IEEE Transactions on Mobile Computing

The ever-increasing number of ubiquitous Internet of Things (IoT) applications entails a high demand for scarce communication and network resources. To meet this stringent requirement, mobile edge computing (MEC) is envisioned as a transformative ...

Lightweight and layered structure containers in serverless edge computing (SEC) provide flexible service configurations and computing for vehicles with diverse service requests in the Vehicle-Infrastructure Collaboration (VIC) environment. Despite ...

With the wide spread of data-driven deep learning applications, a growing number of users outsource compute-intensive inference processes to the cloud. To protect inference privacy, Liu et al. (INFOCOM 2022) proposed two steganography-based solutions, ...

Federated learning (FL) has emerged as a prominent distributed learning paradigm, enabling collaborative training of neural network models across local devices with raw data stay local. However, FL systems often encounter significant challenges due to ...

Distributed learning on edges aims at training the AI model collaboratively in a network of edge devices via frequent model aggregations. Achieving the desired training performance requires the aggregation structure and frequency to fit well with the ...

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (UAV-MEC) has been proposed to offer computing resources for smart devices and user equipment. UAV cluster aided MEC rather than one UAV-aided MEC as edge pool is the newest edge computing ...

In vehicular networks, caching content on an edge server (ES) is a popular method for quickly responding to massive vehicle service requests, reducing communication delays, and enhancing driver and passenger service experiences. However, after integrating ...

In this work, we address a novel framework for next-generation distributed radio access. In contrast with existing studies, where all remote radio heads (RRHs) in a distributed network are directly connected to a central unit (CU), an alternative ...

Blink detection is essential for various human-computer interaction scenarios, such as virtual reality and driving state detection. It has gained significant attention from industry and academia alike in recent years. Existing non-contact detection ...

Wireless traffic prediction is indispensable for network planning and resource management. Due to different population distributions and user behavior, there exist strong spatial-temporal variations in wireless traffic across different regions. Most of ...

Indoor self-localization has become a highly desirable system function for smartphones. The existing systems based on imaging, radio frequency, and geomagnetic sensing may have sub-optimal performance when their limiting factors prevail. In this paper, we ...

Millimeter wave radar is gaining traction recently for enabling privacy-preserving human sensing. However, the lack of large-scale, dynamic radar datasets impedes progress in developing robust and generalized deep learning models for mobile sensing ...

Services provided by mobile edge clouds offer low-latency responses for large-scale and real-time applications. Dynamic service management algorithms generate live service migration requests to support user mobility and ensure service latency in mobile ...

For the capability of concentrating radiation energy along a direction, using Directional Mobile Chargers (DMCs) for charging the nodes in a Wireless Rechargeable Sensor Network (WRSN) via wireless power transfer has become a research hotspot. However, ...

Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, ...

In the Internet of Things (IoT) employing centralized machine learning, security is a major concern due to the heterogeneity of end devices. Malicious devices could launch poisoning attacks to degrade machine learning models. Distributed machine learning (...

Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant performance degradation due to heterogeneous data at clients. Common solutions ...

The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to resource-rich edge/cloud servers for DNN inference. Common video encoding ...

While the Mean Opinion Score (MOS) is the most well-known way to quantify Quality of Experience (QoE), it only provides average insight. In this paper, we will demonstrate that instead of only relying on the MOS value, predicting users’ perceived ...

The advancements in information and communication technology have led to the emergence of innovative edge computing models that incorporate the computing power of vehicles into the energy sector. Electric vehicles (EVs), functioning as edge computing ...

With the worldwide mobility electrification initiative to reduce air pollution and energy security, more and more for-hire vehicles are being replaced with electric ones. A key difference between gas for-hire vehicles and electric for-hire vehicles (EFHV) ...

Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service ...

In recent years, advances in immersive multimedia technologies, such as extended reality (XR) technologies, have led to more realistic and user-friendly devices. However, these devices are often bulky and uncomfortable, still requiring tether connectivity ...

Head motion tracking is a promising research field with vast applications in ubiquitous human-computer interaction (HCI) scenarios. Unfortunately, solutions based on vision and wireless sensing have shortcomings in user privacy and tracking range, ...

Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In ...

Traffic sensor networks are widely applied in smart cities to monitor traffic in real-time. Exploiting such data to forecast future traffic conditions has the potential to enhance the decision-making capabilities of intelligent transportation systems, ...

Typical mobile edge computing infrastructures have to contend with unreliable computing devices at their end-points. The limited resource capacities of mobile edge devices gives rise to frequent contentions, node overloads or failures. This is exacerbated ...

In a cellular network, User Equipments (UEs) have to conduct the Random Access (RA) procedure with the Base Station (BS) before transmitting data. In the conventional four-step RA procedure, UEs perform RA competition by exchanging four messages with the ...

Multi-access edge computing (MEC)-enabled Internet of Things (IoT) has become a powerful solution to run computation-intensive applications on end devices. These applications are composed of multiple dependent tasks, which can be abstracted as directed ...

Parking violation is a common urban problem in major cities all over the world. Traditional approaches for detecting parking violations mainly rely on fixed deployed sensors and enforcement agencies, which suffer from high deployment costs and limited ...