Convergence of edge computing and deep learning: A comprehensive survey
Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …
massive amounts of data, and ever-increasing computing power is driving the core of …
Fog computing for sustainable smart cities in the IoT era: Caching techniques and enabling technologies-an overview
H Zahmatkesh, F Al-Turjman - Sustainable cities and society, 2020 - Elsevier
In recent decade, the number of devices involved with the Internet of Things (IoT)
phenomena has increased dramatically. Parallel to this, fog computing paradigm has been …
phenomena has increased dramatically. Parallel to this, fog computing paradigm has been …
Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems
Mobile Edge Computing (MEC) is one of the most promising techniques for next-generation
wireless communication systems. In this paper, we study the problem of dynamic caching …
wireless communication systems. In this paper, we study the problem of dynamic caching …
Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning
To improve the quality of computation experience for mobile devices, mobile-edge
computing (MEC) is a promising paradigm by providing computing capabilities in close …
computing (MEC) is a promising paradigm by providing computing capabilities in close …
Age of information aware radio resource management in vehicular networks: A proactive deep reinforcement learning perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource
management for expected long-term performance optimization in a Manhattan grid vehicle …
management for expected long-term performance optimization in a Manhattan grid vehicle …
Decentralized computation offloading for multi-user mobile edge computing: A deep reinforcement learning approach
Z Chen, X Wang - EURASIP Journal on Wireless Communications and …, 2020 - Springer
Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-
limited mobile devices from computation-intensive tasks, which enables devices to offload …
limited mobile devices from computation-intensive tasks, which enables devices to offload …
Approximate dynamic programming for ambulance redeployment
MS Maxwell, M Restrepo… - INFORMS Journal …, 2010 - pubsonline.informs.org
We present an approximate dynamic programming approach for making ambulance
redeployment decisions in an emergency medical service system. The primary decision is …
redeployment decisions in an emergency medical service system. The primary decision is …
Dynamic multi-appointment patient scheduling for radiation therapy
Seeking to reduce the potential impact of delays on radiation therapy cancer patients such
as psychological distress, deterioration in quality of life and decreased cancer control and …
as psychological distress, deterioration in quality of life and decreased cancer control and …
Information freshness-aware task offloading in air-ground integrated edge computing systems
This paper investigates an air-ground integrated multi-access edge computing system,
which is deployed by an infrastructure provider (InP). Under a business agreement with the …
which is deployed by an infrastructure provider (InP). Under a business agreement with the …
Information relaxations and duality in stochastic dynamic programs
We describe a general technique for determining upper bounds on maximal values (or lower
bounds on minimal costs) in stochastic dynamic programs. In this approach, we relax the …
bounds on minimal costs) in stochastic dynamic programs. In this approach, we relax the …