Socially-Motivated Cooperative Mobile Edge Computing

Chen, Xu; Zhou, Zhi; Wu, Weigang; Wu, Di; Zhang, Junshan

doi:10.1109/mnet.2018.1700354

Cited by 39 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To comprehensively enhance security in D2D task offloading frameworks, social networking characteristics and UEs heterogeneity should also be considered. A sociallymotivated cooperative approach has been presented in [156] to improve the security level of task offloading by leveraging the social tie structure among UEs. The cooperative approach incorporates the social tie structure with the UE computation and network resource sharing processes.…”

Section: F Security Enhancementmentioning

confidence: 99%

See 1 more Smart Citation

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

View full text Add to dashboard Cite

Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

show abstract

Section: F Security Enhancementmentioning

confidence: 99%

“…The main drawback of the approach in [156] is that the UE mobility has not been considered. However, UE mobility has a very strong impact on the social graph of the UEs, i.e., the UEs' social ties and relationships.…”

Section: F Security Enhancementmentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

View full text Add to dashboard Cite

show abstract

“…A successive convex approximation (SCA)-based algorithm-based algorithm and a decomposition and iteration (DAI)-based algorithm are proposed to tackle the nonconvex problem. [27] proposes a novel paradigm of sociallymotivated cooperative mobile edge computing, which leverages the social tie structure among mobile and wearable device users for achieving effective and trustworthy cooperation in task executions.…”

Section: Related Workmentioning

confidence: 99%

Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

et al. 2020

View full text Add to dashboard Cite

Edge computing can be widely used in unmanned aerial vehicle (UAV) inspection, field operation control, power consumption information collection and other businesses in the power Internet of Things scene. Edge computing offloads functions such as data processing and applications to network edge nodes near the terminals to provide low-latency services and ensure service quality. However, with the explosive growth of business terminals, the capacity of single edge node is limited and it is difficult to meet all business requirements at the same time. Therefore, this paper proposes a task allocation mechanism based on cooperative edge computing. Firstly, a task allocation model based on cooperation of two edge nodes is established to minimize the average task completion delay while meeting business requirements. Secondly, the Two-edge-node Cooperative-task Allocation based on Improved Particle Swarm Optimization (TCA-IPSO) algorithm is proposed, which applies the crossover and mutation strategy in genetic algorithm to improve the particle swarm optimization algorithm, and solves the problem that the task allocation scheme in cooperation is prone to fall into a local optimum. Finally the simulation results show that the proposed TCA-IPSO algorithm reduces the average task completion delay by 53.8% and 36.0% compared to the benchmark and QoS-based Task Distribution (QBTD) algorithm. INDEX TERMS Cooperative edge computing, Power Internet of Things, Task completion delay, Task allocation.

show abstract

“…Many previous works in mobile cloud computing have focused on the computation offloading problem (e.g., [5]- [7], [21], [22] and references therein). Wen et al in [5] presented an efficient offloading policy by jointly configuring the clock frequency in the mobile device and scheduling the data transmission to minimize the energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing

et al. 2018

IEEE Trans. Veh. Technol.

Self Cite

111

View full text Add to dashboard Cite

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud service, in this paper we propose a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation (JCC) resource allocation mechanism for network operator. Specifically, we first study the resource-efficient computation offloading problem for a user, in order to reduce user's resource occupation by determining its optimal communication and computation resource profile with minimum resource occupation and meanwhile satisfying the QoS constraint. We then tackle the critical problem of user admission control for JCC resource allocation, in order to properly select the set of users for resource demand satisfaction. We show the admission control problem is NP-hard, and hence develop an efficient approximation solution of a low complexity by carefully designing the user ranking criteria and rigourously derive its performance guarantee. To prevent the manipulation that some users may untruthfully report their valuations in acquiring mobile-edge cloud service, we further resort to the powerful tool of critical value approach to design truthful pricing scheme for JCC resource allocation. Extensive performance evaluation demonstrates that the proposed schemes can achieve superior performance for on-demand mobile-edge cloud computing.

show abstract

Socially-Motivated Cooperative Mobile Edge Computing

Cited by 39 publications

References 14 publications

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing

Contact Info

Product

Resources

About