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Energy efficiency is crucial in the design of battery-powered end devices, such as smart sensors for the Internet of Things applications. Wireless communication between these distributed smart devices consumes significant energy, and even more when data need to reach several kilometers in distance. Low-power and long-range communication technologies such as LoRaWAN are becoming popular in IoT applications. However, LoRaWAN has drawbacks in terms of (i) data latency; (ii) limited control over the end devices by the gateway; and (iii) high rate of packet collisions in a dense network. To overcome these drawbacks, we present an energy-efficient network architecture and a high-efficiency on-demand time-division multiple access (TDMA) communication protocol for IoT improving both the energy efficiency and the latency of standard LoRa networks. We combine the capabilities of short-range wake-up radios to achieve ultra-low power states and asynchronous communication together with the long-range connectivity of LoRa. The proposed approach still works with the standard LoRa protocol, but improves performance with an on-demand TDMA. Thanks to the proposed network and protocol, we achieve a packet delivery ratio of 100% by eliminating the possibility of packet collisions. The network also achieves a round-trip latency on the order of milliseconds with sensing devices dissipating less than 46 mJ when active and 1.83 μ W during periods of inactivity and can last up to three years on a 1200-mAh lithium polymer battery.

Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical functions are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical functions of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.

Low-power energy harvesting has been demonstrated as a feasible alternative for the power supply of next-generation smart sensors and IoT end devices. In many cases, the output of kinetic energy harvesters is an alternating current (AC) requiring rectification in order to supply the electronic load. The rectifier design and selection can have a considerable influence on the energy harvesting system performance in terms of extracted output power and conversion losses. This paper presents a quantitative comparison of three passive rectifiers in a low-power, low-voltage electromagnetic energy harvesting sub-system, namely the full-wave bridge rectifier (FWR), the voltage doubler (VD), and the negative voltage converter rectifier (NVC). Based on a variable reluctance energy harvesting system, we investigate each of the rectifiers with respect to their performance and their effect on the overall energy extraction. We conduct experiments under the conditions of a low-speed rotational energy harvesting application with rotational speeds of 5 rpm to 20 rpm, and verify the experiments in an end-to-end energy harvesting evaluation. Two performance metrics—power conversion efficiency (PCE) and power extraction efficiency (PEE)—are obtained from the measurements to evaluate the performance of the system implementation adopting each of the rectifiers. The results show that the FWR with PEEs of 20% at 5 rpm to 40% at 20 rpm has a low performance in comparison to the VD (40–60%) and NVC (20–70%) rectifiers. The VD-based interface circuit demonstrates the best performance under low rotational speeds, whereas the NVC outperforms the VD at higher speeds (>18 rpm). Finally, the end-to-end system evaluation is conducted with a self-powered rpm sensing system, which demonstrates an improved performance with the VD rectifier implementation reaching the system’s maximum sampling rate (40 Hz) at a rotational speed of approximately 15.5 rpm.

Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

LoRa is popular for internet of things applications as this communication technology offers both a long range and a low power consumption. However, LoRaWAN, the standard MAC protocol that uses LoRa as physical layer, has the bottleneck of a high downlink latency to achieve energy efficiency. To overcome this drawback we explore the use of wake-up radio combined with LoRa, and propose an adequate MAC protocol that takes profit of both these heterogeneous and complementary technologies. This protocol allows an opportunistic selection of a cluster head that forwards commands from the gateway to the nodes in the same cluster. Furthermore, to achieve self-sustainability, sensor nodes might include an energy harvesting sub-system, for instance to scavenge energy from the light, and their quality of service can be tuned, according to their available energy. To have an effective self-sustaining LoRa system, we propose a new energy manager that allows less fluctuations of the quality of service between days and nights. Latency and energy are modeled in a hybrid manner, i.e., leveraging microbenchmarks on real hardware platforms, to explore the influence of the energy harvesting conditions on the quality of service of this heterogeneous network. It is clearly demonstrated that the cooperation of nodes within a cluster drastically reduces the latency of LoRa base station commands, e.g., by almost 90% compared to traditional LoRa scheme for a 10 nodes cluster.

