Special Session SS10 on Edge Computing in Energy Harvesting Networks
Motivation and Background
We foresee future networks where energy harvesting technology (e.g., small factor solar panels, vibration, heat, radio energy backscattering, etc.) will be the key to provide self-sufficiency. These networks range from user equipment and IoT objects, whose operation is often limited by battery life, to 5G mobile networks, whose operation is responsible for a significant amount of energy drained worldwide from the power grid. Together with energy storage, energy harvesting can achieve system self-sustainability and can drastically diminish the carbon footprint, as well as the system operation and maintenance costs. Energy harvesting and storage technologies have been studied for low-power IoT devices, and they have been recently utilized for base stations, remote radioheads and data centres. The use of such technologies is expected to grow further thanks to the ever-increasing penetration of renewable energy sources and the increasing capacity and efficiency of batteries. A parallel trend is that of edge computing, where computing power is placed closer to the data sources. This new paradigm enables rapid deployment and shift of computation tasks, in a software defined fashion, providing advantages such as ultra low latency, and higher bandwidth, that are not possible with current cloud computing architectures.
The aim of this special session is to attract papers that jointly cover edge computing and energy harvesting, dealing with the challenges that arise in this setting, such as how to create and move computation resources according to traffic and energy patterns, how to optimally distribute energy computing resources within federations of micro edge servers and how this technology can be mapped onto 5G architectures.
Topics of interest include, but are not limited to:
- Mobile Edge Computing (MEC)
- 5G Mobile Systems
- Internet of Things (IoT)
- Energy Efficiency
- Optimal Control of MEC Platforms
- Energy efficient MEC for IoT
- Energy Harvesting
- Energy-aware Computation Scheduling
- Data Analytics for Energy Harvesting Networks
Prospective authors are invited to submit technical papers of their previously unpublished work. Accepted special session papers will be included in the conference proceedings and will appear in IEEE Xplore. Papers should be submitted via EDAS; the links are available at http://pimrc2018.ieee-pimrc.org under “Authors”.
Papers should follow the same author guidelines of the general symposium, which are available at http://pimrc2018.ieee-pimrc.org/authors/submission-guidelines/.
Paper submission: May 18, 2018
Acceptance notification: June 15, 2018
Final paper due: June 29, 2018
Wednesday, September 12, 14.00 – 15.30
Room: Da Vinci
Chair: Michele Rossi
- Wireless Backhauling for Energy Harvesting Ultra-Dense Networks
- Soheil Rostami (Huawei Technologies, Finland); Kari Heiska (Huawei, Finland); Oleksandr Puchko (Huawei (Finland), Finland); Georgios P. Koudouridis (Huawei Technologies R&D Center Sweden, Sweden); Kari Leppanen (Huawei Technologies, Finland); Mikko Valkama (Tampere University of Technology, Finland)
- Dynamic Functional Split Selection in Energy Harvesting Virtual Small Cells Using Temporal Difference Learning
- Dagnachew Azene Temesgene (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain); Marco Miozzo (CTTC/CERCA, Spain); Paolo Dini (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain)
- Online Resource Management in Energy Harvesting BS Sites Through Prediction and Soft-Scaling of Computing Resources
- Thembelihle Dlamini and Angel Fernandez Gambin (University of Padova, Italy); Daniele Munaretto (Athonet, Italy); Michele Rossi (University of Padova, Italy)
- Mobile Traffic Prediction from Raw Data Using LSTM Networks
- Hoang Duy Trinh (Centre Tecnològic de Telecomunicacions de Catalunya, Spain); Lorenza Giupponi and Paolo Dini (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain)
- Aggressive Fragmentation Strategy for Enhanced Network Performance in Dense LPWANs
- Ioana Suciu (Polytechnic University of Catalonia & Worldsensing, Spain); Xavier Vilajosana (WorldSensing, Spain) and Ferran Adelantado (Universitat Oberta de Catalunya, Spain)