Battery Life Of Devices Research Articles

TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions

As the COVID-19 pandemic emerged, everyone’s attention was brought to the topic of the health and safety of the entire human population. It has been proven that wearing a face mask can help limit the spread of the virus. Despite the enormous efforts of people around the world, there still exists a group of people that wear face masks incorrectly. In order to provide the best level of safety for everyone, face masks must be worn correctly, especially indoors, for example, in shops, cinemas and theaters. As security guards can only handle a limited area of the frequently visited objects, intelligent sensors can be used. In order to mount them on the shelves in the shops or near the cinema cash register queues, they need to be capable of battery operation. This restricts the sensor to be as energy-efficient as possible, in order to prolong the battery life of such devices. The cost is also a factor, as cheaper devices will result in higher accessibility. An interesting and quite novel approach that can answer all these challenges is a TinyML system, that can be defined as a combination of two concepts: Machine Learning (ML) and Internet of Things (IoT). The TinyML approach enables the usage of ML algorithms on boards equipped with low-cost, low-power microcontrollers without sacrificing the classifier quality. The main goal of this paper is to propose a battery-operated TinyML system that can be used for verification whether the face mask is worn properly. To this end, we carefully analyze several ML approaches to find the best method for the considered task. After detailed analysis of computation and memory complexity as well as after some preliminary experiments, we propose to apply the K-means algorithm with carefully designed filters and a sliding window technique, since this method provides high accuracy with the required energy-efficiency for the considered classification problem related to verification of using the face mask. The STM32F411 chip is selected as the best microcontroller for the considered task. Next, we perform wide experiments to verify the proposed ML framework implemented in the selected hardware platform. The obtained results show that the developed ML-system offers satisfactory performance in terms of high accuracy and lower power consumption. It should be underlined that the low-power aspect makes it possible to install the proposed system in places without the access to power, as well as reducing the carbon footprint of AI-focused industry which is not negligible. Our proposed TinyML system solution is able to deliver very high-quality metric values with accuracy, True Positive Ratio (TPR), True Negative Ratio (TNR), precision and recall being over 96% for masked face classification while being able to reach up to 145 days of uptime using a typical 18650 battery with capacity of 2500 mAh and nominal voltage of 3.7 V. The results are obtained using a STM32F411 microcontroller with 100 MHz ARM Cortex M4, which proves that execution of complex computer vision tasks is possible on such low-power devices. It should be noted that the STM32F411 microcontroller draws only 33 mW during operation.

SMART CITY INFORMATION SYSTEM BASED ON LORA TECHNOLOGYANNOTATION

This article discusses issues related to the concept of the Internet of Things and its application to the development of the Smart City. Smart City or "Smart City" is a new concept for the introduction of technologies (information and communication) to manage the life of the modern city. Existing technologies for long-distance data transmission in the Internet of Things are analyzed. Based on the calculations, the justification of the efficiency of LORA technology is substantiated, the energy potential of the communication line for LORA technology is given, the capacity of the LoRa network is determined. The network architecture has the best relationship between increasing the battery life of IoT devices and providing communication range. The protocol operates in a non-licensed range, and this is its uniqueness in cost and speed of implementation. Based on the study of the experience of implementing the protocol in the city of Lviv, the advantages and problems of implementation were identified. The possibility of using the technology in large cities was assessed. Software has been developed that allows the user to check that everything in his house is in order and that there are no gas leaks, water leaks and no movement in the house. The practical use of IoT in the "Smart City", the use of LoRaWAN technology, the principle of the network are shown. The experience of using the LoRaWAN protocol in Ukraine has been studied and analyzed. The experience of cities on the introduction of Internet of Things technologies in the housing system is considered.

Content-Adaptable ROI-Aware Video Storage for Power-Quality Scalable Mobile Streaming

The demand for mobile video streams is constantly increasing. With this demand comes a need for mobile devices to receive more videos at ever increasing quality. However, due to the large size of video data and intensive computational requirements, video streaming requires frequent memory access that consume a substantial amount of mobile device power; as a result, the battery life of mobile devices is limited. In this paper, we present a video content-adaptable Region-of-Interest (ROI)-aware video storage technique that promotes power savings. During the video encoding process on the transmitting server, based on the macroblock variance and ROI characterization, the “macroblocks of interest” are identified and embedded in the encoded bitstream. In the decoding process, a new frame buffer with dynamic power-quality trade-off is presented to adapt to the macroblock characteristics during run-time. Results from the system-level and circuit-level simulations show that the proposed technique enables substantially more truncated bits and significant power savings while delivering similar or better video quality as compared to other state-of-the-art solutions.

