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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: 30 July 2021.

Special Issue Editors

Dr. Behnam Dezfouli
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Santa Clara University, Santa Clara, 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
Dr. Yuhong Liu
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Santa Clara University, Santa Clara, 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 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 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 2200 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 (3 papers)

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Research

Open AccessArticle
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
Viewed by 236
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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Open AccessArticle
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
Viewed by 263
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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Open AccessArticle
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
Viewed by 492
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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