Adaptive Duty Cycle Research Articles

IoT-based framework for optimizing energy efficiency and reliability in acoustic sensor networks using mobile sinks

In today’s world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues. This framework optimizes energy consumption and enhances data reliability by incorporating advanced energy management strategies such as adaptive duty cycling and efficient data transmission mechanisms by minimizing forwarding nodes. Simulation results demonstrate that REEFSM reduces energy consumption by up to 43% and increases data reliability by 35% compared to protocols like EERBCR and DEADS. REEFSM ensures zero dead nodes, minimizes packet drops, and maintains high data accuracy throughout the simulation. This research outcome provides a sustainable and reliable solution for energy-efficient data collection in underwater environments. The future research directions, including integrating autonomous path planning, energy harvesting, and machine learning techniques, hold great potential for further advancements in the field.

ENIAO: Energy Aware Faulty Node Re-Placement Integrated With Duty Cycling and Improved Harmony Search Based Clustering through Adaptive Fish School Search Routing for WSN Optimization

In wireless sensor networks (WSNs), sensor nodes are constrained by resource constraints. The limited energy supply and susceptibility to failure greatly affect WSN lifespan, hindering long-term deployment. ENIAO is an Integrated cross-layer Optimized Routing Approach for WSNs that is fault-tolerant and energy-efficient. A bio-inspired clustering architecture and adaptive duty cycling are incorporated into ENIAO's routing optimization. In a clustering protocol, the network is partitioned, and paths are dynamically optimized within and between clusters. By optimizing active/sleep schedules, duty cycling optimizes energy efficiency. A variety of network conditions have been simulated to assess ENIAO's performance. Regarding fault tolerance and energy consumption, ENIAO significantly prolongs the network lifetime. As compared to benchmark protocols, it achieves higher throughput. As a result of the cross-layer design, ENIAO is automatically adapted to optimize energy usage and routing reliability. In the long run, large-scale IoT deployments are possible with ENIAO due to the integrated approach.

EADA: Energy-Aware Adaptive Duty-Cycle Adjustment in Superframe for IEEE 802.15.6-Based Wireless Body Area Networks

EADA: Energy-Aware Adaptive Duty-Cycle Adjustment in Superframe for IEEE 802.15.6-Based Wireless Body Area Networks

A dual-mode digital controller with a high accuracy load current estimator and Gaussian adaptive duty cycle switching for fast transient recovery in DC-DC buck converters

To improve the dynamic performance, this paper proposes a dual-mode digital controller with a high accuracy load current estimator and Gaussian adaptive duty cycle switching for DC-DC buck converters with an auxiliary power stage. The proposed digital controller has two operating modes: steady state and transient. In steady-state mode, a conventional PID control module is used to regulate the main power stage and the output voltage. In transient mode, a charge balance control module is responsible for controlling the auxiliary power stage to improve the dynamic performance. A long short-term memory (LSTM)-based load current estimator is proposed to obtain high accuracy load current. It employs a multiple-to-one structure and estimates the load current based on time series data (a sequence of data points indexed in time order). A Gaussian adaptive duty cycle switching is proposed to suppress the output voltage chattering when transitioning between the auxiliary and the main power stages. Simulation and experimental results demonstrate that the proposed controller has a superior dynamic performance when compared to controllers using linear-based load current estimator and direct duty cycle switching. The overshoot/undershoot and settling time can be reduced by over 50 % for a 1.9 A load transient in a 5 to 1.8 V buck converter operating switching at 2 MHz..

