The Experts below are selected from a list of 1926 Experts worldwide ranked by ideXlab platform
Lan Yao - One of the best experts on this subject based on the ideXlab platform.
-
a novel reputation incentive mechanism and game theory analysis for service caching in software defined vehicle edge computing
Peer-to-peer Networking and Applications, 2021Co-Authors: Feng Zeng, Yaojia Chen, Lan YaoAbstract:Service caching can improve the QoS of computationally intensive vehicle Applications by pre-storing the necessary Application programs and related data for computing tasks on edge servers. In this paper, we propose a new vehicle edge computing framework based on software defined networks, which introduces the reputation to measure the contribution of each vehicle as the basis for providing different quality of services. The process is divided into two phases: in the first phase, the vehicle requests the Offload Application task from the edge server; and in the second phase, the edge server makes the service caching decision after processing the task. We design the whole interaction process as a kind of incentive mechanism based on reputation via using Stackelberg game modeling, and analyze the optimal strategy for both sides of the game by reverse induction. Furthermore, we also prove the existence and uniqueness of Stackelberg equilibrium in two-stage game, and a genetic optimization algorithm is designed to quickly obtain the optimal strategy for both sides of the game. Experimental results show that the proposed scheme not only brings more profits to the edge server side, but also reduces the average delay by 76 % compared with the ordinary mobile edge computing scheme.
Feng Zeng - One of the best experts on this subject based on the ideXlab platform.
-
a novel reputation incentive mechanism and game theory analysis for service caching in software defined vehicle edge computing
Peer-to-peer Networking and Applications, 2021Co-Authors: Feng Zeng, Yaojia Chen, Lan YaoAbstract:Service caching can improve the QoS of computationally intensive vehicle Applications by pre-storing the necessary Application programs and related data for computing tasks on edge servers. In this paper, we propose a new vehicle edge computing framework based on software defined networks, which introduces the reputation to measure the contribution of each vehicle as the basis for providing different quality of services. The process is divided into two phases: in the first phase, the vehicle requests the Offload Application task from the edge server; and in the second phase, the edge server makes the service caching decision after processing the task. We design the whole interaction process as a kind of incentive mechanism based on reputation via using Stackelberg game modeling, and analyze the optimal strategy for both sides of the game by reverse induction. Furthermore, we also prove the existence and uniqueness of Stackelberg equilibrium in two-stage game, and a genetic optimization algorithm is designed to quickly obtain the optimal strategy for both sides of the game. Experimental results show that the proposed scheme not only brings more profits to the edge server side, but also reduces the average delay by 76 % compared with the ordinary mobile edge computing scheme.
Yaojia Chen - One of the best experts on this subject based on the ideXlab platform.
-
a novel reputation incentive mechanism and game theory analysis for service caching in software defined vehicle edge computing
Peer-to-peer Networking and Applications, 2021Co-Authors: Feng Zeng, Yaojia Chen, Lan YaoAbstract:Service caching can improve the QoS of computationally intensive vehicle Applications by pre-storing the necessary Application programs and related data for computing tasks on edge servers. In this paper, we propose a new vehicle edge computing framework based on software defined networks, which introduces the reputation to measure the contribution of each vehicle as the basis for providing different quality of services. The process is divided into two phases: in the first phase, the vehicle requests the Offload Application task from the edge server; and in the second phase, the edge server makes the service caching decision after processing the task. We design the whole interaction process as a kind of incentive mechanism based on reputation via using Stackelberg game modeling, and analyze the optimal strategy for both sides of the game by reverse induction. Furthermore, we also prove the existence and uniqueness of Stackelberg equilibrium in two-stage game, and a genetic optimization algorithm is designed to quickly obtain the optimal strategy for both sides of the game. Experimental results show that the proposed scheme not only brings more profits to the edge server side, but also reduces the average delay by 76 % compared with the ordinary mobile edge computing scheme.
Neves Calheiros Rodrigo - One of the best experts on this subject based on the ideXlab platform.
-
Smart food scanner system based on mobile edge computing
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Javadi Bahman, Trieu, Quoc Lap, Matawie, Kenan M., Neves Calheiros RodrigoAbstract:Smart Applications, including Internet of Things (IoT) and Big Data analytics, are traditionally hosted by cloud infrastructures, which can result in high latency and cost beyond users expectation. Edge computing has emerged as a paradigm that can alleviate the pressure on clouds by delegating parts of the computation to devices in the edge of the network, at closer proximity to end users and IoT devices. In this paper, we discuss a smart Application, built on top of mobile edge computing concept, to enables users to measure and analyse their food intake and support nutritional decision-making. The approach utilizes mobile edge computing to Offload Application computations and communications to the edge, thus saving battery life, increasing the processing capacity, and improving user comfort. In order to develop this system, we propose a loosely coupled architecture for a smart food scanner and then implement it using various IoT sensors. The performance evaluation results reveal that the implemented system can be used as an interactive appliance by users with minimum dependency and usage of their mobile phones
Javadi Bahman - One of the best experts on this subject based on the ideXlab platform.
-
Smart food scanner system based on mobile edge computing
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Javadi Bahman, Trieu, Quoc Lap, Matawie, Kenan M., Neves Calheiros RodrigoAbstract:Smart Applications, including Internet of Things (IoT) and Big Data analytics, are traditionally hosted by cloud infrastructures, which can result in high latency and cost beyond users expectation. Edge computing has emerged as a paradigm that can alleviate the pressure on clouds by delegating parts of the computation to devices in the edge of the network, at closer proximity to end users and IoT devices. In this paper, we discuss a smart Application, built on top of mobile edge computing concept, to enables users to measure and analyse their food intake and support nutritional decision-making. The approach utilizes mobile edge computing to Offload Application computations and communications to the edge, thus saving battery life, increasing the processing capacity, and improving user comfort. In order to develop this system, we propose a loosely coupled architecture for a smart food scanner and then implement it using various IoT sensors. The performance evaluation results reveal that the implemented system can be used as an interactive appliance by users with minimum dependency and usage of their mobile phones
