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Pervasive applications are revolutionizing the perception that users have towards the environment. Indeed, pervasive applications perform resource intensive computations over large amounts of stream sensor data collected from multiple sources. This allows applications to provide richer and deep insights into the natural characteristics that govern everything that surrounds us. A key limitation of these applications is that they have high energy footprints, which in turn hampers the quality of experience of users. While cloud and edge computing solutions can be applied to alleviate the problem, these solutions are hard to adopt in existing architecture and far from become ubiquitous. Fortunately, cloudlets are becoming portable enough, such that they can be transported and integrated into any environment easily and dynamically. In this article, we investigate how cloudlets can be transported by unmanned autonomous vehicles (UAV)s to provide computation support on the edge. Based on our study, we develop GEESE, a novel UAVbased system that enables the dynamic deployment of an edge computing infrastructure through the cooperation of multiple UAVs carrying cloudlets. By using GEESE, we conduct rigorous experiments to analyze the effort to deliver cloudlets using aerial, ground, and underwater UAVs. Our results indicate that UAVs can work in a cooperative manner to enable edge computing in the wild.
In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. The proposed algorithm is designed under the consideration of delay, ene
Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, the onsite resource tradi
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The rigid MPI programming model and batch scheduling dominate high-performance computing. While clouds brought new levels of elasticity into the world of computing, supercomputers still suffer from low resource utilization rates. To enhance supercomp
Advancements in artificial intelligence (AI) gives a great opportunity to develop an autonomous devices. The contribution of this work is an improved convolutional neural network (CNN) model and its implementation for the detection of road cracks, po