Optimize Power Consumption Research Articles

Research on vulnerability assessment technology of power consumption based on data analysis

In this paper, an evaluation model for the vulnerability of power consumption in enterprises is proposed. Firstly, we establish the customer electricity vulnerability index system, which includes the organizational structure, hidden trouble and optimized power consumption. Then, we calculate the index weight and assignment based on the analytic hierarchy process (AHP). And the comprehensive index method is employed to get the customer electricity vulnerability. At last, an example is employed to show our results clearly.

Analysis of Energy Consumption on IaaS Cloud Using Simulation Tool

The cloud-computing technique provides services to the customer globally. The large-scale datacenters arrangement is very complex due to demand for optimization of resources utilization and power consumption. Simulation tools provide an easy way to model complex research problems through GUI representation. Cloudreports is a GUI based simulation tool that generates reports and provides an API with extensions and user defined plugins. In the present paper using this simulation tool user may set the simulation scenario for various VM’s and resources consumption, and simulation environment for the provider, customer, SAN, network performance etc. The resource utilization and overall resource utilization reports by various data centers, customers are also generated. PUE and DCIE, is also computed for effective power usage within datacenters.

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

The smart energy system, viewed as an “Energy Internet”, consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter ( S T U K F ) and modified Bayes’ classification-based Modified Three Sigma test ( M T S ), abbreviated as S F B T , for smart energy networks. The theoretical analysis and simulations indicate that S F B T detects faults with a high accuracy and a low complexity of O ( n ) .

A semi-physical static model for optimizing power consumption of HVAC systems

Mathematical models of heating, ventilation and air conditioning (HVAC) systems is of great significance for optimizing power consumptions in large buildings. This paper proposes a semi-physical model to describe static characters of a typical HVAC system. Major components of the HVAC system are modeled individually according to their thermodynamic principles or input–output relations, and are integrated into an overall model by choosing input and output variables from a perspective of optimizing power consumptions. One set of experimental data samples is used to estimate unknown model coefficients, and more data sets are taken to validate the estimated model. Based on the estimated model, genetic algorithms are exploited to find the optimal operating point of the HAVC system for minimizing power consumptions under constraints on indoor temperatures.

Optimized Bit Matrix based Power Aware Load Distribution Policy for Federated Cloud

Federated Cloud is a next generation cloud computing model which works towards a new dynamic scenario in smart ecosystems. It consolidates individual clouds together to form single large cloud to satisfy all on-demand requirements of the users. Federated cloud is currently facing many challenges including: monitoring of individual cloud services, maintaining service level agreement, fair load distribution, power usage etc. Hence, to address these challenges this paper presents a brokerage model “Optimized Bit Matrix based Power Aware Load Distribution Policy for Federated Cloud (OBMPLP)”. Presented model includes two novel concepts: Bit Matrix and Load Distribution Factor. Bit Matrix constructed by individual cloud members representing resource availability status and environment in executing the user’s request. Load Distribution Factor representing load distribution level at individual cloud. OBMPLP policy dispenses the incoming requests among multiple clouds by analyzing bits pattern and load distribution factor that not only performs fair load distribution among multiple clouds, but also improves response time, alleviate power consumption at each cloud and achieve better quality service. The performance of the proposed policy is evaluated and results demonstrate reduction in response time, optimized power consumption and fair distribution of load compared to existing approaches.

Distributed multi-hop routing algorithm for wireless sensor networks

In a Wireless Sensor Network (WSN), routing is the process of finding a cost effective route in terms of power consumption. As an evaluation criterion for the WSN performance, network lifetime is directly affected by the routing method. In a wide variety of WSNs, different techniques are used as routing methods, such as shortest distance path. In this paper, we propose a novel algorithm, optimizing power consumption in WSN nodes, based on the shortest path algorithm. In this approach, the energy level of nodes and their geographical distance from each other contribute to the weight of the connecting path. The proposed algorithm is used as a data dissemination method in WSNs with randomly scattered nodes. We also apply Dijkstra?s shortest path algorithm to the same networks. The results showed that the proposed algorithm increases the network lifetime up to 30 % by preventing nodes with low charge levels from early disconnection.

