Energy Efficient Cooperation in Underlay RFID Cognitive Networks for a Water Smart Home

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