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Wildfires are an escalating environmental concern, closely linked to power grid infrastructure in two significant ways. High-voltage power lines can inadvertently spark wildfires when they contact vegetation, while wildfires originating elsewhere can damage the power grid, causing severe disruptions. This paper proposes a self-powered cyber-physical system framework with sensing, processing, and communication capabilities to enable early wildfire detection. The proposed framework first analyzes the probability of the presence of a wildfire using lightweight smoke detection models that can be deployed on embedded processors at the edge. Then, it identifies the Pareto-optimal configurations that co-optimize the wildfire detection probability and expected time to detect a wildfire under energy constraints. Experimental evaluations on Jetson Orin Nano and STM Nucleo boards show that the Pareto-optimal solutions achieve wildfire detection within 5–15 minutes while consuming 1.2–3.5x lower energy than transmitting images to the cloud. 

Abstract The bioactive lysophospholipid sphingosine-1-phosphate (S1P) acts via five different subtypes of S1P receptors (S1PRs) - S1P_1-5. S1P₅is predominantly expressed in nervous and immune systems, regulating the egress of natural killer cells from lymph nodes and playing a role in immune and neurodegenerative disorders, as well as carcinogenesis. Several S1PR therapeutic drugs have been developed to treat these diseases; however, they lack receptor subtype selectivity, which leads to side effects. In this article, we describe a 2.2 Å resolution room temperature crystal structure of the human S1P₅receptor in complex with a selective inverse agonist determined by serial femtosecond crystallography (SFX) at the Pohang Accelerator Laboratory X-Ray Free Electron Laser (PAL-XFEL) and analyze its structure-activity relationship data. The structure demonstrates a unique ligand-binding mode, involving an allosteric sub-pocket, which clarifies the receptor subtype selectivity and provides a template for structure-based drug design. Together with previously published S1PR structures in complex with antagonists and agonists, our structure with S1P₅-inverse agonist sheds light on the activation mechanism and reveals structural determinants of the inverse agonism in the S1PR family. 

Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization. 

Advances in integrated sensors and low-power electronics have led to an increase in the use of wearable devices for health and activity monitoring applications. These devices have severe limitations on weight, form-factor, and battery size since they have to be comfortable to wear. Therefore, they must minimize the total platform energy consumption while satisfying functionality (e.g., accuracy) and performance requirements. Optimizing the platform-level energy efficiency requires considering both the sensor and processing subsystems. To this end, this paper presents a sensor-classifier co-optimization technique with human activity recognition as a driver application. The proposed technique dynamically powers down the accelerometer sensors and controls their sampling rate as a function of the user activity. It leads to a 49% reduction in total platform energy consumption with less than 1% decrease in activity recognition accuracy. 

Small form factor and low-cost wearable devices enable a variety of applications including gesture recognition, health monitoring, and activity tracking. Energy harvesting and optimal energy management are critical for the adoption of these devices, since they are severely constrained by battery capacity. This paper considers optimal gesture recognition using self-powered devices. We propose an approach to maximize the number of gestures that can be recognized under energy budget and accuracy constraints. We construct a computationally efficient optimization algorithm with the help of analytical models derived using the energy consumption breakdown of a wearable device. Our empirical evaluations demonstrate up to 2.4 x increase in the number of recognized gestures compared to a manually optimized solution.