Be Good Neighbors: A Novel Application Isolation Metric Used to Optimize the Initial Container Placement in CaaS

Many cloud providers benefit from fine-grained resource management in Container as a Service (CaaS), e.g., energy savings. However, while optimizing power consumption, cloud providers also need to consider service performance. Few studies in the literature performed a balanced optimization of the two objectives in the initial container placement (ICP). As such, the obtained solutions are often locally optimal. In this work, we model the ICP as a bi-objective optimization problem. Unfortunately, directly applying a conventional performance modeling method in solving the problem is complex and costly, especially for a large-scale application scenario. It also fails to capture the impacts of some non-resource constraints such as high availability constraints. To tackle these issues, we propose a novel application isolation metric to quantify the overall service performance. With regard to the power consumption objective, we opt for a non-linear model since a linear one makes power consumption indistinguishable in a homogeneous setting. With the two building blocks, we introduce an optimization algorithm named First Fit based improving Genetic Algorithm (denoted by FF-based-IGA) to find the best ICP solution. Our experimental results are as follows: 1) the proposed application isolation metric is effective at improving service performance, and 2) FF-based-IGA achieves a desirable balance between power consumption and service performance.

Power Consumption Optimization Using Gradient Boosting Aided Deep Q-Network in C-RANs

Cloud Radio Access Networks (C-RANs) have the potential to enable growing data traffic in 5G networks. However, with the complex states, resource allocation in C-RANs is time-consuming and computational-expensive, making it challenging to meet the demands of energy efficiency and low latency in real-time wireless applications. In this paper, we propose a gradient boosting decision tree (GBDT)-based deep Q-network (DQN) framework for solving the dynamic resource allocation (DRA) problem in a real-time C-RAN, where the heavy computation to solve SOCP problems is cut down and significant power consumption can be saved. First, we apply the GBDT to the regression task to approximate the solutions of second order cone programming (SOCP) problem formulated from beamforming design which consumes heavy computing resources by traditional algorithms. Then, we design a deep Q-network (DQN), coming from a common deep reinforcement learning, to autonomously generate the robust policy that controls the status of remote radio heads (RRHs) and saves the power consumption in long term. The DQN deploys deep neural networks (DNN) to solve the problem of innumerable states in the real-time C-RAN system and generates the policy by observing the state and the reward engendered by GBDT. The generated policy is error-tolerant considering that the gradient boosting regression may not be strictly subject to the constraints of the original problem. Simulation results validate its advantages in terms of the performance and computational complexity for power consumption saving compared with existing methods.

Power Consumption Optimization in IoT based Wireless Sensor Node Using ESP8266

In this paper, we present the design and prototype implementation of a weather monitoring system for a college campus powered by a renewable energy source which is based on solar panel for remote applications. The device is powered with the help of rechargeable battery which is recharged through solar energy. Sensors are connected to the device and device uploads these sensor data on cloud with the help of Wi-Fi and internet connection. The main objective of this research is to power this device on renewable energy and continuous sensor data uploading on cloud. To keep device in running mode on battery and to save power deep sleep function of the device is used which consume very less power, which makes it sufficient to run on solar power for long time.

Fuzzy Based Particle Swarm Optimization for Modeling Home Appliances Towards Energy Saving and Cost Reduction Under Demand Response Consideration

Recently, homes consume around 40% of world power and produce 21% of the total greenhouse gas emissions. Thus, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, an intelligent home energy management system (HEMS) is developed to control domestic appliances load. The motivation of this work is reduced the electricity cost and power consumption from all the appliances by maintaining the customer’s high comfort level using an efficient optimized controller. The domestic household appliances such as heating ventilation and air conditioning (HVAC), electric water heater (EWH) and lighting were modelled and analysed using Simulink/Matlab. The developed models analysed the appliances’ energy consumption and cost sceneries during peak, off-peak and both peak and off-peak hours. Fuzzy logic controller (FLC) was developed for the HEMS to perform energy utilization estimation and cost analysis during these periods taking the Malaysian tariff for domestic use into consideration. To improve the FLC outcomes and the membership function constraint, particle swarm optimization (PSO) is developed to ensure an optimal cost and power consumption. The results showed that the developed FLC controller minimized the cost and energy consumption for peak period by 19.72% and 20.34%, 26.71% and 26.67%, 37.5% and 33.33% for HVAC, EWH, and dimmable lamps, respectively. To validate the optimal performance, the obtained results shows that the FLC-PSO can control the home appliances more significantly compared to FLC only. In this regard, the FLC-PSO based optimum scheduled controller for the HEMS minimized power and cost by 36.17%-36.54%, 54.54%-55.76%, and 62.5%-58% per day for HVAC, EWH, and light, respectively. In sum, the PSO shows good performance to reduce the cost and power consumption toward efficient HEMS. Thus, the developed fuzzy-based heuristic optimized controller of HEMS is beneficial towards sustainable energy utilization.

