Recent progress on Remote Sensing Foundation Models (RSFMs) aims toward universal representations for Earth observation imagery. However, current efforts often scale up in size significantly without addressing efficiency constraints critical for real-world applications (e.g., onboard processing, rapid disaster response) or treat multispectral (MS) data as generic imagery, overlooking valuable physical priors. We introduce PhySwin, a foundation model for MS data that integrates physical priors with computational efficiency. PhySwin combines three innovations: (i) physics-informed pretraining objectives leveraging radiometric constraints to enhance feature learning; (ii) an efficient MixMAE formulation tailored to SwinV2 for low-FLOP, scalable pretraining; and (iii) token-efficient spectral embedding to retain spectral detail without increasing token counts. Pretrained on over 1M Sentinel-2 tiles, PhySwin achieves SOTA results (+1.32\% mIoU segmentation, +0.80\% F1 change detection) while reducing inference latency by up to 14.4$\times$ and computational complexity by up to 43.6$\times$ compared to ViT-based RSFMs.

Intermittent computing (IC) is a key enabler for the vision of a trillion Internet of Things devices. By harvesting energy from the environment and leveraging nonvolatile memory (NVM) to retain computational progress across power cycles, IC enables untethered and battery-free devices to perform computation whenever ambient energy is available. The backbone of state retention is NVM, and recent advances in energy-efficient NVM have the potential to expand the application domain of IC significantly. Utilizing emerging NVM at the level of bit cells, researchers have proposed nonvolatile processors. However, these do not leverage hardware–software co-design, which can be used to overcome hardware limitations and to provide support for application-level constraints such as atomicity. In this article, we propose MEMIC, a memory architecture tailored for IC devices with byte-addressable NVM. A core focus of MEMIC is to combine volatile and NVM in such a way that the operations of IC are as efficient as possible, while also maximizing computational performance per joule. MEMIC uses volatile memory for energy efficiency and NVM for data retention. To avoid double-buffered checkpoints and costly roll backs when code needs to be reexecuted, MEMIC is designed to track and minimize writes to NVM during failure-atomic sections. Our evaluation shows that MEMIC’s instruction cache reduces workload completion time under intermittent operation by 41%–70% and its data cache provides a further reduction of 13%–39%.

There has been significant innovation in the domain of Internet of Things (IoT) as nowadays wireless data transmission is playing an essential role in various organizations like agriculture, defence, transportation, etc. Batteries are the most common option to power wireless devices. However, using batteries to power IoT devices has drawbacks including the cost and disruption of frequent battery replacement, and environmental concerns about battery disposal. Solar energy harvesting is a promising solution for long-term operation applications. However, solar energy harvesting varies drastically over location and time. Due to fluctuating weather conditions and the environmental effects on PV surface condition, output could be reduced and become insufficient. Environmental conditions including temperature, wind, solar irradiance, humidity, tilt angle and the dust accumulated over time on the photovoltaic (PV) module surface affects the amount of energy harvested. To address this issue, a novel solution is required to autonomously predict the harvested energy and plan the IoT device tasks accordingly, to enhance its performance and lifetime. Using Machine Learning (ML) algorithms could make it possible to predict how much energy can be harvested using weather forecast data. This research is ongoing, and aims to apply ML algorithms on historical weather data including environmental factors to generate solar energy predictions for IoT device energy budget planning.

Owing to the shorter wavelength in the millimeter-wave (mmWave) spectrum, miniaturized antennas can receive power with a higher efficiency than UHF bands, promising sustainable mmWave-powered Internet of Things (IoT) devices. Nevertheless, the performance of a mmWave power receiver has not been compared, numerically or experimentally, to its sub6-GHz counterpart. In this article, the performance of mmWave-powered receivers is evaluated based on a novel wearable textile-based higher order mode microstrip antenna, showing the benefits of wireless power transmission (WPT). First, a broadband antenna is proposed maintaining a stable wearable measured bandwidth from 24.9 to 31.1 GHz, over threefold improvement compared to a conventional patch. The proposed antenna has a measured 8.2 dBi co-polarized gain with the highest thickness-normalized efficiency of a wearable antenna. When evaluated for compact power receivers, the measured path gain shows that WPT at 26 GHz outperforms 2.4 GHz by 11 dB. A rectenna array based on the proposed antenna is then evaluated analytically showing the potential for up to$6.3\times $higher power reception compared to a UHF patch, based on the proposed antenna’s gain and an empirical path-loss model. Both use cases demonstrate that mmWave-powered rectennas are suitable for area-constrained and large-area wearable IoT applications.

