Real-world performance of sub-1 GHz and 2.4 GHz textile antennas for RF-powered body area networks

In Radio Frequency (RF)-powered networks, peak antenna gains and path-loss models are often used to predict the power that can be received by a rectenna. However, this leads to significant over-estimation of the harvested power when using rectennas in a dynamic setting. This work proposes more realistic parameters for evaluating RF-powered Body Area Networks (BANs), and utilizes them to analyze and compare the performance of an RF-powered BAN based on 915 MHz and 2.4 GHz rectennas. Two fully-textile antennas: a 915 MHz monopole and a 2.4 GHz patch, are designed and fabricated for numerical radiation pattern analysis and practical forward transmission measurements. The antennas' radiation properties are used to calculate the power delivered to a wireless-powered BAN formed of four antennas at different body positions. The mean angular gain is proposed as a more insightful metric for evaluating RFEH networks with unknown transmitter-receiver alignment. It is concluded that, when considering the mean gain, an RF-powered BAN using an omnidirectional 915 MHz antenna outperforms a 2.4 GHz BAN with higher-gain antenna, despite lack of shielding, by 15.4× higher DC power. Furthermore, a transmitter located above the user can result in 1× and 9× higher DC power at 915 MHz and 2.4 GHz, respectively, compared to a horizontal transmitter. Finally, it is suggested that the mean and angular gain should be considered instead of the peak gain. This accounts for the antennas' angular misalignment resulting from the receiver's mobility, which can vary the received power by an order of magnitude

Distributed sensing with low-cost mobile sensors towards a sustainable IoT

Cities are monitored by sparsely positioned high-cost reference stations that fail to capture local variations. Although these stations must be ubiquitous to achieve high spatio-temporal resolutions, the required capital expenditure makes that infeasible. Here, low-cost IoT devices come into prominence; however, non-disposable and often non-rechargeable batteries they have pose a huge risk for the environment. The projected numbers of required IoT devices will also yield to heavy network traffic, thereby crippling the RF spectrum. To tackle these problems and ensure a more sustainable IoT, the cities must be monitored with fewer devices extracting highly granular data in a self-sufficient manner. Hence, this paper introduces a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies. Based on the experience gained through real-world trials, a detailed overview of the technical challenges encountered when using low-cost sensors and the requirements for achieving high spatio-temporal resolutions in the 3D space are highlighted. Finally, to show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of COVID-19 pandemic is presented. The results showed that using mobile sensors is as accurate as using stationary ones with the potential of reducing device numbers, leading to a more sustainable IoT

An efficient indoor photovoltaic power harvesting system for energy-aware wireless sensor nodes

Wireless sensor nodes are autonomous devices which sense parameters and communicate them wirelessly. Their independent operation precludes the use of wired power supplies, and energy harvesting is becoming an attractive alternative to batteries. This paper presents the circuit design and embedded software of a photovoltaic power harvesting system for indoor wireless sensor nodes, which delivers energy-awareness and improved efficiency levels