This paper presents a low-cost low-power CMOS humidity sensor for passive RFID sensing applications. The humidity sensing element is implemented in standard CMOS technology without any further post-processing, which results in low fabrication costs. The interface of this humidity sensor employs a PLL-based architecture transferring sensor signal processing from the voltage domain to the frequency domain. Therefore this architecture allows the use of a fully digital circuit, which can operate on ultra-low supply voltage and thus achieves low-power consumption. The proposed humidity sensor has been fabricated in the TSMC 0.18 μm CMOS process. The measurements show this humidity sensor exhibits excellent linearity and stability within the relative humidity range. The sensor interface circuit consumes only 1.05 µW at 0.5 V supply voltage and reduces it at least by an order of magnitude compared to previous designs.

According to the advantages of integrating wireless sensors networks (WSN) and radio frequency identification (RFID), this paper proposes a novel method for methane gas density monitoring and predicting based on a passive RFID sensor tag and a convolutional neural networks (CNN) algorithm. The proposed wireless sensor is based on electronic product code (EPC) generation2 (G2) protocol and the sensor data is embedded into the identification (ID) information of the RFID chip. The wireless sensor consists of a communication section, radio-frequency (RF) front-end section, and digital section. The communication section is used to perform the transmission and reception of wireless signals, modulation, and demodulation. The RF front-end section is adopted to provide the stable supply voltage for other parts. The digital section is employed to achieve sensor data and control the overall operation of the wireless sensor based on EPC protocol. Because the miscellaneous noises will decrease the accuracy during the process of data wireless transmission, the CNN algorithm is adopted to extract the robust feature from raw data. The measurement results show that the exploited RFID sensor can realize a maximum communication distance of 10.3 m and can accurately measure and predict the methane gas density in an underground mine. The RFID sensor technology is a beneficial supplement to the current underground WSN monitoring system.

The tower is a key component of the transmission line system and its stability and reliability is the guarantee for the safety of entire transmission line system. This paper proposes a transmission tower monitoring network integrating wireless sensor network (WSN) and radio frequency identification (RFID). Compared to current designs, the proposed monitoring network shows the advantages of low cost, fast fault locating, tracking and management of grid equipment, and long-time monitoring. Low delay design is critical for the proposed monitoring network. In order to achieve balance between transmission delay and network cost, this paper establishes the transmission delay model for sink node optimization. For the redundant data of RFID, this paper presents a novel data cleaning scheme specially for the transmission tower monitoring, which can effectively reduce the redundant data at the reader end. The simulation results show that the optimal group number is 10 according to the proposed delay model.

The authors wish to make the following corrections to this paper [1].