Energy Consumption Of Devices Research Articles

Analysis of Energy and Environmental Indicators for Sustainable Operation of Mexican Hotels in Tropical Climate Aided by Artificial Intelligence

This study assessed the energy-use index and carbon-footprint performance of nine medium-category Mexican hotels (two–four stars) located in tropical-climate regions. The consumption of electrical and thermal energies of each hotel was collected during audits. Based on this, various scenarios of the partial replacement of the most energy-consuming devices were evaluated and synthesized in an expert model based on artificial neural networks. The artificial-intelligence model was designed to simultaneously associate the energy-consumption indicators, environmental impact, and economic savings of hotels based on their category, location, room number, number of existing electrical or thermal devices, and their percentage of substitution with more energy-efficient technologies. The model was used to compare the various partial-technology-substitution alternatives in each hotel that could reduce energy consumption and CO2 emissions based on the current values reported by the energy-use and environmental-impact indicators. The results of the proposed approach showed that even without making total replacements of equipment such as interior and exterior lighting or air conditioners, it was possible to identify configurations that could reduce the hotels’ energy use per room-year by 9–12%. In the environmental case, using more efficient technologies could reduce environmental mitigation. The proposed methodology represents an attractive option to facilitate the analyses and the decision making of administrators according to the needs of the type of hotel to improve its performance, which also affects the reduction in operating costs.

Energy-Efficient Computation Offloading in Mobile Edge Computing Systems With Uncertainties

Computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC systems are subject to various uncertainties rendering these assumptions impractical. In this paper, we investigate the energy-efficient computation offloading problem by relaxing those common assumptions and considering intrinsic uncertainties in the network. Specifically, we minimize the worst-case expected energy consumption of a local device when executing a time-critical application modeled as a directed acyclic graph. We employ the extreme value theory to bound the occurrence probability of uncertain events. To solve the formulated problem, we develop an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -bounded approximation algorithm based on column generation. The proposed algorithm can efficiently identify a feasible solution that is less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1+\epsilon)$ </tex-math></inline-formula> of the optimal one. We implement the proposed scheme on an Android smartphone and conduct extensive experiments using a real-world application. Experiment results corroborate that it will lead to lower energy consumption for the client device by considering the intrinsic uncertainties during computation offloading. The proposed computation offloading scheme also significantly outperforms other schemes in terms of energy saving.

Intelligent-Reflecting-Surface-Aided Mobile Edge Computing With Binary Offloading: Energy Minimization for IoT Devices

Mobile edge computing (MEC) is envisioned as a promising technique to support computation-intensive and time-critical applications in future Internet of Things (IoT) era. However, the uplink transmission performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Recently, intelligent reflecting surface (IRS) has drawn much attention because of its capability to control the wireless environments so as to enhance the spectrum and energy efficiencies of wireless communications. In this article, we consider an IRS-aided multidevice MEC system where each IoT device follows the binary offloading policy, i.e., a task has to be computed as a whole either locally or remotely at the edge server. We aim to minimize the total energy consumption of devices by jointly optimizing the binary offloading modes, the CPU frequencies, the offloading powers, the offloading times, and the IRS phase shifts for all devices. Two algorithms, which are greedy based and penalty based, are proposed to solve the challenging nonconvex and discontinuous problem. It is found that the penalty-based method has only linear complexity with respect to the number of devices, but it performs close to the greedy-based method with cubic complexity with respect to the number of devices. Furthermore, binary offloading via IRS indeed saves more energy compared to the case without IRS.

