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

Bolisetti, S.K. and Patwary, Mohammad 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

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 , andIoTiswellsuitedtoaddress 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 efficient 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 wave form 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:	(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Identification Number:	10.1109/ACCESS.2017.2728372
Dates:	Date Event 20 July 2017 Published 6 July 2017 Accepted
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Oana-Andreea Dumitrascu
Date Deposited:	31 Aug 2017 11:27
Last Modified:	22 Mar 2023 12:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/5103

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