This paper introduces a novel compact ultralightweight multiband RF energy harvester fabricated on a paper substrate. The proposed rectenna is designed to operate in all recently released LTE bands (range 0.79-0.96 GHz; 1.71-2.17 GHz; and 2.5-2.69 GHz). High compactness and ease of integration between antenna and rectifier are achieved by using a topology of nested annular slots. The proposed rectifier features an RF-to-dc conversion efficiency in the range of 5%-16% for an available input power of -20 dBm in all bands of interest, which increases up to 11%-30% at -15 dBm. The rectenna has been finally tested both in laboratory and in realistic scenarios featuring a superior performance to other state-of-the-art RF harvesters on flexible substrates.

This invited paper presents potential solutions for tackling some of the main underlying challenges toward developing sustainable Internet-of-things (IoT) devices with a focus on eco-friendly manufacturing, sustainable powering, and wireless connectivity for next-generation IoT devices. The diverse applications of IoT systems, such as smart cities, wearable devices, self-driving cars, and industrial automation, are driving up the number of IoT systems at an unprecedented rate. In recent years, the rapidly-increasing number of IoT devices and the diverse application-specific system requirements have resulted in a paradigm shift in manufacturing processes, powering methods, and wireless connectivity solutions. The traditional cloud-centering IoT systems are moving toward distributed intelligence schemes that impose strict requirements on IoT devices, e.g., operating range, latency, and reliability. In this article, we provide an overview of hardware-related research trends and application use cases of emerging IoT systems and highlight the enabling technologies of next-generation IoT. We review eco-friendly manufacturing for next-generation IoT devices, present alternative biodegradable and eco-friendly options to replace existing materials, and discuss sustainable powering IoT devices by exploiting energy harvesting and wireless power transfer. Finally, we present (ultra-)low-power wireless connectivity solutions that meet the stringent energy efficiency and data rate requirements of future IoT systems that are compatible with a batteryless operation.

Radio frequency (RF) energy harvesting and wireless power transmission (WPT) technologies —both near-field and far-field—have attracted significant interest for wireless applications and RFID systems. We already utilize near-field WPT products in our life and it is expected that RF EH and far-field WPT systems can drive the future low-power wireless systems. In this article, we initially present a brief historical overview of these technologies. The main technical challenges of rectennas and WPT transmitters are discussed. Furthermore, this paper presents the recent advances on the development of these technologies, including the possibility of powering RFID systems through the millimeter wave power from 5G networks, the trends in flexible rectennas design and the technological developments on the simultaneous wireless information and power transfer (SWIPT).

This paper presents a holistic and authoritative review of the role of microwave technologies in enabling a new generation of wearable devices. A human-centric Internet of Things (IoT) covering remote healthcare, distributed sensing, and consumer electronics, calls for high-performance wearable devices integrated into clothing, which require interdisciplinary research efforts to emerge. Microwaves, the “interconnect” of wireless networks, can enable, rather than solely connect, the next generation of autonomous, sustainable, and wearable-friendly electronics. First, enabling technologies including wireless power transmission and RF energy harvesting, backscattering and passive communication, RFID, and electromagnetic sensing are reviewed. We then discuss the key integration platforms, covering smart fabrics and electronic textiles, additive manufacturing, printed electronics, natively-flexible and organic RF semiconductors, and fully-integrated CMOS systems, where opportunities for hybrid integration are highlighted. The emerging research trends, from mmWave 6G, RF sensing and imaging, to healthcare applications including neural implants, drug delivery, and safety upon exposure to microwaves are re-visited and discussed, presenting a future roadmap for interdisciplinary research towards sustainable and reliable next-generation wearables.

Radio-Frequency IDentification (RFID) devices and sensors are among the main innovations of the last years, with an enormous impact on the Internet of Things (IoT) physical communication layer as well as on logistics and robotics. The aim of the present paper is to review the main technologies available for RFID sensors, and to identify the corresponding state-of-the-art when these technologies are applied to realistic IoT scenarios. Firstly, the concepts of radio backscattering and harmonic backscattering are analyzed, highlighting the pros and cons of each approach. Then, state-of-the-art solutions are reported, and the performance of each of them are discussed, to provide an overview of the potential of RFID-based sensing in different scenarios.

River discharge is an important variable to measure in order to predict droughts and flood occurrences. Once the cross-sectional geometry of the river is known, discharge can be inferred from water level and surface flow velocity measurements. Since river discharges are of particular interest during extreme weather events, when river sites cannot be safely accessed, noncontact sensing technologies are particularly appealing. To this purpose, this work proposes a prototype of a low-cost continuous wave (CW) Doppler radar sensor, which is able to monitor the surface flow velocity of rivers. The prototype is tested at two gauged sites in central Italy, along the Tiber River. The surface flow velocity distribution across the river is monitored by means of the analysis of received Doppler signal. The surface velocity statistics are then extracted using a novel algorithm that is optimized to run on a microprocessor platform with minimal computing power (ArduinoUNO). In particular, the radar measurements are used to initialize a 2-D entropy-based velocity model (EVM) that is able to estimate river discharges in any flow condition. Finally, the results concerning the observed discharge provided by the EVM prove to be comparable with those obtained with more expensive commercial solutions. The results are important since the described methodology can be extended to small-size Doppler radar sensors onboard unmanned aerial vehicles (UAVs), the latter providing a method for mapping surface velocity of rivers.