12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China

Research Article

Edge Computing Enabled Cognitive Portable Ground Penetrating Radar

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  • @INPROCEEDINGS{10.4108/eai.29-6-2019.2282886,
        author={Dalei  Wu and Maxwell M. Omwenga and Yu  Liang and Li  Yang and Dryver  Huston and Tian  Xia},
        title={Edge Computing Enabled Cognitive Portable Ground Penetrating Radar},
        proceedings={12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2019},
        month={6},
        keywords={gpr edge computing image processing cognitive intelligence},
        doi={10.4108/eai.29-6-2019.2282886}
    }
    
  • Dalei Wu
    Maxwell M. Omwenga
    Yu Liang
    Li Yang
    Dryver Huston
    Tian Xia
    Year: 2019
    Edge Computing Enabled Cognitive Portable Ground Penetrating Radar
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.29-6-2019.2282886
Dalei Wu,*, Maxwell M. Omwenga1, Yu Liang1, Li Yang1, Dryver Huston2, Tian Xia2
  • 1: University of Tennessee at Chattanooga
  • 2: University of Vermont
*Contact email: dalei-wu@utc.edu

Abstract

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 tradeoffs 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.