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Special Issue "Edge and Fog Computing for Internet of Things Systems"

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

Deadline for manuscript submissions: closed (10 April 2022) | Viewed by 17877

Special Issue Editors

Dr. Behnam Dezfouli
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Santa Clara University, Santa Clara, CA, USA
Interests: wireless networking and security mechanisms for internet of things systems; edge and fog computing (SDN, virtualization technologies, resource allocation); traffic flow and channel access control methods using machine learning and scheduling; empirical, simulation-based, and theoretical performance evaluation of IoT systems; mobile computing and energy-efficient software development; design and interfacing of hardware platforms for energy measurement and calibration of IoT devices
Special Issues, Collections and Topics in MDPI journals
Dr. Yuhong Liu
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Santa Clara University, Santa Clara, CA, USA
Interests: trust, security, and privacy issues for internet of things systems; machine learning and AI in edge/fog devices; secure and energy-efficient edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Employing edge and fog computing for building IoT systems is essential considering the massive amount of data generated by sensing devices, the delay requirements of IoT applications, the high burden of data processing on cloud platforms, and the need to take immediate actions against security threats. By pushing processing and storage closer to IoT devices, it is possible to reduce the amount of data sent to the cloud, while also reducing communication delay. To this end, new data aggregation and processing methods are required to distribute computation across the edge to the cloud continuum. Edge and fog computing can also be used to facilitate communication and resource discovery, and enhance the security of IoT devices. New architectures are required to facilitate the communication between IoT devices and servers, depending on the type of application. From the data analytics point of view, efficient and scalable data processing at the edge or task offloading to trustworthy edge/fog nodes is critical to avoid significant delays and network congestion. Meanwhile, the massive and rapidly increasing amount of resource-constrained IoT edge devices has also significantly extended the attack surface, creating new challenges to ensuring data privacy and communication security against emerging threats and establishing trust among multiple communication parties.

For this Special Issue the following topics are of particular interest:

  • Sensor data processing by edge/fog
  • Architectures for building edge/fog system
  • Network function virtualization
  • Traffic control and traffic shaping
  • Allocation of computation and communication resources
  • Edge/fog computing applications, such as healthcare, smart homes, smart cities, intelligent transportation.
  • Multi-layer collaboration from edge to the cloud
  • Security, privacy, and trust issues
  • Secure communication across the edge to cloud continuum
  • Energy-efficient solutions for edge and fog computing
  • Signal processing and artificial intelligence

Dr. Behnam Dezfouli
Dr. Yuhong Liu
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.

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 2400 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.

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Published Papers (10 papers)

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Editorial

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Editorial
Editorial: Special Issue “Edge and Fog Computing for Internet of Things Systems”
Sensors 2022, 22(12), 4387; https://doi.org/10.3390/s22124387 - 10 Jun 2022
Cited by 1 | Viewed by 651
Abstract
Employing edge and fog computing for building IoT systems is essential, especially because of the massive number of data generated by sensing devices, the delay requirements of IoT applications, the high burden of data processing on cloud platforms, and the need to take [...] Read more.
Employing edge and fog computing for building IoT systems is essential, especially because of the massive number of data generated by sensing devices, the delay requirements of IoT applications, the high burden of data processing on cloud platforms, and the need to take immediate actions against security threats.  Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)

