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.

RF Wireless Power Transfer (WPT) emerges as a technology for charging autonomous devices, enabling simultaneous power and information transfer. However, with increasing distance, single-input, single-channel rectenna systems are not able to meet the power requirements of large scale IoT applications. In this paper, we tackle this problem on two levels. First, we minimize the energy consumption at the energy-constrained device on three levels. Second, we evolve to a dual-band solution increasing RF WPT. One frequency band is used to provide a base charge to many nodes in a shared transmission. Beam steering, on the other hand, allows for more power hungry operations while introducing as minimal interference as possible. We showcase this method for a hybrid RF-acoustic positioning system. Practical measurements conducted in a multi-antenna indoor testbed (Techtile) show the additional power gain and positioning rate.

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.

For over more than a decade, ultrasound phased arrays have been playing a leading role in domains such as noninvasive diagnostic examination, nondestructive testing and sonar, and will continue to be indispensable in a wide range of new applications. Unlike the aforementioned traditional scanning systems, many new applications require a transmit-only configuration and work in the low ultrasound frequency range, consequently posing new challenges in low-cost phased array design. However, two sought-after features are still missing in current low-cost ultrasound phased array systems, namely broadband transmission and wide angular beam steering. In this work, a low-cost ultrasound phased array system is presented that enables the transmission of both narrowband and broadband signals. Accurate beam steering is achieved through the concept of Direct Digital Synthesis (DDS). The design features small, wide bandwidth MEMS transducers enabling grating lobe free beam patterns at high frequency and a broad angular beam steering range. The presented prototype supports 64 speaker elements and allows very fine amplitude and phase control over each transducer stage with respectively 10-bit and 12-bit resolution. The beam steering performance is validated for a ${1}\times {10}$ calibrated uniform linear array design. In these measurements, an accuracy in the order of just a few degrees was observed, even at a steering angle of 70° off-axis.

We present a hybrid signaling approach to position energy-neutral devices. Our method combines the best of two worlds: instant RF signals for both communication and energy transfer, and slower propagating acoustic waves for accurate distance measurements. We introduce advanced energy loading of an ’E-buffer’ through beamformed RF signals. The scientific contribution of our approach is twofold, advancing both the positioning performance and the energy harvesting efficiency. On the one hand, we overcome current distance limitations in RF backscattering-based indoor localization. On the other hand, it enhances the energy harvesting by using the calculated position and beamforming a higher amount of directed RF energy into the mobile node. We provide a functional architecture for both sides of the system and present a proof-of-concept. Practical measurements in representative use cases show an update rate of 10 positions per hour within the regulatory constraints in the 868MHz band for distances up to 4.5 m. An energy and power model are drawn up to provide insights into the performance trade-offs.