Edge Computing for the IoT

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 28781

Special Issue Editors


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Guest Editor
Department of Computer Science & Engineering (DISI), University of Bologna, 40136 Bologna, Italy
Interests: wireless sensor and actuator networks; middleware for sensor and actuator networks; vehicular sensor networks; edge computing; fog computing; online stream processing of sensing dataflows; IoT and big data processing; pervasive and mobile computing; cooperative networking; cyber physical systems for Industry 4.0
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Guest Editor
ABV-Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh 474015, India
Interests: distributed systems; wireless sensor networks; mobile edge computing; mobile cyber-physical systems; Internet of Things

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Guest Editor
NIMBUS Centre, Munster Technological University, T12 P928 Cork, Ireland
Interests: edge computing & edge enabled distributed machine learning; networked systems & IoT

Special Issue Information

The Edge paradigm (exploitation of decentralized and virtualized processing, storage, and networking resources at network edges, such as in fog computing, 5G/6G, Edge-cloud computing, etc.) is revitalized by a number of research areas, including autonomous vehicles, personal assistants, smart cities, smart industry, and increased resiliency. These distributed Edge computing systems with varying degrees of reasoning abilities are continuously evolving as sophisticated cyber-physical and/or sociotechnical systems. Rapid progress has been witnessed in the recent past in the modeling of these systems via machine learning, semantic computing, deductive systems, mathematical and statistical heuristic-oriented approaches, etc. This Special Issue aims at hosting articles from computing sciences and multidisciplinary application-oriented research to provide a timely and comprehensive overview of the current state-of-the-art in terms of innovations and technological advances towards exploiting various aspects of the Edge paradigm applied to different application domains of IoT networks. The topics of interest include but are not limited to:

  1. Edge deployment architectures and models;
  2. Edge programming paradigms for the IoT;
  3. Edge resource management and orchestration;
  4. Evaluation of network technology interfaces at Edge nodes;
  5. Protocols and architectures for information-centric wireless Edge networking;
  6. Computation offloading vis-à-vis enriching on-device hardware/software capabilities at handheld edges;
  7. Edge handoff mechanisms, strategies, and management;
  8. Baseline performance evaluation for Edge infrastructure and applications;
  9. Industry adoption use cases of Edge computing paradigm;
  10. Edge computing for scalable smart city applications;
  11. Edge computing for vehicular clouds;
  12. Edge computing for personal assistance and mobile services;
  13. Machine learning for Edge computing and Edge computing for distributed machine learning;
  14. Sustainable digitalization of the manufacturing industry for futuristic technologies (Industry 5.0, 6G);
  15. Security and privacy aspects of federated Edge learning systems;
  16. Trust management for Edge-enabled IoT ecosystems.

Prof. Dr. Paolo Bellavista
Dr. Kiran Kumar Pattanaik
Dr. Sourabh Bharti
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 submissions that pass pre-check are 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.

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Published Papers (9 papers)