Batteryless energy-harvesting devices promise to deliver a sustainable Internet of Things. Intermittent computing is an emerging area, where the application forward progress, i.e., computation beneficial to the progress of the active application, is maintained by saving the volatile computing state into nonvolatile memory before power interruptions, and restored afterward. Conventional intermittent computing approaches typically minimize energy storage to reduce device dimensions and interruption periods, but this can result in high state-saving and -restoring overheads and impede forward progress. In this article, we argue that adding a small amount of energy storage can significantly improve the forward progress. We develop an intermittent computing model that accurately estimates the forward progress, with an experimentally validated mean error of 0.5%. Using this model, we show that sizing energy storage can improve the forward progress by up to 65% with a constant current supply, and 43% with real-world photovoltaic sources. An extension to this approach, which uses a cost function to trade off the energy storage size against forward progress, can save 83% of capacitor volume and 91% of interruption periods while maintaining 93% of the maximum forward progress.

Emerging applications for Internet of Things (IoT) devices demand smaller mass, size, and cost whilst increasing capability and reliability. Energy harvesting can provide power to these ultra-constrained devices, but introduces unreliability, unpredictability, and intermittency. Schemes for wireless sensors without batteries or supercapacitors overcome intermittency through saving system state into nonvolatile memory before the supply drops below the minimum operating voltage, termed transient, or intermittent computing. However, this introduces significant time and energy overheads. This article presents two schemes that significantly reduce these overheads: entering a sleep mode to avoid saving state and utilizing direct memory access (DMA) when state saves are required. Time and energy previously wasted on state saves can instead be used to perform useful computation, termed “forward progress.” We practically validate the proposed approaches across a range of energy sources and IoT benchmarks and demonstrate up to 46.8% and 40.3% increase in forward progress and up to 91.1% and 85.6% reduction in overheads for each scheme, respectively.

Energy-driven computing is an emerging paradigm that aims to fuel the proliferation of tiny and low-cost IoT sensing and monitoring devices. Energy-driven computers are generally powered by energy harvesting sources, and adapt their operation at runtime according to energy availability; thus, they must be designed and tested according to the expected dynamics of their power source. However, today's processor simulators and debuggers typically assume that power is always available, so they are unable to correctly model the interactions between power supply, power consumption and energy-driven execution. To address this shortcoming, we propose Fused, an open source full-system simulator for energy-driven computers. Fused models execution, power consumption, and power supply in a closed loop, thus correctly models the interaction between them. It targets energy-driven embedded systems, and employs SystemC for digital and mixed-signal simulation to model a microcontroller and mixed-signal circuitry, enabling hardware-software codesign and design space exploration. Fused includes a high-level power modelling methodology, whereby events recorded during simulation are correlated to power measurements of real hardware to extract features for power modelling. Results show that Fused can model the execution time and power consumption of a commercially available microcontroller with a geometric mean error of 0.2% and 3.4% respectively, across a wide range of workloads. Through a case-study, we demonstrate that Fused can accurately model a state-of-the art intermittent computing system, where execution is heavily dependent on energy availability: although up to 70 power cycles were needed to complete the tested workload on the constrained energy supply, Fused modelled the completion time with less than 7% error.

Reactive transient computing systems preserve computational progress despite frequent power failures by suspending (saving state to nonvolatile memory) when detecting a power failure, and restoring once power returns. Existing methods inefficiently save and restore all allocated memory. We propose lightweight memory management that applies the concept of paging to load pages only when needed, and save only modified pages. We then develop a model that maximises available execution time by dynamically adjusting the suspend and restore voltage thresholds. Experiments on an MSP430FR5994 microcontroller show that our method reduces state retention overheads by up to 86.9% and executes algorithms up to 5.3× faster than the state-of-the-art.

Recent research has looked to supplement or even replace the batteries in embedded computing systems with energy harvesting, where energy is derived from the device’s environment. However, such supplies are generally unpredictable and highly variable, and hence systems typically incorporate large external energy buffers (e.g., supercapacitors) to sustain computation; however, these pose environmental issues and increase system size and cost. This article proposes Momentum , a general power-neutral methodology, with intrinsic system-wide maximum power point tracking, that can be applied to a wide range of different computing systems, where the system dynamically scales its performance (and hence power consumption) to optimize computational progress depending on the power availability. Momentum enables the system to operate around an efficient operating voltage, maximizing forward application execution, without adding any external tracking or control units. This methodology combines at runtime (1) a hierarchical control strategy that utilizes available power management controls (such as dynamic voltage and frequency scaling, and core hot-plugging) to achieve efficient power-neutral operation; (2) a software-based maximum power point tracking scheme (unlike existing approaches, this does not require any additional hardware), which adapts the system power consumption so that it can work at the optimal operating voltage, considering the efficiency of the entire system rather than just the energy harvester; and (3) experimental validation on two different scales of computing system: a low power microcontroller (operating from the already-present 4.7μF decoupling capacitance) and a multi-processor system-on-chip (operating from 15.4mF added capacitance). Experimental results from both a controlled supply and energy harvesting source show that Momentum operates correctly on both platforms and exhibits improvements in forward application execution of up to 11% when compared to existing power-neutral approaches and 46% compared to existing static approaches.

