Duty Cycle Scheme Research Articles

Energy Efficient Priority-Based Hybrid MAC Protocol for IoT-Enabled WBAN Systems

Among the wireless body area network (WBAN) scarce resources, energy resource is an essential resource on which most of the WBAN biomedical devices activities depend upon. The biomedical devices are usually battery-powered and if they fail to operate as required because of battery power drain, the WBAN system would become unreliable, and this could lead to life-threatening situations. Consequently, it would be of advantage and logical to minimize energy consumption and energy wastage issues to achieve an energy efficient WBAN system. Following this, we proposed a coordinated superframe duty cycle hybrid MAC (SDC-HYMAC) protocol to enhance energy efficiency and to prolong the biomedical devices lifetime. To improve the energy efficiency of the WBAN system, we introduced different energy resource management strategies including the design of a priority-based slot-allocation scheme to minimize timeslot and energy wastage. Also, we proposed a coordinated superframe duty cycle (SDC) scheme to accurately select an appropriate superframe order (SO) based on the traffic information and the priority level of the biomedical devices to save energy and prolong the devices lifetime. We compared the SDC-HYMAC protocol with other related protocols like MG-HYMAC, HyMAC, and CPMAC for the sake of validation, and is simulated in MATLAB. The outcome of the simulation results revealed that the SDC-HYMAC protocol performed better than the existing protocols using performance metrics like convergence speed, energy efficiency, delay, packet drop ratio, and devices lifetime.

Controlling Action Space of Reinforcement-Learning-Based Energy Management in Batteryless Applications

Duty cycle management is critical for energy-neutral operation of batteryless devices. Many efforts have been made to develop an effective duty cycling method, including machine learning-based approaches, but existing methods can barely handle the dynamic harvesting environments of batteryless devices. Specifically, most machine learning-based methods require the harvesting patterns to be collected in advance, as well as manual configuration of the duty-cycle boundaries. In this paper, we propose a configuration-free duty cycling scheme for batteryless devices, called CTRL, with which energy harvesting nodes tune the duty cycle themselves adapting to the surrounding environment without user intervention. This approach combines reinforcement learning (RL) with a control system to allow the learning algorithm to explore all possible search space automatically. The learning algorithm sets the target state of charge (SoC) of the energy storage, instead of explicitly setting the target task frequency at a given time. The control system then satisfies the target SoC by controlling the duty cycle. An evaluation based on real implementation of the system using publicly available trace data shows that CTRL outperforms state-of-the-art approaches, resulting in 40% less frequent power failures in energy-scarce environments, while achieving more than ten times the task frequency in energy-rich environments.

Evaluation of Current-Mode Controller for Active Battery Cells Balancing With Peak Efficiency Operation

This article presents a current-mode controller for switched-inductor topology to achieve voltage balancing between battery cells or modules in a battery pack while operating at peak efficiency. The presented method yields several advantages and/or eliminates several drawbacks of the existing conventional fixed duty cycle scheme. Among these advantages: 1) the balancing current is well controlled instead of being a function of battery cells’ voltages and system parasitic elements, which might result in exceeding the current ratings of the components, and 2) the balancing current reference is selected such that the balancing circuit operates at its maximum efficiency point and, therefore, yielding lower balancing power losses. This article also presents a battery balancing algorithm for utilization with the presented controller that achieves open circuit voltage balancing, which is a more accurate indicator of state-of-charge compared to terminal voltage. Experimental results obtained from a laboratory prototype of a three-cell battery pack are presented to evaluate the presented current control and balancing method under different modes of operation.

