Energy Efficient Target Detection Through Waveform Selection for Multi-Sensor RF Sensing Based Internet of Things

Bolisetti, S.K. and Sharma, M. and Patwary, Mohammad and Soliman, Abdel-Hamid and Benkhelifa, E. and Maguid, M. (2017) Energy Efficient Target Detection Through Waveform Selection for Multi-Sensor RF Sensing Based Internet of Things. In: 2017 10th IFIP Wireless and Mobile Networking Conference (WMNC), 25-27 September, Valencia. (In Press)

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Abstract

In this paper, we explore multi-sensor Radio Frequency (RF) sensing based Internet of Things (IoT) for surveillance applications. RF sensing techniques are the next generation technologies which offer distinct advantages over traditional means of sensing. Traditionally, Energy detection (ED) has been
used for surveillance applications due to its low computational complexity. However, ED is unreliable due to high false detection rates. There is a need to develop surveillance strategies which offer reliable target detection rates. In this paper, we have proposed a multi-sensor RF sensing based target detection architecture for IoT. To perform surveillance within IoT, multiple
sensor nodes are required to co-exist while performing the desired
tasks. Interfering waveforms from the neighbouring sensor nodes
have a significant impact on the target detection reliability of IoT.
In this paper, a waveform selection criterion has been proposed to
optimise the target detection reliability and power consumption
within IoT in the presence of interfering waveforms.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Energy efficiency, internet of things (IoT), multisensor, RF sensing
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cloud Computing
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: $ Ian McDonald
Date Deposited: 01 Sep 2017 11:19
Last Modified: 27 Sep 2017 03:00
URI: http://www.open-access.bcu.ac.uk/id/eprint/5116

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