An effective and robust time synchronization scheme is essential for many wireless sensor network (WSN) applications. Conventional synchronization methods assume the use of highly accurate crystal oscillators (10-100 ppm) for synchronization, only correcting for small errors. This paper suggests a novel method for time synchronization in a multihop, fully-distributed WSN using imprecise CMOS oscillators (up to 15 000 ppm). The DiStiNCT technique is power-efficient, computationally simple, and robust to packet loss and complex topologies. Effectiveness has been demonstrated in simulations of fully connected, grid, and unidirectional ring topologies. The method has been validated in hardware on a grid of nine sensor nodes, synchronizing to within a mean error of 6.6 ms after 40 iterations.

Transient computing systems do not have energy storage, and operate directly from energy harvesting. These systems are often faced with the inherent challenge of low-current or transient power supply. In this paper, we propose “power-neutral” operation, a new paradigm for such systems, whereby the instantaneous power consumption of the system must match the instantaneous harvested power. Power neutrality is achieved using a control algorithm for dynamic frequency scaling, modulating system performance gracefully in response to the incoming power. Detailed system model is used to determine design parameters for selecting the system voltage thresholds where the operating frequency will be raised or lowered, or the system will be hibernated. The proposed control algorithm for power-neutral operation is experimentally validated using a microcontroller incorporating voltage threshold-based interrupts for frequency scaling. The microcontroller is powered directly from real energy harvesters; results demonstrate that a power-neutral system sustains operation for 4%-88% longer with up to 21% speedup in application execution.

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass, and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This paper proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over state-of-the-art approaches.

Street lighting is an important resource; it has been shown to reduce crime, improve road safety, and increase economic activity. These benefits, however, come with a cost: an annual emission of 64 million tonnes of CO2. Solar-powered street lighting is attractive for its use of renewable energy and its ease of installation (particularly in off-grid applications), but sizing and control is a non-trivial task. This paper describes TALiSMaN-Green, a traffic-aware street lighting scheme which takes account of road users as well as the available energy to dynamically adjust lighting levels. Simulations using real traffic and sunlight data illustrate that solar-powered streetlights can be managed to deliver consistent usefulness throughout the night.

The design of sensing systems powered from energy harvesting can be complex. Design decisions are required concerning the properties and parameters of energy harvesting, conversion, and storage devices. The quantity and properties of environmental energy are typically both temporally and spatially variant, while the current consumption of the load electronics also changes dynamically. In this paper we describe Enspect, an open-source hardware/software tool which simplifies the design of energy harvesting sensing systems by assisting in the specification of harvesting and storage devices. It does this by enabling the long-term collection of data on energy availability, and modeling and simulating the performance of a complete system.

Energy harvesting allows low-power embedded devices to be powered from naturally-ocurring or unwanted environmental energy (e.g. light, vibration, or temperature difference). While a number of systems incorporating energy harvesters are now available commercially, they are specific to certain types of energy source. Energy availability can be a temporal as well as spatial effect. To address this issue, ‘hybrid’ energy harvesting systems combine multiple harvesters on the same platform, but the design of these systems is not straight-forward. This paper surveys their design, including trade-offs affecting their efficiency, applicability, and ease of deployment. This survey, and the taxonomy of multi-source energy harvesting systems that it presents, will be of benefit to designers of future systems. Furthermore, we identify and comment upon the current and future research directions in this field.

This paper proposes a linearised state-space technique to accelerate the simulation of tunable vibration energy harvesting systems by at least two orders of magnitude. The paper provides evidence that currently available simulation tools are inadequate for simulating complete energy harvesting systems where prohibitive CPU times are encountered due to disparate time scales. In the proposed technique, the model of a complete mixed-technology energy harvesting system is divided into component blocks whose mechanical and analogue electrical parts are modelled by local state equations and terminal variables while the digital electrical part is modelled as a digital process. Unlike existing simulation tools that use Newton-Raphson method, the proposed technique uses explicit integration such as Adams-Bashforth method to solve the state equations of the complete energy harvester model in short simulation time. Experimental measurements of a practical tunable energy harvester have been carried out to validate the proposed technique.

