Energy-efficient Architecture Research Articles

Energy efficient architecture for mitigating the hot-spot problem in wireless sensor networks

Energy efficient architecture for mitigating the hot-spot problem in wireless sensor networks

Thermal and energy performance of a user-responsive microalgae bioreactive façade for climate adaptability

As a recent trend in the energy-efficient architecture, microalgae bio-reactive façades can control buildings’ thermal loads by the responsiveness to solar radiations and adaptive variations in culture density. Although these smart systems can provide adaptable shading during the year, they cannot meet the varying thermal comfort needs of building users in a short time because the microalgae medium’s culture remains almost unchanged during the day. This paper reports on an innovative method that helps microalgae bioreactive façade respond to solar radiation and users’ thermal needs in a short time. To achieve it, a smart window panel will be introduced, which contains two remotely-controlled adjustable bioreactors which can regulate the algae medium in height based on the users’ thermal needs. This novel panel can serve as a bio-adaptable sunshade integrated with the building facade. Thus, the internal building thermal loads can be adjusted via the height of the bioreactor façade. Experimental and simulation research was conducted to compare the thermal performance of bioreactor facades at different microalgae medium height levels in the BSk climate zone. The results indicate that indoor and outdoor temperature differences for full, ¾, ½, and ¼ medium height level every 15-minute time interval are 12.55, 11.50, 10.87, and 6.53, respectively, indicating that the full-height level has the most influence to control the thermal load of the system. According to the results, the bioreactor façade with adjustable medium height greatly impacts building thermal control in a short time.

An Energy Efficient Architecture for Furnace Monitor and Control in Foundry Based on Industry 4.0 Using IoT

The global standards in the field of industrial automation are maintained in industries by completely digitizing their manufacturing process with industry 4.0 standard. Internet of Things (IoT) enables the conservation of cultural heritage with proper assistance on data management on the data collected from the sensors. However, energy efficient conservation is required to monitor the IoT sensors in order to deal with building a better infrastructure. In this paper, we develop a bio-inspired algorithm which can automate the entire furnace monitoring and controlling system in order to eliminate the human intervention involved in the physical process. The algorithm is blended as a web-based remote application for the better control of the tasks involved, energy utilized, and its subsequent log-report maintenance. The entire system employs Wi-Fi communication for data transfer from device to cloud where the stored data including temperature log, forth coming schedule, and process graphic are maintained by the proposed algorithm to predict the machine failure at an earlier stage. The real-time prototype system is supported by a heat treatment process that is completely automated using IoT to monitor and maintain the temperature during the production of metal casting process.

Power consumption analysis of access network in 5G mobile communication infrastructures — An analytical quantification model

Energy consumption growth of the fifth-generation (5G) mobile network infrastructure can be significant due to the increased traffic demand for a massive number of end-users with increasing traffic volume, user density, and data rate. The emerging technologies of radio access networks (RAN), e.g., millimeter-wave (mm-wave) communication and large-scale antennas, make a considerable contribution to such an increase in energy consumption. The multiband 2-tier heterogeneous network (HetNet), cloud radio access network (C-RAN), and heterogeneous cloud radio access network (H-CRAN) are considered the prospective RAN architectures of the 5G mobile communication. This paper explores these novel architectures from the energy consumption and network power efficiency perspective considering the varying high volume traffic load, the number of antennas, varying bandwidth, and varying density of low power nodes (LPNs), integrated with mm-wave communication and large-scale multiple antennas. The architectural differences of these networks are highlighted and power consumption analytical models that characterize the energy consumption of radio resource heads (RRHs), base band unit (BBU) pool, fronthaul, macro base station (MBS), and small cell base stations (SCBs) in HetNet, C-RAN, and H-CRAN are developed. The network power efficiency with the consideration of propagation environment and network constraints is investigated to identify the energy-efficient architecture for the 5G mobile network. The simulation results reveal that the power consumption of all these architectures increases in all considered scenarios due to an increase in power consumption of radio frequency components and computation power. Moreover, CRAN is the most energy-efficient RAN architecture due to its cooperative processing and decreased cooling and site support devices and H-CRAN consumes most of the energy compared to other 5G RAN architectures mainly due to a high level of heterogeneity.

