sensors-logo

Journal Browser

Journal Browser

Special Issue "Recent Advancement in Edge/Fog Computing for Intelligent IoT Applications"

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

Deadline for manuscript submissions: closed (31 July 2020).

Special Issue Editors

Dr. Christos Anagnostopoulos
Website SciProfiles
Guest Editor
School of Computing Science, University of Glasgow, Lilybank Gardens, G12 8QQ, Glasgow, UK
Interests: distributed machine learning; stochastic optimization; mobile computing; inferential analytics at the edge
Special Issues and Collections in MDPI journals
Dr. Kolomvatsos Kostas
Website
Co-Guest Editor
Department of Informatics and Telecommunications, University of Athens, Panepistimiopolis, Ilisia, Athens, Greece
Interests: intelligent systems; distributed systems; distributed machine learning; computational intelligence; soft computing
Special Issues and Collections in MDPI journals
Dr. Dimitrios Pezaros
Website
Co-Guest Editor
School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
Interests: network and service management; network function virtualisation (NFV); software-defined networking (SDN); network security and resilience; cloud data centre networking; closed-loop; measurement-based network control; adaptive resource provisioning; network traffic analysis and modelling; network anomaly detection

Special Issue Information

Dear Colleagues,

In recent years, Fog & Edge Computing (FEC) has appeared as a new paradigm that extends existing fundamental computing infrastructure to the network edges to provide computation, networking, and intelligent real-time services between end/mobile devices, IoT devices, networking elements, sensors, and data centers. The FEC paradigm has been considered to effectively mitigate loads on data centers, to support Artificial Intelligence (AI)-led services, and to increase 5G services at the network edge. The FEC eco-system is capable of processing large amounts of contextual data locally for supporting in-network decision making, data analytics (predictive analytics, exploratory data analysis, network analytics etc) and knowledge/data management in the network edge. Therefore, FEC applications along with the IoT field are essential technical directions that enable smart homes/hospitals/cities, intelligent vehicles/unmanned vehicles, intelligent network management services, smart supply chain, e-health, automation, and a variety of other AI-led computing and networking environments. These features make FEC paradigm suitable for context-aware, intelligent, real-time and location-sensitive applications as it can be envisaged as an interface between IoT and Cloud. This Special Issue intends to collect cutting-edge research from both academia and industry, with emphasis on current intelligent application developments and future directions in distributed intelligence, self-organizing 5G networks, AI-led applications for IoT, distributed computation and analytics in IoT environments providing a unique opportunity for both technology and applied science to meet. We invite authors to submit their original papers. Potential topics include:

• Theoretical Foundation and (Distributed) Computing models for FEC Applications
• Distributed Intelligence and Distributed Machine Learning for FEC Applications
• Advances in AI-led SDN/NFV for FEC Applications
• Intelligent IoT Management and Networking Services
• Self-evolving, self-organizing FEC Networking Applications
• FEC-based Intelligent Data and/or Knowledge Management Services (data analytics)
• Intelligent Tasks Management for supporting FEC Applications
• Intelligent caching for large-scale contextual data in FEC Applications
• Quality of Experience/Quality of Service for FEC
• Bio-inspired FEC Applications in large-scale networks (e.g., UxVs, WSNs, IoT)
• Advances in Soft-computing for FEC Applications
• Decision Support & novel Services in FEC-based IoT

Dr. Christos Anagnostopoulos
Dr. Dimitrios Pezaros
Dr. Kostas Kolomvatsos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:
Open AccessArticle
Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching
Sensors 2020, 20(6), 1582; https://doi.org/10.3390/s20061582 - 12 Mar 2020
Cited by 1
Abstract
This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., [...] Read more.
This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio. Full article
Show Figures

