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EFIOT'19 2019 : MobiQuitous Workshop on Edge, Fog, and Cloud Computing for the Internet of Things (EFIOT)

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Link: http://mobiquitous.org/call-workshop1/
 
When Nov 12, 2019 - Nov 14, 2019
Where Houston, United States
Submission Deadline Sep 15, 2019
Notification Due Oct 1, 2019
Final Version Due Oct 10, 2019
Categories    edge computing   internet of things   fog computing   machine learning
 

Call For Papers

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)

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