Wireless power transfer provides a most convenient solution to charge devices remotely and without contacts. R&D has advanced the capabilities, variety, and maturity of solutions greatly in recent years. This survey provides a comprehensive overview of the state of the art on different technological concepts, including electromagnetic coupled and uncoupled systems and acoustic technologies. Solutions to transfer mW to MW of power, over distances ranging from millimeters to kilometers, and exploiting wave concepts from kHz to THz, are covered. It is an attractive charging option for many existing applications and moreover opens new opportunities. Various technologies are proposed to provide wireless power to these devices. The main challenges reside in the efficiency and range of the transfer. We highlight innovation in beamforming and UV-assisted approaches. Of particular interest for designers is the discussion of implementation and operational aspects, standards, and safety relating to regulations. A high-level catalog of potential applications maps these to adequate technological options for wireless power transfer.

The digital transformation is exciting the uptake of Internet-of-Things technologies, and raises the questions surrounding our knowledge of the positions of many of these things. A review of indoor localization technologies summarized in this paper shows that with conventional RF-based techniques, a significant challenge exists in terms of achieving good accuracy with a low power consumption at the device side. We present hybrid RF-acoustic approaches as an interesting alternative: the slow propagation speed of sound allows for accurate distance measurements, while RF can easily provide synchronization, data, and power to the devices. We explain how the combination of adequate signaling realizing a late wake-up of the devices with backscattering could position energy-neutral devices. Experiments in a real-life testbed confirmed the potential 10 cm-accuracy based on RF-harvested energy. Nonetheless, these also expose open challenges to be resolved in order to achieve accurate 3D positioning.

Received signal strength (RSS)-based optical wireless positioning (OWP) systems are becoming popular for indoor localization because they are low-cost and accurate. However, few open-source datasets are available to test and analyze RSSbased OWP systems. In this paper, we collected RSS values at a sampling frequency of 27 Hz, inertial measurement unit (IMU) at a sampling frequency of 200 Hz and the ground truth at a sampling frequency of 160 Hz in three indoor environments. The first scenario is obstacle-free, the second contains a metal column obstacle, and the third contains a paper rectangular obstacle, with both obstacles representing different non-line-of-sight (NLOS) scenarios. We recorded data with a vehicle at three different speeds (low, medium and high). The dataset includes over 160 k data points and covers more than 110 min. We also provide benchmark tests to show localization performance using only RSS-based OWP and improve accuracy by combining IMU data via extended kalman filter or transformer. The dataset OWPIMU and accompanying benchmark results are open source to support further research on indoor localization methods

With increasing interest in indoor positioning systems across various domains, such as industry, retail, and healthcare, the search for optimal solutions to meet the needs of different applications has gained significant momentum. This work highlights the potential of hybrid RF-acoustic systems combined with advanced machine learning models for robust, scalable, and energy-efficient indoor localization. The focus is on enhancing positioning algorithms for energy-neutral devices to improve accuracy, precision, reliability, and ease of installation. Traditional model-based (MB) methods, relying on line-of-sight (LoS) components, often struggle in challenging nonline-of-sight (NLoS) and reverberant environments. To address this, we propose data-driven neural network (NN) approaches capable of harnessing multipath components (MPCs) as additional information. The echoes in the room are exploited to improve accuracy. Various NN architectures, including multilayer perceptrons, (circular) convolutional neural networks, and graph neural networks (GNNs) are evaluated, in first instance using synthetic data. Results demonstrate that especially GNNs outperform MB methods, achieving superior accuracy in both LoS and NLoS scenarios. During the second phase, extensive real-life experiments are carried out. The GNN is evaluated using cross-validation, training on measurement data, and transfer learning (TL) within a reverberant NLoS environment. The cross-validation and TL demonstrate the practical feasibility. We report over 80% of improvement in 3-D positioning error compared to the MB technique.

Emerging applications in fields such as extended reality require both a high throughput and low latency. The millimeter-wave (mmWave) spectrum is considered because of the potential in the large available bandwidth. The present work studies mmWave Wi-Fi physical layer latency management mechanisms, a key factor in providing low-latency communications for time-critical applications. We calculate physical layer latency in an ideal scenario and simulate it using a tailor-made simulation framework, based on the IEEE 802.11ad standard. Assessing data reception quality over a noisy channel yielded latency’s dependency on transmission parameters, channel noise, and digital baseband tuning. Latency in function of the modulation and coding scheme was found to span 0.28–2.71 ms in the ideal scenario, whereas simulation results also revealed its tight bond with the demapping algorithm and the number of low-density parity-check decoder iterations. The findings yielded tuning parameter combinations for reaching Pareto optimality either by constraining the bit error rate and optimizing latency or the other way around. Our assessment shows that trade-offs can and have to be made to provide sufficiently reliable low-latency communication. In good channel conditions, one may benefit from both the very high throughput and low latency; yet, in more adverse situations, lower modulation orders and additional coding overhead are a necessity.

The proposed infrastructure, named Techtile, provides a unique R&D facility as features dispersed electronics enables transmission and capturing of a multitude of signals in 3D. Specific available equipment that enhances the design process from smooth prototyping to a commercial product is discussed. The acoustic parameters of the room, particularly the reverberation and ambient noise, are measured to take these into account for future innovative acoustic indoor positioning and sensing systems. This can have a positive influence on the accuracy and precision. The wooden construction represents an acoustically challenging room for audible sound with a maximum measured RT60 value of 1.17s at 5kHz, which is comparable to the reverberation properties of a lecture or small concert hall. For ultrasound it is rather challenging due to the present ambient noise sources. In general, the Techtile room can be compared with a home or quiet office environment, in terms of sound pressure levels (SPLs). In addition to the acoustic properties, possible research and development options are discussed in combination with the associated challenges. Many of the designs described are available through open source.