Elsevier Information Systems Special Issue on
Emerging Trends and Challenges in Edge-Fog-Cloud Interplay in the Internet of Things (IoT)


The Intelligent Internet of Things (IoT) tsunami and public embracement, and the ubiquitous adoption of devices in virtually every industry is affecting every aspect of life, ranging from smart cars, smart homes, smart cities, smart factories to smart health, and smart environments. The integration of IoT and Cloud Computing has created another paradigm, the cloud IoT, to address some of the major challenges of IoT, such as advanced analytics capabilities and big data storage. However, in the cloud IoT model, the massive amount of data coming from “smart things” needs to be uploaded to the cloud, demanding a considerable amount of available communication bandwidth. Cloud-based IoT model cannot meet the strict computing time requirement in latency-critical applications requiring a real-time operation. An excellent example of such a case is eHealth applications such as arrhythmia monitoring and classification in which volume, variety, and velocity, as well as end-to-end response time and communication bandwidth, should be handled efficiently. Edge or Fog Computing has emerged as a solution to address the drawbacks of Cloud-based IoT solutions in which computing and storage resources are located not only in the cloud but also at the edges near the source of data. Hierarchical collaborative edge-fog-cloud architecture brings tremendous benefits as it makes possible to distribute the intelligence and computation —including data analysis, machine learning (ML) training, and decision making—to achieve an optimal solution while satisfying the given constraints (i.e., optimization for energy versus optimization for latency) of each use case. However, due to the hierarchical, cross-layer, and distributed nature of this IoT model, many challenges from smart things, to network, architecture, algorithms/software, and security still need to be addressed to develop consistent, suitable, scalable, safe, flexible and power-efficient systems. The main objective of this Special Issue (SI) is to address all important aspects of emerging technologies for edge-fog-cloud computing in IoT covering architectures, techniques, protocols, policies, applications, distributed machine learnings, as well as the interaction between edge, fog and cloud analytics. Authors are invited to submit high-quality papers containing original work from either academia or industry reporting novel advances in (but not limited to) the following topics:

• Distributed architectures and reference models.
• Resource Management Mechanisms.
• Service placement, migration and adaptation.
• Low-latency High-reliability energy-efficient network protocols and communications in edge-fog-cloud.
• The impact of 5G technology on edge-fog-cloud interplay.
• Edge-fog-cloud management protocols and policies for workload communication and distribution.
• Privacy and security issues including secure firmware, communications, and strategies to detect and mitigate attacks, as well as Over the air updates for safety IoT devices.
• Trust-Oriented Designs of next-generation hierarchical IoT systems.
• Optimization of the utility-privacy tradeoffs.
• Big-data analytics, machine learning algorithms, and scalable/parallel/distributed algorithms.
• Collaborative distributed machine learning and data analytics from Edge to Fog and Cloud.
• Privacy-preserving Machine Learning and Data Processing solutions in hierarchical IoT solutions.
• Privacy-Preserving Machine Learning (PPML) and Multi-party computation (MPC) techniques.
• Performance monitoring & evaluation.
• Real-world experiences and use cases (eHealth, automotive, transportation and logistics, retail, industry 4.0, etc.)



Important Dates
Submissions Deadline: June 15, 2020
First Reviews Due: August 15, 2020
Revision Due: September 15, 2020
Second Reviews Due/Notification: October 15, 2020
Final Manuscript Due: October 30, 2020
Publication Date: 2020


Submission Guidelines
Solicited original submissions must not be currently under consideration for publication in other venues. Author guidelines are at https://www.journals.elsevier.com/information-systems/.


Guest Editors
Farshad Firouzi, Duke University
Sebastián Ventura, University of Córdoba
Bahar Farahani, Shahid Beheshti University
Alysson Bessani, Universidade de Lisboa



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