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Cloud and Edge Computing in Wireless Sensor Networks and Internet of Things

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 13774

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Prof. Dr. Fatos Xhafa

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Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: cloud computing; distributed computing; security and privacy
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Isaac Woungang

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Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: next generation wireless networks; cloud computing; computational intelligence; telecommunications network design; management and control; distributing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, cloud computing has become more and more popular with the increasing demand for reducing local storage and computing costs. However, due to the huge energy consumption and difficult internet access for many resource-constrained equipments when outsourcing, cloud computing cannot be adapted smoothly. Edge computing can be a solution to the above challenges. Compared with cloud computing, edge computing provides users with various services, such as more easily computing power, storage service and communication bandawidth, in a location closer to the user side. Its advantages include lower response delay, smaller core network bandwidth pressure, and more effective privacy protection and data security. Our Special Issue foucuses on the topic of edge and cloud computing in wireless sensor networks and the Internet of Things:

Network/cloud/edge protocol in cloud and edge computing;
Network protocols in cloud and edge computing;
Multimedia contents analysis in cloud and edge computing;
Security, trust, and privacy in cloud and edge computing;
Intelligent data processing of cloud and edge computing;
Resource management and task scheduling of cloud and edge computing;
Scalability problems and solutions of cloud and edge computing;
Fog computing and Internet of Things technology for cloud and edge computing

Prof. Dr. Fatos Xhafa
Prof. Dr. Isaac Woungang
Guest Editors

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Published Papers (5 papers)

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Research

24 pages, 7780 KiB

Open AccessArticle

Fuzzy-Based Microservice Resource Management Platform for Edge Computing in the Internet of Things

by David Chunhu Li, Chiing-Ting Huang, Chia-Wei Tseng and Li-Der Chou

Sensors 2021, 21(11), 3800; https://doi.org/10.3390/s21113800 - 31 May 2021

Cited by 7 | Viewed by 2987

Abstract

Edge computing exhibits the advantages of real-time operation, low latency, and low network cost. It has become a key technology for realizing smart Internet of Things applications. Microservices are being used by an increasing number of edge computing networks because of their sufficiently small code, reduced program complexity, and flexible deployment. However, edge computing has more limited resources than cloud computing, and thus edge computing networks have higher requirements for the overall resource scheduling of running microservices. Accordingly, the resource management of microservice applications in edge computing networks is a crucial issue. In this study, we developed and implemented a microservice resource management platform for edge computing networks. We designed a fuzzy-based microservice computing resource scaling (FMCRS) algorithm that can dynamically control the resource expansion scale of microservices. We proposed and implemented two microservice resource expansion methods based on the resource usage of edge network computing nodes. We conducted the experimental analysis in six scenarios and the experimental results proved that the designed microservice resource management platform can reduce the response time for microservice resource adjustments and dynamically expand microservices horizontally and vertically. Compared with other state-of-the-art microservice resource management methods, FMCRS can reduce sudden surges in overall network resource allocation, and thus, it is more suitable for the edge computing microservice management environment. Full article

(This article belongs to the Special Issue Cloud and Edge Computing in Wireless Sensor Networks and Internet of Things)

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16 pages, 696 KiB

Open AccessArticle

Study of the Efficiency of Fog Computing in an Optimized LoRaWAN Cloud Architecture

by Jakub Jalowiczor, Jan Rozhon and Miroslav Voznak

Sensors 2021, 21(9), 3159; https://doi.org/10.3390/s21093159 - 2 May 2021

Cited by 9 | Viewed by 2561

Abstract

The technologies of the Internet of Things (IoT) have an increasing influence on our daily lives. The expansion of the IoT is associated with the growing number of IoT devices that are connected to the Internet. As the number of connected devices grows, the demand for speed and data volume is also greater. While most IoT network technologies use cloud computing, this solution becomes inefficient for some use-cases. For example, suppose that a company that uses an IoT network with several sensors to collect data within a production hall. The company may require sharing only selected data to the public cloud and responding faster to specific events. In the case of a large amount of data, the off-loading techniques can be utilized to reach higher efficiency. Meeting these requirements is difficult or impossible for solutions adopting cloud computing. The fog computing paradigm addresses these cases by providing data processing closer to end devices. This paper proposes three possible network architectures that adopt fog computing for LoRaWAN because LoRaWAN is already deployed in many locations and offers long-distance communication with low-power consumption. The architecture proposals are further compared in simulations to select the optimal form in terms of total service time. The resulting optimal communication architecture could be deployed to the existing LoRaWAN with minimal cost and effort of the network operator. Full article