Research

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Article
Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing
Sensors 2022, 22(3), 1026; https://doi.org/10.3390/s22031026 - 28 Jan 2022
Cited by 4 | Viewed by 1364
Abstract
Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration [...] Read more.
Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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Article
A Framework for Managing Device Association and Offloading the Transport Layer’s Security Overhead of WiFi Device to Access Points
Sensors 2021, 21(19), 6433; https://doi.org/10.3390/s21196433 - 26 Sep 2021
Cited by 2 | Viewed by 1324
Abstract
Considering the resource constraints of Internet of Things (IoT) stations, establishing secure communication between stations and remote servers imposes a significant overhead on these stations in terms of energy cost and processing load. This overhead, in particular, is considerable in networks providing high [...] Read more.
Considering the resource constraints of Internet of Things (IoT) stations, establishing secure communication between stations and remote servers imposes a significant overhead on these stations in terms of energy cost and processing load. This overhead, in particular, is considerable in networks providing high communication rates and frequent data exchange, such as those relying on the IEEE 802.11 (WiFi) standard. This paper proposes a framework for offloading the processing overhead of secure communication protocols to WiFi access points (APs) in deployments where multiple APs exist. Within this framework, the main problem is finding the AP with sufficient computation and communication capacities to ensure secure and efficient transmissions for the stations associated with that AP. Based on the data-driven profiles obtained from empirical measurements, the proposed framework offloads most heavy security computations from the stations to the APs. We model the association problem as an optimization process with a multi-objective function. The goal is to achieve maximum network throughput via the minimum number of APs while satisfying the security requirements and the APs’ computation and communication capacities. The optimization problem is solved using genetic algorithms (GAs) with constraints extracted from a physical testbed. Experimental results demonstrate the practicality and feasibility of our comprehensive framework in terms of task and energy efficiency as well as security. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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Article
Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets
Sensors 2021, 21(18), 6253; https://doi.org/10.3390/s21186253 - 17 Sep 2021
Cited by 5 | Viewed by 1492
Abstract
The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help [...] Read more.
The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help diminish the burden and chaos of medical management and operations. Previous studies contributed tremendous progress on the dependability quantification of existing computing paradigms (e.g., cloud, grid computing) at remote data centers, while a few works investigated the performance of provided medical services under the constraints of operational availability of devices and systems at local medical centers. Therefore, it is critical to rapidly develop appropriate models to quantify the operational metrics of medical services provided and sustained by medical information systems (MIS) even before practical implementation. In this paper, we propose a comprehensive performability SRN model of an edge/fog based MIS for the performability quantification of medical data transaction and services in local hospitals or medical centers. The model elaborates different failure modes of fog nodes and their VMs under the implementation of fail-over mechanisms. Sophisticated behaviors and dependencies between the performance and availability of data transactions are elaborated in a comprehensive manner when adopting three main load-balancing techniques including: (i) probability-based, (ii) random-based and (iii) shortest queue-based approaches for medical data distribution from edge to fog layers along with/without fail-over mechanisms in the cases of component failures at two levels of fog nodes and fog virtual machines (VMs). Different performability metrics of interest are analyzed including (i) recover token rate, (ii) mean response time, (iii) drop probability, (iv) throughput, (v) queue utilization of network devices and fog nodes to assimilate the impact of load-balancing techniques and fail-over mechanisms. Discrete-event simulation results highlight the effectiveness of the combination of these for enhancing the performability of medical services provided by an MIS. Particularly, performability metrics of medical service continuity and quality are improved with fail-over mechanisms in the MIS while load balancing techniques help to enhance system performance metrics. The implementation of both load balancing techniques along with fail-over mechanisms provide better performability metrics compared to the separate cases. The harmony of the integrated strategies eventually provides the trustworthiness of medical services at a high level of performability. This study can help improve the design of MIS systems integrated with different load-balancing techniques and fail-over mechanisms to maintain continuous performance under the availability constraints of medical services with heavy computing workloads in local hospitals/medical centers, to combat with new waves of virus pandemics. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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Article
IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models
Sensors 2021, 21(16), 5660; https://doi.org/10.3390/s21165660 - 23 Aug 2021
Cited by 4 | Viewed by 1313
Abstract
Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks [...] Read more.
Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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Article
Quality of Service Provision in Fog Computing: Network-Aware Scheduling of Containers
Sensors 2021, 21(12), 3978; https://doi.org/10.3390/s21123978 - 09 Jun 2021
Cited by 7 | Viewed by 1624
Abstract
State-of-the-art scenarios, such as Internet of Things (IoT) and Smart Cities, have recently arisen. They involve the processing of huge data sets under strict time requirements, rendering the use of cloud resources unfeasible. For this reason, Fog computing has been proposed as a [...] Read more.