PP428 Rechargeable Versus Recharge-Free Sacral Neurostimulation: An Australian Health Economics Analysis

IntroductionSacral neuromodulation is a well-established therapy for urinary and fecal incontinence. Currently available sacral neurostimulators require replacement every three to five years due to battery depletion. New rechargeable sacral neurostimulators with a potential 15-year battery life now have regulatory approval in Australia. However, the initial outlay for the device is higher than for the predicate devices. Our objective was to assess the economic value of rechargeable devices, compared with recharge-free implants, and to identify the patients most likely to benefit from the introduction of this novel technology in Australia.MethodsThe Medicare database was reviewed to quantify populations likely to derive benefits from rechargeable technology. Cost minimization and budget impact analyses were conducted from a payer perspective. Cost inputs were derived from Medicare and the Private Hospital Data Bureau. Two scenarios were modeled comparing the three and five-year battery life of the recharge-free devices with 15 years for a rechargeable device. Sensitivity testing was conducted based on potential uptake and dropout rates (due to death, dementia, etc.).ResultsRechargeable neurostimulators were found to be dominant (cost-saving) in all modeled scenarios, facilitated by a reduction in the frequency of battery replacement procedures and their associated risks for patients. Rechargeability also facilitated higher power settings for optimal symptom control, without trading off device longevity. Younger patients are expected to derive the greatest benefit from the extended battery life as data showed that 40 percent of the implantations were for patients younger than 65 years. The key uncertainty in this analysis was the limited real-world data on patient selection and preferences, which may influence uptake and dropout rates.ConclusionsRechargeable sacral neurostimulators deliver cost savings to the healthcare system due to their extended battery life. Fewer replacement surgeries are an important patient-relevant outcome, especially for younger populations. This finding is important because it demonstrates the economic value of rechargeability to payers and provides robust evidence supporting therapy access for privately insured patients in Australia.

Energy-Efficient Optimal Power Allocation for SWIPT Based IoT-Enabled Smart Meter.

This paper presents an internet of things (IoTs) enabled smart meter with energy-efficient simultaneous wireless information and power transfer (SWIPT) for the wireless powered smart grid communication network. The SWIPT technique with energy harvesting (EH) is an attractive solution for prolonging the battery life of ultra-low power devices. The motivation for energy efficiency (EE) maximization is to increase the efficient use of energy and improve the battery life of the IoT devices embedded in smart meter. In the system model, the smart meter is equipped with an IoT device, which implements the SWIPT technique in power splitting (PS) mode. This paper aims at the EE maximization and considers the orthogonal frequency division multiplexing distributed antenna system (OFDM-DAS) for the smart meters in the downlink with IoT enabled PS-SWIPT system. The EE maximization is a nonlinear and non-convex optimization problem. We propose an optimal power allocation algorithm for the non-convex EE maximization problem by the Lagrange method and proportional fairness to optimal power allocation among smart meters. The proposed algorithm shows a clear advantage, where total power consumption is considered in the EE maximization with energy constraints. Furthermore, EE vs. spectral efficiency (SE) tradeoff is investigated. The results of our algorithm reveal that EE improves with EH requirements.

A 148-nW Reconfigurable Event-Driven Intelligent Wake-Up System for AIoT Nodes Using an Asynchronous Pulse-Based Feature Extractor and a Convolutional Neural Network

This article presents a 148-nW always-on wake-up system that drastically reduces the system power consumption of Internet of Things (IoT) sensor nodes while oftentimes operating in random-sparse-event (RSE) scenarios. To significantly reduce the long-term average (LTA) power consumption and realize multiapplication and intelligent event detection, three techniques are proposed: 1) In a three-stage pipelined event-driven architecture, a frame generator and a convolutional neural network intelligent inference engine (CNN IIE) in stage III are event-driven by the preliminary detectors in stage II, and the detectors are triggered by a level-crossing (LC) analog-to-digital converter (ADC), i.e., stage I, dramatically reducing the overall power consumption. 2) The clock-free pulse-based instant rate of change (IROC) feature extractor directly processes the asynchronous pulses of the LC-ADC outputs in the temporal domain instead of utilizing a conventional power-hungry frequency-domain method. 3) A reconfigurable IROC, the frame generator, and the CNN IIE provide adaptive intelligence for various IoT events, enhancing the accuracy of multipurpose detection with ultralow power. We demonstrate two artificial intelligence IoT (AIoT) applications at 0.6-V V_DD. For electrocardiogram (ECG) recognition, one example works at a typical event rate (ER) of &#x007E;4800/h, with an active power of 1.68 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> and a precision of up to 99&#x0025;; the other is used for keyword spotting (KWS), where the chip achieves 378 nW at &#x007E;720/h ER and 94&#x0025; accuracy. The LTA power is bounded to 148 nW, while the event-driven chip is on call and waiting for events; this chip dominates the AIoT device battery life in RSE scenarios.

A Student in My Pocket: Development of a Virtual Internal Medicine Hospital Rotation During the COVID-19 Pandemic.

Purpose: It is imperative for physician assistant (PA) students to be exposed to hospital medicine as 40% of practicing PAs identify the hospital as their principal clinical practice setting. 1 Many of these rotations were canceled due to the COVID-19 pandemic, negatively impacting the 70% of PA programs not affiliated with an academic medical center. 2 To address this need and provide a comparable experience for PA students, medical educators at the University of Chicago created a Virtual PA Rotation (VPAR). The purpose of this project was to develop a high-yield clinical rotation for private PA programs negatively impacted by COVID-19 by implementing and evaluating a 4-week-long VPAR comprising (1) direct patient care, (2) medical educator-facilitated breakout sessions, and (3) asynchronous learning. Approach: Five students worked with 3 preceptors for the VPAR and were assigned 3–6 patients daily. Direct patient experiences included virtual interviews using FaceTime, preceptor staffing via Zoom, and completion of daily progress notes. This was augmented with medical educator-facilitated breakout sessions and didactic sessions. Evaluation of the VPAR included: (1) comparison of virtual vs traditional in-person rotation patient logs, (2) postcurriculum survey of students and medical educators’ satisfaction and self-efficacy, and (3) student pass rate on the end of rotation (EOR) examination. Outcomes: Diagnoses and patient encounter logs were collected from the 5 virtual rotation students and compared with 4 students who completed traditional in-person rotations in 2019–2020. Mean diagnoses logged per virtual student was 206 (standard deviation [SD] = 155), compared with mean 414 diagnoses (SD = 130) for traditional students (t = 2.14, P = .07). Mean patient encounters logged per virtual student was 34.2 (SD = 20.7), compared with 80.8 (SD = 29.5) for traditional students (t = 2.79, P = .03). One hundred percent of students responded to a survey. Interactive patient interviews, preceptor group, case presentations, and electronic health record review were rated as the most useful educational experiences. Students felt most prepared in ability to review hospital charts, analyze results, and obtain a patient history. All 5 students felt this rotation provided an adequate experience to understand Internal Medicine in a hospital setting. Fifty-nine percent (13/22) of medical educators responded to the postrotation survey. Eighty-five percent (11/13) were moderately or extremely satisfied with their participation in teaching in the rotation, 77% (10/13) were moderately or extremely likely to recommend the rotation to other medical educators, and 100% felt it made them more open to future interprofessional education experiences. Discussion: We were able to create an innovative, high-yield hospital medicine rotation to meet the need for PA students negatively impacted by COVID-19. The VPAR overall was a success as measured by 100% EOR examination pass rate, high student ratings, and comparable patient encounters and diagnoses. The VPAR was also well received by medical educators, though most felt it was more time-consuming, proving to be the biggest limitation to this model. Lessons learned from the preceptors included limitations of technology such as decreased device battery life, need for troubleshooting, and loss of organic educational experiences for students. They also highlighted concerns regarding increased time dedicated to logistics of the rotation while managing highly complex patients. Strengths of the VPAR include its feasibility, low cost of implementation, and ease of application of this model to other practice settings, specialties, and professions. The VPAR also has the capability to adjust for different rotation lengths of time. Significance: We believe the results above showcase that this innovative model of education can meet the needs of PA students negatively impacted by COVID-19. Therefore, as COVID-19 continues and health systems respond, a VPAR is a reasonable alternative to in-person clinical rotations. Beyond COVID, this pilot rotation’s success demonstrates that VPARs or a hybrid of a virtual and in-person rotation may be an interesting addition to PA education, particularly for unaffiliated programs with a significant need for access to inpatient rotations and tertiary care centers. Furthermore, this model could easily translate to other professional disciplines similarly impacted by visiting student restrictions.

