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Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge nodes. It is crucial to accurately predict the content that…
Smart cities rely on dynamic and real-time data to enable smart urban applications such as intelligent transport and epidemics detection. However, the streaming of big data from IoT devices, especially from mobile platforms like pedestrians…
Owing to the large volume of sensed data from the enormous number of IoT devices in operation today, centralized machine learning algorithms operating on such data incur an unbearable training time, and thus cannot satisfy the requirements…
Next generation technologies such as smart healthcare, self-driving cars, and smart cities require new approaches to deal with the network traffic generated by the Internet of Things (IoT) devices, as well as efficient programming models to…
Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting…
This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the…
Vehicle computing represents a fundamental shift in how autonomous vehicles are designed and deployed, transforming them from isolated transportation systems into mobile computing platforms that support both safety-critical, real-time…
Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However,…
Recently, edge computing has emerged as a promising paradigm to support mobile access in IoT multinetworks. However, coexistence of heterogeneous wireless communication schemes brings about new challenges to the mobility management and…
We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices that perform computations on local dataset partitions. Edge devices transmit the…
Vehicular cloud computing is gaining popularity thanks to the rapid advancements in next generation wireless communication networks. Similarly, Edge Computing, along with its standard proposals such as European Telecommunications Standards…
Deep learning technologies have demonstrated remarkable effectiveness in a wide range of tasks, and deep learning holds the potential to advance a multitude of applications, including in edge computing, where deep models are deployed on…
Application Service Providers (ASPs) obtaining resources from multiple clouds have to contend with different management and control platforms employed by the cloud service providers (CSPs) and network service providers (NSP). Distributing…
For autonomous vehicles to be fully aware of its environment, it needs to collect data consistently from other vehicles and Road Side Units (RSU) in the surroundings. This heavy exchange increases latency and cybersecurity threats. This…
With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and performance. However, existing autoscaling tools for edge…
Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices. On-device computations combined with other privacy and…
Autonomous driving has gained significant advancements in recent years. However, obtaining a robust control policy for driving remains challenging as it requires training data from a variety of scenarios, including rare situations (e.g.,…
With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning…
Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on utilizing deep learning algorithms to extract and represent the design features…
To enhance the quality and speed of data processing and protect the privacy and security of the data, edge computing has been extensively applied to support data-intensive intelligent processing services at edge. Among these data-intensive…