相关论文: Age of Processing-Based Data Offloading for Autono…
The rapid increase in the number of connected vehicles has led to the generation of vast amounts of data. As a significant portion of this data pertains to vehicle-to-vehicle and vehicle-to-infrastructure communications, it is predominantly…
Vehicle edge computing (VEC) brings abundant computing resources close to vehicles by deploying them at roadside units (RSUs) or base stations, thereby enabling diverse computation-intensive and delay sensitive applications. Existing task…
The increasing complexity of Intelligent Transportation Systems (ITS) has led to significant interest in computational offloading to external infrastructures such as edge servers, vehicular nodes, and UAVs. These dynamic and heterogeneous…
Environmental perception is a key element of autonomous driving because the information received from the perception module influences core driving decisions. An outstanding challenge in real-time perception for autonomous driving lies in…
Task offloading to mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of mobile devices and decrease service latency for the computation-intensive applications. Device battery consumption is…
During the last few years, the use of Unmanned Aerial Vehicles (UAVs) equipped with sensors and cameras has emerged as a cutting-edge technology to provide services such as surveillance, infrastructure inspections, and target acquisition.…
Modern applications increasingly demand ultra-low latency for data processing, often facilitated by host-controlled accelerators like GPUs and FPGAs. However, significant delays result from host involvement in accessing accelerators. To…
This paper considers minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. We consider…
The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for…
With the rapid advancement of devices requiring intensive computation, such as Internet of Things (IoT) devices, smart sensors, and wearable technology, the computational demands on individual platforms with limited resources have…
The smart vehicles construct Vehicle of Internet which can execute various intelligent services. Although the computation capability of the vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for vehicular…
Fog-computing network combines the cloud computing and fog access points (FAPs) equipped with mobile edge computing (MEC) servers together to support computation-intensive tasks for mobile users. However, as FAPs have limited computational…
In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system. We divide the original problem into two stages: the offloading decision making stage and the request…
The huge amount of data generated by the Internet of things (IoT) devices needs the computational power and storage capacity provided by cloud, edge, and fog computing paradigms. Each of these computing paradigms has its own pros and cons.…
With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In…
The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an…
Vehicular Edge Computing (VEC) has emerged as a promising paradigm for enhancing the computational efficiency and service quality in intelligent transportation systems by enabling vehicles to wirelessly offload computation-intensive tasks…
Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to…
Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based…
The rapid growth of end-user AI applications, such as computer vision and generative AI, has led to immense data and processing demands often exceeding user devices' capabilities. Edge AI addresses this by offloading computation to the…