To explore the association of sleep modes with metabolic dysfunction-associated steatotic liver disease (MASLD) in type 2 diabetes mellitus (T2DM). A total of 1900 patients with T2DM were enrolled between March 2017 and December 2024. Sleep behaviors were collected via questionnaires. Four sleep modes were defined by combining nocturnal sleep factors and nap: good nocturnal sleep pattern with short/long nap (GNSP-SN/LN) and poor nocturnal sleep pattern with short/long nap (PNSP-SN/LN). Cox regression analysis was used to examine the association between sleep and MASLD. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) quantified the added value of sleep modes to the Fatty Liver Index (FLI). Over an average follow-up of 3.23years, 379 new-onset MASLD events were identified based on ultrasound criteria in this retrospective cohort study. Four nocturnal sleep factors and napping duration were positively related to the MASLD in T2DM patients (HR for adverse sleep behaviors ranged from 1.64 to 2.26). Compared to GNSP-SN, GNSP-LN (HR = 1.88, 95% CI:1.37-2.61), PNSP-SN (HR = 2.54, 95% CI: 1.91-3.37), and PNSP-LN (HR = 3.51, 95% CI: 2.53-4.87) were associated with higher MASLD risk. Napping more than 30min can increase the MASLD risk (HR = 1.82, 95% CI:1.30-2.53, in GNSP-LN; HR = 1.40, 95% CI:1.01-1.93, in PNSP-LN). Incorporating sleep modes into the FLI improved prediction, with an NRI of 0.21 and an IDI of 0.06. Poor nocturnal sleep was independently associated with a substantially higher risk of MASLD. Prolonged napping might increase the MASLD risk, regardless of the pattern of nocturnal sleep. Combining sleep modes with FLI provides exploratory evidence of improved discrimination for incident MASLD.

This work explores the key capabilities of emerging sensing technologies in the context of Structural Health Monitoring (SHM) of civil infrastructures, aiming to contribute to research on integrated and intelligent systems for more accessible and efficient monitoring solutions. As a case study, it focuses on the analysis of the static and dynamic behavior of the Edgar Cardoso stay-cable bridge during its rehabilitation, using fully customized transducers and equipment. The developed system integrates sensors capable of measuring accelerations, displacements, and temperature, which are connected to an autonomous data acquisition and transmission network. A digital interface was also developed to store, process, and visualize the collected data, enabling remote access for subsequent interpretation and analysis. The main contribution of this research lies in the use of optimized wireless monitoring systems with extended autonomy. This is achieved by employing edge computing techniques to minimize energy consumption during data transmission, as well as by managing the sleep modes of the sensor nodes. At same time, a methodology was proposed for the automatic and real-time estimation of axial forces in cables. This approach relies on the use of innovative edge computing tools, combined with the taut string theory as a simplified modelling framework. The results confirm the effectiveness of the developed system in achieving long-term operation without compromising monitoring performance. In addition, the developed system enabled the identification of the structure’s dynamic properties, particularly natural frequencies. The temperature profiles in critical sections, as well as displacements in the expansion joint were also measured and evaluated. The results demonstrate the potential of customized sensing solutions as effective tools for the management, maintenance, and long-term preservation of strategic infrastructures.

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above across one to three hops, with average end-to-end delays between s and s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching packet delivery ratio at 50 m and at 200 m. Energy measurements further indicate that the sensor nodes consume only in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems.

Fully maintenance-free smart collars for range cattle, sheep and deer must survive years of uncontrolled grazing under highly variable shade and motion conditions. This paper presents an ultra-low-power buck converter governed by a fast integral terminal sliding mode controller (FITSMC) with a fixed-time observer. A new reaching law retains the initial sliding manifold and a negative-power term maintains the constant switching gain to preserve robustness near the surface while attenuating chattering without widening the bandwidth. The fixed-time observer estimates the irradiance and load changes and provides a feed-forward correction, tightening the output regulation regardless of initial conditions. Load step tests with moderate resistance swings showed the proposed method recovers noticeably faster and exhibits slightly lower overshoot than a recent method based on a two-phase power reaching law, while visible inductor current spikes are also suppressed. Simulations under daily grazing profiles confirmed tight output regulation adequate for microwatt data logging and periodic long-range (LoRa) bursts. The sleep mode quiescent current remained in the 9 microamps range, eliminating the need for manual recharge across multi-season field deployments. By integrating robust power electronics with collar-grade solar harvesting, the circuit offers a truly maintenance-free energy path for untethered livestock wearables and supports sustainable precision agriculture.