Reinforcement learning based energy-neutral operation for hybrid EH powered TBAN

The aging population, outbreak of new infectious diseases and shortage of medical resources promote rapid development of telemedicine. Wireless textile body area network (TBAN), which combines functional textile and wireless body area network (WBAN), is gaining great attention as an efficient medium of remote medical care. This is because of its unique materials and application scenario, as well as its convenience and friendliness to the elderly. Moreover, it is an effective application for integrating edge computing with next generation of wearable technology. Nonetheless, it is unavoidable that TBAN has to deal with reliability and energy issues. Given these deficiencies and challenges, this paper focuses on the feasibility of achieving wearable energy neutral operation (ENO) in TBAN while maintaining robustness. In addition to adding user posture factors regarding network specifics, we combine hybrid energy harvesting (EH) techniques and duty cycle schemes. A hybrid radio frequency (RF) energy and Triboelectric nanogenerator (TENG) EH-assisted TBAN system is built in this work. We analyze and discuss the delay, data rate and packet error rate (PER) under five typical daily activities (standing, sitting, lying, walking, and running). To optimize the ENO problem, two reinforcement learning (Q-learning and Deep Q-Network (DQN)) based algorithms are proposed. According to numerical results, both algorithms ultimately lead to stable power levels compared to the continuous decline of battery power without optimization. DQN-based optimization performs better than Q-Learning. For instance, 14% and 56% improvements in PER and battery power, respectively.

Design of boundary pulse width modulation controller for a class of heat equations

This paper studies the asymptotic stability of a single heat equation and the synchronization of a class of networked heat equations under boundary pulse width modulation (PWM) control inputs. Firstly, two duty cycle schemes are modulated according to the whole state and the boundary output state to realize the asymptotic stability of a single heat equation. During each duty cycle, the amplitude of the pulse is fixed. Secondly, assuming that the networked heat equations are connected through undirected strongly connected topology, a boundary PWM controller for each subsystem is constructed based on boundary output state at each sampling instant and consensus protocols,which provides an alternative digital implementation strategy for the consensus control of networked heat equations. In addition, the effectiveness of the method is verified by numerical simulation.

Modeling of Intelligent Sensor Duty Cycling for Smart Home Automation

The advancement of wireless sensor networks (WSNs) improves various smart home automation services and home users’ living standards. However, efficiently collecting data and automating smart home services require the extensive deployment of the sensors. Thus, one of the crucial and challenging tasks is to minimize the sensors’ energy consumption for monitoring and automating various activities in a smart home. In this article, we present a solution to control the excessive energy consumption of sensors used to detect various activities of daily living (ADL) of a smart home resident. The sensors within a smart home network are divided into various groups employing the recurrent neural network (RNN) and dynamic time warping (DTW) techniques to predict the activities with high accuracy and less energy consumption. The smart home users’ future activities are forecast with bidirectional long short-term memory (BLSTM) RNN model to select those sensors that are likely to predict the upcoming activities. Similarly, to predict the home users’ unusual activities, a guard sensor is elected among sensors with high similarities with each other using DTW. The sensor’s role is evenly switched between different modes to maintain a fair tradeoff between energy and accuracy. An extensive set of simulations is performed to validate the proposed scheme’s work integrating datasets from authentic sources. Finally, the proposed system significantly reduces the sensors’ energy consumption and prolongs the battery lifetime to approximately 137 days. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article presented an energy-efficient duty-cycling scheme for automating sensors’ operations in a smart home scenario. The traditional duty-cycling schemes mainly provide solutions based on appointing sentries or predicting smart home users’ next activity using models, such as Bayesian networks. We design a system that integrates the advantages of both sentry and prediction-based schemes to reduce the amount of energy required by sensors to detect and automate smart home users’ activities with high accuracy and precision. The active sensors are appointed using a bidirectional long short-term memory recurrent neural network. Similarly, the guard sensors are assigned to detect unusual activities using the similarities among idle sensors. This study could be used to automate the smart home sensors for detecting home user’s activities with less energy, which ultimately prolongs the battery lifetime of the sensors.

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.