Trust-based Friend Selection Algorithm for navigability in social Internet of Things

The Internet of Things (IoT) is a growing area of millions of heterogeneous objects denoted by intensive interactions. The social Internet of Things (SIoT) is a promising approach for expediting object interaction issues by integrating the IoT’s social networking notion. The object discovery and service search are promoted with the right friend selection and trust management on that ground. The number of friends and complexity of devoting relation types affect the navigability in a network. On the other hand, constraints in the storage area and battery life of SIoT devices are two crucial challenges that highlighted the importance of mechanisms to increase the lifetime and robustness of devices. Therefore, this scenario addressed an intelligent friend selection strategy by considering objects’ characteristics, typology, and associated functionalities. This issue is leveraged by trust value to eliminate interception of untrustworthy aspects. We designed a generic reference model and utilized optimization decision theory for optimal friend choice to reduce resource consumption to this effect. The simulation results illuminated that a rational selection of friends per service exploration fortifies global navigability in measuring average path length, degree distribution, and the number of links. The efficiency of our solution was revealed by lessening the average distance and promoting the number of links to achieve a well-tied network and benefiting scale-free degree distribution, which hinted existence of hubs or high degree nodes.

A Novel Framework for Mobile-Edge Computing by Optimizing Task Offloading

With the emergence of mobile computing offloading paradigms, such as mobile-edge computing (MEC), many Internet of Things applications can take advantage of the computing powers of end devices to perform local tasks without the need to rely on a centralized server. Computation offloading is becoming a promising technique that helps to prolong the device's battery life and reduces the computing tasks' execution time. Many previous works have discussed task offloading to the cloud. However, these schemes do not differentiate between types of application tasks. It is not reasonable to offload all application tasks into the cloud. Some application tasks with low computing and high communication cost are more suitable to be executed on the end devices. On the other hand, most resources on the end devices are idle and can be used to process tasks with low computing and high communication cost. In this article, a three-layer task offloading framework named DCC is proposed, which consists of the device layer, cloudlet layer and cloud layer. In DCC, the tasks with high computing requirement are offloaded to the cloudlet layer and cloud layer. Whereas tasks with low computing and high communication cost are executed on the device layer, hence DCC avoids transmitting large amount of data to the cloud, and can effectively reduce the processing delay. We have introduced a greedy task graph partition offloading algorithm, where the tasks scheduling process is assisted according to the device computing capabilities following a greedy optimization approach to minimize the tasks communication cost. To show the effectiveness of the proposed framework, We have implemented a facial recognition system as usecase scenario. Furthermore, experiment and simulation results show that DCC can achieve high performance when compared to state-of-the-art computational offloading techniques.

Collaborative Computation Offloading and Resource Allocation in Multi-UAV-Assisted IoT Networks: A Deep Reinforcement Learning Approach

In the fifth-generation (5G) wireless networks, Edge-Internet-of-Things (EIoT) devices are envisioned to generate huge amounts of data. Due to the limitation of computation capacity and battery life of devices, all tasks cannot be processed by these devices. However, mobile-edge computing (MEC) is a very promising solution enabling offloading of tasks to nearby MEC servers to improve quality of service. Also, during emergency situations in areas where network failure exists, unmanned aerial vehicles (UAVs) can be deployed to restore the network by acting as Aerial Base Stations and computational nodes for the edge network. In this article, we consider a central network controller who trains observations and broadcasts the trained data to a multi-UAV cluster network. Each UAV cluster head acts as an agent and autonomously allocates resources to EIoT devices in a decentralized fashion. We propose model-free deep reinforcement learning (DRL)-based collaborative computation offloading and resource allocation (CCORA-DRL) scheme in an aerial to ground (A2G) network for emergency situations, which can control the continuous action space. Each agent learns efficient computation offloading policies independently in the network and checks the statuses of the UAVs through Jain’s Fairness index. The objective is minimizing task execution delay and energy consumption and acquiring an efficient solution by adaptive learning from the dynamic A2G network. Simulation results reveal that our scheme through deep deterministic policy gradient, effectively learns the optimal policy, outperforming A3C, deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network and greedy-based offloading for local computation in stochastic dynamic environments.

ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT devices in large numbers approach edge for computation offloading requests. This research article presents a three-tier energy-efficient framework to address the scalability issue in edge computing. We introduced an energy-efficient recursive clustering technique at the IoT layer that prioritizes the tasks based on weight. Each selected task with the highest weight value offloads to the edge server for execution. A lightweight client–server architecture affirms to reduce the computation offloading overhead. The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. The energy-efficient framework minimizes the energy consumption of IoT devices, decreases computation time and computation overhead, and scales the edge server. Numerical results show that the proposed framework satisfies the quality of service requirements of both delay-sensitive and delay-tolerant applications by minimizing energy and increasing the lifetime of devices.

Energy-Efficient MAC for Cellular IoT: State-of-the-Art, Challenges, and Standardization

In the modern world, the connectivity-as-we-go model is gaining popularity. Internet-of-Things (IoT) envisions a future in which human beings communicate with each other and with devices that have identities and virtual personalities, as well as sensing, processing, and networking capabilities, which will allow the developing of smart environments that operate with little or no human intervention. In such IoT environments, that will have battery-operated sensors and devices, energy efficiency becomes a fundamental concern. Thus, energy-efficient (EE) connectivity is gaining significant attention from the industrial and academic communities. This work aims to provide a comprehensive state-of-the-art survey on the energy efficiency of medium access control (MAC) protocols for cellular IoT. we provide a detailed discussion on the sources of energy dissipation at the MAC layer and then propose solutions. In addition to reviewing the proposed MAC designs, we also provide insights and suggestions that can guide practitioners and researchers in designing EE MAC protocols that extend the battery life of IoT devices. Finally, we identify a range of challenging open problems that should be solved for providing EE MAC services for IoT devices, along with corresponding opportunities and future research ideas to address these challenges.

Spectrum Based Power Management for Congested IoT Networks.

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

Memory and Cache Contention Denial-of-Service Attack in Mobile Edge Devices

In this paper, we introduce a memory and cache contention denial-of-service attack and its hardware-based countermeasure. Our attack can significantly degrade the performance of the benign programs by hindering the shared resource accesses of the benign programs. It can be achieved by a simple C-based malicious code while degrading the performance of the benign programs by 47.6% on average. As another side-effect, our attack also leads to greater energy consumption of the system by 2.1× on average, which may cause shorter battery life in the mobile edge devices. We also propose detection and mitigation techniques for thwarting our attack. By analyzing L1 data cache miss request patterns, we effectively detect the malicious program for the memory and cache contention denial-of-service attack. For mitigation, we propose using instruction fetch width throttling techniques to restrict the malicious accesses to the shared resources. When employing our malicious program detection with the instruction fetch width throttling technique, we recover the system performance and energy by 92.4% and 94.7%, respectively, which means that the adverse impacts from the malicious programs are almost removed.

Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

A High Conversion Gain Envelope Detector with Wide Input Range for Simultaneous Wireless Information and Power Transfer System