Edge computing is now transforming how data is processed by shifting the computing devices closer to the source of data generation. Even though this transformation helps reduce latency and bandwidth consumption, it introduces a critical challenge. The edge devices operate with strict hardware constraints. The conventional microcontrollers such as ATmega328p and ESP32 offer simple and reliable design while they come with lack of architectural mechanism for advanced energy optimization. This research proposes the idea of designing an edge oriented, System on Chip (SoC) implemented using Verilog, integrating a 32bit Reduce Instruction Set Computing V(RISC-V/ RV32I) core with essential peripherals for the proposed microcontroller design. The new architecture explores energy minimization strategies including Dynamic Voltage and Frequency Scaling (DVFS), Sleep Modes, Clock Gating, Approximate ALU (Arithmetic and Logic Unit) in a separate manner. All the techniques will be implemented and evaluated separately. After thorough evaluation all techniques will be synergized and evaluated in one system. By means of Xilinx Vivado simulation and power analysis, a structured experimental matrix compares the baseline design against the optimized variants mentioned above. To represent edge workloads an integer multiplication (N32/64) and FIR (Finite Impulse Response) filtering will be complied using RISCV32 GCC toolchain under Ubuntu Operating System and executed on the soft SoC. The estimations of power, latency and Hardware areas such as LUTSs (Look Up Tables), Registers, BRAM (Block Random Access Memory) will be measured and compared to evaluate energy, latency and area tradeoffs. The study leverages recent research in energy efficient RISC-V microarchitectures, approximate computing and adaptive DVFS policies. This research contributes a reproducible methodology for architectural energy optimization in edge computing by providing a quantitative evaluation within a unified FPGA (Field Programmable Gate Array) based framework. The expected outcome is a demonstrable reduction is dynamic and static power while maintaining an acceptable performance degradation, setting up design guidelines for next generation energy-aware embedded architectures.

The study concentrates on improving the dependability and operational efficacy of LoRa-based Wireless Sensor Networks (WSNs), which are extensively utilized in IoT applications, especially for long-range private networks. It seeks to deal with the problems that arise when a single node or communication line fails, which can have a big effect on network performance. The research utilizes a Markovian matrix theoretical framework to examine and simulate the behavior of LoRa-based Wireless Sensor Networks (WSNs), incorporating states such as Sleep (S), Idle (I), Transmit (T), and Receive (R) mode. A Python software program was created to put this model into action, allowing for testing and simulation with 50 fake data sets. The method stresses that the network should always be running, that sensor nodes should be replaced quickly, and that the network should be able to handle failures of individual nodes. The simulations indicate that using the Markov chain model in conjunction with detailed step-by-step math computation may yield a more accurate analysis of the data sets. The methodology also helps you evaluate protocols, change control, look at scalability, and make informed choices about how to build a network. This work offers practical benefits for the design, deployment, and maintenance of LoRa-based WSNs in real-world IoT scenarios. It supports network administrators and engineers in predicting power consumption, designing resilient protocols, scaling networks efficiently, and implementing adaptive control measures to ensure continuous and dependable operation. The integration of Markov chain mathematical modeling with Python-based simulation provides a robust solution for ensuring reliable operation of LoRa-based WSNs. The approach mitigates the impact of node failures, supports rapid recovery, and maintains network integrity.

Power-saving sleep mode (SM) is commonly employed to extend devices runtime in battery-powered buck converters. However, conventional mode transitions from active mode (AM) to SM rely on hard-switching actions for power-state conversion, which inject loop noise and may cause output glitches. This brief proposes a seamless power and gain transition (SPGT) scheme to achieve smooth transition from AM to SM and competitive conversion efficiency. Besides, the introduced DCM compensation effectively suppresses subharmonic oscillation to address the instability issue during SM operation. The proposed scheme is implemented in a 180 nm BCD process with a core scheme area of 0.07 μm². Test results show seamless transitions both AM-SM and PWM-PFM without any glitches. No subharmonic oscillation is observed during either steady-state or transient operation. The prototype achieves a peak efficiency of 96% and a 30 mV undershoot voltage and a 7 μs settling time under a 0.1-3A load step.

