Next Article in Journal
A Reliability-Based Multisensor Data Fusion with Application in Target Classification
Next Article in Special Issue
Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach
Previous Article in Journal
An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition
Previous Article in Special Issue
Complex Field Network Coding for Multi-Source Multi-Relay Single-Destination UAV Cooperative Surveillance Networks
Open AccessFeature PaperArticle

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

1
School of Electrical and Computer Engineering, National Technical University of Athens—NTUA, 15780 Zografou, Greece
2
School of Electronics, Electrical Engineering and Computer Science, Queen’s University of Belfast—QUB, Belfast BT7 1NN, UK
3
École de Technologie Supérieure (ÉTS Montreal) | Université du Québec, Montreal, QC H3C 1K3, Canada
4
Inria Lille-Nord Europe, Lille, 59650 Villeneuve d’Ascq, France
5
Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain—UCLouvain, 1348 Louvain-la-Neuve, Belgium
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(8), 2191; https://doi.org/10.3390/s20082191
Received: 23 March 2020 / Revised: 9 April 2020 / Accepted: 10 April 2020 / Published: 13 April 2020
(This article belongs to the Special Issue Optimization and Communication in UAV Networks)
The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. View Full-Text
Keywords: edge computing; internet of things; mobile robots; resource allocation; control co-design edge computing; internet of things; mobile robots; resource allocation; control co-design
Show Figures

Graphical abstract

MDPI and ACS Style

Dechouniotis, D.; Athanasopoulos, N.; Leivadeas, A.; Mitton, N.; Jungers, R.; Papavassiliou, S. Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective. Sensors 2020, 20, 2191. https://doi.org/10.3390/s20082191

AMA Style

Dechouniotis D, Athanasopoulos N, Leivadeas A, Mitton N, Jungers R, Papavassiliou S. Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective. Sensors. 2020; 20(8):2191. https://doi.org/10.3390/s20082191

Chicago/Turabian Style

Dechouniotis, Dimitrios; Athanasopoulos, Nikolaos; Leivadeas, Aris; Mitton, Nathalie; Jungers, Raphael; Papavassiliou, Symeon. 2020. "Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective" Sensors 20, no. 8: 2191. https://doi.org/10.3390/s20082191

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop