Battery Life Of Devices Research Articles

Energy-Efficient Handover Algorithm for Sustainable Mobile Networks: Balancing Connectivity and Power Consumption

In the era of pervasive mobile and heterogeneous networks, maintaining seamless connectivity during handover events while minimizing energy consumption is paramount. Traditional handover mechanisms prioritize metrics such as signal strength, user mobility, and network load, often neglecting the critical aspect of energy consumption. This study presents a novel approach to handover decision-making in mobile networks by incorporating energy-related metrics, such as battery level, energy consumption rate, and environmental context, to make informed handover decisions that balance connectivity quality and energy efficiency. Unlike traditional methods that primarily focus on signal strength and network load, our approach addresses the critical need for energy efficiency, particularly in high-mobility scenarios. This innovative framework not only enhances connectivity but also significantly improves power consumption management, offering a more sustainable solution for modern mobile networks. Through extensive simulations, we demonstrate the effectiveness of our proposed solution in reducing energy usage without compromising network performance. The results reveal significant improvements in energy savings for mobile devices, especially under high-mobility scenarios and varying network conditions. By prioritizing energy-efficient handovers, our approach not only extends the battery life of mobile devices but also contributes to the overall sustainability of mobile networks. This paper underscores the importance of incorporating energy metrics into handover decisions and sets the stage for future research in energy-aware network management.

Integrating Cloud Services with Mobile Applications for Seamless User Experience

In the modern digital era, the integration of cloud services with mobile applications has emerged as a pivotal strategy for delivering a seamless and dynamic user experience. This approach not only enhances the functionality of mobile applications but also ensures that users can access services and data in real-time, regardless of their location. As mobile devices continue to dominate the technology landscape, the demand for responsive, scalable, and user-centric applications has skyrocketed. Cloud services, with their inherent capabilities such as storage, computing power, and advanced analytics, provide the backbone for developing applications that meet these demands. The integration of cloud services with mobile applications offers numerous advantages. It allows for the offloading of heavy computational tasks to cloud servers, thereby preserving the limited processing power and battery life of mobile devices. This capability enables mobile applications to perform complex tasks such as real-time data processing, machine learning inference, and multimedia content rendering without compromising user experience. Additionally, cloud services facilitate the synchronization of data across multiple devices, ensuring that users have consistent access to their data and settings regardless of the device they are using. This is particularly important in scenarios where users switch between devices, such as moving from a smartphone to a tablet or laptop. Moreover, the use of cloud services in mobile applications enhances security and reliability. Cloud providers typically offer robust security features, including data encryption, identity and access management, and continuous monitoring, which are essential for protecting sensitive user data. By leveraging these features, mobile applications can offer a higher level of security than would be feasible with on-device storage and processing. Furthermore, the reliability of cloud services ensures that applications remain available and functional even during peak usage times or when the user is in a location with poor network connectivity. Through techniques such as caching and offline access, cloud-integrated mobile applications can continue to operate smoothly even when real-time access to cloud resources is temporarily unavailable.

Optimization of wind farm energy supply system costs through machine learning

At present, the energy crisis is becoming increasingly severe, and changing the energy structure and developing clean energy have gradually become the consensus of the world. Establishing a clean energy power supply system, using clean energy for power generation, and investing in real power supply are currently unresolved challenges. Based the current high cost of new energy power supply systems, this project proposes an optimization plan based on the wind power supply system, targeting the capacity of lithium battery energy storage equipment, and constructs an optimization model for wind power supply in electric vehicle systems. In addition, in order to use lithium-ion battery devices more efficiently and ensure the evaluation of the efficiency and battery life of lithium-ion battery energy storage devices, we predicted the cyclic aging phenomenon of lithium-ion battery energy storage devices through machine learning technology. This prediction can provide suggestions and references for the use of this lithium battery.

The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

In this paper, the task processing approach at the edge of the Internet of Things (IoT) network in improving energy consumption was investigated. First, the subject of maximizing the use of energy is defined as the main problem and then based on the state of assignment of task processing from end devices at the edge of the network to smart gateways and local servers at the edge using possible low bandwidth and low battery consumption, the problem is divided into two subject which are obtained independently. After modeling by recombining these two sub-problems, the subject of maximizing the utility gain and battery life of the end devices is solved despite the limitations of processing time and energy resources.Considering that the environment of the problem and how the terminal devices are connected with the edge devices are unknown, a repetitive reinforcement learning algorithm has been used to create an optimal solution to maximize the operational benefit of the energy consumed in edge processing. The results achieved from the simulation show the increase in network load and processing overhead with the increase in the number of active devices. The suggested method, while increasing the edge processing speed, decreases the delay and maximizes the performance gain of the IOT edge system compared to the central cloud system. It should be noted that when the number of active end devices dramatically increases, the energy consumption and bandwidth at the network edge improves, and energy consumption at the network edge decreases lonely by 12.5%.

Enhancing longevity: Sustainable channel modeling for wireless-powered implantable BANs

Wireless body area network (WBAN) has revolutionized the healthcare sector by enabling remote monitoring and control of wearable and implantable devices, providing freedom of mobility to patients. However, wireless channel modeling in BAN is a crucial aspect for designing an efficient off-body, on-body and in-body communication. Due to the unique characteristics of the human body, it aims to characterize the signal propagation through skin, tissues, internal organs and biological fluids of a patient’s body. Moreover, it is important to enhance the battery life of the low-powered devices for a sustainable BAN. In this work, we provide a hybrid communication channel model for wireless power transfer in a BAN including both off-body and in-body communication channels. An indoor room scenario is considered in which a movable patient having an implant inside its body is present along with an RF power source (for example, a Wi-Fi access point) situated in a ceiling corner. Implant is assumed to inhibit energy harvesting capability. For practicability, we have considered the effect of path loss, partition walls, floor attenuation factor along with other important body parameters. Specifically, we aim to statistically characterize this hybrid communication system, for which unique closed-form expressions of the probability distribution functions of the received power have been derived, thereby first calculating the instantaneous power at different layers of human body and then obtaining the closed-form expression for average received power. All the derived mathematical expressions have been verified via numerical simulations. Further, for elongating the lifespan of implants, we investigated the average power harvested by an implant and its power outage probability for analyzing the sustainability of implants. The results are numerically validated, considering different types of indoor room scenarios, in addition to providing key design insights.

Unveiling the future of cardiac care: advances in mechanical circulatory support

Congestive heart failure (CHF) is a multifaceted clinical syndrome characterized by the inability of the heart to pump blood effectively, leading to inadequate oxygen and nutrient delivery to the body tissues. Despite advancements in treatment strategies, including guideline-directed medical treatment (GDMT), end-stage CHF remains a significant cause of morbidity and mortality worldwide. Heart transplantation is considered to be the gold standard treatment of end stage CHF but constrained by the lack of organ donors, lengthening waitlists, and the negative side effects of lifelong immunosuppressive medications. Mechanical circulatory support (MCS) has emerged as a pivotal intervention for patients with end-stage CHF, serving as a bridge to recovery, transplantation, or destination therapy. The aim of this narrative review is to highlight the historical development of MCS, to assess the recent status of MCS device technology and discuss current challenges associated with complications of MCS that need to be solved in the future by device development. The history of MCS dates back to pioneering efforts in the 1960s, with significant progress in device development and utilization over decades. MCS devices, including left ventricular assist devices (LVADs), extracorporeal membrane oxygenation (ECMO), and artificial hearts, play a crucial role in providing circulatory support to patients with end-stage CHF. Recent advancements in MCS technology aim to decrease the device size, enhance blood compatibility, reduce thrombo-embolic complications, and prolong device durability and battery life and improve physiological performance of MCS. Continued research and innovation are essential to address these challenges and improve outcomes in patients with end-stage CHF. Artificial intelligence (AI) has emerged as a valuable tool in cardiovascular medicine to facilitate risk prediction, patient selection, and treatment optimization for MCS and heart transplantation. Despite these advancements, challenges persist in MCS device selection, resource allocation, and integration of AI into clinical practice. Continued research and innovation are essential to address these challenges and improve outcomes in patients with advanced heart failure.

A Programmable Ambient Light Sensor with Dark Current Compensation and Wide Dynamic Range.

Ambient light sensors are becoming increasingly popular due to their effectiveness in extending the battery life of portable electronic devices. However, conventional ambient light sensors are large in area and small in dynamic range, and they do not take into account the effects caused due to a dark current. To address the above problems, a programmable ambient light sensor with dark current compensation and a wide dynamic range is proposed in this paper. The proposed ambient light sensor exhibits a low current power consumption of only 7.7 µA in dark environments, and it operates across a wide voltage range (2-5 V) and temperature range (-40-80 °C). It senses ambient light and provides an output current proportional to the ambient light intensity, with built-in dark current compensation to effectively suppress the effects of a dark current. It provides a wide dynamic range over the entire operating temperature range with three selectable output-current gain modes. The proposed ambient light sensor was designed and fabricated using a 0.18 µm standard CMOS process, and the effective area of the chip is 663 µm × 652 µm. The effectiveness of the circuit was verified through testing, making it highly suitable for portable electronic products and fluorescent fiber-optic temperature sensors.

Resource management for computational offload in MEC networks with energy harvesting and relay assistance

In mobile edge computing (MEC) networks, the integrated application of energy harvesting (EH) and relay cooperation is a promising technology, which ensures the real-time performance of the system and computing power supply. Relay and EH cooperation also expand communication range and extend the battery life of energy-constrained devices. However, in wireless powered and relay-assisted MEC networks, it is challenging to jointly dispatch energy, and computing resources to coordinate heterogeneous performance requirements. To fill this gap, this paper examines the fundamental compromise between utility energy efficiency (UEE) and stable queue length in wireless powered and relay-assisted MEC network systems, and the amount of data transferred for each unit of energy is called UEE. The data queue and energy queue models are introduced and the constraints of stability of two queues in long term are included in the formulated problem. To tackle the fractional and nonconvex optimization problems, the Dinkelsbach method is utilized. Since optimization problems change from time to time and the long-term averaging queue stability is considered, the Lyapunov optimization theory is introduced to convert the non-convex problems into the solvable one. An online resource offloading allocation algorithm is proposed to determine the solution. In addition, the algorithm implements the control parameter V to achieve the compromise between UEE and stable queue length, while the impact of various parameters are also revealed on system performance.

Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries

Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications.

Dynamic Co-Operative Energy-Efficient Routing Algorithm Based on Geographic Information Perception in Opportunistic Mobile Networks

Opportunistic mobile networks, as an important supplement to the traditional communication methods in unique environments, are composed of mobile communication devices. It is a network form that realizes message transmission by using the opportune encounter of these mobile communication devices. Consequently, mobile communication devices necessitate periodic contact detection in order to identify potential communication opportunities, thereby leading to a substantial reduction in the already limited battery life of such devices. Previous studies on opportunistic networks have often utilized geographic information in routing design to enhance message delivery rate. However, the significance of geographic information in energy conservation has been overlooked. Furthermore, previous research on energy-efficient routing has lacked diversification in terms of the methods employed. Therefore, this paper proposes a dynamic co-operative energy-efficient routing algorithm based on geographic information perception (DCEE-GIP) to leverage geographic information to facilitate dynamic co-operation among nodes and optimize node sleep time through probabilistic analysis. The DCEE-GIP routing and other existing algorithms were simulated using opportunistic network environment (ONE) simulation. The results demonstrate that DCEE-GIP effectively extends network service time and successfully delivers the most messages. The service time of DCEE-GIP increased by 8.05∼31.11%, and more messages were delivered by 14.82∼115.9%.

Comparison between Power Dissipation and Propagation Delay on 6T SRAM Cell Design Using GDI Logic with Transmission Gate VMSA and Voltage Divider

The rapid evolution of the semiconductor industry has witnessed shrinking portable and mobile devices alongside an increasing demand for extended battery life. Addressing the critical challenges of speed and battery life in digital devices, this paper investigated the effectiveness of innovative low-power design techniques. Focusing on the Gate Diffusion Input (GDI) approach, a recent advancement in the field, a comprehensive analysis revealed its significant potential for reducing power consumption in digital circuits. Additionally, a comparative analysis was conducted to evaluate the performance of conventional 6T GDI SRAM cells and their Modified 6T GDI SRAM with Voltage Divider, considering the influence of Sense Amplifiers. Simulation data demonstrated that Modified 6T SRAM designs, particularly the Voltage Divider and TGVMSA variants, achieved significantly lower power dissipation and delay despite having a larger cell area. Remarkably, the proposed design substantially improved power dissipation and propagation delay, achieving 1.3 ps, and 889.41mV at 1.8V shows that the suggested design enhances power dissipation and propagation delay. These findings suggest that the proposed design offers a promising strategy for enhancing power efficiency and performance in digital devices, thereby mitigating the limitations of battery life and speed in the modern technological landscape.

Precise realtime current consumption measurement in IoT TestBed.

The Internet of Things, similar to wireless sensor networks, has been integrated into the daily life of almost everyone. These wearable, stationary, or mobile devices are in multiple locations, collecting data or monitoring and executing certain tasks. Some can monitor environmental values and interact with the environment, while others are used for data collection, entertainment, or even lifesaving. To achieve the wireless part of the system, the majority of sensor nodes are designed to be battery-powered. While battery power has become increasingly ubiquitous, it tends to increase the global carbon footprint of electronic devices. This issue can be mitigated by employing some form of energy harvesting so that batteries can be refilled and the gadget lasts longer, but this does not alter the reality that batteries are still used and eventually discarded. In this paper, the authors emphasize the significance of power consumption in battery-powered devices. To be able to monitor devices' power consumption, one of the measurable parameters is current. When users know the exact current consumption, they can decrease it by polishing the program or tweaking the duty cycle, making radio transmit fewer data or less frequently, thus decreasing overall power draw. In order to simplify current consumption monitoring, the authors have developed a testbed facility that provides real-time current consumption measurements, which may be used to enhance the duty cycle and battery life of the aforementioned devices. While minimizing total current consumption is a great way to extend the battery life and, thus, the carbon footprint, the primary culprit in the Internet of Things is radio communications. This transmission is the primary source of current consumption. By determining the exact amount of current drawn during transmission and adjusting it, users can significantly extend battery life.

Textured nanofibers inspired by nature for harvesting biomechanical energy and sensing biophysiological signals

Flexible electronics are emerging as a promising new platform for wearables; however, their practical utility is hindered by the limited battery life of electronic devices and the need for frequent battery replacements. Here, we report self-powered, flexible, permeable, tough, and lightweight biomechanical energy harvesting and biophysiological sensing devices using textured nanofibers for wearable electronics applications. The textured structure draws inspiration from two major sources of nature: the interior porous structure of jute fibers and the rough bark texture of trees. Interior pores are introduced to the nanofibers to increase compressibility and breathability like jute fibers. The inspiration from the rough bark texture also leads to a wrinkled surface morphology of the nanofibers to expand the surface area and consequently enhance toughness. To design and fabricate such a textured structure, the vapor-induced phase separation mechanism and electrospinning technique are employed within a controllable high humidity environment. Consequently, the textured nanofiber-based devices exhibit a well-rounded performance in electrical, mechanical, and physical properties, specifically demonstrating a significantly enhanced piezoelectric performance with more than two-fold electrical generation. These textured devices not only effectively harvest biomechanical energy from human movement but also demonstrate sensing capability for self-powered multifunctional biophysiological monitoring applications by leveraging the same piezoelectric mechanism. The novel design strategy for the textured nanofibers and their resultant balanced performance promotes the versatility and practical applicability of piezoelectric fibrous energy harvesting and sensing devices, contributing to the development of next-generation flexible and wearable electronics.

Privacy‐preserving task offloading in mobile edge computing: A deep reinforcement learning approach

AbstractAs machine learning (ML) technologies continue to evolve, there is an increasing demand for data. Mobile crowd sensing (MCS) can motivate more users in the data collection process through reasonable compensation, which can enrich the data scale and coverage. However, nowadays, users are increasingly concerned about their privacy and are unwilling to easily share their personal data. Therefore, protecting privacy has become a crucial issue. In ML, federated learning (FL) is a widely known privacy‐preserving technique where the model training process is performed locally by the data owner, which can protect privacy to a large extent. However, as the model size grows, the weak computing power and battery life of user devices are not sufficient to support training a large number of models locally. With mobile edge computing (MEC), user can offload some of the model training tasks to the edge server for collaborative computation, allowing the edge server to participate in the model training process to improve training efficiency. However, edge servers are not fully trusted, and there is still a risk of privacy leakage if data is directly uploaded to the edge server. To address this issue, we design a local differential privacy (LDP) based data privacy‐preserving algorithm and a deep reinforcement learning (DRL) based task offloading algorithm. We also propose a privacy‐preserving distributed ML framework for MEC and model the cloud‐edge‐mobile collaborative training process. These algorithms not only enable effective utilization of edge computing to accelerate machine learning model training but also significantly enhance user privacy and save device battery power. We have conducted experiments to verify the effectiveness of the framework and algorithms.

A Trust-Aware and Authentication-Based Collaborative Method for Resource Management of Cloud-Edge Computing in Social Internet of Things

The Social Internet of Things (S-IoT) paradigm is focused on topic of the Internet of Things (IoT), which accelerates the object issues by working with the concept of social networks. Searching and finding a new object in the community are considered to manage the number of friends and complex relationships between them and affect the ability to navigate at the cloud-edge layer, and resources, such as battery lifetime of S-IoT devices and energy resources, are important challenges in this field. In the processing of social messages of remote devices, increasing the battery life of devices that require such requirements plays the most important role. In this research, a collaboration scenario is presented to consider object attributes, friend’s functions and intelligent friend selection among objects for group messaging. First, a general reference model is designed and presented to select a friend to access group message remote processing services and minimize cloud-edge resources. The simulation results show that, for the correct communication of friends at the edge of the network and in each service discovery, according to the length of the path in the network, it is possible to establish stable communication and make better service with the least possible. The results show that if we want to develop a method for friendship between objects in communication in cloud computing, the proposed method can greatly improve the effectiveness of providing reliable message processing types.

Morphological analysis of the human earcanal deformations during face-related activities

Ear-related technologies are spreading in our daily life and have become essential in several applications. The comfort, retention and battery life of in-ear devices can be substantially improved by considering the dynamic behavior of the earcanal. A better understanding of the earcanal dynamic motion would not only result in the improved fit and performance of earpieces but could also pave the way to harvest energy from these movements to power future ear-related technologies. The contours of the left and right ears of 18 healthy subjects during closed mouth and 4 activities (mouth opening, turning head left, raising eyebrows and smiling) were discretized. Eight parameters were analyzed to investigate the possible relation between each of these face-related activities and the radial and axial deformations of the earcanal. The largest significant deformations in reference to the closed-mouth geometry were observed during mouth-opening and smiling at the earcanal entrance and between the two bends.

Multi-objective optimization for energy efficient cooperative communication in energy-constrained overlay Cognitive Radio Networks

With the rapid proliferation of wireless devices, energy efficiency and addressing spectrum scarcity have become critical global concerns. To address these challenges, this paper explores radio-frequency energy harvesting in Cognitive Radio Networks (CRNs) as a solution which can significantly extend the battery life of mobile devices. In this setup, energy-constrained secondary users (SUs) use Simultaneous Wireless Information and Power Transfer (SWIPT) technology to harvest energy from primary signals and aid primary transmission. We propose a joint time-switching and power-splitting technique to maximize energy harvesting, achieving a 97.7% accurate sub-optimal solution compared to the optimal (benchmark) result. Furthermore, we investigate optimal power allocation during cooperative communication and secondary transmission, resulting in a near-optimal solution with 98.5% accuracy. We also introduce a cooperative communication framework using the Hedonic game concept, enabling SUs to establish preferred partnerships with suitable primary users (PUs). Numerical results indicate that this cooperative framework enhances the secondary network’s overall utility, average SU satisfaction, and SU participation when compared to the non-cooperative approach.

Lightweight Steganography Technique in The Internet of Things: A survey

In the context of the Internet of Things (IoT), lightweight steganography provides a revolutionary combination of technology and security, where data concealment meets the efficiency requirements of IoT devices. Lightweight steganography, as opposed to conventional steganographic methods, focuses on concealing information inside digital content while using the least amount of computer resources and energy. Secure data transfer is essential in the IoT world because linked devices may communicate with one another without interruption. IoT devices may communicate private data discreetly while conserving system resources, thanks to the elegant approach offered by lightweight steganography. This novel method makes use of complex algorithms that gently change data packets, turning them into bearers of hidden information without raising any red flags. The secrecy of IoT ecosystems is strengthened by lightweight steganography's easy integration of covert communication channels. In addition to improving data security, this technology also optimizes bandwidth usage and increases device battery life. A future when linked devices may transmit information safely, effectively, and surreptitiously is predicted as the IoT environment develops. This would usher in a period of unmatched data security and resource optimization.

Wearable sensing electronic devices with health monitoring function

Wearable mobile terminal (Wearable Devices) is a wearable mobile terminal that integrates sensors, wireless communication and multimedia technology into the human body. With the development of computer technology, wearable devices can perceive, record, analyze, adjust, intervene and even treat various diseases so as to maintain human health. Wearable electronic products refer to the intelligent exchange of information with the outside world using their own internal sensors and chips according to their physiological function or adaptability to the outside world. Wearable devices have been edging research in recent years, this paper introduces the characteristics of smart wearable devices, the materials currently applied, the different modes (types) of sensors, and discusses how the sensor network used for monitoring and transmitting recorded data can process the data network efficiently and accurately. In addition, as the sensor needs to consume more power to record and process the information obtained from the body, the battery life of smart wearable devices shortens. This paper summarizes some major concepts and methods related to the energy-saving of sensor components. Battery life significantly impacts the future personalization and universalization of health monitoring devices.

Piezoelectric nanogenerators for self-powered wearable and implantable bioelectronic devices.

One of the recent innovations in the field of personalized healthcare is the piezoelectric nanogenerators (PENGs) for various clinical applications, including self-powered sensors, drug delivery, tissue regeneration etc. Such innovations are perceived to potentially address some of the unmet clinical needs, e.g., limited life-span of implantable biomedical devices (e.g., pacemaker) and replacement related complications. To this end, the generation of green energy from biomechanical sources for wearable and implantable bioelectronic devices gained considerable attention in the scientific community. In this perspective, this article provides a comprehensive state-of-the-art review on the recent developments in the processing, applications and associated concerns of piezoelectric materials (synthetic/biological) for personalized healthcare applications. In particular, this review briefly discusses the concepts of piezoelectric energy harvesting, piezoelectric materials (ceramics, polymers, nature-inspired), and the various applications of piezoelectric nanogenerators, such as, self-powered sensors, self-powered pacemakers, deep brain stimulators etc. Important distinction has been made in terms of the potential clinical applications of PENGs, either as wearable or implantable bioelectronic devices. While discussing the potential applications as implantable devices, the biocompatibility of the several hybrid devices using large animal models is summarized. This review closes with the futuristic vision of integrating data science approaches in developmental pipeline of PENGs as well as clinical translation of the next generation PENGs. STATEMENT OF SIGNIFICANCE: Piezoelectric nanogenerators (PENGs) hold great promise for transforming personalized healthcare through self-powered sensors, drug delivery systems, and tissue regeneration. The limited battery life of implantable devices like pacemakers presents a significant challenge, leading to complications from repititive surgeries. To address such a critical issue, researchers are focusing on generating green energy from biomechanical sources to power wearable and implantable bioelectronic devices. This comprehensive review critically examines the latest advancements in synthetic and nature-inspired piezoelectric materials for PENGs in personalized healthcare. Moreover, it discusses the potential of piezoelectric materials and data science approaches to enhance the efficiency and reliability of personalized healthcare devices for clinical applications.