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Call For Workshop Papers

1st International Workshop on:

Edge, Fog, and Cloud Computing for the Internet of Things (EFIOT)

(In conjunction with MobiQuitous 2019)

Submit Workshop Paper

In today’s world of mobile healthcare, smart cities, smart homes, and Industry 4.0, almost every device is connected to the Internet. These devices generate data and require computation and storage resources, which cannot be met at the network edge. With the growing number of interconnected devices and IoT applications arises the challenge of handling a massive amount of data in an efficient manner. Cloud computing offers a partial solution to this dilemma by providing massive infrastructure and powerful applications. However, cloud computing is not suitable in real-time and mission-critical applications with strict runtime and latency requirements. Furthermore, cloud computing cannot scale sufficiently to handle the processing, storage, and communication demands of billions of IoT devices. To address this concern, fog computing brings additional computing, storage, and control capabilities closer to the network edge. Fog is intended to work alongside the cloud, forming an edge-fog-cloud continuum to serve various kinds of applications based on their requirements. To this end, new resource allocation mechanisms are necessary to slice both communication and processing resources, depending on the requests made by applications. In addition, with the massive increase in the collected data from IoT devices, machine learning (ML) is a key tool for data inference and decision making, which can be integrated at different processing layers. For resource-constrained IoT devices, the challenge calls for more efficient hardware platforms to carry on ML algorithms as well as optimizing ML techniques to reduce the power consumption, memory requirement, and computation intensity. To leverage the computing resources across edge devices, fog computers and cloud servers, distributed and collaborative ML techniques are required. In addition to data processing, ML can be used in management and organizing IoT systems, such as security and privacy, power/battery management, and resource allocation/task scheduling.


Topics of interest include but are not limited to:

Network Slicing

Real-Time Communication

Virtualization Technologies

Joint Networking and Computational Resource Management

Signal Processing and Edge Mining

Machine Learning and Deep Learning

Collaborative Edge-Fog Algorithms

Energy-Efficient Computing

Mobile Edge Computing (MEC)

Theoretical and Empirical Evaluation

Simulation and Performance Evaluation Tools and Techniques

Role of Edge and Fog Computing in 5G and Beyond


Paper Submission

Papers should be submitted through EAI ‘Confy+‘ system, and have to comply with the ACM format (see Author’s kit section)


Important Dates:

Extended Submission deadline: September 15, 2019 (11:59PM Pacific Time)

Extended Acceptance notification: October 1, 2019

Extended Camera-ready deadline: October 10, 2019

Workshop: November 12, 2019


Workshop Co-chairs

Behnam Dezfouli, Santa Clara University, USA

Ying Liu, Santa Clara University, USA


Technique Committee Members:

 Venkat Pullela, Broadcom, USA

Sundar Sankaran, Ruckus Networks, USA

Jaykumar Sheth, Santa Clara University, USA

Dimitris A. Pados, Florida Atlantic University, USA

Georgios Sklivanitis, Florida Atlantic University, USA

Konstantinos Tountas, Florida Atlantic University, USA

Panagiotis P. Markopoulos, Rochester Institute of Technology, USA

Hongzhi Guo, Norfolk State University, USA

Zhangyu Guan, The State University of New York at Buffalo, USA

Emrecan Demirors, Northeastern University, USA

Ying Mao, Fordham University, USA

Si Chen, West Chester University of Pennsylvania, USA

Liu Cui, West Chester University of Pennsylvania, USA

Jun Zhuang, Indiana University-Purdue University Indianapolis, USA

Ali Nikoukar, Otto-von-Guericke-Universität Magdeburg, Germany

Hossen Fotouhi, Mälardalen University (MDH), Sweden

Puneet Kumar, Santa Clara University, USA

Andrei Negulescu, Santa Clara University, USA

Shabih Hassan, Delos Labs, USA


EAI Institutional Members