A Comprehensive Survey on Mobile Edge Computing: Challenges, Tools, Applications

Vhora, Fatema; Gandhi, Jay

doi:10.1109/iccmc48092.2020.iccmc-0009

Cited by 44 publications

(16 citation statements)

References 29 publications

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“…Cloudlet is a project presented by a group of researchers from Mellon University [21]. The goal of using cloudlets is to have available resources near the IoT devices.…”

Section: Architectures In Service Allocationmentioning

confidence: 99%

Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

et al. 2021

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The expansion of Internet of Things (IoT) services and the huge amount of data generated by different sensors signify the importance of cloud computing services such as Storage as a Service more than ever. IoT traffic imposes such extra constraints on the cloud storage service as sensor data preprocessing capability and load-balancing between data centers and servers in each data center. Furthermore, service allocation should be allegiant to the quality of service (QoS). In the current work, an algorithm is proposed that addresses the QoS in storage service allocation. The proposed hybrid multi-objective water cycle and grey wolf optimizer (MWG) considers different QoS objectives (e.g., energy, processing time, transmission time, and load balancing) in both the fog and cloud Layers, which were not addressed altogether. The MATLAB script is used to simulate and implement our algorithms, and services of different servers, e.g., Amazon, Dropbox, Google Drive, etc., are considered. The MWG has 7%, 13%, and 25% improvement, respectively, in comparison with multi-objective water cycle algorithm (MOWCA), k-means based GA (KGA), and non-dominated sorting genetic algorithm (NSGAII) in metric of spacing. Moreover, the MWG has 4%, 4.7%, and 7.3% optimization in metric of quality in comparison to MOWCA, KGA, and NSGAII, respectively. The new hybrid algorithm, MWG, not only yielded to the consideration of three objectives in service selection but also improved the performance compared to the works that considered one or two objective(s). The overall optimization shows that the MWG algorithm has 7.8%, 17%, and 21.6% better performance than MOWCA, KGA, and NSGAII in the obtained best result by considering different objectives, respectively.

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“…Cloudlet is a project presented by a group of researchers from Mellon University [21]. The goal of using cloudlets is to have available resources near the IoT devices.…”

Section: Architectures In Service Allocationmentioning

confidence: 99%

Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

et al. 2021

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“…where (a) is due to the assumption of Z(0) � 0, and (b) is based on (9). Moreover, E(ϖ) is an arbitrary solution from the Lyapunov function.…”

Section: □ Bmentioning

confidence: 99%

Distributed Optimization for Mobile Robots under Mobile Edge Computing Environment

Luo

Yin

2021

Complexity

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Driven by the development of the Internet industry, mobile robots (MRs) technology has become increasingly mature and widely used in all walks of life. Since MRs are densely distributed in the network system, how to establish a reliable communication architecture to achieve good cooperation and resource sharing between MRs has become a research hotspot. In this respect, mobile edge computing (MEC) technology and millimeter wave (mmW) technology can provide powerful support. This paper proposes a mmW communication network architecture for distributed MRs in MEC environment. The mmW base station provides reliable communication services for MRs under the coverage of information cloud (IC). We design a joint resource and power allocation strategy aimed at minimizing network energy consumption. First, we use the Lyapunov optimization technique to transform the original infinite horizon Markov decision process (MDP) problem. Then, a semidistributed algorithm is introduced to solve the distributed optimization problem in the mmW network. By improving the autonomous decision-making ability of the mmW base station, the signaling overheads caused by information interaction are reduced, and information leakage is effectively avoided. Finally, the global optimal solution is obtained. Simulation results demonstrate the superiority of the proposed strategy.

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“…To overcome these limitations, researchers developed a solution called edge computing. Cloudlets [4], [5] are a type of edge computing that aims to bring cloud applications, capabilities, and services to the network edge and closer to users, specifically, a single hop away [6]. Figure 1 depicts a simplified cloudlet architecture.…”

Section: Introductionmentioning

confidence: 99%

“…Discusses the potential of cloudlets to provide continuous service in hostile conditions, such as emergencies and military operations, and data-intensive applications that benefit from data-staging techniques in cloudlets and communication-intensive applications that fancy a network as a service (NaaS) technique that leverages cloudlets substantially in difficult conditions 6 Presents significant open research issues that require attention…”

Section: Introductionmentioning

confidence: 99%

Cloudlet Computing: Recent Advances, Taxonomy, and Challenges

et al. 2021

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A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resourceconstrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail. INDEX TERMS Cloud computing, Edge computing, Cloudlets, Internet of things, Computation offloading, Smart city, Smart health.

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A Comprehensive Survey on Mobile Edge Computing: Challenges, Tools, Applications

Cited by 44 publications

References 29 publications

Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

Distributed Optimization for Mobile Robots under Mobile Edge Computing Environment

Cloudlet Computing: Recent Advances, Taxonomy, and Challenges

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