Wireless sensors networks (WSN) have been gradually facilitating the pervasive connectivity of wireless sensor nodes. A greater number of wireless sensors have been used in different aspects of our life. However, limited device battery life restricts the applications of large-scale WSN. This paper presents a batteryless envelope detector with radio frequency energy harvesting (RFEH) for wireless sensor nodes, which enables simultaneous wireless information and power transfer (SWIPT). The envelope detector is designed for small modulation index AM signals with large amplitude variations. Therefore, the envelope detector is supposed to have wide input range while achieving a high conversion gain. We proposed an adaptive biasing technique in order to extend the input range of envelope detector. The input differential pair is adaptively biased through a feedback loop to overcome the variation of bias point when the amplitude of input signal changes. The cross coupled rectifier and DC-DC boost converter with maximum power point tracking (MPPT) are presented against power conversion efficiency (PCE) degradation of RF rectifier with the input power varying. The adaptive biased envelope detector is theoretically analyzed by square law MOSFET model. Designed with 0.18 μm complementary-metal-oxide-semiconductor (CMOS) standard process, the power consumption of proposed envelope detector is 9 μW. Simulated with a 915 MHz AM input signal with 2 Mbps data rate and 0.05 modulation index, the proposed envelope detector achieves 20.37 dB maximum conversion gain when the amplitude of input signal is 0.5 V, and the PCE of energy harvesting circuits achieves 55.2% when input power is –12.5 dBm.

Towards Optimizing WLANs Power Saving: Context-Aware Listen Interval

Despite the rapid growth of Wireless Local Area Networks (WLANs), the energy consumption caused by wireless communication remains a significant factor in reducing the battery life of power-constrained wireless devices. To reduce the energy consumption, static and adaptive power saving mechanisms have been deployed in WLANs. However, some inherent drawbacks and limitations remain. We have developed the concept of Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. In this paper we develop the power saving modes of CALI. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to Pyles’ et al. SAPSM power saving approach, which also employs an ML classifier.

Low Power Clock Generator Design With CMOS Signaling

The requirements for computing with higher energy efficiency in the datacenter and for longer battery life in laptop computers, cell phones, and other IoT devices while increasing performance with higher frequency and more cores, drive the needs for more clock generators with increased performance (frequency and jitter) and lower power budgets. The traditional current mode low swing clock generators were used widely in industry about 10 years ago. Although it had the advantage of higher supply noise rejection due to the differential nature of the architectures, however, it had the disadvantages of high-power consumption, large layout area, and not friendly to process scaling. Contrary to current mode low swing design, clock generator architectures with CMOS large swing signaling, which have advantages of low power consumption, small area, and based on circuits friendly to process scaling, have been widely adopted for clocking generation in the industry since 2009. In this paper, phase locked loops, delay locked loops, phase interpolators, high resolution digital to time converter and clock distribution techniques with CMOS large swing signaling will be discussed and reviewed.

Strategy for Reducing Delays and Energy Consumption in Cloudlet-Based Mobile Cloud Computing

In the paper, the problem of using cloudlet-based mobile cloud computing to solve the issues (resource deficiency and limited energy) that require large computing and memory resources on mobile devices has been studied. Energy-saving of mobile devices, resource limitations in mobile devices, and elimination of network delays are analyzed. It is shown that the solution of mobile users' tasks in the cloud leads to the increased battery life of mobile devices and reduces network delays, which is shown by mathematical calculations. The article considers the balanced distribution of the tasks in the cloudlet network. The paper also deals with the selection of cloudlets according to user requirements. The cases for which a cloud application can be loaded by the user were evaluated and a model was proposed using the possible values that determine the importance of cloudlets (user proximity, high reliability, etc.).

Performance Analysis of FDSOI based Gate Diffusion Input Logic Gates at 28nm Technology Node

In this paper, Fully Depleted Silicon on Insulator (FDSOI) MOSFET at 28nm channel length are simulated in COGENDA Visual TCAD tool. Buried Oxide layer (BOX) between channel and substrate in FDSOI improves the electric field inside the device. The device characteristics are compared in terms of threshold voltage, transconductance parameter, Ion/Ioff ratio and Short Channel Effects (SCEs). To implement VLSI circuits, Complementary Metal Oxide Semiconductor (CMOS) logic is a common choice. However, in the case of CMOS, the logic implementation requires a transistor count equal to twice the number of logic inputs. Therefore, the area is always an issue in the case of the CMOS logic family. To address this issue, Gate Diffusion Input (GDI) based logic has been investigated because it uses less number of transistors. Hence FDSOI MOSFET based GDI logic gates are proposed in this article. FDSOI MOSFET exhibits improved electrical characteristics which can reduce the power consumption considerably and help in expanding the battery life of devices while the GDI logic helps in designing the compact circuit for portable equipment.