfuzz.2022.3158000

Cited by 100 publications

(51 citation statements)

References 32 publications

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“…The fitness value of the path is calculated in step 5, and the new solution is added into the solution archive in step 6. (11)…”

Section: The Procedures For Solutionmentioning

confidence: 99%

“…The emergence of EC and CloudIoT brings many new applications, such as tasks offloading strategies [7][8][9], dynamic resource management [10], internet of vehicles [11,12], geographical Point-of-Interest (POI) recommendation [13], privacy security and recommender systems [14], cloud-based big data technique [15], smart city [16,17], fault-tolerant placement for cloud systems [18], data forecasting [19], convergence technology of computing, communication and caching [20], edge-cloud collaboration method [21], the efficient data collection with UAVs [22,23] and et al. Among them, the application of UAV can significantly improve the capabilities of IoT devices by processing the data of these devices.…”

Section: Introductionmentioning

confidence: 99%

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Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

et al. 2022

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In recent years, with the development of Unmanned Aerial Vehicle (UAV) and Cloud Internet-of-Things (Cloud IoT) technology, data collection using UAVs has become a new technology hotspot for many Cloud IoT applications. Due to constraints such as the limited power life, weak computing power of UAV and no-fly zones restrictions in the environment, it is necessary to use cloud server with powerful computing power in the Internet of Things to plan the path for UAV. This paper proposes a coverage path planning algorithm called Parallel Self-Adaptive Ant Colony Optimization Algorithm (PSAACO). In the proposed algorithm, we apply grid technique to map the area, adopt inversion and insertion operators to modify paths, use self-adaptive parameter setting to tune the pattern, and employ parallel computing to improve performance. This work also addresses an additional challenge of using the dynamic Floyd algorithm to avoid no-fly zones. The proposal is extensively evaluated. Some experiments show that the performance of the PSAACO algorithm is significantly improved by using parallel computing and self-adaptive parameter configuration. Especially, the algorithm has greater advantages when the areas are large or the no-fly zones are complex. Other experiments, in comparison with other algorithms and existing works, show that the path planned by PSAACO has the least energy consumption and the shortest completion time.

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“…The fitness value of the path is calculated in step 5, and the new solution is added into the solution archive in step 6. (11)…”

Section: The Procedures For Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

et al. 2022

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“…Dai et al [9] proposed an efficient task offloading approach based on DQN to minimize the processing delay of tasks, which jointly considered the edge-cloud opportunities and the convergence of deep reinforcement learning. Xu et al [8] presented a game theory-based service offloading approach to minimize tasks processing latency of users, where both predictions of traffic flow and the allocation of resources are considered. Qiu et al [24] propose a distributed and collective DRL algorithm, namely DC-DRL, to solve the multi-user computation offloading problem.…”

Section: Related Workmentioning

confidence: 99%

“…To compensate for the shortcomings of could computing in data transmission, vehicular edge computing (VEC) is emerging as a novel computing paradigm [8,9], which greatly reduces the distance of data transmission by deploying edge servers in roadside units or base stations relatively close to roads, thus largely enhancing the users' experience.…”

Section: Introductionmentioning

confidence: 99%

Dependency-Aware Task Offloading for Vehicular Edge Computing with End-Edge-Cloud Collaborative Computing

Dai

Huang

et al. 2022

Preprint

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Vehicular edge computing (VEC) is emerging as a new computing paradigm to improve the quality of vehicular services and enhance the capabilities of vehicles. It aids vehicles execution tasks with low latency by deploying computing and storage resources close to the vehicle side. However, the traditional task offloading schemes waste the fine-grained offloading opportunities for subtasks by neglecting the decomposability and dependency of tasks. Furthermore, the continuous control problem caused by the learning-based offloading schemes should be taken into account. In this paper, we proposed an efficient dependency-aware task offloading scheme, which minimizes the average processing delay of tasks by fully utilizing the end-edge-cloud collaborative computing. Specifically, first, the directed acyclic graph (DAG) technique is utilized to model the inter-subtask dependency, which establishes the priority of subtask execution. Second, a task offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) in Reinforcement Learning (RL) was proposed to obtain the optimal offloading strategy in a dynamic environment, which efficiently solves continuous control problems and helps reach fast convergence. Finally, we conduct extensive simulation experiments, and the experimental results show that the proposed dependency-aware task offloading scheme can achieve a good performance.

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“…Last but not least, as the application of AI technologies can significantly improve the resource utilization [7][8][9][10][11], the maturity model should consider the AI dimension.…”

Section: Introductionmentioning

confidence: 99%

A maturity model for AI-empowered cloud-native databases: from the perspective of resource management

et al. 2022

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Cloud-native database systems have started to gain broad support and popularity due to more and more applications and systems moving to the cloud. Various cloud-native databases have been emerging in recent years, but their developments are still in the primary stage. At this stage, database developers are generally confused about improving the performance of the database by applying AI technologies. The maturity model can help database developers formulate the measures and clarify the improvement path during development. However, the current maturity models are unsuitable for cloud-native databases since their architecture and resource management differ from traditional databases. Hence, we propose a maturity model for AI-empowered cloud-native databases from the perspective of resource management. We employ a systematic literature review and expert interviews to conduct the maturity model. Also, we develop an assessment tool based on the maturity model to help developers assess cloud-native databases. And we provide an assessment case to prove our maturity model. The assessment case results show that the database’s development direction conforms to the maturity model. It proves the effectiveness of the maturity model.

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Game Theory for Distributed IoV Task Offloading With Fuzzy Neural Network in Edge Computing

Cited by 100 publications

References 32 publications

Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

Dependency-Aware Task Offloading for Vehicular Edge Computing with End-Edge-Cloud Collaborative Computing

A maturity model for AI-empowered cloud-native databases: from the perspective of resource management

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