DADC: A Novel Duty-cycling Scheme for IEEE 802.15.4 Cluster-tree-based IoT Applications

The IEEE 802.15.4 standard is one of the widely adopted specifications for realizing different applications of the Internet of Things. It defines several physical layer options and Medium Access Control (MAC) sub-layer for devices with low-power operating at low data rates. As devices implementing this standard are primarily battery-powered, minimizing their power consumption is a significant concern. Duty-cycling is one such power conserving mechanism that allows a device to schedule its active and inactive radio periods effectively, thus preventing energy drain due to idle listening. The standard specifies two parameters, beacon order and superframe order, which define the active and inactive period of a device. However, it does not specify a duty-cycling scheme to adapt these parameters for varying network conditions. Existing works in this direction are either based on superframe occupation ratio or buffer/queue length of devices. In this article, the particular limitations of both the approaches mentioned above are presented. Later, a novel duty-cycling mechanism based on MAC parameters is proposed. Also, we analyze the role of synchronization schemes in achieving efficient duty-cycles in synchronized cluster-tree network topologies. A Markov model has also been developed for the MAC protocol to estimate the delay and energy consumption during frame transmission.

A Low-latency Strategy with dynamic Duty-Cycle in Opportunistic Mobile Crowdsensing Scenario

In Mobile Crowdsensing Scenario (MCS), most of the mobile devices transfer data to each other relay on encounter opportunities. MCS energy consumption and latency are the key indicators of networks under the application scenarios. On one hand, the neighbor idle scanning and listening mechanism of mobile devices usually consumes the energy that could be saved. Therefore, keeping devices to work in a low duty cycle can avoid this part of energy waste effectively, but it will bring serious network latency. Aim to this, the duty cycle strategy with a lower latency strategy is focused to discuss in this paper. A method, named Low latency Duty Cycle (DC) with MSFO, is proposed to reduce network latency which mainly compares the size of data packets to be transmitted by the device. Besides, small data packets have priority in the transmission queue for enhanced network performances. Extensive simulation results show that the proposed method can significantly reduce network latency in terms of MCS with a duty cycle strategy.

Tailoring the chemical composition and microstructure of CrxN deposited by HiPIMS through duty-cycle modifications

The development of homogeneous, dense, and smooth hard coatings is a challenging task, and that is why the high power impulse magnetron sputtering (HiPIMS) method has been proposed to tailor and control the microstructure through the modification of the duty cycle. HiPIMS offers extensive possibilities to obtain good quality films with fine and dense morphology. In this work, the synthesis of Cr x N coatings by HiPIMS with the variation of duty cycle (τ = 2.7% and 12%), substrate bias voltages (V b = −150 and − 250 V), and gas ratio ( f N 2/ Ar = 0.2 and 0.4) was evaluated. The influence of these processing conditions on the chemical composition, microstructure, and adhesion of HiPIMS Cr x N was investigated. A significant increment of the deposition rate and the chromium content in the Cr x N coatings is observed at τ = 12%; meanwhile, at τ = 2.7%, films showed high nitrogen content. The crystalline phases like α-Cr + h-Cr 2 N, h-Cr 2 N, and h-Cr 2 N + c-CrN were identified in the as-deposited films. The Cr-rich coatings presented faceted columns with cauliflower-like surface morphology , while the growth of the h-Cr 2 N phase caused a grain size refinement and a denser microstructure. The surface morphology of the h-Cr 2 N coatings changed from pyramidal to stacked pyramids with the reduction of the duty cycle. The transformation of the h-Cr 2 N to c-CrN leads to highly dense columnar microstructures with a fine granular morphology and a smooth surface. The duty cycle strategies can be applied to develop a particular microstructure with specific chemical composition and crystallographic phases, resulting in an alternative method to produce graded or multilayer systems. The duty cycle reduction increases the interfacial adhesion strength of the coatings. • Duty cycle modifies composition, phases, microstructure, and morphology. • Higher duty cycle enhances target current and coating deposition rates. • Duty cycle strategy is a good option to deposit graded or multilayer systems.

Small-Signal Models of Resonant Converter With Consideration of Different Duty-Cycle Control Schemes

Duty-cycle control is a widely used control method of resonant converters. In this article, the existing model of duty-cycle controlled resonant converters is found to have discrepancy compared with the simulation and experimental results. The reason behind is investigated, showing the phase change in the output voltage of the inverter bridge caused by the duty-cycle perturbation was ignored in the existing model. The phase change law is highly dependent on the duty-cycle scheme used, which is further determined by the forms of the carriers and the switching sequences of the resonant converters. With this consideration, improved models of both full-bridge and half-bridge resonant converters with different duty-cycle control schemes are rederived. It is revealed that different duty-cycle control schemes lead to different phase curves of the duty-cycle-to-output-voltage transfer functions, therefore care should be taken when selecting the appropriate duty-cycle control scheme. In the end, a series-series compensated wireless power transfer system is built to verify the validity of the proposed models.

