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The smart grid utilizes many Internet of Things (IoT) applications to support its intelligent grid monitoring and control. The requirements of the IoT applications vary due to different tasks in the smart grid. In this paper, we propose a…
The exponential growth of Internet of Things (IoT) devices has intensified the demand for efficient and responsive services. To address this demand, fog and edge computing have emerged as distributed paradigms that bring computational…
Cloud computing, despite its inherent advantages (e.g., resource efficiency) still faces several challenges. the wide are network used to connect the cloud to end-users could cause high latency, which may not be tolerable for some…
With the rapid increase in the Internet of Things (IoT), the amount of data produced and processed is also increased. Cloud Computing facilitates the storage, processing, and analysis of data as needed. However, cloud computing devices are…
Code offloading is promising to accelerate mobile applications and save energy of mobile devices by shifting some computation to cloud. However, existing code offloading systems suffer from a long communication delay between mobile devices…
Applications are sets of interacting components that can be executed in sequence, in parallel, or by using more complex constructs such as selections and loops. They can, therefore, be modeled as structured graphs with sub-structures…
With the increasing development of advanced communication technologies, vehicles are becoming smarter and more connected. Due to the tremendous growth of various vehicular applications, a huge amount of data is generated through advanced…
The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly…
Fog computing serves as a computing layer that sits between the edge devices and the cloud in the network topology. They have more compute capacity than the edge but much less so than cloud data centers. They typically have high uptime and…
Edge computing is an emerging paradigm to enable low-latency applications, like mobile augmented reality, because it takes the computation on processing devices that are closer to the users. On the other hand, the need for highly scalable…
Vehicular cloud computing has emerged as a promising paradigm for realizing user requirements in computation-intensive tasks in modern driving environments. In this paper, a novel framework of multi-task offloading over vehicular clouds…
For effective use of edge computing in an IoT application, we need to partition the application into tasks and map them into the cloud, fog (edge server), device levels such that the resources at the different levels are optimally used to…
Edge Computing (EC) is a computational paradigm that involves deploying resources such as CPUs and GPUs near end-users, enabling low-latency applications like augmented reality and real-time gaming. However, deploying and maintaining a vast…
Modern-day cars are equipped with numerous cameras and sensors, typically integrated with advanced decision-control systems that enable the vehicle to perceive its surroundings and navigate autonomously. Efficient processing of data from…
This work considers a parallel task execution strategy in vehicular edge computing (VEC) networks, where edge servers are deployed along the roadside to process offloaded computational tasks of vehicular users. To minimize the overall…
Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, it is still a challenge to fully unleash the…
While the success of edge and fog computing increased with the proliferation of the Internet of Things (IoT) solutions, such novel computing paradigm, that moves compute resources closer to the source of data and services, must address many…
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…
The demand for distributed applications has significantly increased over the past decade, with improvements in machine learning techniques fueling this growth. These applications predominantly utilize Cloud data centers for high-performance…
In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed,…