Adaptive SARIMA modelling for continuous chamber temperature tracking in ultra-low temperature freezers

Ultra-low temperature (ULT) freezers are vital for storing perishable biological materials, requiring continuous monitoring of chamber thermal conditions to ensure sample integrity. A reliable dynamic data-driven model is key for smart proactive surveillance of the ULT freezers through real-time status tracking. However, the dynamic nature of ULT freezers, influenced by external disturbances, demands adaptive models to sustain performance. Complex models thus face challenges due to time-consuming parameter updates. To ensure model prediction performance while minimising model updating time, this study proposes a Seasonal Autoregressive Integrated Moving Average (SARIMA) model-based approach for short-term ULT freezer chamber temperature prediction. This method solely relies on historical chamber temperature data, eliminating the need for additional measurements. Moreover, two adaptive duty cycle length (DCL) selection strategies and a one-step ahead DCL estimation method are developed to address auto-correlation issues caused by recurring duty cycles. Comparative analyses with reference models demonstrate that the SARIMA model, incorporating future DCL effects, excels in one-step prediction and multi-step forecast under stable and variant DCL periods. Moreover, it outperforms the reference models in auto-correlation representation, affirming its reliability in predicting future states. The proposed modelling approach promises an effective method for freezing chamber temperature prediction, enabling continuous status tracking and early fault detection. Moreover, it holds great potential for modelling other critical temperatures in refrigeration systems and facilitating flexible energy operations.

Multi-Cell-to-Multi-Cell active equalization method based on k-means clustering and battery pack SOC estimation

In this paper, a MC2MC (Multi-Cell-to-Multi-Cell) battery pack active equalization scheme based on reconfigurable circuits and Buck-Boost converters is proposed and validated for the problem of inconsistent single cell power in battery packs. This scheme can simultaneously equalize multiple non-adjacent cells in a single equalization process, improving the equalization time efficiency. Then, based on the relationship between duty cycle and voltage difference, the MC2MC battery pack active equalization control strategy based on adaptive duty cycle is designed. In addition, a k-means based clustering algorithm is used to cluster and reorganize batteries with different charges, and the SOC (State of charge) estimation algorithm of the battery pack is improved and introduced into the equalization control among multiple batteries. Finally, AC2AC (Any-Cell-to-any -Cell), MC2MC, and MC2MC simulations considering the SOC estimation of the battery pack are validated and analyzed for the unbalanced battery packs under MATLAB/Simulink environment, respectively. On the basis of verifying the accuracy of the simulation process, the effectiveness of the MC2MC equalization control method considering the SOC estimation of the battery pack is verified through the comparative analysis of four aspects, namely, equalization time, computational workload, resultant variance, and energy efficiency.

Optimizing Energy-Efficient Communication Protocols for IoT Devices in Smart Cities Using Narrowband IoT and LTE-M Technology

In the context of optimizing energy-efficient communication protocols for IoT devices in smart cities, this research addresses the pressing need for sustainable and reliable solutions. The research provides a comprehensive understanding of the strategies that contribute to minimizing energy consumption in smart city IoT deployments, with a specific focus on Narrowband IoT (NB-IoT) technology. Employing a structured framework and an integrated system architecture, incorporating LTE-M technology and an application server, the research leverages various optimization strategies. These include Adaptive Power Control, Duty Cycling, Data Aggregation, Protocol Optimization, and Network Topology Optimization. Methodologically, the study adopts a quantitative approach, utilizing metrics such as duty cycle percentages and energy consumption reductions. Data is collected through simulations and experiments, and the analysis involves assessing the impact of each optimization strategy on energy efficiency. Key findings demonstrate significant reductions in energy consumption, ranging from 15% to 25%, depending on the specific strategy employed. Adaptive Power Control and Network Topology Optimization emerge as particularly effective approaches. The implications of these findings underscore the importance of adopting tailored optimization strategies in smart city IoT deployments to achieve both energy efficiency and reliability. The study discusses the broader significance of these results and acknowledges potential limitations, paving the way for future research endeavors in the realm of sustainable and resource-conserving smart cities.

Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

Wireless sensor networks (WSNs) have undergone rapid development but still suffer from energy shortage issues. Meanwhile, existing energy-efficient routing approaches lack a cross-layer cooperation mechanism and cannot offer intelligent decision-making capabilities for resource-constrained nodes. In this paper, we design an intelligent routing algorithm with adaptive duty cycling for application in software-defined WSNs (SDWSNs) to maximize the network lifetime and data transmission reliability subject to a long-term data queue stability constraint. In particular, we introduce Lyapunov optimization to decouple the multistage routing optimization problem into per-frame deterministic problems with delay guarantee. To optimize the routing algorithm and adaptively adjust the duty-cycling mechanism, we propose a cooperative deep reinforcement learning (CDRL) model. Moreover, we develop a centralized training and distributed execution framework to decouple the model training and inference processes, which provides the capability of intelligent routing decision-making for resource-constrained nodes with low computational complexity. In addition, we design a new hybrid routing metric and a unique feature construction and extraction scheme to capture fine-grained information from our hybrid routing metric. The proposed CDRL algorithm adopts a cooperative and iterative model optimization strategy to achieve highly reliable data transmission and optimal long-term network performance. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of network lifetime, packet delivery ratio, and statistical delay performance under various scenarios compared to the EBR-RL, EA-AGG, and QLRP algorithms.

Distributed Data Collection Algorithm With Unknown Objective Function in Adaptive Duty-Cycle WSNs

With the rapid development of wireless sensor networks (WSNs), higher requirements are raised for the flexibility and applicability of data collection algorithms. Therefore, a novel distributed algorithm for data collection in adaptive duty-cycle WSNs is proposed in this article. On the one hand, the priority in data transmission is considered, in which the queues are used to realize the data storage and data transmission with different priorities and then a scheme for selecting the optimal transmission path for the sensor nodes is developed to ensure that high-priority data can be transmitted more efficiently. Next, a method that can adaptively adjust the duty cycle of the sensor nodes is designed to reduce the transmission delay, where each sensor can update its own duty cycle according to the amount of data with different priorities. On the other hand, the relation between the objective function and the variables in the data collection problem may be difficult to establish accurately or even unknown due to the complexity of WSNs, while the most existing data collection algorithms require the objective function to be explicitly expressed, making them inapplicable to the above scenarios. Hence, the data collection problem with unknown objective function in adaptive duty-cycle WSNs is also investigated in this article. Since the objective function cannot be established, a distributed optimization algorithm with randomized gradient-free is developed to solve that problem. In particular, the proposed algorithm solely relies on the row-stochastic weight matrix, where each sensor needs to communicate only with its neighbors to make decision and locally allocates the information weight of its neighbors, making the algorithm easier to implement. Under some reasonable assumptions, the explicit analysis for the convergence of the proposed algorithm is provided. Finally, the convergence and effectiveness of the proposed algorithm are substantiated through numerical simulations.

Machine Learning Prediction Based Adaptive Duty Cycle MAC Protocol for Solar Energy Harvesting Wireless Sensor Networks

The dynamic nature of energy harvesting rate, arising because of ever changing weather conditions, raises new concerns in energy harvesting based wireless sensor networks (EH-WSNs). Therefore, this drives the development of energy aware EH solutions. Formerly, many Medium Access Control (MAC) protocols have been developed for EH-WSNs. However, optimizing MAC protocol performance by incorporating predicted future energy intake is relatively new in EH-WSNs. Furthermore, existing MAC protocols do not fully harness the high harvested energy to perform aggressively despite the availability of sufficient energy resources. Therefore, a prediction-based adaptive duty cycle (PADC) MAC protocol has been proposed, called PADC-MAC, that incorporates current and future harvested energy information using the mathematical formulation to improve network performance. Furthermore, a machine learning model, namely nonlinear autoregressive (NAR) neural network, is employed that achieves good prediction accuracy under dynamic harvesting scenarios. As a result, it enables the receiver node to perform aggressively better when there is sufficient inflow of incoming harvesting energy. In addition, PADC-MAC uses a self-adaptation technique that reduces energy consumption. The performance of PADC-MAC is evaluated using GreenCastalia in terms of packet delay, network throughput, packet delivery ratio, energy consumption per bit, receiver energy consumption, and total network energy consumption using realistic harvesting data for 96 consecutive hours under dynamic solar harvesting conditions. The simulation results show that PADC-MAC provides lower average packet delay of the highest priority packets and all packets, energy consumption per bit, and total energy consumption by more than 10.7%, 7.8%, 81%, and 76.4%, respectively when compared to three state-of-the-art protocols for EH-WSNs.

A hybrid approach for energy‐efficient routing in IoT using duty cycling and improved ant colony

AbstractThe promising technology of IoT in the fourth industrial revolution connects everything to the Internet in the digital world of the current era. Though the gigantic connectivity among things through the blend of multifarious technologies offers potential opportunities, it also increases the overhead to optimise the energy consumption in IoT networks. Energy optimisation has become the major concern in the IoT realm because of the continuous sensing of the constrained sensor nodes and data transmission to longer distances. The state‐of‐the‐art studies do not take into account the management of duty‐cycling and the process of efficient route discovery hand in hand. Also, the prevalent studies focus on the management of the sensor node's duty cycling (DC) solely instead of adaptive DC scheduling of the communication unit of the sensor node that consumes more energy during transmission. Therefore, keeping in view the existing tribulations regarding energy consumption, the authors attempt to devise an energy‐efficient routing approach using On‐Demand Duty Cycling and Ant‐Colony optimisation (DC‐ACO) for IoT. The ACO‐based routing approach is applicable in IoT networks because the ants’ environment is conceptualised as a distributed set of interconnected graph nodes. The proposed approach poses the empirical notion to manage the energy consumption of the IoT network by considering the key performance indicators (KPI) like energy consumption, packet delivery rate, average residual energy, mobility factor, distance, throughput, and network lifespan to accomplish the tangible outputs. The proposed approach is modelled using Data flow Diagrams (DFDs) and algorithms supported by results. Experimental results show significant improvement in relative throughput, network lifetime, and energy efficiency by 45%, 78%, and 68%, respectively, after simulating for successive iterations.

EICO: Energy-Harvesting Long-Range Environmental Sensor Nodes With Energy-Information Dynamic Co-Optimization

Intensive research on energy-harvested sensor nodes has been driven by the challenges in achieving stringent design goals of battery lifetime, information accuracy, transmission distance, and cost. This challenge is further amplified by the inherent power-intensive nature of long-range communication when sensor networks are required to span vast areas, such as agricultural fields and remote terrain. Solar power is a common energy source in wireless sensor nodes, however, it is not reliable due to fluctuations in available power stemming from the changing seasons and weather conditions. This article tackles these issues by presenting a perpetually powered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long-range communication by dynamically co-optimizing energy consumption and information transfer, termed as energy-information dynamic co-optimization (EICO). This energy-information intelligence is achieved by adaptive dutycycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing in-sensor analytics (ISA) to minimize loss of information. This is the first reported sensor node < 35 cm2 in dimension, which is capable of long-range communication over >1 km at continuous information transfer rates of up to 1 packet/second which is enabled by EICO and ISA.

Optimized maximum power point tracker for partially shaded PV system: adaptive duty cycle control

Optimized maximum power point tracker for partially shaded PV system: adaptive duty cycle control

A Simple Modulation Strategy for Full ZVS of Single-Stage Electrolytic Capacitor-Less EV Charger With Universal Input

The single-stage electrolytic capacitor-less on-board charger (OBC) is attracting attention with the possibility of achieving high efficiency, high power density, and low component count. However, under universal input and wide battery voltage, the single-stage OBC has a difficulty in achieving ZVS turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> while maintaining unity power factor. In order to achieve ZVS under wide voltage range, the conventional single-stage OBC uses multiple control variables including switching frequency where the switching frequency usually varies widely, which complicates the converter design and optimization. In this regard, a totem-pole based single-stage electrolytic capacitor-less OBC was introduced in [36]. It uses one control variable and fixed switching frequency. However, this topology is not able to achieve full ZVS under wide voltage range and has high grid current total harmonic distortion (THD). This article proposes a simple modulation strategy that uses an adaptive duty-cycle and phase-shift modulation to achieve full ZVS under universal input and wide battery voltage. In addition, the fourth-harmonic injection method greatly reduces the grid current THD. The effectiveness of the proposed modulation was validated on a 3.84-kW prototype with 100–240 V ac input and 460–800 V battery voltage. The efficiency is also increased up to 2.3%p under low-voltage grid compared to the previous work.

