This paper proposes a thermoelectric generator (TEG)-powered ultrasonic sensing system for non-intrusive water flow rate measurement. The limited power provided by the TEGs is handled by a dedicated energy management unit (EMU), allowing reliable sensing, computation, and transmission tasks. First, we introduce the delta time-of-flight (ΔToF)-based ultrasonic sensing and thermoelectric energy generation theory. Then, the design is given, followed by the system evaluation under different harvesting conditions to show their impact on average sensing and transmission times. The results revealed that our method could achieve high measurement accuracy (±1.4%), comparable to intrusive and battery-powered counterparts, thereby offering a "plug&play+deploy&forget" hybrid solution.
LoRaWAN, the MAC layer protocol built on top of LoRa modulation, adopts Adaptive Data Rate (ADR) to optimize communications by dynamically adjusting parameters like spreading factor (SF), transmitting power, and bandwidth based on signal quality metrics, e.g., signal-to-noise ratio (SNR). However, since energy harvesting (EH) IoT devices function only on the intervals of intermittent energy availability, the generic ADR mechanism has to be energy-aware to allow LoRaWAN operation under energy constraints. This expectedly introduces major challenges, such as preserving critical system information during power outages and managing the varying energy needs at run-time. To address these challenges, this paper introduces an energy management unit (EMU) meeting the ADR requirements based on the harvested energy whilst retaining critical information through the microcontroller unit (MCU) when no power is available. The proposed EMU dynamically selects the energy storage size and the operating voltage, which refers to the multiple operating points, to provide the amount of energy required for each task, e.g., LoRa transmission at the SF requested by the ADR. Considering the varying needs of IoT devices, this multiple operating points approach, compared to the conventional way of using one big storage for all tasks, avoids lengthened start-ups, and therefore approximates the operation to a battery-powered system. Experimental results demonstrate that the proposed design can retain the EMU operating points and enable intermittent LoRaWAN communications by managing the available energy at run-time.
The intermittent and varying nature of energy harvesting (EH) entails dedicated energy management with large energy storage, which is a limiting factor for low-power/cost systems with small form factors. Transient computing allows system operations to be performed in the presence of power outages by saving the system state into a non-volatile memory (NVM), thereby reducing the size of this storage. These systems are often designed with a task-based strategy, which requires the storage to be sized for the most energy consuming task. That is, however, not ideal for most systems since their tasks have varying energy requirements, i.e., energy storage size and operating voltage. Hence, to overcome this issue, this paper proposes a novel energy management unit (EMU), tailored for multi-source EH transient systems, that allows selecting the storage size and operating voltage for the next task to be performed at run-time, thereby optimizing task-specific energy needs and startup times based on application requirements. For the first time in literature, we adopted a hybrid NVM+VM approach allowing our EMU to reliably and efficiently retain its internal state, i.e., stateful EMU, under even the most severe EH conditions. Extensive empirical evaluations validated the operation of the proposed stateful EMU at a small overhead (0.07mJ of energy to update the EMU state and a $\simeq 4\mu\mathrm{A}$ of static current consumption of the EMU).
Residential water meters accommodate various methods of power provisioning. Electromagnetic and ultrasonic meters, for example, often rely on a battery-like external power source, whereas mechanical meters harvest energy from water flow through an impeller. Although energy harvesting (EH) minimizes maintenance needs driven by battery depletion/replenishment, placing a physical element into the flow adversely affects water pressure. This intrusive EH/sensing technique is not user-friendly either since the meters with impellers need to be embedded into pipes by skilled personnel. Hence, this paper proposes a non-intrusive sensor system powered by thermoelectric generators (TEGs) for plug-and-play water flow rate measurement. This system, equipped with a custom-made energy management unit (EMU), adopts ultrasonic sensors, a task-based computing scheme, and a LoRa module for autonomous sensing and reporting of the flow rate. After summarizing thermoelectricity and delta time-of-flight ( $\Delta$ ToF)-based ultrasonic sensing theory, we provide the system model and design details with a particular focus on the EMU. Then, we experimentally evaluate the system under varying conditions, demonstrating their impact on average sensing and transmission periods. The results unveil that our proposal can achieve high measurement precision ( $\pm 1.4\%$ ), comparable to its intrusive and battery-powered counterparts, and thus has the potential of replacing the residential water meters.
Neural networks are exerting burgeoning influence in emerging artificial intelligence applications at the micro-edge, such as sensing systems and image processing. As many of these systems are typically self-powered, their circuits are expected to be resilient and efficient in the presence of continuous power variations caused by the harvesters. In this paper, we propose a novel mixed-signal (i.e. analogue/digital) approach of designing a power-elastic perceptron using the principle of pulse width modulation (PWM). Fundamental to the design are a number of parallel inverters that transcode the input-weight pairs based on the principle of PWM duty cycle. Since PWM-based inverters are typically agnostic to amplitude and frequency variations, the perceptron shows a high degree of power elasticity and robustness under these variations. We show extensive design analysis in Cadence Analog Design Environment tool using a 3 × 3 perceptron circuit as a case study to demonstrate the resilience in the presence of parameric variations.
Energy management in energy harvesting (EH) transient computing systems is challenging due to the common reliance on volatile memory (VM) elements, which require the energy management units (EMUs) of these systems to be powered at all times. Such a requirement is unattainable due to the intermittent and varying nature of EH. Additionally, these EMUs often use only one large energy storage to power the systems, which is not optimal considering the distinct energy needs of different system tasks. We addressed these issues in our recent study by proposing an EMU capable of selecting task-specific operating voltage levels and energy storage sizes at runtime while reliably retaining this information (internal EMU state) on the EMU side, thanks to the non-volatile memory (NVM) elements used. However, this solution had only two options for voltage and storage selection, preventing the system from providing the precise energy levels required by each task. Hence, this study extends these options for greater granularity in optimizing task-specific energy needs via a multi-storage EMU approach, offering an ever-efficient state retention unit (SRU) solution. We use the Signal Transition Graph (STG) method to design SRU's control logic that handles the NVM elements for retaining the internal EMU state. The empirical measurements reveal that the actual energy overhead added by the SRU is as low as 0.1mJ to update the EMU state while the static current consumption is $\simeq3\mu$A.
Neural networks are exerting burgeoning influence in emerging artificial intelligence applications at the micro-edge, such as sensing systems and image processing. As many of these systems are typically self-powered, their circuits are expected to be resilient and efficient in the presence of continuous power variations caused by the harvesters.
In this paper, we propose a novel mixed-signal (i.e. analogue/digital) approach of designing a power-elastic perceptron using the principle of pulse width modulation (PWM). Fundamental to the design are a number of parallel inverters that transcode the input-weight pairs based on the principle of PWM duty cycle. Since PWM-based inverters are typically agnostic to amplitude and frequency variations, the perceptron shows a high degree of power elasticity and robustness under these variations. We show extensive design analysis in Cadence Analog Design Environment tool using a 3x3 perceptron circuit as a case study to demonstrate the resilience in the presence of parameric variations.
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