Optimum Functional Splits for Optimizing Energy Consumption in V-RAN

A virtualized radio access network (V-RAN) is considered one of the key research points in the development of 5G and the interception of machine learning algorithms in the Telecom industry. Recent technological advancements in Network Function Virtualization (NFV) and Software Defined Radio (SDR) are the main blocks towards V-RAN that have enabled the virtualization of dual-site processing instead of all BBU processing as in the traditional RAN. As a result, several types of research discussed the trade-off between power and bandwidth consumption in V-RAN. Processing at remote locations instead of BBU reduces mid-haul bandwidth at the expense of power consumption and vice versa. As a result, the integration of NFV and SDR in V-RAN facilitates dynamic power consumption and processing whenever relaxation is needed. This paper studies several functional splits proposed by ETSI in the NFV of the dual-site network. In addition, network performance is analyzed in terms of data rate, power consumption, and energy efficiency (EE) optimization. Furthermore, the combined optimization of power consumption and mid-haul bandwidth are investigated, and optimal operating parameters are recommended for similar network operators. Thus, regulators/operators can adjust their networks with these parameters to achieve the best performance. Additionally, the UEs switching scheme is introduced to sleep some RRHs in low-density traffic to lessen power consumption.

Design of Double-Gate Tri-Active Layer Channel Based IGZO Thin-Film Transistor for Improved Performance of Ultra-Low-Power RFID Rectifier

In a Radio Frequency Identification (RFID) circuit, the rectifier plays a vital role since it converts the received RF energy from a distant transmitter (reader) to power the entire RF circuit of the passive tag. The thin-film transistors have the potential to affect the rectifier circuit performance using the channel structural modification in the device to accomplish better gate-control for low-power operations. In this paper, the authors have presented a novel design of a double-gate amorphous In-Ga-Zn-O (IGZO) based thin-film transistor with a tri-active layer channel structure. Further, the experimental characterization of the device has been implemented using the SPICE model to analyze the device performance in the rectifier circuits. The simulation reports the competitive results with an improved rectifier performance using the proposed structure. The performance of ultra-low-power rectifier topologies has been compared with the conventional rectifier circuit. Among others, the differential rectifier circuit arrangement has a high power conversion efficiency of 20.06 % for low input 0.45 V-0.61 V, whereas Self-$\text{V}_{\mathrm {TH}}$ -Cancellation (SVC) configuration optimum power consumption is $0.14~\mu W$ . The promising results suggest the device deployment in the passive RFID tags and implantable devices in ultra-low-power integrated circuits.

Joint Computation Offloading and URLLC Resource Allocation for Collaborative MEC Assisted Cellular-V2X Networks

By leveraging the 5G enabled V2X networks, the vehicles connected by cellular base-stations can support a wide variety of computation-intensive services. In order to solve the arisen challenges in end-to-end low-latency transmission and backhaul resources, mobile edge computing (MEC) is now regarded as a promising paradigm for 5G-V2X communications. Considering the importance of both reliability and delay in vehicle communication, this article innovatively envisions a joint computation and URLLC resource allocation strategy for collaborative MEC assisted cellular-V2X networks and formulate a jointly power consumption optimization problem while guaranteeing the network stability. To solve this NP hard problem, we decouple it into two sub-problems: URLLC resource allocation for multi-cells to multi-vehicles and computation resource decisions among local vehicle, serving MEC server and collaborative MEC server. Secondly, non-cooperative game and bipartite graph are introduced to reduce the inter-cell interference and decide the channel allocation, which aims to maximize the throughput with a guarantee of reliability in URLLC V2X communication. Then, an online Lyapunov optimization method is proposed to solve computation resource allocation to get a trade-off between the average weighted power consumption and delay where CPU frequency are calculated using Gauss-Seidel method. Finally, the simulation results demonstrate that our proposed strategy can get better trade-off performance among power consumption, overflow probability and execution delay than the one based on centralized MEC assisted V2X.