An energy efficient reconfigurable architecture for multi-lead ECG signal compression

An energy efficient reconfigurable architecture for multi-lead ECG signal compression

An Energy-Efficient 12b 2.56 MS/s SAR ADC Using Successive Scaling of Reference Voltages

This paper presents an energy efficient architecture for successive approximation register (SAR) analog to digital converter (ADC). SAR ADCs with a capacitor array structure have been widely used because of its simple architecture and relatively high speed. However, conventional SAR ADCs consume relatively high energy due to the large number of capacitors used in the capacitor array and their sizes scaled up along with the number of bits. The proposed architecture reduces the energy consumption as well as the capacitor size by employing a new array architecture that scales down the reference voltages instead of scaling up the capacitor sizes. The proposed 12-bit SAR ADC is implemented in Complementary Metal Oxide Semiconductor (CMOS) library using Cadence Virtuoso design tool. Simulation results and mathematical model demonstrate the overall energy savings of up to 97.3% compared with conventional SAR ADC, compared with the SAR ADC with split capacitor, and compared with the resistor and capacitor (R&C) Hybrid SAR ADC. The ADC achieves an effective number of bits (ENOB) of bits and consumes at sampling rate of , offering an energy consumption of per conversion step. The proposed SAR ADC offers 95.5% reduction in chip core area compared to conventional architecture, while occupying an active area of .

Fog-IoT based smart home fusion billing system

In this paper, we propose an energy efficient architecture of Fog-IoT based real time fusion billing system. We present a two-layer prototype for the smart building include like internet of things (IoT), Fog and Cloud. The architecture helps to increase the revenue generation by service provider due to the real time data. The proposed system provides automatic utilisation of resources and a billing system. The user is able to take advantage of the proposed architecture to pay a bill at any moment based on real-time calculation. Our approach uses the fog computing for better bandwidth and latency. It provides real-time smart bills for single homes as an entity. The bill generated is accompanied with all the bills (Electrical, water, Gas pipeline, etc.). The proposed method will be used for generating the revenue every time that may be used for further purpose. The end-user may submit all bills at a moment, whenever they want to submit. The bill generated is user-friendly, the end-user may interact and able to know and understand, which appliance they are using in what amount. So they keep monitoring the same and may alert and try to save the energy.

Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

This article proposes a proof-of-concept device to continuously assess the usage of handheld power tools and detect construction working tasks (e.g., different drilling works) along with potential misusages, e.g., drops, with an energy-efficient architecture design. The designed device is based on Bluetooth low energy (BLE) and NFC connectivity. BLE is used to exchange data with a gateway, whereas NFC has been chosen as an energy-efficient wake-up mechanism. A temperature and humidity sensor is embedded to monitor storage conditions and an accelerometer for tool usage monitoring. The ARM Cortex-M4 core embedded in the BLE module is exploited to process the information at the edge. A Tiny Machine Learning (TinyML) algorithm is proposed to process the data directly on board and achieve low latency and high energy efficiency. The TinyML algorithm has been developed embedded in the proposed device to detect four different usage classes (tool transportation, no-load, metal, and wood drilling). A dataset containing more than 280 min of three-axis accelerations during different activities has been acquired with the device attached to a construction rotary hammer drill and used to train and validate the algorithm. A neural architecture search has been performed to optimize the trade-off between accuracy and complexity, achieving an accuracy of 90.6% with a model size of roughly 30 kB. The experimental results showed an ultralow power consumption in sleep mode of 550 nA and a peak power consumption of 8 mA while running TinyML on the edge. This results in a balanced combination of edge processing capabilities and low power consumption, enabling to obtain a smart Internet of Things (IoT) device in the field with a long lifetime of up to four years in operation and 17 years in shelf mode with a standard 250-mAh coin battery. This work enables a long battery lifetime operation of device degradation and utility analysis, further closing the gap between edge processing and fine granularity data evaluation.