Introduction Energy efficiency is a critical challenge in Beyond-5G (B5G) cellular networks, where ground base stations (GBSs) are responsible for a substantial portion of network energy consumption. Reducing this consumption while maintaining minimum user data rate requirements remains a key research problem. Methods This paper proposes an Aerial Base Station (ABS)-assisted energy optimization framework that integrates ABS deployment with low-power sleep states of GBSs. Traffic is selectively offloaded from lightly loaded GBSs to ABSs, enabling energy savings without violating user quality-of-service constraints. A Deep Deterministic Policy Gradient (DDPG) algorithm is employed to jointly optimize ABS positioning, GBS sleep mode scheduling, and resource allocation under dynamic traffic conditions. Results Simulation results demonstrate that the proposed DDPG-based framework significantly reduces network energy consumption while improving achievable user data rates compared to baseline schemes without ABS assistance or learning-based optimization. Discussion The results highlight the effectiveness of integrating ABSs with GBS low-power sleep states using reinforcement learning. By enforcing minimum data rate constraints and dynamically adapting to traffic variations, the proposed approach provides a scalable and energy-efficient solution for sustainable operation.

The rapid evolution of wireless communication toward Fifth Generation (5G) networks has enabled unprecedented performance improvement in terms of data rate, latency, reliability, sustainability, and connectivity. Recent years have witnessed an excessive deployment of new 5G networks worldwide. This deployment lead to an exponential growth in traffic flow and a massive number of connected devices requiring a new generation of energy-hungry base stations (BSs). This results in increased power consumption, higher operational costs, and greater environmental impact, making energy efficiency (EE) a critical research challenge. This paper presents a comprehensive survey of EE optimization strategies in 5G networks. It reviews the transition from traditional methods such as resources allocation, energy harvesting, BS sleep modes, and power control to modern artificial intelligence (AI)-driven solutions employing machine learning, deep reinforcement learning, and self-organizing networks (SON). Comparative analyses highlight the trade-offs between energy savings, network performance, and implementation complexity. Finally, the paper outlines key open issues and future directions toward sustainable 5G and beyond-5G (B5G/Sixth Generation (6G)) systems, emphasizing explainable AI, zero-energy communications, and holistic green network design.

ABSTRACT This paper designs and investigates an STM32-based multicore load-balanced communication platform tailored for industrial field environments. The platform fully leverages the low power consumption and high integration characteristics of the STM32 microcontroller, incorporating multiple optimised communication modules. These include an anti-interference RS485 communication system based on the Modbus protocol, a high-speed communication mechanism between the STM32 and an FPGA coprocessor via the FSMC parallel interface, and a wireless laser communication subsystem equipped with multipath channel estimation and parallel equalisation capabilities. For wireless communication, the platform employs dynamic clock frequency adjustment and sleep mode strategies, significantly reducing the power consumption of both the processor and the RF module, achieving a low power level of 10–30 μJ under continuous data transmission. Experimental results obtained using the SMARTConvert test platform show significant improvements in communication stability, energy efficiency, and throughput. These results validate the feasibility and advantages of the STM32 multicore architecture in achieving load-balanced communication for industrial applications.

Data-retention flip-flop (DRFF) efficiently maintains data during sleep mode and retains state during transitions between active and sleep mode. This paper proposes a novel source-biased stacked inverter (SBS-Inverter) and a low-leakage, structure-reused DRFF. The sleep latch circuit constructed using the SBS-Inverter can effectively reduce the power of DRFF when storing data. Reuse of the structure improves the situation of redundant transistors in certain DRFF. Fine-grained inverter level optimization reduces delay and power during the active mode. The DRFF was implemented using a 55 nm process and subjected to comprehensive analysis. Post-layout simulation results at a supply voltage of 0.4 V indicate that the proposed DRFF’s data retention leakage power is only 5.3 pW. At a supply voltage of 0.8 V, the power-delay product is only 0.146 nW*ns@20 MHz. Monte Carlo simulation results considering process, voltage and temperature (PVT) variations show that the proposed DRFF can operate reliably down to a supply voltage of 0.4 V.

