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• W2889969436 abstract "Radiological pollution, which poses a great threat to public health and environment safety, is one of the critical concerns in nuclear industries. The ever growing IIoT is a promising network paradigm for monitoring the radiological pollution level in uranium mill tailings. Coverage, as one of the most decisive indicators for evaluating the success of IIoT-based radiological pollution monitoring, reflects how well the sensing fields of interest are monitored. Nevertheless, the possible coverage holes resulting from sensors' random deployment, energy depletion, and hazardous conditions may remarkably lower the QoS, especially the coverage performance of IIoT-based radiological pollution monitoring. In order to avoid the potential emerging coverage holes' negative effects on the network QoS, this article focuses on the coverage hole detection issue from an energy-efficient perspective. By exploiting the merits of the CIC model, this article develops an EECICHD that detects the CIC holes by fully considering the sensor nodes' energy dissipation as well as their communication abilities. The EECICHD makes full use of the intrinsic spatial distribution correlation of monitored variables and collaborative sensing among neighboring sensors for improving the efficiency of CIC holes detection. Through simulation experiments, we show that the proposed EECICHD scheme can efficiently localize and determine the locations and number of the CIC holes." @default.
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• W2889969436 date "2018-09-01" @default.
• W2889969436 modified "2023-10-12" @default.
• W2889969436 title "Detecting Confident Information Coverage Holes in Industrial Internet of Things: An Energy-Efficient Perspective" @default.
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• W2889969436 doi "https://doi.org/10.1109/mcom.2018.1701195" @default.
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