Patient Behavioral Analysis with Smart Healthcare and IoT.

Patient behavioral analysis is the key factor for providing treatment to patients who may suffer from various difficulties including neurological disease, head trauma, and mental disease. Analyzing the patient's behavior helps in determining the root cause of the disease. In traditional healthcare, patient behavioral analysis has lots of challenges that were much more difficult. The patient behavior can be easily analyzed with the development of smart healthcare. Information technology plays a key role in understanding the concept of smart healthcare. A new generation of information technologies including IoT and cloud computing is used for changing the traditional healthcare system in all ways. Using Internet of Things in the healthcare institution enhances the effectiveness as well as makes it more personalized and convenient to the patients. The first thing that will be discussed in the article is the technologies that have been used to support the smart class, and further, there will be a discussion on the existing problems with the smart healthcare system and how these problems can be solved. This study can provide essential information about the role of smart healthcare and IoT in maintaining behavior of patent. Various biomarkers are maintained properly with the help of these technologies. This study can provide effective information about importance of smart health system. This smart healthcare is conducted with the involvement of proper architecture. This is treated as effective energy efficiency architecture. Artificial intelligence is used increasingly in healthcare to maintain diagnosis and other important factors of healthcare. This application is also used to maintain patient engagement, which is also included in this study. Major hardware components are also included in this technology such as CO sensor and CO2 sensor.

High throughput and energy efficient linear phase FIR filter architectures

High throughput, low complex and energy efficient linear phase FIR filter structures with low latency are highly desirable for most of the portable signal processing applications. Improving the throughput and energy efficiency while ensuring low latency and complexity is a difficult problem due to the trade-off between performance metrics. To this end, the design of linear phase filter based on the application of retiming and pipelining is proposed. The CPD, PDP, ADP and ELT of the proposed 16-tap linear phase FIR filter is reduced by 64.83%, 80.1%, 61.58%, and 62.49% respectively in comparison with FIR filter using flexible retiming and by 18.29%, 30.99%, 16.56%, and 12.82% respectively in comparison with the recent retiming based high throughput FIR filter. The 16-tap ECG denoising filter developed from the proposed filter structure provides excellent performances in terms of CPD, PDP, ADP and ELT as the algorithmic transformations are applied to an exact multiplier-less linear phase filter structure. It filters out PLI of 50 Hz, its harmonics and high frequency noises while providing improved denoising performances. The CPD, PDP, ADP and ELT of the proposed multiplier-less ECG denoising filter is reduced by 82.44%, 97.55%, 93.51%, and 81.27% respectively in comparison with a typical 17-tap FIR notch filter and by 70.06%, 99.82%, 99.68%, and 98.12% respectively in comparison with a typical Bartlett window based 256-tap FIR notch filter. Structural model VHDL coding is used for developing filters. For synthesis, Cadence software with gpdk 45 nm standard cell library is used and for simulation modelsim is used.

InFeMo: Flexible Big Data Management Through a Federated Cloud System

This paper introduces and describes a novel architecture scenario based on Cloud Computing and counts on the innovative model of Federated Learning. The proposed model is named Integrated Federated Model , with the acronym InFeMo . InFeMo incorporates all the existing Cloud models with a federated learning scenario, as well as other related technologies that may have integrated use with each other, offering a novel integrated scenario. In addition to this, the proposed model is motivated to deliver a more energy efficient system architecture and environment for the users, which aims to the scope of data management. Also, by applying the InFeMo the user would have less waiting time in every procedure queue. The proposed system was built on the resources made available by Cloud Service Providers (CSPs) and by using the PaaS (Platform as a Service) model, in order to be able to handle user requests better and faster. This research tries to fill a scientific gap in the field of federated Cloud systems. Thus, taking advantage of the existing scenarios of FedAvg and CO-OP, we were keen to end up with a new federated scenario that merges these two algorithms, and aiming for a more efficient model that is able to select, depending on the occasion, if it “trains” the model locally in client or globally in server.