An $S$ -Band GaAs Multifunction Chip for Transmit/Receive Modules

In this article, a highly integrated $S$ -band prototype multifunction chip (MFC) featuring high integration level, bidirectional operation, reduced number of pads, and optimized power consumption is demonstrated with a GaAs 0.15- $\mu \text{m}$ pHEMT technology. In order to pursue a high integration level, the MFC utilizes four amplifiers, two bidirectional mixers, and compact integrated passive filters. For the sake of power supply pads, the self-biasing technique is adopted with the on-chip power-combining technique. As for the power consumption minimization, when the system voltage is constant, the current reuse technique is selected with optimized transistor size without degradation of the key specifications. The main function for the MFC is to provide double-conversion and gain amplification both in the transmission path and the receiving path on one chip. In transmit mode, the MFC achieves 0-dB gain and 7-dBm $P_{-1\,\mathrm {dB}}$ from 2.6 to 3.2 GHz. The obtained local oscillator (LO)-radio frequency (RF) isolation is better than 50 dB, and the intermediate frequency (IF)-RF isolation is 85 dB. In receive mode, it achieves −5-dB gain with $V_{\mathrm {DD}}$ of the MFC is 5 V. The fabricated MFC occupied 5 mm $\times 5$ mm, which is the largest record area that the process can withstand.

Design technique for regulated cascode transimpedance amplifier using Gm/ID methodology

This paper presents an approach using gm/ID methodology for the design of the regulated cascode circuit (RGC) transimpedance amplifier (TIA) for optical receivers. The framework uses lookup tables produced using the gm/ID methodology to define the sizing of the transistors needed to reach the required specifications. The presented framework has the advantage of setting limits on the design space using intuitive equations derived from the circuit analyses. This gives insight into the effect of changing the value of different circuit parameters on the resulting design performance while decreasing the calculation time to reach the desired design. The proposed method can reach the required specifications with the flexibility to minimize the DC power consumption or the total input referred noise. The framework is implemented in a 130 ​nm CMOS process with a 1.5 ​V supply voltage producing two different designs. Both designs met the required specifications while optimizing power consumption or noise and were validated using simulations. The results obtained are discussed and compared against other similar designs from the state-of-the-art.

Computer Simulation of a Smart Building

Smart Building is a technology that is on the rise in developed countries. The idea behind it is that a smart building should be able to recognize a variety of situations in itself and respond accordingly. An automated building management system is set up to automate the controls and to coordinate the separate building engineering systems; the system comprises subsystems to automate this or that segment of the equipment. Mathematical modeling and computer simulation is necessary to study the various modes, in which the engineering subsystems and the system as a whole may operate. From the mathematical standpoint, a smart building is a continuous-discrete or hybrid system that comprises multiple coordinated components of varying nature that can be described as continuous or discrete processes. This paper proposes a smart building model that can simulate the functioning of the core engineering subsystems as well as the algorithms behind them. The model is based on Matlab Simulink and uses the Simscape physical modeling library as well as the Stateflow library. The model employs specialized control algorithms to coordinate subsystems and optimize power consumption.

Multilayer Virtual Cell-Based Resource Allocation in Low-Power Wide-Area Networks

The Internet of Things (IoT) technology has attracted widespread attention since it can connect a huge number of heterogeneous devices to construct an intelligent environment to offer various services. Driven by the decreasing expense and significant convenience, it is expected that the total number of IoT devices will reach as high as 50 billion by 2020. And, these devices will provide diversified Internet services, including smart home, elder care, and vehicle-to-everything (V2X) communications. For the IoT technology, the network performance as well as the energy efficiency are both important since most end devices are battery limited. In this article, we focus on the IoT network based on the long-range wide-area network (LoRaWAN) protocol since its chirp modulation technology can adopt different spreading factors (SFs) to realize flexible communications. We propose a novel resource allocation strategy which utilizes multilayer virtual cell-based spatial time division multiple access (STDMA) scheme. This new method can not only simplify the calculation of the interference but also enables the cell radius to be adjusted to fit the communication distance. Moreover, the relationship between the power consumption and the network data rate is also analyzed in this article. We numerically analyze the optimal power consumption to achieve the highest data rate. The final performance evaluation demonstrates the significant improvement of our proposal compared with the conventional method.

