An Intelligent Edge Computing-based Scalable Architecture for Large-scale Smart Farm System

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

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A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

Engineering Proceedings ◽

10.3390/ecsa-7-08261 ◽

2020 ◽

Vol 2 (1) ◽

pp. 92

Author(s):

Rahim Rahmani ◽

Ramin Firouzi ◽

Sachiko Lim ◽

Mahbub Alam

Keyword(s):

Large Scale ◽

Cluster Head ◽

Edge Computing ◽

Computing Technology ◽

Proximal Algorithm ◽

Data Intensive ◽

Distributed Ledger ◽

Smart Contract ◽

Distributed Ledger Technology ◽

Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

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DESA: Dependable, Efficient, Scalable Architecture for Management of Large-Scale Batteries

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2011.2166771 ◽

2012 ◽

Vol 8 (2) ◽

pp. 406-417 ◽

Cited By ~ 41

Author(s):

Hahnsang Kim ◽

Kang G. Shin

Keyword(s):

Large Scale ◽

Scalable Architecture

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Evaluation of Large Scale RoI Mining Applications in Edge Computing Environments

10.1109/ds-rt52167.2021.9576131 ◽

2021 ◽

Author(s):

Loris Belcastro ◽

Alberto Falcone ◽

Alfredo Garro ◽

Fabrizio Marozzo

Keyword(s):

Large Scale ◽

Edge Computing ◽

Computing Environments

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Edge Computing-Empowered Large-Scale Traffic Data Recovery Leveraging Low-Rank Theory

IEEE Transactions on Network Science and Engineering ◽

10.1109/tnse.2020.2984658 ◽

2020 ◽

Vol 7 (4) ◽

pp. 2205-2218 ◽

Cited By ~ 1

Author(s):

Chaocan Xiang ◽

Zhao Zhang ◽

Yuben Qu ◽

Dongyu Lu ◽

Xiaochen Fan ◽

...

Keyword(s):

Large Scale ◽

Edge Computing ◽

Data Recovery ◽

Low Rank ◽

Traffic Data

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Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

Future Internet ◽

10.3390/fi11040100 ◽

2019 ◽

Vol 11 (4) ◽

pp. 100 ◽

Cited By ~ 20

Author(s):

Maurizio Capra ◽

Riccardo Peloso ◽

Guido Masera ◽

Massimo Ruo Roch ◽

Maurizio Martina

Keyword(s):

Internet Of Things ◽

Mobile Devices ◽

Information Exchange ◽

Large Scale ◽

Operating Time ◽

Edge Computing ◽

The Internet ◽

Power Load ◽

Battery Capacity ◽

Iot Devices

In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

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