Topic
Edge computing
About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.
Papers published on a yearly basis
Papers
More filters
08 May 2017
TL;DR: A new simulator tool called EdgeCloudSim streamlined for Edge Computing scenarios is proposed, which builds upon CloudSim to address the specific demands of Edge Computing research and support necessary functionality in terms of computation and networking abilities.
Abstract: Edge Computing is a fast growing field of research covering a spectrum of technologies such as Cloudlets, Fog Computing and Mobile Edge Computing (MEC). Edge Computing involves technically more sophisticated setup when compared with the pure Cloud Computing and pure Mobile Computing cases since both computational and network resources should be considered simultaneously. In that respect, it provides a larger design space with many parameters rendering a variety of novel approaches feasible. Given the complexity, Edge Computing designs deserve scientific scrutiny for sound assessment of their feasibility. However, despite increasing research activity, this field lacks a simulation tool compatible with the requirements. Starting from available simulators a significant programming effort is required to obtain a simulation tool meeting the actual needs. To decrease the barriers, a new simulator tool called EdgeCloudSim streamlined for Edge Computing scenarios is proposed in this work. EdgeCloudSim builds upon CloudSim to address the specific demands of Edge Computing research and support necessary functionality in terms of computation and networking abilities. To demonstrate the capabilities of EdgeCloudSim an experiment setup based on different edge architectures is simulated and the effect of the computational and networking system parameters on the results are depicted.
156 citations
04 Jan 2018
TL;DR: A Fog-based IoT-Healthcare solution structure is introduced and the integration of Cloud-Fog services in interoperable Healthcare solutions extended upon the traditional Cloud-based structure is explored.
Abstract: The issue of utilizing Internet of Things (IoT) in Healthcare solutions relates to the problems of latency sensitivity, uneven data load, diverse user expectations and heterogeneity of the applications. Current explorations consider Cloud Computing as the base stone to create IoT-Enable solution. Nonetheless, this environment entails limitations in terms of multi-hop distance from the data source, geographical centralized architecture, economical aspects, etc. To address these limitations, there is a surge of solutions that apply Fog Computing as an approach to bring computing resources closer to the data sources. This approach is being fomented by the growing availability of powerful edge computing at lower cost and commercial developments in the area. Nonetheless, the implementation of Cloud-Fog interoperability and integration implies in complex coordination of applications and services and the demand for intelligent service orchestrations so that solutions can make the best use of distributed resources without compromising stability, quality of services, and security. In this paper, we introduce a Fog-based IoT-Healthcare solution structure and explore the integration of Cloud-Fog services in interoperable Healthcare solutions extended upon the traditional Cloud-based structure. The scenarios are evaluated through simulations using the iFogSim simulator and the results analyzed in relation to distributed computing, reduction of latency, optimization of data communication, and power consumption. The experimental results point towards improvement in instance cost, network delay and energy usage.
156 citations
TL;DR: A proposal for a tiered architecture with a modular approach that allows to manage the complexity of solutions not only for Industry 4.0 environments but also for other scenarios such as smart cities, smart energy, healthcare or precision agrotechnology.
155 citations
TL;DR: The proposed privacy preserving data aggregation scheme not only guarantees data privacy of the TDs but also provides source authentication and integrity, and is very suitable for MEC assisted IoT applications.
Abstract: As the rapid development of 5G and Internet of Things (IoT) techniques, more and more mobile devices with specific sensing capabilities access to the network and large amounts of data. The traditional architecture of the cloud computing cannot satisfy the requirements, such as low latency, fast data access for IoT applications. Mobile edge computing (MEC) can solve these problems, and improve the execution efficiency of the system. In this paper, we propose a privacy preserving data aggregation scheme for MEC assisted IoT applications. In our model, there are three participants, i.e., terminal device (TD), edge server (ES), and public cloud center (PCC). The data generated by the TDs is encrypted and transmitted to the ES, then the ES aggregates the data of the TDs and submits the aggregated data to the PCC. At last, the aggregated plaintext data can be recovered by PCC through its private key. Our scheme not only guarantees data privacy of the TDs but also provides source authentication and integrity. Compared with traditional model, our scheme can save half of communication cost, and is very suitable for MEC assisted IoT applications.
155 citations
TL;DR: This paper integrates the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D1D device.
Abstract: The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.
154 citations