State-of-the-art scenarios, such as Internet of Things (IoT) and Smart Cities, have recently arisen. They involve the processing of huge data sets under strict time requirements, rendering the use of cloud resources unfeasible. For this reason, Fog computing has been proposed as a solution; however, there remains a need for intelligent allocation decisions, in order to make it a fully usable solution in such contexts. In this paper, a network-aware scheduling algorithm is presented, which aims to select the fog node most suitable for the execution of an application within a given deadline. This decision is made taking the status of the network into account. This scheduling algorithm was implemented as an extension to the Kubernetes default scheduler, and compared with existing proposals in the literature. The comparison shows that our proposal is the only one that can execute all the submitted jobs within their deadlines (i.e., no job is rejected or executed exceeding its deadline) with certain configurations in some of the scenarios tested, thus obtaining an optimal solution in such scenarios. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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Article
Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network
Sensors 2021, 21(9), 3135; https://doi.org/10.3390/s21093135 - 30 Apr 2021
Cited by 3 | Viewed by 1100
Abstract
With the exponential growth of mobile devices and the emergence of computationally intensive and delay-sensitive tasks, the enormous demand for data and computing resources has become a big challenge. Fortunately, the combination of mobile edge computing (MEC) and ultra-dense network (UDN) is considered [...] Read more.
With the exponential growth of mobile devices and the emergence of computationally intensive and delay-sensitive tasks, the enormous demand for data and computing resources has become a big challenge. Fortunately, the combination of mobile edge computing (MEC) and ultra-dense network (UDN) is considered to be an effective way to solve these challenges. Due to the highly dynamic mobility of mobile devices and the randomness of the work requests, the load imbalance between MEC servers will affect the performance of the entire network. In this paper, the software defined network (SDN) is applied to the task allocation in the MEC scenario of UDN, which is based on routing of corresponding information between MEC servers. Secondly, a new load balancing algorithm based on load estimation by user load prediction is proposed to solve the NP-hard problem in task offloading. Furthermore, a genetic algorithm (GA) is used to prove the effectiveness and rapidity of the algorithm. At present, if the load balancing algorithm only depends on the actual load of each MEC, it usually leads to ping-pong effect. It is worth mentioning that our method can effectively reduce the impact of ping-pong effect. In addition, this paper also discusses the subtask offloading problem of divisible tasks and the corresponding solutions. At last, simulation results demonstrate the efficiency of our method in balancing load among MEC servers and its ability to optimize systematic stability. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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Article
FASDQ: Fault-Tolerant Adaptive Scheduling with Dynamic QoS-Awareness in Edge Containers for Delay-Sensitive Tasks
Sensors 2021, 21(9), 2973; https://doi.org/10.3390/s21092973 - 23 Apr 2021
Cited by 3 | Viewed by 1213
Abstract
As the requirement for real-time data analysis increases, edge computing is being implemented to leverage the resources of edge devices to reduce system response times and decrease the latency. However, due to the resource constraints of edge clouds, edge servers are more prone [...] Read more.
As the requirement for real-time data analysis increases, edge computing is being implemented to leverage the resources of edge devices to reduce system response times and decrease the latency. However, due to the resource constraints of edge clouds, edge servers are more prone to failures than other systems. Therefore, guaranteeing the reliability of services in edge clouds is critical. In this paper, we propose a fault-tolerant adaptive scheduling mechanism with dynamic quality of service (QoS) awareness (FASDQ), which extends the primary/backup (PB) model by applying QoS on demand to task copies. The aim of the method is to reduce the latency and achieve reliable service for tasks by changing the execution time of task copies. This paper also proposes a container resource-adaptive adjustment mechanism, which adjusts the timing of resources when the available resources cannot meet the task copy requirements. Finally, this paper reports the results of simulation experiments on the EdgeCloudSim platform to evaluate the difference in performance between FASDQ and other methods. The results show that the mechanism effectively reduces the execution time of task copies and outperforms other methods in terms of reliability and general resource utilization. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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Article
Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds
Sensors 2021, 21(1), 229; https://doi.org/10.3390/s21010229 - 01 Jan 2021
Cited by 10 | Viewed by 1764
Abstract
The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way [...] Read more.
The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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Review

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Review
A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture
Sensors 2021, 21(17), 5922; https://doi.org/10.3390/s21175922 - 03 Sep 2021
Cited by 21 | Viewed by 3614
Abstract
Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the [...] Read more.
Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades. This article aims to provide a systematic literature review of current works that have been done in Cloud, Fog, and Edge Computing applications in the smart agriculture domain between 2015 and up-to-date. The key objective of this review is to identify all relevant research on new computing paradigms with smart agriculture and propose a new architecture model with the combinations of Cloud–Fog–Edge. Furthermore, it also analyses and examines the agricultural application domains, research approaches, and the application of used combinations. Moreover, this survey discusses the components used in the architecture models and briefly explores the communication protocols used to interact from one layer to another. Finally, the challenges of smart agriculture and future research directions are briefly pointed out in this article. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems)
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