The mitigation of rapid mass movements involves a subtle interplay between field surveys, numerical modelling, and experience. Hazard engineers rely on a combination of best practices and, if available, historical facts as a vital prerequisite in establishing reproducible and accurate hazard zoning. Full-scale field tests have been performed to reinforce the physical understanding of debris flows and snow avalanches. Rockfall dynamics are - especially the quantification of energy dissipation during the complex rock-ground interaction - largely unknown. The awareness of rock shape dependence is growing, but presently, there exists little experimental basis on how rockfall hazard scales with rock mass, size, and shape. Here, we present a unique data set of induced single-block rockfall events comprising data from equant and wheel-shaped blocks with masses up to 2670 kg, quantifying the influence of rock shape and mass on lateral spreading and longitudinal runout and hence challenging common practices in rockfall hazard assessment. The awareness of rock shape dependence in rockfall hazard assessment is growing, but experimental and field studies are scarce. This study presents a large data set of induced single block rockfall events quantifying the influence of rock shape and mass on its complex kinematic behaviour.

IPS is a crucial technology that enables medical staff and hospital management to accurately locate and track persons or assets inside medical buildings. Among other technologies, readily available BLE can be exploited to achieve an energy-efficient and low-cost solution. This work presents the design, implementation and comparison of a RSSI-based and a MCPD-based indoor localization system. The implementation is based on a lightweight wkNN algorithm that processes RSSI and MCPD distance data from connection-less BLE Beacons. The designed hardware and firmware are implemented around the state-of-the-art SoC for BLE, the nRF5340 from Nordic Semiconductor. Experimental evaluation with real-time data processing has been evaluated and presented in a 7.3 m × 8.9 m room with furniture and six beacon nodes. The experimental results on randomly chosen validation points within the room show an average error of only 0.50 m for the MCPD approach, whereas the RSSI approach achieved an error of 1.39 m.

Remote communication in small-scale robotics offers a powerful way to enhance their capabilities, introducing options for state monitoring, multi-agent collaboration, and autonomous operation. Integrating common remote communication tools, such as antennas, into microrobots is challenging with conventional design and manufacturing techniques. We propose a concept that integrates shape-reconfigurable soft microrobots with flexible electronics, leveraging their elastic mechanical properties to enable remote communication. This approach, based on photolithography processes, is scalable and adaptable to various sensing applications. As a proof of concept, we present a microrobot, which integrates a thermoresponsive magnetic hydrogel, an anisotropic support structure, and a flexible dipole antenna into a cohesive three-layered design. The microrobot can morph from a helical shape at low-temperatures to a planar shape at high-temperatures. This shape transformation can be remotely detected by external radio communication receivers, enabling shape-state recognition and environmental temperature sensing. Furthermore, we show that the collective behavior of multiple microrobots enhances the recognition performance by amplifying the signal. The concept represents a significant advancement in co-engineering smart materials and flexible electronics, illustrating an approach of microrobotic embodied intelligence by integrating environmental monitoring, magnetic navigation, and remote signaling. Real-time recognition of microrobot state remains challenging due to their small size and imaging limitations. The authors integrate flexible RF antennas into shape-reconfigurable soft microrobots, enabling remote detection of shape transformations.

We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade’s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually.

Optogenetics provide a potential alternative approach to the treatment of chronic pain, in which complex pathology often hampers efficacy of standard pharmacological approaches. Technological advancements in the development of thin, wireless, and mechanically flexible optoelectronic implants offer new routes to control the activity of subsets of neurons and nerve fibers . This study reports a novel and advanced design of battery-free, flexible, and lightweight devices equipped with one or two miniaturized LEDs, which can be individually controlled in real time. Two proof-of-concept experiments in mice demonstrate the feasibility of these devices. First, we show that blue-light devices implanted on top of the lumbar spinal cord can excite channelrhodopsin expressing nociceptors to induce place aversion. Second, we show that nocifensive withdrawal responses can be suppressed by green-light optogenetic (Archaerhodopsin-mediated) inhibition of action potential propagation along the sciatic nerve. One salient feature of these devices is that they can be operated via modern tablets and smartphones without bulky and complex lab instrumentation. In addition to the optical stimulation, the design enables the simultaneously wireless recording of the temperature in proximity of the stimulation area. As such, these devices are primed for translation to human patients with implications in the treatment of neurological and psychiatric conditions far beyond chronic pain syndromes.