Low-Delay Opportunistic Routing with Reducing Overhead in Asynchronous Duty-Cycled Wireless Sensor Networks

The Internet of Things (IoT) and cloud computing are well developed and applied in different services. Among these services, some of them take latency and overhead as the metrics to evaluate the quality of services. This paper introduces how an opportunistic routing (OR) protocol reduces latency and overhead when it is applied in wireless sensor network (WSN) application among lightweight devices. OR mitigates the delay problem and balances the energy consumption and delay of the nodes in WSNs. However, OR, with the nature of broadcasting, can easily cause heavy overhead such as redundant data and control packets in the forwarding process. In the current solution, the number of candidates (potential forwarders) is not limited by the dynamics of forwarding process. Also, the overhead of establishing the candidate sets is high due to global information calculation. Therefore, a low-delay OR protocol with reducing overhead which designs the dynamic candidate area (DCA) to establish candidate sets is first proposed in this paper. The main work is as follows. (a) The duty cycle of a node is adjusted according to the distance between the node and sink node in the initialized network. (b) A method to establish dynamic candidate sets is proposed based on the adaptive duty cycle. Before and during the forwarding process, the dynamic candidate area is adjusted in time. (c) According to the feature of the candidate area, the corresponding routing metric is proposed to complete the cooperative communication among candidates. Through further theoretical analysis and simulations, the results indicate that this protocol achieves better performance in terms of transmission delay, data and control overhead, and network lifetime compared to the state-of-the-art solutions.

Data Fairness Transmission and Adaptive Duty Cycle through Machine Learning in wireless Sensor Networks

In this paper, we propose the data fairness transmission and adaptive duty cycle through machine learning in wireless sensor networks. The mechanism of this paper is mainly composed of two parts. The proposed mechanism is based on the sleep-wake structure, which is one of the methods to increase the lifespan of the entire network by efficiently using the energy of the nodes. The first is a mechanism to support priority and data fairness. To this end, data input to the node is divided into priority classes according to transmission urgency and stored. Introduces the concept of cross-layer to rearrange data destined for the same destination. In addition, we propose a fair data transmission mechanism that allows even low-priority data to participate in transmission after a certain period. The second is an adaptive duty cycle mechanism through machine learning. For this purpose, public data related to forest fires are collected. The collected data is refined into data for each forest fire location and data for each forest fire time. For the refined data, an SVM (Support Vector Machine) model of supervised learning is used for machine learning, and a mechanism for adaptively adjusting the duty cycle of each node through the trained model is proposed. The computer language used for machine learning is Python language, and Google's Psychic Learn is used for the machine learning library. It was compared with the existing MAC protocol for evaluation, and it was confirmed that excellent energy efficiency results were obtained.

Time-Multiplexed PWM LED Driver With Grayscale Enhancement Techniques for Signage Display

In this article, a pulsewidth modulation (PWM) LED driver is proposed to overcome the challenges of time-multiplexed driving and to enhance the quality of displays sufficient for signage applications. First, adaptive duty cycle restoration (ADCR) compensates for the brightness degradation in low grayscale, where a simple analog timer circuit measures the limited charging time and compensates its effect to ensure the desired emission time. Second, amplitude-segmented PWM (ASPWM) improves the grayscale resolution without increasing the grayscale clock frequency; thus, the electromagnetic interference performance is not sacrificed. The proposed LED driver is fabricated in a 0.18-μm Bipolar-CMOS-DMOS (BCD) process. The measurement results show that the driver Integrated Circuit (IC) achieves a duty cycle restoration factor (DCRF) greater than 0.95 out of ten samples, whereas the DCRF is limited to 0.67 on average when the ADCR scheme is disabled. Resolution enhancement with the proposed ASPWM is verified, achieving 16-b grayscale resolution. The prototype driver IC occupies 3.56 × 3.00 mm of chip area, operated from a 4.5 V supply voltage.

