Unobtrusive monitoring of physiological signals in natural settings is important for precision diagnostics. Fully-passive wireless body-worn sensors are viable and promising for unobtrusive monitoring. In this study, the authors present a new class of fully-passive sensor, namely wireless resistive analog passive (WRAP) sensor. It uses resistive transducers at the sensors for converting physical stimulus to load modulation of carrier wireless signal at 13.56 MHz at low power (–20 to 0 dBm). The sensor is simply composed of a loop antenna, a tuning capacitor, and a resistive transducer suitable for the type of physiological signals to be measured. The authors report the characterisation of WRAP sensors for various resistive loads of 1.2 ω to 82 kω at various co-axial distances (5–40 mm) between the TX and RX antennas. They have prototyped and characterised multiple WRAP sensors with several practical measurements of physiological signals such as heart rate, temperature, and pulse oximetry. They also demonstrate bio-potential measurement (down to 400 μV _pp ) using metal–oxide–semiconductor field-effect transistor as the transducer. These results show the feasibility of developing a new type of body-worn fully-passive WRAP sensors for unobtrusive physiological signal monitoring at real-life settings for precision diagnostics of many disorders and tracking person-centric therapy efficacy.

Wireless sensor networks (WSNs) have been widely applied in many areas for real-time event detection. They are designed using both mobile and static sensor nodes (SNs) for different applications such as smart parking, environmental monitoring, health care systems, automotive industries, sports, open space surveillance, and so on. WSNs communicate through wireless mediums and are accessible to anyone, which make SNs susceptible to different types of attacks. Distributed denial of service (DDoS) is one such attack. It wastes the limited energy of SNs and causes loss of data packets within a network. A DDoS attack launches a coordinated attack by flooding the target nodes with bogus requests, thus exhausting their resources, and forcing them to deny service to legitimate member nodes. In this study, the authors propose a message analyser scheme for WSNs. The method is capable of detecting compromised SNs vulnerable to a DDoS attack. In addition, it is able to detect all compromised messages transmitted by the attackers to the base station through the sender nodes. The proposed method is compared with other related protocols. The results show that their method can effectively detect and defend against DDoS attacks in WSNs.

A novel distributed approach for searching and tracking of targets is presented for sensor network environments in which physical distance measurement using techniques such as signal strength is not feasible. The solution consists of a robust Kalman filter combined with a non-linear least-square method, and maximum likelihood topology maps. The primary input for estimating target location and direction of motion is provided by time stamps recorded by the sensor nodes when the target is detected within their sensing range. An autonomous robot following the target collects this information from sensors in its neighbourhood to determine its own path in search of the target. While the maximum likelihood topology coordinate space is a robust alternative to physical coordinates, it contains significant non-linear distortions when compared with physical distances between nodes. The authors overcome this using time stamps corresponding to target detection by nodes instead of relying on distances. The performance of the proposed algorithm is compared with recently proposed pseudo gradient algorithm based on hop count and received signal strength. Even though the proposed algorithm does not depend on distance measurements, the results show that it is able to track the target effectively even when the target changes its moving pattern frequently.