In green massive MIMO networks, reducing power consumption (PC) while ensuring user quality of service (QoS) is critical for sustainable operation. To this end, we propose a robust and scalable reinforcement learning framework based on independent proximal policy optimization (IPPO), enabling intelligent base station (BS) control through a three-dimensional configuration of antenna activation, sleep mode transitions, and user offloading. Compared to a non-learning simple energy-saving policy, our proposed IPPO algorithm achieves approximately a 20.3% reduction in PC and a 49% improvement in energy efficiency (EE). In addition, it demonstrates significantly faster convergence and better scalability than multi-agent PPO (MAPPO), reducing convergence time by approximately 75% with 49 BSs and by around 90% with 81 BSs.

Fasting presents unique metabolic challenges for individuals with T1D. The 25-h Yom Kippur complete fast provides an opportunity to evaluate whether automated insulin delivery (AID) systems can maintain metabolic stability, prevent hypoglycemia and ketosis, and determine basal insulin requirements during prolonged fasting. This real-world, noninterventional study included 54 adolescents and young adults with T1D (mean age 17.3 ± 3.3 years, HbA1c 6.8 ± 1.0%). Participants used MiniMed 780 G (n = 34), Control-IQ (n = 10), or open-source AID systems (n = 10). Common system-specific adjustments included setting a 150 mg/dL exercise target, activating sleep mode, and modifying basal or glucose targets, while 11 participants made no changes. Ketone levels were measured after the 25-h fast and a routine overnight fast. Analyses compared glucose and insulin across fasting periods and different 780 G settings and assessed predictors of hypoglycemia and ketone levels. All participants successfully completed the fast. Mean TIR increased from 71.6 ± 13.9% during routine days to 82 ± 13.2% during fasting (P < 0.01), while time <70 mg/dL decreased from 2.6% to 2.2% (P = 0.017). Ten mild hypoglycemic events occurred after the pre-fast meal and one during fasting. A higher baseline percentage of time <70 mg/dL was the only predictor of hypoglycemia. No significant difference was found between 780 G users with exercise mode and those with no or minor changes. Participants received 43.4 ± 16.8% (range 9.3%-90%) of their usual insulin dose. Median (IQR) end-of-fast ketone levels were 0.4 (0.3, 0.7) mmol/L vs 0.1 (0, 0.1) mmol/L on a regular morning (n = 31); insulin doses <30% of usual dose were associated with higher ketone levels. No severe hypoglycemia or serious adverse events occurred. AID systems enable safe 25-h fasting by maintaining glucose control and reducing the risk of hypoglycemia and ketonuria. Fasting adjustments should be individualized and can often be minor.

ABSTRACT In wireless sensor networks (WSNs) used for continuous surveillance, the problem of monitoring critical data transmitted infrequently is an extreme challenge of energy usage and latency requirements. Current medium access control (MAC) protocols often have high energy consumption, primarily owing to idle listening, collision, and excessive data transmission, and as a result, are not suitable for such uses. This study proposes a novel protocol to optimize energy consumption and transmission delays in WSNs used to monitor infrequent critical data. This protocol is named OWuR‐MAC, that is, “Optimized Wake‐up Radio based Medium Access Control.” OWuR‐MAC implements an event‐driven wake‐up strategy utilizing wake‐up receivers so that devices can stay in the low‐power sleep mode until data transmission is necessary. Sensor nodes use wake‐up receivers, which allow them to remain at low‐energy sleep times until there is relevant data transmission that can wake them up. However, OWuR‐MAC dynamically modifies the wake‐up receiver sensitivity and transmission timing based on the characteristics of networked activity and environmental conditions. The protocol was implemented and compared with Fully Asynchronous Wake‐up Radio MAC (FAWR‐MAC) and Opportunistic Wake‐up Radio MAC (OPWUM) protocols of a similar category. The results indicate that OWuR‐MAC achieves lower rates of energy consumption, lower latency, and higher packet delivery ratios than the other two protocols.

