Multi-agent DRL for task offloading and resource allocation in multi-UAV enabled IoT edge network
The Internet of Things (IoT) edge network has connected lots of heterogeneous smart
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
Reinforcement learning-empowered mobile edge computing for 6G edge intelligence
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive
and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its …
and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its …
Distributed resource scheduling in edge computing: Problems, solutions, and opportunities
Edge computing has become popular in the last decade and will advance in future to
support real-time actionable analytics at the devices. One of the fundamental problems for …
support real-time actionable analytics at the devices. One of the fundamental problems for …
Edge computing technology enablers: A systematic lecture study
With the increasing stringent QoS constraints (eg, latency, bandwidth, jitter) imposed by
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …
Mobile computation offloading in mobile edge computing based on artificial intelligence approach: a review and future directions
Mobile computation offloading (MCO) is one of the significant processes in mobile edge
computing (MEC). MCO is a promising approach to contract with the restrictions in client …
computing (MEC). MCO is a promising approach to contract with the restrictions in client …
Cache-assisted collaborative task offloading and resource allocation strategy: A metareinforcement learning approach
S Chen, L Rui, Z Gao, W Li, X Qiu - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Multiaccess edge computing (MEC) provides users with better Quality of Experience (QoE)
via offloading tasks to the nearby edge. However, the emergence of new Internet of Things …
via offloading tasks to the nearby edge. However, the emergence of new Internet of Things …
Large-scale computation offloading using a multi-agent reinforcement learning in heterogeneous multi-access edge computing
Z Gao, L Yang, Y Dai - IEEE Transactions on Mobile Computing, 2022 - ieeexplore.ieee.org
Recently, existing computation offloading methods have provided extremely low service
latency for mobile users (MUs) in multi-access edge computing (MEC). However, this …
latency for mobile users (MUs) in multi-access edge computing (MEC). However, this …
Scalable primal-dual actor-critic method for safe multi-agent rl with general utilities
We investigate safe multi-agent reinforcement learning, where agents seek to collectively
maximize an aggregate sum of local objectives while satisfying their own safety constraints …
maximize an aggregate sum of local objectives while satisfying their own safety constraints …
Cost-efficient resources scheduling for mobile edge computing in ultra-dense networks
With the development of 5G communication technologies and smart mobile devices, various
computation-intensive and delay-sensitive tasks continue to increase. The combination of …
computation-intensive and delay-sensitive tasks continue to increase. The combination of …
When hierarchical federated learning meets stochastic game: toward an intelligent UAV charging in urban prosumers
Unmanned aerial vehicles (UAVs) nowadays are developing rapidly for various applications
such as UAV taxis and delivery drones. However, the limited battery energy restricts the …
such as UAV taxis and delivery drones. However, the limited battery energy restricts the …