Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.

Socially Aware Dynamic Computation Offloading Scheme for Fog Computing System With Energy Harvesting Devices

Abstract: Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting (EH) techniques, the system performance could be further improved. In this paper, we take the social relationships of the EH mobile devices (MDs) into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost, we advocate game theoretic approach and propose a dynamic computation offloading scheme designing the offloading process in fog computing system with EH MDs. Different queue models are applied to model the energy cost and delay performance. It can be seen that the proposed problem can be formulated as a generalized Nash equilibrium problem (GNEP) and we can use exponential penalty function method to transform the original GNEP into a classical Nash equilibrium problem and address it with semi-smooth Newton method with Armijo line search. The simulation results demonstrate the effectiveness of the proposed scheme.

Cooperative Service Caching and Workload Scheduling in Mobile Edge Computing

Abstract: Mobile edge computing is beneficial for reducing service response time and core network traffic by pushing cloud functionalities to network edge. Equipped with storage and computation capacities, edge nodes can cache services of resource-intensive and delay-sensitive mobile applications and process the corresponding computation tasks without outsourcing to central clouds. However, the heterogeneity of edge resource capacities and mismatch of edge storage and computation capacities make it difficult to fully utilize both the storage and computation capacities in the absence of edge cooperation. To address this issue, we consider cooperation among edge nodes and investigate cooperative service caching and workload scheduling in mobile edge computing. This problem can be formulated as a mixed integer nonlinear programming problem, which has non-polynomial computation complexity. Addressing this problem faces challenges of sub-problem coupling, computation-communication tradeoff, and edge node heterogeneity. We develop an iterative algorithm named ICE to solve this problem. It is designed based on Gibbs sampling, which has provably near-optimal performance, and the idea of water filling, which has polynomial computation complexity. Simulation results demonstrate that our algorithm can jointly reduce the service response time and the outsourcing traffic, compared with the benchmark algorithms.

Providing Task Allocation and Secure Deduplication for Mobile Crowdsensing via Fog Computing

Abstract: Mobile crowdsensing enables a crowd of individuals to cooperatively collect data for special interest customers using their mobile devices. The success of mobile crowdsensing largely depends on the participating mobile users. The broader participation, the more sensing data are collected; nevertheless, the more replicate data may be generated, thereby bringing unnecessary heavy communication overhead. Hence it is critical to eliminate duplicate data to improve communication efficiency, a.k.a., data deduplication. Unfortunately, sensing data is usually protected, making its deduplication challenging. In this paper, we propose a fog-assisted mobile crowdsensing framework, enabling fog nodes to allocate tasks based on user mobility for improving the accuracy of task assignment. Further, a fog-assisted secure data deduplication scheme (Fo-SDD) is introduced to improve communication efficiency while guaranteeing data confidentiality. Specifically, a BLS-oblivious pseudo-random function is designed to enable fog nodes to detect and remove replicate data in sensing reports without exposing the content of reports. To protect the privacy of mobile users, we further extend the Fo-SDD to hide users’ identities during data collection. In doing so, Chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous mobile users. Finally, we demonstrate that both schemes achieve secure, efficient data deduplication.

Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems

Abstract: In this paper, we propose a multilayer data flow processing system, i.e., EdgeFlow, to integrally utilize the computing capacity throughout the whole network, i.e., the cloud center (CC) on the top layer, the mobile edge computing (MEC) servers on the middle layer, and the edge devices (EDs) on the bottom layer. To realize the efficient data processing in EdgeFlow, we optimally assign the tasks to multiple layers, and allocate the wireless transmission resources between the MEC servers and EDs as well as the wired transmission resources between the CC and MEC servers. We prove that the system is naturally classified into two states, the nonblocking state and the blocking state, according to various data generation speed at the EDs. The system latency is minimized for the nonblocking state even though the problem is nonconvex. As for the blocking state, the recovery time is minimized through solving a min-max problem. Based on the analytical results, the EdgeFlow system is implemented on the universal software radio peripheral and the Intel next units of computing. A typical Internet of Things application, photo recording and face recognition, is used for the simulation and the experiment, and indicates that the EdgeFlow can achieve a low latency and recovery time than the previous distributed frameworks, e.g., the Cloudlet and the Markov decision process.

Emerging Technologies for 5G-IoV Networks: Applications, Trends and Opportunities

Abstract: With the evolution of the technologies for IoV to be intelligent and interconnected, V2X communication technology serves as a core technology for information interaction among intelligent connected vehicles, and an important technology to realize environment sensing for future autonomous driving. In order to provide wireless communication services with ultra-low delay, ultra-high reliability and ultra-large bandwidth, this article proposes architectures of 5G-V2X communication networks by exploiting the technologies of 5G new radio (NR), network slicing, and deviceto- device communications. We discuss their principles and key features, and foresee the challenges, opportunities, and future research trends. The applications of related technologies of the software defined network (SND) and multi-access edge computing (MEC) are also introduced. Finally, the technologies of information security and privacy protection are identified to support the diverse services and applications in the future 5G-V2X networks.