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Research

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20 pages, 6711 KiB  
Article
DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things
by Rachid Mafamane, Mourad Ouadou, Hajar Sahbani, Nisrine Ibadah and Khalid Minaoui
Computers 2022, 11(7), 107; https://doi.org/10.3390/computers11070107 - 30 Jun 2022
Cited by 4 | Viewed by 2156
Abstract
Radio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and [...] Read more.
Radio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and reduces the performance of the RFID system. Therefore, communication resource management is required to avoid such problems. In this paper, we propose an anti-collision protocol based on feed-forward Artificial Neural Network methodology for distributed learning between RFID readers to predict collisions and ensure efficient resource allocation (DMLAR) by considering the mobility of tags and readers. The evaluation of our anti-collision protocol is performed for different mobility scenarios in healthcare where the collected data are critical and must respect the terms of throughput, delay, overload, integrity and energy. The dataset created and distributed by the readers allows an efficient learning process and, therefore, a high collision detection to increase throughput and minimize data loss. In the application phase, the readers do not need to exchange control packets with each other to control the resource allocation, which avoids network overload and communication delay. Simulation results show the robustness and effectiveness of the anti-collision protocol by the number of readers and resources used. The model used allows a large number of readers to use the most suitable frequency and time resources for simultaneous and successful tag interrogation. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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21 pages, 511 KiB  
Article
A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
by Emma Fitzgerald, Michał Pióro, Harsh Tataria, Gilles Callebaut, Sara Gunnarsson and Liesbet Van der Perre
Computers 2022, 11(6), 98; https://doi.org/10.3390/computers11060098 - 16 Jun 2022
Viewed by 1630
Abstract
Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that [...] Read more.
Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. Here we propose node partitioning strategies that do not require full channel state information, but rather are based on nodes’ respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realize a simple partitioning method, requiring minimal information to be collected from the nodes, and in which this information typically remains stable over the long term, thus promoting the system’s autonomy and energy efficiency. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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14 pages, 798 KiB  
Article
Can We Trust Edge Computing Simulations? An Experimental Assessment
by Gonçalo Carvalho, Filipe Magalhães, Bruno Cabral, Vasco Pereira and Jorge Bernardino
Computers 2022, 11(6), 90; https://doi.org/10.3390/computers11060090 - 31 May 2022
Cited by 1 | Viewed by 1917
Abstract
Simulators allow for the simulation of real-world environments that would otherwise be financially costly and difficult to implement at a technical level. Thus, a simulation environment facilitates the implementation and development of use cases, rendering such development cost-effective and faster, and it can [...] Read more.
Simulators allow for the simulation of real-world environments that would otherwise be financially costly and difficult to implement at a technical level. Thus, a simulation environment facilitates the implementation and development of use cases, rendering such development cost-effective and faster, and it can be used in several scenarios. There are some works about simulation environments in Edge Computing (EC), but there is a gap of studies that state the validity of these simulators. This paper compares the execution of the EdgeBench benchmark in a real-world environment and in a simulation environment using FogComputingSim, an EC simulator. Overall, the simulated environment was 0.2% faster than the real world, thus allowing for us to state that we can trust EC simulations, and to conclude that it is possible to implement and validate proofs of concept with FogComputingSim. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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17 pages, 1080 KiB  
Article
Design of a Cattle-Health-Monitoring System Using Microservices and IoT Devices
by Isak Shabani, Tonit Biba and Betim Çiço
Computers 2022, 11(5), 79; https://doi.org/10.3390/computers11050079 - 12 May 2022
Cited by 11 | Viewed by 5945
Abstract
This article proposes a new concept of microservice-based architecture for the future of distributed systems. This architecture is a bridge between Internet-of-Things (IoT) devices and applications that are used to monitor cattle health in real time for the physical and health parameters of [...] Read more.
This article proposes a new concept of microservice-based architecture for the future of distributed systems. This architecture is a bridge between Internet-of-Things (IoT) devices and applications that are used to monitor cattle health in real time for the physical and health parameters of cattle, where microservice architecture is introduced that enables this form of monitoring. Within this architecture, machine-learning algorithms were used to predict cattle health and inform farmers about the health of each cattle in real time. Within this architecture, six microservices were proposed that had the tasks of receiving, processing, and sending data upon request. In addition, within the six microservices, a microservice was developed for the prediction of cattle health using algorithms from machine learning using the LightGBM algorithm. Through this algorithm, it is possible to determine the percentage value of the health of each head of cattle in the moment, based on the parameters that are sent from the mobile node. If health problems are identified in the cattle, the architecture notifies the farmer in real time about the problems that the cattle have. Based on the proposed solution, farmers will have 24 h online access to monitor the following parameters for each head of cattle: body temperature, heart rate, humidity, and position. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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23 pages, 3248 KiB  
Article
Performance of a Live Multi-Gateway LoRaWAN and Interference Measurement across Indoor and Outdoor Localities
by Eugen Harinda, Andrew J. Wixted, Ayyaz-UI-Haq Qureshi, Hadi Larijani and Ryan M. Gibson
Computers 2022, 11(2), 25; https://doi.org/10.3390/computers11020025 - 11 Feb 2022
Cited by 4 | Viewed by 3317
Abstract
Little work has been reported on the magnitude and impact of interference with the performance of Internet of Things (IoT) applications operated by Long-Range Wide-Area Network (LoRaWAN) in the unlicensed 868 MHz Industrial, Scientific, and Medical (ISM) band. The propagation performance and signal [...] Read more.
Little work has been reported on the magnitude and impact of interference with the performance of Internet of Things (IoT) applications operated by Long-Range Wide-Area Network (LoRaWAN) in the unlicensed 868 MHz Industrial, Scientific, and Medical (ISM) band. The propagation performance and signal activity measurement of such technologies can give many insights to effectively build long-range wireless communications in a Non-Line of Sight (NLOS) environment. In this paper, the performance of a live multi-gateway in indoor office site in Glasgow city was analysed in 26 days of traffic measurement. The indoor network performances were compared to similar performance measurements from outdoor LoRaWAN test traffic generated across Glasgow Central Business District (CBD) and elsewhere on the same LoRaWAN. The results revealed 99.95% packet transfer success on the first attempt in the indoor site compared to 95.7% at the external site. The analysis shows that interference is attributed to nearly 50 X greater LoRaWAN outdoor packet loss than indoor. The interference measurement results showed a 13.2–97.3% and 4.8–54% probability of interfering signals, respectively, in the mandatory Long-Range (LoRa) uplink and downlink channels, capable of limiting LoRa coverage in some areas. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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21 pages, 27858 KiB  
Article
Adaptive Contextual Risk-Based Model to Tackle Confidentiality-Based Attacks in Fog-IoT Paradigm
by Satiaseelan Selvan and Manmeet Mahinderjit Singh
Computers 2022, 11(2), 16; https://doi.org/10.3390/computers11020016 - 24 Jan 2022
Cited by 5 | Viewed by 2814
Abstract
The Internet of Things (IoT) allows billions of physical objects to be connected to gather and exchange information to offer numerous applications. It has unsupported features such as low latency, location awareness, and geographic distribution that are important for a few IoT applications. [...] Read more.
The Internet of Things (IoT) allows billions of physical objects to be connected to gather and exchange information to offer numerous applications. It has unsupported features such as low latency, location awareness, and geographic distribution that are important for a few IoT applications. Fog computing is integrated into IoT to aid these features to increase computing, storage, and networking resources to the network edge. Unfortunately, it is faced with numerous security and privacy risks, raising severe concerns among users. Therefore, this research proposes a contextual risk-based access control model for Fog-IoT technology that considers real-time data information requests for IoT devices and gives dynamic feedback. The proposed model uses Fog-IoT environment features to estimate the security risk associated with each access request using device context, resource sensitivity, action severity, and risk history as inputs for the fuzzy risk model to compute the risk factor. Then, the proposed model uses a security agent in a fog node to provide adaptive features in which the device’s behaviour is monitored to detect any abnormal actions from authorised devices. The proposed model is then evaluated against the existing model to benchmark the results. The fuzzy-based risk assessment model with enhanced MQTT authentication protocol and adaptive security agent showed an accurate risk score for seven random scenarios tested compared to the simple risk score calculations. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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30 pages, 36489 KiB  
Article
Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms
by Alexander Vodyaho, Nataly Zhukova, Igor Kulikov and Saddam Abbas
Computers 2021, 10(8), 101; https://doi.org/10.3390/computers10080101 - 18 Aug 2021
Viewed by 2090
Abstract
The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity [...] Read more.
The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realization of these procedures. A distinctive feature of the developed approach is that the models are built in runtime automatically. As a top-level model, a multi-level relative finite state operational automaton is used. The automaton state is described using a multi-level structural-behavioral model, which is a superposition of four graphs: the workflow graph, the data flow graph, the request flow graph and the resource graph. To implement the data acquisition procedure using the model, the context-sensitive policy mechanism is used. The article discusses possible approaches to implementation of suggested mechanisms and describes an example of application. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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11 pages, 3476 KiB  
Article
Energy Aware and Quality of Service Routing Mechanism for Hybrid Internet of Things Network
by Eyassu Dilla Diratie, Durga Prasad Sharma and Khaldoun Al Agha
Computers 2021, 10(8), 93; https://doi.org/10.3390/computers10080093 - 3 Aug 2021
Cited by 2 | Viewed by 2049
Abstract
Wireless Multimedia Sensor Networks (WMSNs) based on IEEE 802.11 mesh networks are effective and suitable solutions for video surveillance systems in detecting intrusions in selected monitored areas. The IEEE 802.11-based WMSNs offer high bit rate video transmissions but are challenged by energy inefficiency [...] Read more.
Wireless Multimedia Sensor Networks (WMSNs) based on IEEE 802.11 mesh networks are effective and suitable solutions for video surveillance systems in detecting intrusions in selected monitored areas. The IEEE 802.11-based WMSNs offer high bit rate video transmissions but are challenged by energy inefficiency issues and concerns. To resolve the energy inefficiency challenges, the salient research studies proposed a hybrid architecture. This newly evolved architecture is based on the integration of IEEE 802.11-based mesh WMSNs along with the LoRa network to form an autonomous and high bitrate, energy-efficient video surveillance system. This paper proposes an energy-aware and Quality of Service (QoS) routing mechanism for mesh-connected visual sensor nodes in a hybrid Internet of Things (IoT) network. The routing algorithm allows routing a set of video streams with guaranteed bandwidth and limited delay using as few visual sensor nodes as possible in the network. The remaining idle visual sensor nodes can be turned off completely, and thus it can significantly minimize the overall energy consumption of the network. The proposed algorithm is numerically simulated, and the results show that the proposed approach can help in saving a significant amount of energy consumption while guaranteeing bandwidth and limited delay. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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Review