A novel energy efficient architecture for wireless body area networks

A novel energy efficient architecture for wireless body area networks

Energy Efficiency of a Vernacular Building Design and Materials in Hot Arid Climate: Experimental and Numerical Approach

Morocco faces tremendous climate constraints; the climate is hot and dry in most parts of the country, and when selecting an energy-saving approach, the architectural landscape becomes essential.Designer and building professionals seem to have neglected this large-scale integration. Sustainable development programs in terms of sustainable architecture are ongoing in countries around the world. One part of this trend is the growing concern shown in the high environmental efficiency of vernacular architecture. It is within this prescriptive framework that this research study is being conducted, which reveals novel architectural style integrating thermal comfort, energy efficient characteristics, passive solar elements architecture, and construction techniques inspired from the vernacular Ksourian architectural configurations. The goal of the present research study is to identify features of energy efficient vernacular architecture and thermal performances that affect indoor thermal comfort conditions for adaptation to current lifestyles in modern architecture. The key characteristics developed are; built mass structure, building orientation, space planning, availability of sunspace, building techniques, and new coating materials for manufacturing and roofing. The suggested methodology enables to analyze the thermal performance analysis, applying an experimental research using experimental testing measurement and comparative optimization processes for thermal efficiency and comfort evaluation of a traditional vernacular earthen house.Series of experimental thermophysical characterization measurements have been carried out in order to quantify on a real scale the thermophysical properties that characterize the Rissani earth. Thusthermophysical characterization results are operated as input data for the thermal dynamic simulation for the purpose to evaluate thermal performances and comfort under the weather conditions and control natural comfort in both summer and winter, without using heating or cooling systems. Ultimately, the simulations carried out make it possible to identify the optimal orientation, revealing an effective decrease in interior temperatures during summer and providing good thermal comfort in winter.

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes and consumes an average power of . The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment.

Energy Efficient Buildings with Algae

The biggest part of the energy consumption of buildings is for thermal comfort. Awareness on climate change and concerns about the depletion of natural resources made the necessity to use renewable energy sources in buildings evident. In this context, microalgae have high surface efficiency and consume inorganic carbon, thus enabling carbon-neutral operation. They can be integrated into building façades with photobioreactors to reduce energy demands. This paper aims to clarify and discuss the role of microalgal technologies in energy-efficient architecture. The thermal performance and energy generation properties of microalgae façades are comprehensively reviewed. The results show that microalgae provide dynamic shading and thermal insulation, thus have the potential to significantly reduce the thermal load and energy demands of buildings and increase the building performance. Consequently, besides the thermal performance of microalgae façades, evaluation of daylight, lighting, environmental and cost performance, technical applicability and aesthetics are necessary.

Rationalizing Resource Utilization in Cloud Computing Using Coalition Formation Strategy

Even though the contribution of cloud computing towards the Sustainable Development (SD) of communities is still under research investigation, cloud computing has become an integral part of many ICT solutions that shape our daily lives. Thus, some researchers recommend taking considerable actions to point cloud computing development towards supporting SD. In this research, an approach to designing energy efficient cloud architecture as a way of supporting SD is proposed. Resource allocation is a challenging process in cloud management, the goal is to allocate the exact amount of resources needed throughout the service duration; tight enough to avoid unnecessarily wasting resources and loose enough to prevent any degradation in Quality of Service (QoS) that may lead to the violation of the Service Level Agreement (SLA) between the service provider and the cloud user. This study aims to achieve the desired balance by benefiting from the history of the user’s behaviour and from sharing resources – more specifically Virtual Machines (VM) – among a coalition of users. Coalition formation strategy is used to build groups of cloud users based on their cloud behaviour history. Users are grouped in a way that their usage patterns complement each other, either to avoid the loss stemming from VM excess reserved space or from idle times. A type of architecture that fulfils this improvement process is proposed and implemented on Google Compute Engine (GCE). The contribution of this research is that it applies the Coalition formation strategy in cloud computing resource management in a novel way and experiments show that there are scenarios where the efficiency of resource management has improved. Evaluation of the performance of the proposed architecture is done by comparing resource utilization for both the cloud following this architecture and the cloud that runs the basic GCE strategy. In conclusion, it is observed that improvements depend on accuracy of the prediction of usage pattern of the user. Results show that in certain scenarios, improvements can be made to up to 24% of VM usage and, in other scenarios, it can minimize the number of required VMs, thus contributing to green computing.

