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Edge computing is the natural progression from Cloud computing, where, instead of collecting all data and processing it centrally, like in a cloud computing environment, we distribute the computing power and try to do as much processing as…
The rapid evolution of malware attacks calls for the development of innovative detection methods, especially in resource-constrained edge computing. Traditional detection techniques struggle to keep up with modern malware's sophistication…
Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the…
In this paper, we investigate the recent studies on multimedia edge computing, from sensing not only traditional visual/audio data but also individuals' geographical preference and mobility behaviors, to performing distributed machine…
The rise in embedded and IoT device usage comes with an increase in LTE usage as well. About 70\% of an estimated 18 billion IoT devices will be using cellular LTE networks for efficient connections. This introduces several challenges such…
The advent of distributed computing systems will offer great flexibility for application workloads, while also imposing more attention to security, where the future advent and adoption of quantum technology can introduce new security…
We consider an edge computing scenario where users want to perform a linear computation on local, private data and a network-wide, public matrix. Users offload computations to edge servers located at the edge of the network, but do not want…
Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement…
Vehicles are sophisticated machines equipped with sensors that provide real-time data for onboard driving assistance systems. Due to the wide variety of traffic, road, and weather conditions, continuous system enhancements are essential.…
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency…
Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for real-time analytics. Geo-distributed streaming systems, where cloud-based queries utilize data streams from multiple…
Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible…
The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively.…
With the rapid growth of the Internet of Things (IoT) and a wide range of mobile devices, the conventional cloud computing paradigm faces significant challenges (high latency, bandwidth cost, etc.). Motivated by those constraints and…
As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of…
The rapid technological advances in the Internet of Things (IoT) allows the blueprint of Smart Cities to become feasible by integrating heterogeneous cloud/fog/edge computing paradigms to collaboratively provide variant smart services in…
Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data. With the increasing deployment of machine learning models in distributed…
Many IoT applications at the network edge demand intelligent decisions in a real-time manner. The edge device alone, however, often cannot achieve real-time edge intelligence due to its constrained computing resources and limited local…
Centralized clouds processing the large amount of data generated by Internet-of-Things (IoT) can lead to unacceptable latencies for the end user. Against this backdrop, Edge Computing (EC) is an emerging paradigm that can address the…
The increasing prevalence of adversarial attacks on Artificial Intelligence (AI) systems has created a need for innovative security measures. However, the current methods of defending against these attacks often come with a high computing…