Event Driven Duty Cycling with Reinforcement Learning and Monte Carlo Technique for Wireless Network

Reducing transmission delay and maximizing the network lifetime are important issues for wireless sensor networks (WSN). The existing approaches commonly let the nodes periodically sleep to minimize energy consumption, which adversely increases packet forwarding latency. In this study, a novel scheme is proposed, which effectively determines the duty cycle of the nodes and packet forwarding path according to the network condition by employing the event-based mechanism and reinforcement learning technique. This allows low-latency energy-efficient scheduling and reduces the transmission collision between the nodes on the path. The Monte Carlo evaluation method is also adopted to minimize the overhead of the computation of each node in making the decision. Computer simulation reveals that the proposed scheme significantly improves end-to-end latency, waiting time, packet delivery ratio, and energy efficiency compared to the existing schemes including S-MAC and event-driven adaptive duty cycling scheme.

Dual sensing scheduling algorithm for WSN based road network surveillance

In this paper, a dual sensing scheduling algorithm is proposed which is a modified version of VISA technique for sensing scheduling in road networks where targets can enter from both sides of the road. VISA and similar algorithms are based on the idea of designated entrance points and protection points and are very suitable for military scenarios. In comparison, civilian applications mostly use two-way roads and dual carriageways with entrance points on both ends of the roads calling for a modification of the VISA technique to make it suitable for two-way detection. The proposed algorithm achieves detection on a two-way road by using two parallel scan waves originating from the midpoint sensor on the road segment but in opposite directions. The proposed modification of the VISA algorithm improves the detection time by reducing it to half as compared to VISA but at the cost of decreased network lifetime. The proposed algorithm is also compared to Duty Cycling and Always-Awake schemes. In addition, a very simple hole handling method is proposed which applies a comprehensive labeling scheme on all the sensors of the road network represented as vertices in an undirected graph of road network.

WITHDRAWN: Power factor correction controller with Buck Boost converter for fast charging of electrical vehicles

The Buck/Boost Regulator has been implemented to maintain the power factor efficiently for battery charging electric vehicles has been described. The converter operate in buck or boost modes with respect to the voltage from the rectifier at load. An effective frequency with shaping of current control without disturbing power factor near to unity has been proposed. The paper also focuses on the harmonic compensation from the grid as per the standards. The aim is to maintain the power factor with the harmonic distortion in the control circuit with buck/boost mechanism with flexible duty cycle scheme. Control should not be complex even with better performance which is been well suited for open as well as feedback schemes of Matlab Simulink platform.

Wireless Sensor Network Nodal Development for Low Cost and Low Power PH Soil Monitoring Agriculture 4.0 Application

According to a report from the United Nations estimates that by 2030 about 800 million people worldwide will suffer from hunger. To combat this problem the agricultural production needs to be increased by at least 60%. One of many parameters that need to be monitor to increase agricultural output was soil PH level. Soil PH is very influential on the development of rice plants, where high acidity soil will make plant unhealthy and die. In order to be able monitor soil PH level, we need low cost and long life WSN node that can monitor soil PH level. In this paper we have manage to a long life WSN node that can monitor PH level with low cost at about less than 15 US dollar. this WSN node PH meter are using couple of aluminum and lead couple electrode as sensor, and its deviation at about 0.73 compare with ATC 009 PH meter which only 0.55. This device also capable to operate for so long (which is 2 years operational time) using 0.1 duty cycle scheme.

Resource Allocation for 5G-Enabled Vehicular Networks in Unlicensed Frequency Bands

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs.

Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning.

