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The development of novel applications causes increased demands on the computational capabilities of Vehicular Edge Computing (VEC). Current works have introduced Unmanned Aerial Vehicles (UAVs) into VEC to solve the resource-constrained problem. However, ...

The very edge of the rapidly evolving computing continuum is characterized by a wide variety of different hardware architectures, software platforms, and in general diverse device constraints and capabilities. While in-network computing and smaller ...

In the evolving landscape of distributed computing frameworks, Wasimoff emerges as an innovative middleware with a browser-based execution environment designed to facilitate computation offloading to heterogeneous volunteer devices. By leveraging ...

Edge AI aims to enable distributed machine learning (DML) on edge resources to fulfill the need for data privacy and low latency. Meanwhile, the challenge of device heterogeneity and discrepancy in data distribution requires more sophisticated DML ...

The Mobile Cloud Computing paradigm has revolutionized the concepts of mobile computing and the Internet of Things (IoT). This paradigm allows outsourcing the workload of mobile devices, or other connected “things,” to be computed in the Cloud. ...

This survey on mobile cloud computing provides a critical analysis of the operational concerns, challenges, and open issues of this paradigm, highlighting its potential to revolutionize mobile computing and IoT. The work offers new research directions and ...

Centralised servers can provide on-demand resources to edge devices for offloading workloads. Resource-constrained computing nodes send requests to execute a task on the server for higher quality or faster execution. These requests are best characterised ...

Covert communication can provide higher security by protecting communication behavior. In this paper, we utilize covert communication to protect the communication behavior in vehicular edge computing (VEC) networks, that is covert VEC networks, where ...

Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of ...

The utilization of green edge has emerged as a promising paradigm for the development of new energy vehicle (NEV). Nevertheless, the recharging of these vehicles poses a significant challenge in due to limited power resources and enormous transmission ...

This paper addressed the trade-off between timeliness and reliability in joint communication and over-the-air computation offloading (JCACO) system under short-packet communications (SPCs). The inevitable decoding errors introduced by SPC lead to errors ...

The Cloud Computing (CC) paradigm has risen in recent years as a solution to a need for computation and battery-constrained User Equipment (UE) to run increasingly intensive computation tasks. Nevertheless, given the centralized nature of the CC paradigm,...

Increasing demand for computationally intensive jobs on mobile devices is driving interest in computation offloading to the edge/cloud servers. This paper presents a comprehensive framework for managing offloading of stochastic and heterogeneous user(s)-...

Computation offloading in Mobile Edge Computing (MEC) represents a key technology for the future of the Internet of Vehicles (IoV), reducing the time and energy consumption of vehicles for computation tasks, while Unmanned Aerial Vehicles (UAVs) ...

DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that ...

Unmanned Aerial Vehicles (UAVs) play an important role in the Internet of Things and form the paradigm of the Internet of UAVs, due to their characteristics of flexibility, mobility, and low costs. However, resource constraints such as dynamic wireless ...

As IoT devices require more and more mobile data and computing power, but the resource-limited edge devices cannot provide the corresponding requirements, such as applications, data processing, and deep neural network computing. Then deep learning based ...

We propose an efficient Resource Augmentation Framework (RAF) for resource-constrained UAVs through EdgeCPS. Typical UAVs with small form factors have limited computation power which hinders their ability to perform critical or computation-intensive ...

Growing technologies like virtualization and artificial intelligence have become more popular on mobile devices. But lack of resources faced for processing these applications is still major hurdle. Collaborative edge and cloud computing are one of the ...

Underwater environments are emerging as a new frontier for data science thanks to an increase in deployments of underwater sensor technology. Challenges in operating computing underwater combined with a lack of high-speed communication technology covering ...

Mobile edge computing (MEC) driven by 5G cellular systems has recently emerged as a promising paradigm, enabling mobile devices (MDs) with limited computing resources to offload various computation-intensive tasks (such as autopilot, online game) to edge ...