Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges
Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been
proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth …
proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth …
Integrated Sensing and Communication Enabled Multi-Device Multi-Target Cooperative Sensing: A Fairness-aware Design
Integrated sensing and communication (ISAC) provides a spectrum-efficient approach for
simultaneously enabling reliable data transmission and high-quality sensing. This paper …
simultaneously enabling reliable data transmission and high-quality sensing. This paper …
S2E-DECI: Secrecy and Energy-Efficient Dual-Aware Device-Edge Co-Inference for AIoT
This article proposes a secrecy and energy-efficient device-edge co-inference scheme for
resource-constrained Artificial Intelligence of Things (AIoT) devices with physical layer …
resource-constrained Artificial Intelligence of Things (AIoT) devices with physical layer …
On-the-fly Communication-and-Computing to Enable Representation Learning for Distributed Point Clouds
X Chen, H Wu, K Huang - arxiv preprint arxiv:2407.20710, 2024 - arxiv.org
The advent of sixth-generation (6G) mobile networks introduces two groundbreaking
capabilities: sensing and artificial intelligence (AI). Sensing leverages multi-modal sensors …
capabilities: sensing and artificial intelligence (AI). Sensing leverages multi-modal sensors …
Joint User Scheduling and Precoding for RIS-Aided MU-MISO Systems: A MADRL Approach
With the increasing demand for spectrum efficiency and energy efficiency, reconfigurable
intelligent surfaces (RISs) have attracted massive attention due to its low-cost and capability …
intelligent surfaces (RISs) have attracted massive attention due to its low-cost and capability …
Joint Optimization of Device Placement and Model Partitioning for Cooperative DNN Inference in Heterogeneous Edge Computing
EdgeAI represents a compelling approach for deploying DNN models at network edge
through model partitioning. However, most existing partitioning strategies have primarily …
through model partitioning. However, most existing partitioning strategies have primarily …
Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G
Sensing and edge artificial intelligence (AI) are envisioned as two essential and
interconnected functions in sixth-generation (6G) mobile networks. On the one hand …
interconnected functions in sixth-generation (6G) mobile networks. On the one hand …
Task-oriented Feature Re-encoding and Forwarding for Relay-assisted Device-edge Co-inference
In this paper, we explore a task-oriented relay strategy for enhancing classification task
performance in device-edge co-inference systems. In particular, we propose a deep neural …
performance in device-edge co-inference systems. In particular, we propose a deep neural …
Neural Edge-cloud Computing with Information Cascade Attack
In recent years, a considerable body of work has employed machine learning methods to
analyze edge computing network behavior. Previous research predominantly relied on …
analyze edge computing network behavior. Previous research predominantly relied on …
Using efficient deep learning techniques for mobile crowd sensing detection in an IOTA-based framework
MN Alatawi - Discover Computing, 2024 - Springer
This paper introduces a novel approach for securing mobile crowd sensing (MCS) systems,
with a focus on improving the safety and efficiency of crowd management during the Hajj …
with a focus on improving the safety and efficiency of crowd management during the Hajj …