Wireless Monitoring of Household Electrical Power Meter Using Embedded RFID with Wireless Sensor Network Platform

Abstract

Tracking and monitoring system using radio frequency identification (RFID) have gained a lot of improvements especially for applications that need automation with reduction in human intervention and become more interesting nowadays with the increasing market demand for internet of things (IoT) technologies. The objective of this study is to improve the machine-tomachine (M2M) communication using active RFID with wireless sensor networks (WSNs) with heterogeneous data transfer (regardless of power meter type) for monitoring and identification of household electrical consumption. M2M is a popular technology and has become a part of our daily life. WSN through ZigBee technology is applied to monitor data and to read the load consumption from the electrical household power meter by embedding a tag module into the meter. In-house built-in tag was embedded into an electrical power meter with a power management circuit communicating to a reader at an RF signal of 2.45 GHz based on the star network topology with the sleep mode function. The experimental results indicated that the electrical power meters with embedded tags successfully worked wirelessly with acceptable mean value of electrical consumption difference in Watts more than those without tags (standalone meter), which is in the range of 2.44 W to 2.5 W based on 10-hour measurements.

Info-Greedy Sequential Adaptive Compressed Sensing

“…Early compressed sensing works usually assume to be random, which does have benefits for universality regardless of the signal distribution. However, when there is prior knowledge about the signal distribution, one can optimize to minimize the number of measurements subject to a total sensing power constraint (2) for some constant . In the following, we either vary power for each measurement , or fix them to be unit power (for example, due to physical constraint) and use repeated measurements times in the direction of , which is equivalent to measuring using an integer valued power.…”

“…This example has some practical implications: the compressed measurements here correspond to collecting the total power consumption over a region of the power network. This collection process can be achieved automatically by new technologies such as the wireless sensor network platform using embedded RFID in [2] and, hence, our Info-Greedy Sensing may be an efficient solution to monitoring of power consumption of each node in a large power network.…”

“…N OWADAYS ubiquitous big data applications (image processing [1], power network monitoring [2], and large scale sensor networks [3]) call for more efficient information sensing techniques. Often these techniques are sequential in that the measurements are taken one after another.…”

Sequential sensing with model mismatch

“…Note that we can further bound the 1 norm of the error term as We may recover the true covariance matrix from the sketches γ using the convex optimization problem (5).…”

On the effect of model mismatch for sequential Info-Greedy Sensing

“…Sequential compressed sensing is a promising new information acquisition and recovery technique to process big data that arises in various applications such as compressive imaging [1][2][3], power network monitoring [4], and large-scale sensor networks [5]. The sequential nature of the problems is either because the measurements are taken one after another or due to the fact that the data is obtained in a streaming fashion so that it has to be processed in one pass.…”