An enhanced adaptive duty cycle scheme for energy efficiency and QoS awareness in wireless sensor networks

A Wireless Sensor Network (WSN) is a small device that has batteries and radios to connect to the internet. The main problem with WSNs is a limited energy source, energy studies needed to ensure that these sensor nodes can last longer, especially since a lot of energy is wasted during idle listening, overhearing, and data collision that occurs at the medium access control (MAC) layer. The common mechanism used for saving energy in WSN, specifically at the MAC layer is the duty cycle schedule. Duty cycling coordinates sleep-wake time sensor nodes to maximize network lifetime while achieving specific application goals such as high throughput or low latency. Duty cycling of every node should be adjusted separately at any runtime depending on the network conditions to achieve desired delay guarantees and energy efficiency. Recently, a few adaptive duty cycle schemes were introduced, these schemes have reduced energy consumption by some degree, this leaves an open end to the degradation of the quality of service. In this study, adaptive duty cycles enhanced with a priority queue where packet size is the parameter to adjust the duty cycle in order to get efficient energy consumption. A variant of packet size tested to ensure optimum quality of services (QoS). These factors determine the duration of a node’s listen period for various packet transmission scenarios and requirements. As the result, the proposed Enhanced S-MAC (ESMAC) shows an improvement in the energy consumption and QoS compared to the default MAC protocol and S-MAC protocol. The success of this project will contribute to the performance improvement of sensing devices.

PPAASS: Practical Power-Aware Duty Cycle Algorithm for Solar Energy Harvesting Sensors

Energy-harvesting sensor networks promise unlimited operation throughout time, benefiting ongoing monitoring applications. However, most energy sources, such as sunlight, vary over time and do not guarantee constant power delivery to a device. In addition, these devices lack a battery with the necessary capacity to store enough energy to operate when harvesting is impossible. Thus, managing energy consumption becomes essential for any device to function correctly. This article proposes PPAASS (Practical Power-Aware Algorithm for Solar Sensors), a novel algorithm that allows devices to change their duty cycle to maximize the use of harvested energy based on the device’s battery backup level and solar irradiance predictions. The algorithm was evaluated through simulations performed in Python and with different solar irradiance conditions depending on the season of the year. PPAASS shows a higher average duty cycle than other algorithms in the literature while maintaining a few changes in the duty cycle, which makes it ideal for constant monitoring applications. Furthermore, the implementation of the algorithm shows that the real-time duty cycle adaptation allows a device to react quickly to energy harvesting prediction failures and take full advantage of all the harvested energy. It will enable the device to avoid running out of power while preventing the battery from recharging up to its maximum capacity so it can always store harvested energy.

ADCC: An effective adaptive duty cycle control scheme for real time big data in Green IoT

Currently, large number of devices have been connected to the Internet of Things (IoT). Ubiquitous IoT devices encounter emergencies and generate much data which may lead to congestion and urgency to be processed timely. To alleviate it, many approaches have been proposed and Adaptive Duty Cycle Control (ADCC) scheme is an effective one. The main contributions of this paper are as follows: (a) Unlike previous studies that mostly the efficiency of congestion control and real-time processing is experiment-oriented, specially, this paper theoretically gives a targeted optimization of duty cycle ratio, which can effectively guide the design of ADCC scheme for real time big data. (b) A general design principle is proposed to guide the scheme designing in order to improve the efficiency and performance of networks. (c) A comprehensive congestion avoidance and real-time processing scheme combining dynamic duty cycle adjustment and full utilization of residual energy is proposed. Thus, these ideas can meet the concept of Green IoT. Through our extensively theoretical and experimental analysis, the principle can effectively guide the design of ADCC scheme, and can reduce the delay, data drop ratio by 20.95% − 77.85% and 29.63% − 100% respectively, without affecting network lifetime, compared with previous scheme.