Power-saving queueing model of a wireless sensor node operating in two modes subject to random occurrences of sleep orders

Energy efficient sleep mode strategies for communication and computing devices in cellular networks with edge computing

The integration of Internet of Things (IoT) technologies into precision poultry farming has revolutionized environmental monitoring; yet, high energy consumption in sensor networks remains a significant barrier to scalability and sustainability. This study presents a hybrid sleep scheduling algorithm for energy-efficient IoT-based poultry environmental monitoring. The algorithm enables dynamic transitions between Active, Modem Sleep, and Light Sleep modes according to environmental stability and data variability. Analytical models of system cycle time and power consumption were developed to optimise node behaviour under varying farm conditions. A prototype built with Wemos D1 Mini microcontrollers, DHT22, and MQ135 sensors was experimentally validated in a live poultry environment. Results show an average energy reduction of 68.4% compared to always-active systems, while maintaining latency below 2 seconds and measurement errors within ±0.4°C, ±1.3% RH, and ±7 ppm. The proposed framework offers a scalable, low-power architecture suitable for remote, battery-powered farms, advancing the sustainability of IoT-enabled livestock management and supporting the United Nations’ SDGs 2 and 12.

This paper presents a dual-source energy harvesting interface for battery-free IoT devices, utilizing a serial-stacked single-inductor triple-input triple-output (SS-SITITO) buck-boost converter topology. In each charging cycle, the converter allows simultaneous power extraction from two energy harvesting sources and the usage of recycled energy in a storage capacitor. A two-dimensional maximum power point tracking (2D-MPPT) with triple-modulation realizes 98.3% accuracy in tracking the maximum power point for each input with minimal power cost. To provide sufficient output power under limited inputs, the system features energy recycling by automatically switching between sleep and active modes, and introduces a quad-phase buck-boost operation scheme, achieving up to 26mW maximum output power. To ensure self-sustainability with low quiescent power and enhanced power conversion efficiency, the circuit incorporates an edge-triggered feedback path, a power-optimized undervoltage lockout circuit, a low-voltage-supportable bandgap reference generator, and an ultra-low-power relaxation oscillator. Experimental results show a quiescent power consumption of 805nW, a 7000x power range, and a peak efficiency of 85.5%.

This research presents the design, development, and deployment of a GSM-enabled integrated electronic system aimed at enhancing street lighting in the Caraga Region.The system uses solar-powered streetlights with IoT capabilities via the ESP32 TTGO T-Call microcontroller, addressing high maintenance costs and lack of real-time monitoring in Geographically Isolated and Disadvantaged Areas (GIDAs).It enables automated control and data tracking through a web and mobile dashboard.Key parameters monitored include battery state of charge (SOC), voltage, and current from the solar panel, load, and battery.Features like emergency 5V charging and deep sleep mode optimize performance and energy use.This scalable solution aligns with the Philippine government's goal of 100% electrification by 2028 and offers strong potential for smart infrastructure deployment in remote communities.

Disclosure: A. Ramachandran: None. S.K. Azad: None. V. Perugu: None. S. Agarwal: None.We present a 69-year-old male with Type 2 Diabetes with Gastroparesis on a T:Slim X2 insulin pump with U-200 insulin for two years. He has occasional nausea and vomiting managed by diet modification. Continuous Glucose Monitoring (CGM) showed a Time in range (TIR) (70-180) of 61%, high(>180) of 36.5%, low(<70) of 2.4. Interpretation revealed variable blood sugars with post prandial hyperglycemia followed by hypoglycemia. He was administering meal bolus 2 hours after the meal and entered extra carbohydrates to manage hyperglycemia. However, due to variable absorption he receives auto-boluses from the pump prior to meal bolus causing insulin stacking. We used ‘sleep mode’ to turn off auto-bolus with the advice to bolus 30 minutes in an extended bolus form, post prandially. Discussion: Patients with gastroparesis often have inadequate glycemic control (GC). Management is challenging as delayed absorption complicates timing and dosing of insulin which exacerbates glycemic variability. Although no clear guidelines have been established for management of glycemia in diabetic gastroparesis, ADA recommends automated insulin delivery (AID) as it can dynamically adjust insulin delivery based on real-time CGM which improves TIR and reduces HbA1c without increasing hypoglycemia. Our case highlights that insulin stacking could occur with background auto-bolus use with AID in such cases and the importance of tailoring management.Presentation: Monday, July 14, 2025