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Distributing Computing in the Internet of Things (IoT): Cloud, Fog...
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Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 11411

Special Issue Editor

Dr. Antonio Esposito

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli, 81031 Aversa, CE, Italy
Interests: cloud computing; cloud patterns; design patterns; semantics; machine learning; deep learning

Special Issue Information

Dear Colleagues,

Recent advancements in Internet of Things (IoT) technologies, coupled with highly distributed environments such as the cloud–edge continuum, have facilitated the development of complex, heterogeneous, and distributed applications that harness the local processing capabilities of smart devices and the immense computational power provided by cloud servers. However, the inherent complexity of such distributed environments inevitably gives rise to a variety of issues, such as interface and data interoperability problems arising from the high heterogeneity of interfaced resources, privacy and security concerns relating to data management, and the need for optimal resource scheduling and energy consumption to effectively orchestrate these applications. These issues represent only a fraction of the potential research areas where such problems may arise.

The main objective of this Special Issue is to collect contributions, including theoretical research and practical applications, focusing on IoT-based distributed applications that leverage the cloud–edge continuum. The focus is on the critical aspects of these technologies that still present challenges and the possible solutions that could be implemented to address them.

Dr. Antonio Esposito
Guest Editor

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. Future Internet is an international peer-reviewed open access monthly 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 1600 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.

Keywords

  • cloud-edge continuum
  • distributed programming models
  • energy-aware computing
  • fog computing
  • Internet of Things
  • interoperability and portability
  • privacy-aware data processing
  • secure data management

Published Papers (7 papers)

