Glucose sensing is an important way to improve the efficiency of diagnosis and treatment of diabetic patients. A glucose sensing system with low-cost, penetrability and safety is necessary. In this paper, a wireless sensing system for continuous glucose monitoring based on dual-tag radio frequency identification (RFID) technology was proposed. A modified system model incorporating Cole-Cole dielectric model is built to describe the relationship among glucose concentration, matching capacitance of tags and operating frequency, and the related parameters were determined by particle swarm optimization based on linear descending weights (LDW-PSO). The experiments were performed in the frequency range from 902 MHz to 928 MHz, and the results show that the maximum relative error between measured glucose concentration and estimated one is 4.15%, and all the measured glucose concentration lie in the reasonable region in Clarke’s error grid, indicating that our proposed monitoring method has a great potential in the healthcare field.

As a crucial part in sewage, high-salt wastewater contains abundant corrosive components and heavy metal elements, which makes it difficult for invasive monitoring. Therefore, based on coupling Radio Frequency Identification(RFID) tags, this paper proposes a non-invasive monitoring method for concentration of inorganic salt in sewage. In this method, Cole-Cole model and Slit Cylindrical Capacitance(SCC) model are combined to explore the relationship between capacitance of RFID and concentration of inorganic salt in sewage pipeline. A New Efficient Levenberg-Marquardt(NELM) algorithm is used for regression between relative permittivity and concentration of inorganic salt categorically. Then, measurement data is processed under modified relative permittivity mixing rule of electrolyte and solvent. Finally the concentration of each component in the multi-component inorganic salt wastewater are obtained. Experimental results show that the maximal relative error (MRE) and average relative error(ARE) of single inorganic salt solution are 1.1% and 0.5%, while mixed solution are 2.6% and 1.89%. Such results indicate significant improvement comparing with previous works. Great potential of RFID in working sewage monitoring is sufficiently confirmed.

Topology control is important for extending networks lifetime and reducing interference. The accuracy of topology identification plays a crucial role in topology control. Traditional passive interception can only identify the connectivity among cooperative sensor networks with known protocol. This paper proposes a novel method called Active Interfere and Passive Interception (AIPI) to identify the topology of non-cooperative sensor networks by using both active and passive interceptions. Active interception uses full duplex sensors to disrupt communication until frequency hopped to acquire distance information, and thus, infer their connectivity and calculate the location after modifying error in a non-cooperative sensor network. Passive interception uses Granger causality to infer the connectivity between two communication nodes after getting the time frame structure in physical layer. Passive interception is applied to conserve power consumption after obtaining physical information via active interception. Simulation results indicate that AIPI can identify the topology of non-cooperative sensor networks with a higher accuracy than traditional method.

By leveraging the high sensitivity of acousto-optic modulators to microwave signals, their integration into RFID systems can significantly improve the detection of faint microwave signals. Traditional acousto-optic modulators, which use bidirectional uniform comb transducers (IDTs), suffer from a 50% microwave signal loss due to the diffraction efficiency being less than 50%, complicating the accurate identification of already weak signals. This paper presents a solution to the 50% loss issue by replacing the IDT with a floating electrode unidirectional transducer (FEUDT), a key structure in detecting microwave signals. The resulting samples exhibited an optical waveguide insertion loss of 8.73 dB, a mode field diameter of 6.48 $\mathsf{\mu }$m, an operating frequency of 141.2 MHz, and a diffraction efficiency of 61.72%, and required only 0.57 W of driving power. Comparative empirical tests with traditional transducers under identical conditions demonstrated that the FEUDT-based acousto-optic modulator surpassed the 50% diffraction efficiency limit of conventional modulators, which facilitated the detection of weak microwave signals.

Accurate parameter estimation is essential for effective monitoring and treatment of high-salinity industrial wastewater. Traditional methods such as spectroscopy, ion chromatography, and electrochemical analysis offer high sensitivity but are often complex, costly, and unsuitable for real-time monitoring. This research integrates Deep Neural Networks (DNNs) with the Levenberg–Marquardt (LM) algorithm to develop an advanced RFID-based sensing system for real-time monitoring of sodium chloride solutions in wastewater. The DNN extracts essential features from raw data, while the LM algorithm optimizes parameter estimation for enhanced precision and stability. Experimental results show that the dielectric constant sample variance at various flow rates under wireless frequency is 0.08509, while the sample total variance is 0.06807, both below 0.1. Additionally, the sample standard deviation and total standard deviation are both below 0.3, at 0.26090 and 0.29169, respectively. These findings confirm that the proposed system is robust against flow rate variations, ensuring accurate, real-time monitoring and supporting sustainable industrial practices.