Photovoltaic (PV) energy harvesting is commonly used to power wireless sensor nodes. To optimise harvesting efficiency, maximum power point tracking (MPPT) techniques are often used. Recently-reported techniques focus solely on outdoor applications, being too power-hungry for use under indoor lighting. Additionally, some techniques have required light sensors (or pilot cells) to control their operating point. This paper describes an ultra low-power MPPT technique which is based on a novel system design and sample-and-hold arrangement, which enables MPPT across the range of light intensities found indoors and outdoors and is capable of cold-starting. The proposed sample-and-hold based technique has been validated through a prototype system. Its performance compares favourably against state-of-the-art systems, and does not require an additional pilot cell or photodiode. This represents an important contribution, in particular for sensors which may be exposed to different types of lighting (such as body-worn or mobile sensors).

Wireless sensors are normally powered by non- rechargeable batteries, but these must be replaced when depleted. Recent developments in energy harvesting technology allow sensors to be powered by environmental energy where it is present, but the wide range of situations where sensors are deployed means that it is desirable for the energy components of a sensor node (i.e. batteries, supercapacitors, and power generation devices) to be selected and configured at the time of node deployment. Previous energy harvesting-powered systems have been designed for specific energy hardware and been difficult to adapt for different resources. Energy-awareness is useful for state-of-the-art network algorithms, but present systems do not provide a standardized or straightforward way for nodes to monitor and manage their energy hardware. The developments reported in this paper deliver a reconfigurable energy subsystem for wireless autonomous sensors. The new system permits energy modules to be selected and fitted to the sensor node in-situ, in a plug-and-play manner, without the need for reprogramming or the modification of hardware. The node can monitor and intelligently manage its energy resources and assess its overall energy status by analyzing its level of stored energy and rate of power generation. These activities are facilitated by a proposed common hardware interface (which allows multiple energy modules to be connected) and an electronic datasheet structure for the energy modules. The system has been verified through the development and testing of a prototype wireless sensor node which operates from a mix of energy sources.

In this paper, we propose a method for desirably redistributing a wireless sensor network's energy consumption from its sensor nodes (which may have scarce energy resources obtained through energy harvesting, for example) to its central node (which often has an abundant energy resource, such as the mains). At the cost of increasing the central node's decoding complexity, our method facilitates (1) a significant reduction in the number of times the sensor nodes are required to retransmit data owing to transmission errors and/or (2) a reduction of up to 3.99 dB in the sensor node's total transmit energy consumption. We show that our approach can reduce the overall energy consumption of transmitting sensor nodes by more than 20% in practice.

Owing to the limited requirement for sensor processing in early networked sensor nodes, embedded software was generally built around the communication stack. Modern sensor nodes have evolved to contain significant on-board functionality in addition to communications, including sensor processing, energy management, actuation and locationing. The embedded software for this functionality, however, is often implemented in the application layer of the communications stack, resulting in an unstructured, top-heavy and complex stack. In this paper, we propose an embedded system architecture to formally specify multiple interfaces on a sensor node. This architecture differs from existing solutions by providing a sensor node with multiple stacks (each stack implements a separate node function), all linked by a shared application layer. This establishes a structured platform for the formal design, specification and implementation of modern sensor and wireless sensor nodes. We describe a practical prototype of an intelligent sensing, energy-aware, sensor node that has been developed using this architecture, implementing stacks for communications, sensing and energy management. The structure and operation of the intelligent sensing and energy management stacks are described in detail. The proposed architecture promotes structured and modular design, allowing for efficient code reuse and being suitable for future generations of sensor nodes featuring interchangeable components.

The modeling of energy components in wireless sensor network (WSN) simulation is important for obtaining realistic lifetime predictions and ensuring the faithful operation of energy-aware algorithms. The use of supercapacitors as energy stores on WSN nodes is increasing, but their behavior differs from that of batteries. This paper proposes a model for a supercapacitor energy store based upon experimental results, and compares obtained simulation results to those using an 'ideal' energy store model. The proposed model also considers the variety and behavior of energy consumers, and finds that contrary to many existing models, the energy consumed depends on the store voltage (which varies considerably during supercapacitor discharge). Furthermore, energy models in a node's embedded firmware are shown to be paramount for providing energy-aware operation.