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Research

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36 pages, 3893 KiB  
Article
Cloud Security Using Fine-Grained Efficient Information Flow Tracking
by Fahad Alqahtani, Mohammed Almutairi and Frederick T. Sheldon
Future Internet 2024, 16(4), 110; https://doi.org/10.3390/fi16040110 - 25 Mar 2024
Viewed by 707
Abstract
This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the [...] Read more.
This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework. Full article
(This article belongs to the Special Issue Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing)
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31 pages, 1418 KiB  
Article
A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness
by Mahmoud Elkhodr, Samiya Khan and Ergun Gide
Future Internet 2024, 16(1), 22; https://doi.org/10.3390/fi16010022 - 07 Jan 2024
Viewed by 1752
Abstract
In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes [...] Read more.
In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware’s operational efficiency. Full article
(This article belongs to the Special Issue Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing)
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24 pages, 2445 KiB  
Article
Internet-of-Things Traffic Analysis and Device Identification Based on Two-Stage Clustering in Smart Home Environments
by Mizuki Asano, Takumi Miyoshi and Taku Yamazaki
Future Internet 2024, 16(1), 17; https://doi.org/10.3390/fi16010017 - 31 Dec 2023
Viewed by 1923
Abstract
Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices and [...] Read more.
Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices and challenges in replacing or the updating their firmware, adequate security measures have not been implemented. Instead, IoT device identification methods based on traffic analysis have been proposed. Since conventional methods process and analyze traffic data simultaneously, bias in the occurrence rate of traffic patterns has a negative impact on the analysis results. Therefore, this paper proposes an IoT traffic analysis and device identification method based on two-stage clustering in smart home environments. In the first step, traffic patterns are extracted by clustering IoT traffic at a local gateway located in each smart home and subsequently sent to a cloud server. In the second step, the cloud server extracts common traffic units to represent IoT traffic by clustering the patterns obtained in the first step. Two-stage clustering can reduce the impact of data bias, because each cluster extracted in the first clustering is summarized as one value and used as a single data point in the second clustering, regardless of the occurrence rate of traffic patterns. Through the proposed two-stage clustering method, IoT traffic is transformed into time series vector data that consist of common unit patterns and can be identified based on time series representations. Experiments using public IoT traffic datasets indicated that the proposed method could identify 21 IoTs devices with an accuracy of 86.9%. Therefore, we can conclude that traffic analysis using two-stage clustering is effective for improving the clustering quality, device identification, and implementation in distributed environments. Full article
(This article belongs to the Special Issue Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing)
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39 pages, 11982 KiB  
Article
Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
by Anestis Kousis and Christos Tjortjis
Future Internet 2024, 16(1), 3; https://doi.org/10.3390/fi16010003 - 22 Dec 2023
Viewed by 1915
Abstract
In recent years, the emergence of the smart city concept has garnered attention as a promising innovation aimed at addressing the multifactorial challenges arising from the concurrent trends of urban population growth and the climate crisis. In this study, we delve into the [...] Read more.
In recent years, the emergence of the smart city concept has garnered attention as a promising innovation aimed at addressing the multifactorial challenges arising from the concurrent trends of urban population growth and the climate crisis. In this study, we delve into the multifaceted dimensions of the smart city paradigm to unveil its underlying structure, employing a combination of quantitative and qualitative techniques. To achieve this, we collected textual data from three sources: scientific publication abstracts, news blog posts, and social media entries. For the analysis of this textual data, we introduce an innovative semi-automated methodology that integrates topic modeling and thematic analysis. Our findings highlight the intricate nature of the smart city domain, which necessitates examination from three perspectives: applications, technology, and socio-economic perspective. Through our analysis, we identified ten distinct aspects of the smart city paradigm, encompassing mobility, energy, infrastructure, environment, IoT, data, business, planning and administration, security, and people. When comparing the outcomes across the three diverse datasets, we noted a relative lack of attention within the scientific community towards certain aspects, notably in the realm of business, as well as themes relevant to citizens’ everyday lives, such as food, shopping, and green spaces. This work reveals the underlying thematic structure of the smart city concept to help researchers, practitioners, and public administrators participate effectively in smart city transformation initiatives. Furthermore, it introduces a novel data-driven method for conducting thematic analysis on large text datasets. Full article
(This article belongs to the Special Issue Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing)
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0 pages, 5665 KiB  
Article
A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
by Zuopeng Li, Hengshuai Ju and Zepeng Ren
Future Internet 2023, 15(12), 395; https://doi.org/10.3390/fi15120395 - 07 Dec 2023
Viewed by 1368 | Correction
Abstract
The existing research on dependent task offloading and resource allocation assumes that edge servers can provide computational and communication resources free of charge. This paper proposes a two-stage resource allocation method to address this issue. In the first stage, users incentivize edge servers [...] Read more.
The existing research on dependent task offloading and resource allocation assumes that edge servers can provide computational and communication resources free of charge. This paper proposes a two-stage resource allocation method to address this issue. In the first stage, users incentivize edge servers to provide resources. We formulate the incentive problem in this stage as a multivariate Stackelberg game, which takes into account both computational and communication resources. In addition, we also analyze the uniqueness of the Stackelberg equilibrium under information sharing conditions. Considering the privacy issues of the participants, the research is extended to scenarios without information sharing, where the multivariable game problem is modeled as a partially observable Markov decision process (POMDP). In order to obtain the optimal incentive decision in this scenario, a reinforcement learning algorithm based on the learning game is designed. In the second stage, we propose a greedy-based deep reinforcement learning algorithm that is aimed at minimizing task execution time by optimizing resource and task allocation strategies. Finally, the simulation results demonstrate that the algorithm designed for non-information sharing scenarios can effectively approximate the theoretical Stackelberg equilibrium, and its performance is found to be better than that of the other three benchmark methods. After the allocation of resources and sub-tasks by the greedy-based deep reinforcement learning algorithm, the execution delay of the dependent task is significantly lower than that in local processing. Full article
(This article belongs to the Special Issue Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing)
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23 pages, 3075 KiB  
Article
End-to-End Service Availability in Heterogeneous Multi-Tier Cloud–Fog–Edge Networks
by Igor Kabashkin
Future Internet 2023, 15(10), 329; https://doi.org/10.3390/fi15100329 - 06 Oct 2023
Cited by 3 | Viewed by 1322
Abstract
With the evolution towards the interconnected future internet spanning satellites, aerial systems, terrestrial infrastructure, and oceanic networks, availability modeling becomes imperative to ensure reliable service. This paper presents a methodology to assess end-to-end availability in complex multi-tiered architectures using a Markov model tailored [...] Read more.
With the evolution towards the interconnected future internet spanning satellites, aerial systems, terrestrial infrastructure, and oceanic networks, availability modeling becomes imperative to ensure reliable service. This paper presents a methodology to assess end-to-end availability in complex multi-tiered architectures using a Markov model tailored to the unique characteristics of cloud, fog, edge, and IoT layers. By quantifying individual tier reliability and combinations thereof, the approach enables setting availability targets during the design and evaluation of operational systems. In the paper, a methodology is proposed to construct a Markov model for the reliability of discrete tiers and end-to-end service availability in heterogeneous multi-tier cloud–fog–edge networks, and the model is demonstrated through numerical examples assessing availability in multi-tier networks. The numerical examples demonstrate the adaptability of the model to various topologies from conventional three-tier to arbitrary multi-level architectures. As connectivity becomes ubiquitous across heterogeneous devices and networks, the proposed approach and availability modeling provide an effective tool for reinforcing the future internet’s fault tolerance and service quality. Full article
(This article belongs to the Special Issue Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing)
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Review

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22 pages, 828 KiB  
Review
An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
by Javid Misirli and Emiliano Casalicchio
Future Internet 2024, 16(1), 16; https://doi.org/10.3390/fi16010016 - 30 Dec 2023
Cited by 1 | Viewed by 1698
Abstract
The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the [...] Read more.
The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the network and the cloud. This paradigm shift is called fog computing, a concept introduced by Cisco in 2012. Scheduling applications in this decentralized, heterogeneous, and resource-constrained environment is challenging. The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task scheduling problem. We propose a taxonomy of task scheduling algorithms, and we identify the research gaps and challenges. Full article
(This article belongs to the Special Issue Distributing Computing in the Internet of Things (IoT): Cloud, Fog and Edge Computing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Digital Twins Threat survey
Author: Marin Lopez
Highlights: Discusion of the internal structure of Digital Twins Focal points of the security vulnerabilities of DT Main countermeasures

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