With distributed communication, computation, and storage resources close to end users, edge computing has great potentials to support delay-sensitive industrial applications involving intelligent edge devices. Cognitive portable ground penetrating radars (GPRs) are expected to achieve high-quality sensing performance in a variety of industrial environments by operating intelligently and adaptively under varying sensing conditions. Although edge computing makes it very promising to develop cognitive portable GPRs, both strict performance requirement and trade-offs between communication and computation pose significant challenges. This paper presents an edge computing framework for cognitive portable GPRs. Specifically, the system architecture of an EC-enabled cognitive portable GPR is developed. Based on the identification of various involved computation tasks, an offloading policy was proposed to determine whether computation tasks should be executed locally or offloaded to the edge server. Experimental results show the efficacy of the proposed methods. The framework also provides insight into the design of cognitive Internet of things (IoT) supported by edge computing.
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An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm
In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing platform solutions incur. This paper presents a context management framework comprised of sensors, mobile devices such as smartphones and an edge server to enable high performance, context-aware computing at the edge. Key features of this architecture include energy-efficient discovery of available sensors and edge services for the client, an automated mechanism for task planning and execution on the edge server, and a dynamic environment where new sensors and services may be added to the framework. A prototype of this architecture has been implemented, and an experimental evaluation using two computer vision tasks as example services is presented. Performance measurement shows that the execution of the example tasks performs quite well and the proposed framework is well suited for an edge-computing environment.
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- Award ID(s):
- 1816379
- PAR ID:
- 10310801
- Date Published:
- Journal Name:
- Future generation computer systems
- Volume:
- 114
- ISSN:
- 0167-739X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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