(This article belongs to the Special Issue Cloud and Edge Computing in Wireless Sensor Networks and Internet of Things)

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18 pages, 10751 KiB

Open AccessArticle

Managing the Cloud Continuum: Lessons Learnt from a Real Fog-to-Cloud Deployment

by Xavi Masip-Bruin, Eva Marín-Tordera, Sergi Sánchez-López, Jordi Garcia, Admela Jukan, Ana Juan Ferrer, Anna Queralt, Antonio Salis, Andrea Bartoli, Matija Cankar, Cristovao Cordeiro, Jens Jensen and John Kennedy

Sensors 2021, 21(9), 2974; https://doi.org/10.3390/s21092974 - 23 Apr 2021

Cited by 15 | Viewed by 3157

Abstract

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes. Full article

(This article belongs to the Special Issue Cloud and Edge Computing in Wireless Sensor Networks and Internet of Things)

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11 pages, 1409 KiB

Open AccessCommunication

Prioritized Task Distribution Considering Opportunistic Fog Computing Nodes

by Yeunwoong Kyung

Sensors 2021, 21(8), 2635; https://doi.org/10.3390/s21082635 - 9 Apr 2021

Cited by 2 | Viewed by 1586

Abstract

As service latency and core network load relates to performance issues in the conventional cloud-based computing environment, the fog computing system has gained a lot of interest. However, since the load can be concentrated on specific fog computing nodes because of spatial and temporal service characteristics, performance degradation can occur, resulting in quality of service (QoS) degradation, especially for delay-sensitive services. Therefore, this paper proposes a prioritized task distribution scheme, which considers static as well as opportunistic fog computing nodes according to their mobility feature. Based on the requirements of offloaded tasks, the proposed scheme supports delay sensitive task processing at the static fog node and delay in-sensitive tasks by means of opportunistic fog nodes for task distribution. To assess the performance of the proposed scheme, we develop an analytic model for the service response delay. Extensive simulation results are given to validate the analytic model and to show the performance of the proposed scheme, compared to the conventional schemes in terms of service response delay and outage probability. Full article

(This article belongs to the Special Issue Cloud and Edge Computing in Wireless Sensor Networks and Internet of Things)

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32 pages, 716 KiB

Open AccessArticle

Matching IoT Devices to the Fog Service Providers: A Mechanism Design Perspective

by Anjan Bandyopadhyay, Vikash Kumar Singh, Sajal Mukhopadhyay, Ujjwal Rai, Fatos Xhafa and Paul Krause

Sensors 2020, 20(23), 6761; https://doi.org/10.3390/s20236761 - 26 Nov 2020

Cited by 7 | Viewed by 2109

Abstract

In the Internet of Things (IoT) + Fog + Cloud architecture, with the unprecedented growth of IoT devices, one of the challenging issues that needs to be tackled is to allocate Fog service providers (FSPs) to IoT devices, especially in a game-theoretic environment. Here, the issue of allocation of FSPs to the IoT devices is sifted with game-theoretic idea so that utility maximizing agents may be benign. In this scenario, we have multiple IoT devices and multiple FSPs, and the IoT devices give preference ordering over the subset of FSPs. Given such a scenario, the goal is to allocate at most one FSP to each of the IoT devices. We propose mechanisms based on the theory of mechanism design without money to allocate FSPs to the IoT devices. The proposed mechanisms have been designed in a flexible manner to address the long and short duration access of the FSPs to the IoT devices. For analytical results, we have proved the economic robustness, and probabilistic analyses have been carried out for allocation of IoT devices to the FSPs. In simulation, mechanism efficiency is laid out under different scenarios with an implementation in Python. Full article

(This article belongs to the Special Issue Cloud and Edge Computing in Wireless Sensor Networks and Internet of Things)

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