A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion. Therefore, to address these challenges, we propose an Energy and Event Aware-Sensor Duty Cycling scheme. The proposed model predicts the future expected event using the Bi-Directional Long-Short Term Memory model and allocates Predictive Sensors to the predicted event. To detect the unexpected events, the proposed model localizes a Monitor Sensor within a cluster of Hibernate Sensors using the Jaccard Similarity Index. Finally, we optimize the performance of our proposed scheme by employing the Q-Learning algorithm to track the missed or undetected events. The simulation is executed against the conventional Machine Learning algorithms for the sensor duty cycle, scheduling to reduce the sensor energy consumption and improve the activity detection accuracy. The experimental evaluation of our proposed scheme shows significant improvement in activity detection accuracy from 94.12% to 96.12%. Besides, the effective rotation of the Monitor Sensor significantly improves the energy consumption of each sensor with the entire network lifetime.

Reinforcement Learning based Node Sleep or Wake-up Time Scheduling Algorithm for Wireless Sensor Network

A wireless sensor network is a collection of small sensor nodes that have limited energy and are usually not rechargeable. Because of this, the lifetime of wireless sensor networks has always been a challenging area. One of the basic problems of the network has been the ability of the nodes to effectively schedule the sleep and wake-up time to overcome this problem. The motivation behind node sleep or wake-up time scheduling is to take care of nodes in sleep mode for as long as possible (without losing data packet transfer efficiency) and thus extend their useful life. This research going to propose scheduling of nodes sleeps and wake-up time through reinforcement learning. This research is not based on the nodes' duty cycle strategy (which creates a compromise between data packet delivery and nodes energy saving delay) like other existing researches. It is based on the research of reinforcement learning which gives independence to each node to choose its own activity from the transmission of packets, tuning or sleep node in each time band which works in a decentralized way. The simulation results show the qualified performance of the proposed algorithm under different conditions.

DETERMINING NODE DENSITY TO GUARANTEE AVERAGE LATENCY IN DUTY-CYCLED WIRELESS SENSOR NETWORKS

Some applications of Internet of Things (IoTs) require that generated data are delivered to the sink within an end-to-end (E2E) delay bound. Moreover, to save energy consumption or to lengthen the network lifetime, a duty cycle scheme is applied in which sensor nodes periodically switch the radio on and off. In the duty-cycled networks, packet delay is greatly affected by node deployment, therefore we aim to determine node density in a wireless sensor network in order to guarantee the average E2E delay given network parameters using the probability theory. Simulation results show that the proposed node deployment method consumes less energy while requiring a smaller number of sensor nodes than those of other existing works.

Queue and Priority-Aware Adaptive Duty Cycle Scheme for Energy Efficient Wireless Sensor Networks

In Wireless Sensor Networks (WSN), energy efficiency is a fundamental issue that requires attention in the design of communication protocols. Energy waste occurs as a result of collision and idle listening. The widely used mechanism for energy saving in WSN is duty cycling. Duty cycling coordinates the sleep/wake-up time of sensor nodes to maximize the network lifetime while achieving specific application goals such as high throughput or low latency. Most existing works focused more on static duty cycle, which cannot guarantee the desired end-to-end delay at varying network conditions. In applications with specified delay requirements, the duty cycle of every node should be adjusted separately at runtime depending on the network conditions to achieve the desired delay and energy efficiency. In this paper, we present an Energy Efficient and QoS-aware (EEQ) MAC protocol with a duty cycle scheme that adapts the node's duty cycle to the queue size and priority class of a packet to reduce the delay of high priority packets and support time-bounded delivery of priority packets. By checking the queue size and the priority class of packets in the message queue of each node, the node determines whether or not to adjust its duty cycle. In this approach, a node increases the length of its active period in the event of high traffic which provides less waiting time for the packets in the queue. The sender node informs the receiver the duration for which it has to stay awake at the beginning of data transmission, both the sending and the receiver's duty cycle is controlled based on the queue length and the priority of the packets. This approach saves energy and lessen packet latency. Finally, extensive simulation experiments were conducted to evaluate its energy performance within different network topologies. Comparisons with the existing energy-aware MAC protocol verified the effect of EEQ on improving the energy efficiency and extending the lifespan of WSNs.