Efficient acceleration of deep learning inference on resource-constrained edge devices: A review
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …
in breakthroughs in many areas. However, deploying these highly accurate models for data …
Emerging technologies for 6G communication networks: Machine learning approaches
The fifth generation achieved tremendous success, which brings high hopes for the next
generation, as evidenced by the sixth generation (6G) key performance indicators, which …
generation, as evidenced by the sixth generation (6G) key performance indicators, which …
Efficient parallel split learning over resource-constrained wireless edge networks
The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
Vehicle as a service (VaaS): Leverage vehicles to build service networks and capabilities for smart cities
Smart cities demand resources for rich immersive sensing, ubiquitous communications,
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …
Adaptsfl: Adaptive split federated learning in resource-constrained edge networks
The increasing complexity of deep neural networks poses significant barriers to
democratizing them to resource-limited edge devices. To address this challenge, split …
democratizing them to resource-limited edge devices. To address this challenge, split …
Empowering smart cities: High-altitude platforms based Mobile Edge Computing and Wireless Power Transfer for efficient IoT data processing
This work presents an efficient framework that combines High Altitude Platform (HAP)-based
Mobile Edge Computing (MEC) networks with Wireless Power Transfer (WPT) to optimize …
Mobile Edge Computing (MEC) networks with Wireless Power Transfer (WPT) to optimize …
Location-aware and delay-minimizing task offloading in vehicular edge computing networks
Y Xia, H Zhang, X Zhou, D Yuan - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Vehicular edge computing (VEC) has been reported as a new computation paradigm to
meet the low-latency requirement in vehicular networks. In this article, we study a novel …
meet the low-latency requirement in vehicular networks. In this article, we study a novel …
Task offloading in multi-hop relay-aided multi-access edge computing
As demands for multi-access edge computing (MEC) increase exponentially, resource
limitations at individual edge servers (ESs) will inevitably become bottlenecks. Most existing …
limitations at individual edge servers (ESs) will inevitably become bottlenecks. Most existing …
Joint user association, resource allocation, and beamforming in RIS-assisted multi-server MEC systems
Multi-access edge computing (MEC) is a promising solution to supporting resource-intensive
applications on mobile devices (MDs), which enables computation offloading from MDs to …
applications on mobile devices (MDs), which enables computation offloading from MDs to …
A state-of-the-art review of task scheduling for edge computing: A delay-sensitive application perspective
The edge computing paradigm enables mobile devices with limited memory and processing
power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications …
power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications …