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RF Sensing Based Target Detector for Smart Sensing Within Internet of Things in Harsh Sensing Environments

Bolisetti, Siva Karteek, PATWARY, Mohammad, SOLIMAN, Abdel-Hamid and ABDEL-MAGUID, Mohamed (2017) RF Sensing Based Target Detector for Smart Sensing Within Internet of Things in Harsh Sensing Environments. IEEE Access, 5. pp. 13346-13363. ISSN 2169-3536

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Abstract or description

In this paper, we explore surveillance and target detection applications of Internet of
Things (IoT) with radio detection as the primary means of sensing. The problem of surveillance and target
detection has found its place in numerous civilian and military applications, and IoT is well suited to address
this problem. Radio frequency (RF) sensing techniques are the next generation technologies, which offer
distinct advantages over traditional means of sensing used for surveillance and target detection applications
of IoT. However, RF sensing techniques have yet to be widely researched due to lack of transmission
and computational resources within IoT. Recent advancements in sensing, computing, and communication
technologies have made radio detection enabled sensing techniques available to IoT. However, extensive
research is yet to be done in developing reliable and energy ef�cient target detection algorithms for
resource constrained IoT. In this paper, we have proposed a multi-sensor RF sensing-based target detection
architecture for IoT. The proposed target detection architecture is adaptable to interference, which is caused
due to the co-existence of sensor nodes within IoT and adopts smart sensing strategies to reliably detect the
presence of the targets. A waveform selection criterion has been proposed to identify the optimum choice of
transmit waveforms within a given set of sensing conditions to optimize the target detection reliability and
power consumption within the IoT. A dual-stage target detection strategy has been proposed to reduce the
computational burden and increase the lifetime of the sensor nodes.

Item Type: Article
Additional Information: © 2017 IEEE
Faculty: Previous Faculty of Computing, Engineering and Sciences > Engineering
Depositing User: Abdel-Hamid SOLIMAN
Date Deposited: 27 Feb 2019 16:28
Last Modified: 27 Feb 2019 16:28
URI: http://eprints.staffs.ac.uk/id/eprint/5416

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