Energy harvesting has a vital role in building reliable Environmental Wireless Sensor Networks (EWSNs), without needing to replace a discharged battery. Solar energy is one of the main renewable energy sources that can be used to efficiently charge a battery. This paper introduces two solar energy harvesters and their power measurements at different light conditions in order to charge rechargeable AA batteries powering EWSN nodes. The first harvester is a primitive energy harvesting circuit that is built using elementary off-shelf components, while the second harvester is based on a commercial boost converter chip. To prove the effectiveness of harvesting solar energy, five EWSN nodes were distributed at a nature reserve (the Audubon Society of Western Pennsylvania, USA) and the sunlight at their locations was recorded for more than five months. For each recorded illumination, the corresponding harvested energy has been estimated and compared with the average energy consumption of the EWSN with the most power consumption. The results show that the daily harvested energy effectively compensates for the energy consumption of the EWSN nodes, and the battery charge capacity of 295 mAh can reliably support their daily dynamic energy consumption.

Wireless sensor systems powered by batteries are widely used in a variety of applications. For applications with space limitation, their size was reduced, limiting battery energy capacity and memory storage size. A multi-exit neural network enables to overcome these limitations by filtering out data without objects of interest, thereby avoiding computing the entire neural network. This paper proposes to implement a multi-exit convolutional neural network on the ESP32-CAM embedded platform as an image-sensing system with an energy constraint. The multi-exit design saves energy by 42.7% compared with the single-exit condition. A simulation result, based on an exemplary natural outdoor light profile and measured energy consumption of the proposed system, shows that the system can sustain its operation with a 3.2 kJ (275 mAh @ 3.2 V) battery by scarifying the accuracy only by 2.7%.

We propose a low-power voltage reference that enables independent adjustment of temperature sensitivity and output level. This design enhances the temperature sensitivity without impacting the output level distribution, in contrast to previous methods. The proposed circuit achieves this by integrating a separate control system that utilizes diode-connected pMOS transistors and an analog multiplexer for output level adjustment, along with biasing current control to improve the temperature sensitivity. In a 180-nm CMOS process, the prototype circuit generates a stable reference voltage averaging 192 mV, maintaining an accuracy of ±8.8 mV ( $\pm 3\sigma $ ) from 0 °C to 75 °C across ten samples. In addition, it consumes only 35.8 pW at 0.6 V and 25 °C.

Advanced Internet-of-Things (IoT) devices are increasingly used in high-temperature environments, such as automotive, aerospace, defense, and industrial applications. High-temperature voltage references are crucial for these systems. This paper presents two topologies designed for high-temperature operation. The first topology minimizes design costs by using a replica branch to decouple leakage from the core, reducing its impact on the reference voltage. The second topology optimizes the operating temperature by employing one-stage amplifiers as buffers to handle junction leakage while maintaining body voltage. Both designs are fabricated in a 180 nm CMOS process. The first design supports operation up to $140~^{\circ }$ C with an average temperature coefficient (TC) of 70 ppm/°C, while the second design operates up to $170~^{\circ }$ C with a TC of 64 ppm/°C. The designs also exhibit line sensitivities of 0.46 %/V and 0.31 %/V, PSRRs of –37.8 dB and –38.3 dB at 100 Hz, and power consumption of 111 pW and 136.8 pW at room temperature, respectively.