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21 pages, 746 KiB  
Review
IoT Serverless Computing at the Edge: A Systematic Mapping Review
by Vojdan Kjorveziroski, Sonja Filiposka and Vladimir Trajkovik
Computers 2021, 10(10), 130; https://doi.org/10.3390/computers10100130 - 15 Oct 2021
Cited by 23 | Viewed by 4052
Abstract
Serverless computing is a new concept allowing developers to focus on the core functionality of their code, while abstracting away the underlying infrastructure. Even though there are existing commercial serverless cloud providers and open-source solutions, dealing with the explosive growth of new Internet [...] Read more.
Serverless computing is a new concept allowing developers to focus on the core functionality of their code, while abstracting away the underlying infrastructure. Even though there are existing commercial serverless cloud providers and open-source solutions, dealing with the explosive growth of new Internet of Things (IoT) devices requires more efficient bandwidth utilization, reduced latency, and data preprocessing closer to the source, thus reducing the overall data volume and meeting privacy regulations. Moving serverless computing to the edge of the network is a topic that is actively being researched with the aim of solving these issues. This study presents a systematic mapping review of current progress made to this effect, analyzing work published between 1 January 2015 and 1 September 2021. Using a document selection methodology which emphasizes the quality of the papers obtained through querying several popular databases with relevant search terms, we have included 64 entries, which we then further categorized into eight main categories. Results show that there is an increasing interest in this area with rapid progress being made to solve the remaining open issues, which have also been summarized in this paper. Special attention is paid to open-source efforts, as well as open-access contributions. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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