相关论文: SensiX: A Platform for Collaborative Machine Learn…

The multitude of data generated by sensors available on users' mobile devices, combined with advances in machine learning techniques, support context-aware services in recognizing the current situation of a user (i.e., physical context) and…

To support the stringent requirements of the future intelligent and interactive applications, intelligence needs to become an essential part of the resource management in the edge environment. Developing intelligent orchestration solutions…

Deep learning architectures with powerful reasoning capabilities have driven significant advancements in autonomous driving technology. Large language models (LLMs) applied in this field can describe driving scenes and behaviors with a…

Extreme Edge Computing (XEC) distributes streaming workloads across consumer-owned devices, exploiting their proximity to users and ubiquitous availability. Many such workloads are AI-driven, requiring continuous neural network inference…

Nowadays, we are witnessing the advent of the Internet of Things (EC) with numerous devices performing interactions between them or with end users. The huge number of devices leads to huge volumes of collected data that demand the…

WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device-free, cost-effective and…

As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and success recently. With the…

On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest since they are more cost-effective, latency-efficient, and privacy-preserving compared with the cloud paradigm.…

Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local models'…

This article is a position paper which introduces the paradigm of ``Massive Wireless Human Sensing'', i.e. an infrastructure for wireless human sensing based on a plethora of heterogeneous wireless communication signals. More specifically,…

Recent advances in the Internet of Things (IoT) technology have led to a surge on the popularity of sensing applications. As a result, people increasingly rely on information obtained from sensors to make decisions in their daily life.…

The collaboration of large artificial intelligence (AI) models in mobile edge networks has emerged as a promising paradigm to meet the growing demand for intelligent services at the network edge. By enabling multiple devices to…

Real-time video inference on edge devices like mobile phones and drones is challenging due to the high computation cost of Deep Neural Networks. We present Adaptive Model Streaming (AMS), a new approach to improving performance of efficient…

In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing…

The ever-increasing number of Internet of Things (IoT) devices has created a new computing paradigm, called edge computing, where most of the computations are performed at the edge devices, rather than on centralized servers. An edge device…

Wearable sensors have permeated into people's lives, ushering impactful applications in interactive systems and activity recognition. However, practitioners face significant obstacles when dealing with sensing heterogeneities, requiring…

Given the proliferation of wireless sensors and smart mobile devices, an explosive escalation of the volume of data is anticipated. However, restricted by their limited physical sizes and low manufacturing costs, these wireless devices tend…

Synthesizing human motion has advanced rapidly, yet realistic hand motion and bimanual interaction remain underexplored. Whole-body models often miss the fine-grained cues that drive dexterous behavior, finger articulation, contact timing,…

Split Learning (SL) is an emerging privacy-preserving machine learning technique that enables resource constrained edge devices to participate in model training by partitioning a model into client-side and server-side sub-models. While SL…

Next-generation smart city applications, attributed by the power of Internet of Things (IoT) and Cyber-Physical Systems (CPS), significantly rely on the quality of sensing data. With an exponential increase in intelligent applications for…