Implementation of Energy Efficient BIST Architecture by Using Verilog

In a random testing environment, a significant amount of energy is wasted in the LFSR and in the CUT by useless patterns that do not contribute to fault dropping. Another major source of energy drainage is the loss due to random switching activity in the CUT and in the scan path between applications of two successive vectors. In this work, a new built-in self-test (BIST) scheme for scan-based circuits is proposed for reducing such energy consumption. A mapping logic is designed which modifies the state transitions of the LFSR such that only the useful vectors are generated according to a desired sequence. Further, it reduces test application time without affecting fault coverage. Experimental results on circuits reveal a significant amount of energy savings in the LFSR during random testing.

Energy-Efficient On-Platform Target Classification for Electric Air Transportation Systems

Due to the predicted rise of Unmanned Aircraft Systems (UAS) in commercial, civil, and military operations, there is a desire to make UASs more energy efficient so they can proliferate with ease of deployment and maximal life per charge. To address current limitations, a three-tiered approach is investigated to mitigate Unmanned Aerial Vehicle (UAV) hover time, reduce network datalink transmission to a ground station, and provide a real-time framework for Sense-and-Avoidance (SAA) target classification. An energy-efficient UAS architecture framework is presented, and a corresponding SAA prototype is developed using commercial hardware to validate the proposed architecture using an experimental methodology. The proposed architecture utilizes classical computer vision methods within the Detection Subsystem coupled with deeply learned Convolutional Neural Networks (CNN) within the Classification Subsystem. Real-time operations of three frames per second are realized enabling UAV hover time and associated energy consumption during SAA processing to be effectively eliminated. Additional energy improvements are not addressed in the scope of this work. Inference accuracy is improved by 19% over baseline COTS models and current non-adaptive, single-stage SAA architectures. Overall, by pushing SAA processing to the edge of the sensors, network offload transmissions and reductions in processing time and energy consumption are feasible and realistic in future battery-powered electric air transportation systems.

Multivalued Logic Gates: Recent Advances on Multivalued Logic Gates: A Materials Perspective (Adv. Sci. 8/2021)

Emerging materials and device architectures for the realization of multivalued logic operations are investigated. In article number 2004216, Sae Byeok Jo, Joohoon Kang, and Jeong Ho Cho provide an overview of the recent advancements in multivalued logic gates from a materials perspective, in which a paradigm shift toward a new era of hyper Moore's law is manifested through the development of area- and energy-efficient architectures.

Evaluation of Tradeoffs in the Design of FPGA Fabrics Using Electrostrictive 2-D FETs

The electrostrictive 2-D field-effect transistor (EFET) is a steep-slope device that promises to offer aggressive length and voltage scalability. Two key features of this device are its high-drive strength with high ON-OFF current ratio and the isolated back-gate terminal, which provides us the fourth knob to control the transistor drive strength. The disadvantage of the technology is the increased device capacitance incurred due to the additional piezoelectric layer in the transistor structure. Second, although the back-gate biasing of EFETs provides us the fourth knob of control, statically biasing the back gate increases the static power consumption. Despite the idiosyncrasies of the technology, this work shows the use of EFETs in field-programmable gate arrays (FPGAs) to be advantageous because the added energy cost of device capacitance gets amortized by the improvement in performance and energy efficiency of using high-drive EFET transistors in the FPGA interconnect architecture. We also show that co-optimization of back-bias voltage along with transduction efficiency is essential in the FPGA subcircuit level for achieving an energy-efficient architecture. This work highlights the specific design approach tradeoffs that differ from prior CMOS approaches and provides guidance for the engineering parameters necessary for EFETs to evolve as a competitive technology.