scholarly journals Optimized Mobile Edge Computing Framework for IoT based Medical Sensor Network Nodes

2021 ◽  
Vol 3 (1) ◽  
pp. 33-42
Author(s):  
Jennifer S. Raj
Keyword(s):  
Cloud Computing ◽  
Wearable Sensors ◽  
Resource Limitation ◽  
Transmission Efficiency ◽  
Edge Computing ◽  
Task Completion ◽  
Network Nodes ◽  
Computing Paradigm ◽  
Edge Network ◽  
Initial Stage

Edge computing is a new computing paradigm that is rapidly emerging in various fields. Task completion is performed by various edge devices with distributed cloud computing in several conventional applications. Resource limitation, transmission efficiency, functionality and other edge network based circumstantial factors make this system more complex when compared to cloud computing. During cooperation between the edge devices, an instability occurs that cannot be ignored. The edge cooperative network is optimized with a novel framework proposed in this paper. This helps in improving the efficiency of edge computing tasks. The cooperation evaluation metrics are defined in the initial stage. Further, the performance of specific tasks are improved by optimizing the edge network cooperation. Real datasets obtained from elderly people and their wearable sensors is used for demonstrating the performance of the proposed framework. The extensive experimentation also helps in validating the efficiency of the proposed optimization algorithm.

scholarly journals DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3071 ◽  
Author(s):  
Jun-Hong Park ◽  
Hyeong-Su Kim ◽  
Won-Tae Kim
Keyword(s):  
Cloud Computing ◽  
Traffic Congestion ◽  
Data Exchange ◽  
Data Transfer ◽  
Edge Computing ◽  
Computing Paradigm ◽  
Transfer Delay ◽  
Network Usage ◽  
Iot Devices ◽  
Edge Networks

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

scholarly journals Task Balanced Workflow Scheduling Technique considering Task Processing Rate in Spot Market

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Daeyong Jung ◽  
JongBeom Lim ◽  
JoonMin Gil ◽  
Eunyoung Lee ◽  
Heonchang Yu
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Task Completion ◽  
Workflow Scheduling ◽  
Performance Improvements ◽  
Processing Rate ◽  
Computing Paradigm ◽  
Low Performance ◽  
The Cost ◽  
Advanced Computing

Recently, the cloud computing is a computing paradigm that constitutes an advanced computing environment that evolved from the distributed computing. And the cloud computing provides acquired computing resources in a pay-as-you-go manner. For example, Amazon EC2 offers the Infrastructure-as-a-Service (IaaS) instances in three different ways with different price, reliability, and various performances of instances. Our study is based on the environment using spot instances. Spot instances can significantly decrease costs compared to reserved and on-demand instances. However, spot instances give a more unreliable environment than other instances. In this paper, we propose the workflow scheduling scheme that reduces the out-of-bid situation. Consequently, the total task completion time is decreased. The simulation results reveal that, compared to various instance types, our scheme achieves performance improvements in terms of an average combined metric of 12.76% over workflow scheme without considering the processing rate. However, the cost in our scheme is higher than an instance with low performance and is lower than an instance with high performance.

scholarly journals Modified reinforcement learning based- caching system for mobile edge computing

2020 ◽  
pp. 1-16
Author(s):  
Sarra Mehamel ◽  
Samia Bouzefrane ◽  
Soumya Banarjee ◽  
Mehammed Daoui ◽  
Valentina E. Balas
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Mobile Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Network Nodes ◽  
Caching Strategy ◽  
Edge Network ◽  
The Right ◽  
And Storage

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

scholarly journals Edge-Oriented Computing: A Survey on Research and Use Cases

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 452
Author(s):  
Nour Alhuda Sulieman ◽  
Lorenzo Ricciardi Celsi ◽  
Wei Li ◽  
Albert Zomaya ◽  
Massimo Villari
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Autonomous Vehicles ◽  
Research Field ◽  
Edge Computing ◽  
Use Cases ◽  
Comprehensive Overview ◽  
System Architectures ◽  
Computing Paradigm ◽  
Edge Networks

Edge computing is a distributed computing paradigm such that client data are processed at the periphery of the network, as close as possible to the originating source. Since the 21st century has come to be known as the century of data due to the rapid increase in the quantity of exchanged data worldwide (especially in smart city applications such as autonomous vehicles), collecting and processing such data from sensors and Internet of Things devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world is a relevant emerging need. Indeed, edge computing is reshaping information technology and business computing. In this respect, the paper is aimed at providing a comprehensive overview of what edge computing is as well as the most relevant edge use cases, tradeoffs, and implementation considerations. In particular, this review article is focused on highlighting (i) the most recent trends relative to edge computing emerging in the research field and (ii) the main businesses that are taking operations at the edge as well as the most used edge computing platforms (both proprietary and open source). First, the paper summarizes the concept of edge computing and compares it with cloud computing. After that, we discuss the challenges of optimal server placement, data security in edge networks, hybrid edge-cloud computing, simulation platforms for edge computing, and state-of-the-art improved edge networks. Finally, we explain the edge computing applications to 5G/6G networks and industrial internet of things. Several studies review a set of attractive edge features, system architectures, and edge application platforms that impact different industry sectors. The experimental results achieved in the cited works are reported in order to prove how edge computing improves the efficiency of Internet of Things networks. On the other hand, the work highlights possible vulnerabilities and open issues emerging in the context of edge computing architectures, thus proposing future directions to be investigated.