Design and Implementation of a Wake-Up Radio Receiver for Fast 250 kb/s Bit Rate

Wake-up radio (WuR) systems have appeared as a solution for reducing power consumption. As IEEE 802.11 is the preferred radio technology, IEEE 802.11 task group TGba focuses on the IEEE 802.11-based WuR standardization. We present an implementation of a wake-up receiver (WuRx), based on off-the-shelf components, using a microcontroller unit for decodification at the fastest bit rate of 250 kb/s allowed by TGba. The proof-of-concept that has been developed, does not aim at presenting an optimized power consumption solution for WuRx, but at enabling a comparative evaluation for several reception cases based on latency and power utilization. The evaluation of the performance shows the WuRx robustness in presence of frame receptions different from the expected for WuR operation or addressed to other WuRx in the surrounding area. This letter also presents the convenience of implementing a mechanism for avoiding consecutive erroneous WuRx identification matchings within the same WuR procedure.

Mathematical Models and Algorithms of Optimal Load Management of Electricity Consumers

Load profile alignment based on optimal power consumption management is considered to be one of the main ways to ensure efficient operation of energy systems in the short-term planning. Alignment load profile with a view to reducing costs can be implemented with the aid of consumers’ involvement by administrative and economic measures. Administrative measures are associated with the forced restriction of consumer loads in certain intervals of the planning period. On one hand, these measures provide benefits to the power system by alignment load profile, and on the other hand, they cause detriment to consumers. Ultimately, in some cases, for the whole power system, the detriment may be greater than the benefits. Therefore, it is advisable to use administrative measures in conditions of shortage of power and electricity in the power system. Optimal planning of short-term regimes of power systems according to rigid load profile received after alignment can be carried out by traditional methods. The solution of such a problem ought to be initially carried out under conditions of non-rigid load profile resulting from the directive use of administrative and economic measures carried out with the help of specially developed models. In this regard, the paper proposes a mathematical model of the problem of optimizing load profile of regulated electricity consumers to be used for optimal planning of shortterm power system modes, an algorithm for optimal planning of a short-term power system mode with optimizing load profile of regulated power consumers. Also, algorithms are proposed for accounting for simple and functional constraints in the form of equalities and inequalities when optimizing load profile. The effectiveness of the described algorithm for optimizing the short-term mode of the power system, taking into account the optimal load control of adjustable electricity consumers, has been studied using the example of optimal coverage of the load profile of power system, which contains two consumers with adjustable load profile, and two TPPs. Based on the calculated-and-experimental studies, it was determined that the proposed mathematical model of the problem is adequate, while the developed algorithms for optimal planning of short-term power system modes with optimization of load profile of regulated electricity consumers and taking into account various types of limitations are of high computational qualities.

Green-Up: under-provisioning power backup infrastructure for green datacenters

Power backup infrastructure is indispensable but its cost exceeds 20% of the whole power infrastructure expenditure of a traditional datacenter. Under-provisioning the power backup infrastructure has therefore been proposed to reduce the backup cost while still keep power available during grid power outages. However, how to under-provision the power backup facility for a green datacenter using renewable energy has not yet been explored. This paper proposes an approach to under-provision the power backup infrastructure as well as optimize the performance of individual applications with the presence of renewable energy, named Green-Up. It contributes two novel techniques: power source and power load managements. Power source management identifies the suitable power source for power backup whereas power load management selects the best power capping techniques for under-provisioning power backup infrastructure. In addition, we build hierarchical Bayesian Models to optimize power consumption and performance. To evaluate Green-Up, we build an experimental cluster consisting of 10 servers with a simulated solar power generator, and use four representative datacenter benchmarks. The experimental results demonstrate that our theoretical solutions provide a seamless bridge across the whole spectrum of renewable power availability and durations of grid power outages.