Figure 1

Open AccessArticle
Hierarchical MEC Servers Deployment and User-MEC Server Association in C-RANs over WDM Ring Networks
Sensors 2020, 20(5), 1282; https://doi.org/10.3390/s20051282 - 27 Feb 2020
Cited by 1
Abstract
With the increasing number of Internet of Things (IoT) devices, a huge amount of latency-sensitive and computation-intensive IoT applications have been injected into the network. Deploying mobile edge computing (MEC) servers in cloud radio access network (C-RAN) is a promising candidate, which brings [...] Read more.
With the increasing number of Internet of Things (IoT) devices, a huge amount of latency-sensitive and computation-intensive IoT applications have been injected into the network. Deploying mobile edge computing (MEC) servers in cloud radio access network (C-RAN) is a promising candidate, which brings a number of critical IoT applications to the edge network, to reduce the heavy traffic load and the end-to-end latency. The MEC server’s deployment mechanism is highly related to the user allocation. Therefore, in this paper, we study hierarchical deployment of MEC servers and user allocation problem. We first formulate the problem as a mixed integer nonlinear programming (MINLP) model to minimize the deployment cost and average latency. In terms of the MINLP model, we then propose an enumeration algorithm and approximate algorithm based on the improved entropy weight and TOPSIS methods. Numerical results show that the proposed algorithms can reduce the total cost, and the approximate algorithm has lower total cost comparing the heaviest-location first and the latency-based algorithms. Full article
Show Figures

Figure 1

Open AccessArticle
A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction
Sensors 2020, 20(14), 3966; https://doi.org/10.3390/s20143966 - 16 Jul 2020
Abstract
Resource allocation of the availability of certain departments for dealing with emergency recovery is of high importance in municipalities. Efficient planning for facing possible disasters in the coverage area of a municipality provides reassurance for citizens. Citizens can assist with such malfunctions by [...] Read more.
Resource allocation of the availability of certain departments for dealing with emergency recovery is of high importance in municipalities. Efficient planning for facing possible disasters in the coverage area of a municipality provides reassurance for citizens. Citizens can assist with such malfunctions by acting as human sensors at the edge of an infrastructure to provide instant feedback to the appropriate departments fixing the problems. However, municipalities have limited department resources to handle upcoming emergency events. In this study, we propose a smartphone crowdsensing system that is based on citizens’ reactions as human sensors at the edge of a municipality infrastructure to supplement malfunctions exploiting environmental crowdsourcing location-allocation capabilities. A long short-term memory (LSTM) neural network is incorporated to learn the occurrence of such emergencies. The LSTM is able to stochastically predict future emergency situations, acting as an early warning component of the system. Such a mechanism may be used to provide adequate department resource allocation to treat future emergencies. Full article
Show Figures

Figure 1

Open AccessArticle
Monitoring for Rare Events in a Wireless Powered Communication mmWave Sensor Network
Sensors 2020, 20(12), 3341; https://doi.org/10.3390/s20123341 - 12 Jun 2020
Abstract
The use of a wireless sensor network to monitor an area of interest for possible hazardous events has become a common practice. The difficulty of replacing or recharging sensor batteries dictates the use of energy harvesting as a means to extend the network’s [...] Read more.
The use of a wireless sensor network to monitor an area of interest for possible hazardous events has become a common practice. The difficulty of replacing or recharging sensor batteries dictates the use of energy harvesting as a means to extend the network’s lifetime. To this end, energy beamforming is used in a millimeter wave wireless power sensor network with randomly deployed nodes. A simple protocol is proposed that allows nodes to report their charging conditions in an effort to select efficient energy-beamforming strategies. Analytical expressions for the probability of successful information reception and successful reporting are provided for two benchmark schemes: the random and the circular energy-beamforming scheme. A Markov chain is used for the former to model the energy level of sensor nodes. Simple sector selection strategies are presented and their performance, in terms of delay and failure information delivery, is assessed through simulations. Full article
Show Figures

Figure 1

Open AccessReview
When Sensor-Cloud Meets Mobile Edge Computing
Sensors 2019, 19(23), 5324; https://doi.org/10.3390/s19235324 - 03 Dec 2019
Cited by 1
Abstract
Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many [...] Read more.
Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems to be solved in sensor-clouds, such as the limitations of WSNs in terms of communication and energy, the high latency, and the security and privacy issues due to applying a cloud platform as the data processing and control center. In recent years, mobile edge computing has received increasing attention from industry and academia. The core of mobile edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives mobile edge computing great potential in solving the shortcomings of sensor-clouds. In this paper, the latest research status of sensor-clouds is briefly analyzed and the characteristics of the existing sensor-clouds are summarized. After that we discuss the issues of sensor-clouds and propose some applications, especially a trust evaluation mechanism and trustworthy data collection which use mobile edge computing to solve the problems in sensor-clouds. Finally, we discuss research challenges and future research directions in leveraging mobile edge computing for sensor-clouds. Full article
Show Figures

Figure 1

Back to TopTop