Fog Computing

Fog Computing ◽  
2018 ◽  
pp. 198-207 ◽  
Author(s):  
Chintan M. Bhatt ◽  
C. K. Bhensdadia
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real Time ◽  
Fog Computing ◽  
The Internet ◽  
Research Issues ◽  
Computing Paradigm ◽  
Time Interaction ◽  
Edge Network ◽  
The Internet Of Things

The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.

scholarly journals Dependable and Secure Voting Mechanism in Edge Computing

2019 ◽  
Vol 11 (12) ◽  
pp. 262
Author(s):  
Pedro A.R.S. Costa ◽  
Marko Beko
Keyword(s):  
Middle Layer ◽  
Edge Computing ◽  
Cloud Environment ◽  
Computing Paradigm ◽  
Edge Network ◽  
And Storage ◽  
The Internet Of Things ◽  
Multi Cloud ◽  
Voting Mechanism ◽  
Open Issues

Edge computing is a distributed computing paradigm that encompasses data computing and storage and is performed close to the user, efficiently guaranteeing faster response time. This paradigm plays a pivotal role in the world of the Internet of Things (IoT). Moreover, the concept of the distributed edge cloud raises several interesting open issues, e.g., failure recovery and security. In this paper, we propose a system composed of edge nodes and multiple cloud instances, as well as a voting mechanism. The multi-cloud environment aims to perform centralized computations, and edge nodes behave as a middle layer between edge devices and the cloud. Moreover, we present a voting mechanism that leverages the edge network to validate the performed computation that occurred in the centralized environment.

scholarly journals Edge-Computing Architectures for Internet of Things Applications: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6441 ◽  
Author(s):  
Salam Hamdan ◽  
Moussa Ayyash ◽  
Sufyan Almajali
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Resource Limitation ◽  
Edge Computing ◽  
Iot Applications ◽  
Life Tasks ◽  
Iot Devices ◽  
Processing And Storage ◽  
Time Critical ◽  
And Storage

The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

scholarly journals An Efficient Resource Allocation Strategy for Edge-Computing Based Environmental Monitoring System

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6125
Author(s):  
Juan Fang ◽  
Juntao Hu ◽  
Jianhua Wei ◽  
Tong Liu ◽  
Bo Wang
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Environmental Monitoring ◽  
Monitoring System ◽  
Edge Computing ◽  
Resource Allocation Algorithm ◽  
Computing Paradigm ◽  
Environmental Monitoring System ◽  
Monitoring Applications ◽  
Efficient Resource

The cloud computing and microsensor technology has greatly changed environmental monitoring, but it is difficult for cloud-computing based monitoring system to meet the computation demand of smaller monitoring granularity and increasing monitoring applications. As a novel computing paradigm, edge computing deals with this problem by deploying resource on edge network. However, the particularity of environmental monitoring applications is ignored by most previous studies. In this paper, we proposed a resource allocation algorithm and a task scheduling strategy to reduce the average completion latency of environmental monitoring application, when considering the characteristic of environmental monitoring system and dependency among task. Simulations are conducted, and the results show that compared with the traditional algorithms. With considering the emergency task, the proposed methods decrease the average completion latency by 21.6% in the best scenario.

scholarly journals PECSA: Practical Edge Computing Service Architecture Applicable to Adaptive IoT-Based Applications

2021 ◽  
Vol 13 (11) ◽  
pp. 294
Author(s):  
Jianhua Liu ◽  
Zibo Wu
Keyword(s):  
Cloud Computing ◽  
Trust Management ◽  
Computational Cost ◽  
Edge Computing ◽  
Cost Evaluation ◽  
Network Resources ◽  
Service Architecture ◽  
Edge Network ◽  
Edge Networks ◽  
Resource Capacity

The cloud-based Internet of Things (IoT-Cloud) combines the advantages of the IoT and cloud computing, which not only expands the scope of cloud computing but also enhances the data processing capability of the IoT. Users always seek affordable and efficient services, which can be completed by the cooperation of all available network resources, such as edge computing nodes. However, current solutions exhibit significant security and efficiency problems that must be solved. Insider attacks could degrade the performance of the IoT-Cloud due to its natural environment and inherent open construction. Unfortunately, traditional security approaches cannot defend against these attacks effectively. In this paper, a novel practical edge computing service architecture (PECSA), which integrates a trust management methodology with dynamic cost evaluation schemes, is proposed to address these problems. In the architecture, the edge network devices and edge platform cooperate to achieve a shorter response time and/or less economic costs, as well as to enhance the effectiveness of the trust management methodology, respectively. To achieve faster responses for IoT-based requirements, all the edge computing devices and cloud resources cooperate in a reasonable way by evaluating computational cost and runtime resource capacity in the edge networks. Moreover, when cooperated with the edge platform, the edge networks compute trust values of linked nodes and find the best collaborative approach for each user to meet various service requirements. Experimental results demonstrate the efficiency and the security of the proposed architecture.

scholarly journals An Overview of Fog Computing and Edge Computing Security and Privacy Issues

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8226
Author(s):  
Ahmed M. Alwakeel
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Edge Computing ◽  
Security And Privacy ◽  
Local Network ◽  
Security Issues ◽  
Computing Paradigm ◽  
Edge And Fog Computing ◽  
Cloud Technologies ◽  
Privacy Issues

With the advancement of different technologies such as 5G networks and IoT the use of different cloud computing technologies became essential. Cloud computing allowed intensive data processing and warehousing solution. Two different new cloud technologies that inherit some of the traditional cloud computing paradigm are fog computing and edge computing that is aims to simplify some of the complexity of cloud computing and leverage the computing capabilities within the local network in order to preform computation tasks rather than carrying it to the cloud. This makes this technology fits with the properties of IoT systems. However, using such technology introduces several new security and privacy challenges that could be huge obstacle against implementing these technologies. In this paper, we survey some of the main security and privacy challenges that faces fog and edge computing illustrating how these security issues could affect the work and implementation of edge and fog computing. Moreover, we present several countermeasures to mitigate the effect of these security issues.

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