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Sensors (Basel). 2014 Sep 30;14(10):18353-69. doi: 10.3390/s141018353.

Energy efficient cooperation in underlay RFID cognitive networks for a water smart home.

Author information

1
Electrical and Computer Engineering Department, Texas A&M University at Qatar, Doha 23874, Qatar. adnan.nasir@qu.edu.qa.
2
Electrical and Computer Engineering Department, Texas A&M University at Qatar, Doha 23874, Qatar. syed.hussain@qatar.tamu.edu.
3
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore. ebhsoong@ntu.edu.sg.
4
Electrical and Computer Engineering Department, Texas A&M University at Qatar, Doha 23874, Qatar. khalid.qaraqe@qatar.tamu.edu.

Abstract

Shrinking water resources all over the world and increasing costs of water consumption have prompted water users and distribution companies to come up with water conserving strategies. We have proposed an energy-efficient smart water monitoring application in [1], using low power RFIDs. In the home environment, there exist many primary interferences within a room, such as cell-phones, Bluetooth devices, TV signals, cordless phones and WiFi devices. In order to reduce the interference from our proposed RFID network for these primary devices, we have proposed a cooperating underlay RFID cognitive network for our smart application on water. These underlay RFIDs should strictly adhere to the interference thresholds to work in parallel with the primary wireless devices [2]. This work is an extension of our previous ventures proposed in [2,3], and we enhanced the previous efforts by introducing a new system model and RFIDs. Our proposed scheme is mutually energy efficient and maximizes the signal-to-noise ratio (SNR) for the RFID link, while keeping the interference levels for the primary network below a certain threshold. A closed form expression for the probability density function (pdf) of the SNR at the destination reader/writer and outage probability are derived. Analytical results are verified through simulations. It is also shown that in comparison to non-cognitive selective cooperation, this scheme performs better in the low SNR region for cognitive networks. Moreover, the hidden Markov model's (HMM) multi-level variant hierarchical hidden Markov model (HHMM) approach is used for pattern recognition and event detection for the data received for this system [4]. Using this model, a feedback and decision algorithm is also developed. This approach has been applied to simulated water pressure data from RFID motes, which were embedded in metallic water pipes.

PMID:
25271565
PMCID:
PMC4239936
DOI:
10.3390/s141018353
[Indexed for MEDLINE]
Free PMC Article

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