Energy harvesting from human-body-induced motion is mostly challenging due to the low-frequency, high-amplitude nature of the motion, which makes the use of conventional cantilevered spring-mass oscillators unrealizable. Frequency up-conversion by mechanical impact is an effective way to overcome the challenge. However, direct impact on the transducer element (especially, piezoelectric) increases the risk of damaging it and raises questions on the reliability of the energy harvester. In order to overcome this shortcoming, we proposed a transverse mechanical impact driven frequency up-converted hybrid energy harvester for human-limb motion. It utilizes the integration of both piezoelectric and electromagnetic transducers in a given size that allows more energy to be harvested from a single mechanical motion, which, in turn, further improves the power density. While excited by human-limb motion, a freely-movable non-magnetic sphere exerts transverse impact by periodically sliding over a seismic mass attached to a double-clamped piezoelectric bimorph beam. This allows the beam to vibrate at its resonant frequency and generates power by means of the piezoelectric effect. A magnet attached to the beam also takes part in generating power by inducing voltage in a coil adjacent to it. A mathematical model has been developed and experimentally corroborated. At a periodic limb-motion of 5.2 Hz, maximum 93 µW and 61 µW average powers (overall 8 µW·cm⁻³ average power density) were generated by the piezoelectric and the electromagnetic transducers, respectively. Moreover, the prototype successfully demonstrated the application of low-power electronics via suitable AC-DC converters.

In recent years, Wi-Fi infrastructures have become ubiquitous, providing device-free passive-sensing features. Wi-Fi signals can be affected by their reflection, refraction, and absorption by moving objects in their path. The channel state information (CSI), a signal property indicator, of the Wi-Fi signal can be analyzed for human activity recognition (HAR). Deep learning-based HAR models can enhance performance and accuracy without sacrificing computational efficiency. However, to save computational power, an inception network, which uses a variety of techniques to boost speed and accuracy, can be adopted. In contrast, the concept of spatial attention can be applied to obtain refined features. In this paper, we propose a human–human interaction (HHI) classifier, CSI-IANet, which uses a modified inception CNN with a spatial-attention mechanism. The CSI-IANet consists of three steps: i) data processing, ii) feature extraction, and iii) recognition. The data processing layer first uses the second-order Butterworth low-pass filter to denoise the CSI signal and then segment it before feeding it to the model. The feature extraction layer uses a multilayer modified inception CNN with an attention mechanism that uses spatial attention in an intense structure to extract features from captured CSI signals. Finally, the refined features are exploited by the recognition section to determine HHIs correctly. To validate the performance of the proposed CSI-IANet, a publicly available HHIs CSI dataset with a total of 4800 trials of 12 interactions was used. The performance of the proposed model was compared to those of existing state-of-the-art methods. The experimental results show that CSI-IANet achieved an average accuracy of 91.30%, which is better than that of the existing best method by 5%.

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things, represented as any items from our daily life that are enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems has been the main concern for the research community. In this paper, different security threats of Zigbee networks in the IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office/corporate environment. Our proposed SIF can predict and protect against various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting against several potential security attacks efficiently, enabling a more secure way of intelligent monitoring in the IoT platform.

In this paper, we report a wind energy harvesting system for Internet of Things (IoT)-based environment monitoring (e.g., temperature and humidity, etc.) for potential agricultural applications. A wind-driven electromagnetic energy harvester using rotational magnet pole-pairs (rotor) with a back-iron shield was designed, analyzed, fabricated, and characterized. Our analysis (via finite element method magnetic simulations) shows that a back-iron shield enhances the magnetic flux density on the front side of a rotor where the series connected coils interact and convert the captured mechanical energy (wind energy) into electrical energy by means of electromagnetic induction. A prototype energy harvester was fabricated and tested under various wind speeds. A custom power management circuit was also designed, manufactured, and successfully implemented in real-time environmental monitoring. The experimental results show that the harvester can generate a maximum average power of 1.02 mW and maximum power efficiency of 73% (with power management circuit) while operated at 4.5 m/s wind speed. The system-level demonstration shows that this wind-driven energy harvesting system is capable of powering a commercial wireless sensor that transmits temperature and humidity data to a smartphone for more than 200 min after charging its battery for only 10 min. The experimental results indicate that the proposed wind-driven energy harvesting system can potentially be implemented in energetically autonomous IoT for smart agriculture applications.

In signals of opportunity (SOPs)-based positioning utilizing low Earth orbit (LEO) satellites, ephemeris data derived from two-line element files can introduce increasing error over time. To handle the erroneous measurement, an additional base receiver with a known position is often used to compensate for the effect of ephemeris error when positioning the user terminal (UT). However, this approach is insufficient for long baseline (distance between base receiver and UT) as it fails to adequately correct Doppler shift measurement errors caused by ephemeris inaccuracies, resulting in degraded positioning performance. Moreover, the lack of clock synchronization between the base receiver and UT exacerbates erroneous Doppler shift measurements. To address these challenges, we put forth a robust double-difference Doppler shift-based positioning framework, coined 3DPose, to handle the clock synchronization issue between the base receiver and UT, and positioning degradation due to the long baseline. The proposed 3DPose framework leverages double-difference Doppler shift measurements to eliminate the clock synchronization issue and incorporates a novel ephemeris error correction algorithm to enhance UT positioning accuracy in case of the long baseline. The algorithm specifically characterizes and corrects the Doppler shift measurement errors arising from erroneous ephemeris data, focusing on satellite position errors in the tangential direction. To validate the effectiveness of the proposed framework, we conduct comparative analyses across three different scenarios, contrasting its performance with the existing differential Doppler positioning method. The results demonstrate that the proposed 3DPose framework achieves an average reduction of 90% in 3-dimensional positioning errors compared to the benchmark algorithm.

Low Earth orbit (LEO) satellites have recently received considerable attention because they can provide stronger signal power and better bandwidth availability than medium Earth orbit or geosynchronous orbit satellites. However, due to the limited processing capability of a receiver, it is difficult to utilize all the measurements of the available satellites in view when the number of satellites is large. With this motivation, selecting a subset of satellites that are in a good geometry relative to the receiver for precise positioning among a large number of available LEO constellations represents a challenging yet significant problem. Geometric dilution of precision (GDOP) is a metric that provides useful information about the relative geometry between satellites and a receiver. In this study, we put forth a novel GDOP-based satellite selection algorithm that uses efficient matrix decomposition and update rule. Simulation results show that the proposed algorithm achieves a GDOP performance close to the optimal exhaustive search-based schemes while greatly reducing the computational complexity. In particular, the computational complexity is verified in terms of flop counts as well as numerical evaluations.