Multiobjective Optimization for Joint Task Offloading, Power Assignment, and Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) is an emerging computational paradigm for providing storage and computing capabilities in network edge, to improve the experience of users, to shorten the delay, and to reduce the energy consumption of mobile devices. In this article, we consider a multiuser and multiserver scenario, where each user has an application composed of multiple independent tasks that need to be executed, and each MEC server is equipped on a base station (BS) for assisting mobile users to execute computation-intensive and time-sensitive tasks. Multiobjective optimization for joint task offloading, power assignment, and resource allocation is studied to maximize the offloading gains of users. A multivariable and multiobjective optimization problem with three objectives is constructed. An efficient multiobjective evolutionary algorithm is developed to solve the problems of minimizing the response time, minimizing the energy consumption, and minimizing the cost. Simulation results verify the effectiveness of our algorithm, and show the method significantly improves the user’s offloading benefits. According to the author’s knowledge, this is the first paper on the exploration of multiobjective optimization of multiuser with multiple tasks and multiserver MEC system, in which the worst user offloading revenue is regarded as the optimization objectives.

Lotus leaf-inspired and multifunctional Janus carbon felt@Ag composites enabled by in situ asymmetric modification for electromagnetic protection and low-voltage joule heating

Multifunctional materials with electromagnetic interference (EMI) shielding, thermal management, sensing, and comfort characteristics are greatly desirable in the application of intelligent and wearable devices. However, the multifunctional integration and low energy consumption of wearable devices remain a great challenge. Herein, inspired by the lotus leaf, a strategy of in situ asymmetric modification is proposed to construct a highly conductive and multifunctional Janus carbon felt@Ag nanoparticles (AgNPs) composite. Due to the uniform AgNPs layer together with carbon fiber, the resultant multifunctional composite demonstrates high electric conductivity with shielding effectiveness (SE) of ∼65 dB at a thickness of 270 μm. The composite also exhibits a great joule-heating property with the saturated temperature of 61.1 °C at an extremely low voltage of 1 V, as well as boosted thermal durability. More importantly, the asymmetrical feature constructed by a unique nano-micro structure endows the composite with simultaneously hydrophobic and superhydrophilic properties, which may integrate sweat removal and waterproof functions for manufacturing comfortable wearable devices. In addition, the carbon felt@AgNPs composite displays enormous potential as a sensor for human motion monitoring. Therefore, this work would provide a vital sight for fabricating advanced wearable equipment with EMI shielding, thermal management, and multifunctional applications.

Accurate Power Modelling Framework for Medical Images in Embedded System

The display in embedded devices is a significant energy-consuming device, and the display content determines the degree of energy consumption. In the practice of energy saving and emission reduction, it is necessary to build an accurate medical image power prediction model for the display. Current power consumption prediction models for medical images often focus on the performance of a single prediction model, ignoring the ability of multiple single prediction models to improve performance. This paper proposes an accurate medical power framework (AMPF), which consists of three steps. In the first step, the strong predictor is used to compose multiple RGB single channels to obtain the power model. In the second step, the power between RGB channels is represented by errors, and the power generated by RGB channel dependence is obtained by the strong predictor method. The third step is to compose the power of the first step and the second step to get accurate medical image power prediction results. The experimental results show that the accuracy of the AMFP proposed in this paper reaches 0.9798, which is 6.836% higher than that of the single power model. The AMPF implemented by AdaBoost is superior to that implemented by EWM in performance and has about 1.3 times the time advantage in training.

Processing capacity‐based decision mechanism edge computing model for IoT applications

AbstractThe handling of complex tasks in IoT applications becomes difficult due to the limited availability of resources in most IoT devices. There arises a need to offload the IoT tasks with huge processing and storage to resource enriched edge and cloud. In edge computing, factors such as arrival rate, nature and size of task, network conditions, platform differences and energy consumption of IoT end devices impacts in deciding an optimal offloading mechanism. A model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity. This dynamic decision is proposed as processing capacity‐based decision mechanism (PCDM) which takes the offloading decisions on new tasks by scheduling all the available devices based on processing capacity. The target devices are then selected for task execution with respect to energy consumption, task size and network time. PCDM is developed in the EDGECloudSim simulator for four different applications from various categories such as time sensitiveness, smaller in size and less energy consumption. The PCDM offloading methodology is experimented through simulations to compare with multi‐criteria decision support mechanism for IoT offloading (MEDICI). Strategies based on task weightage termed as PCDM‐AI, PCDM‐SI, PCDM‐AN, and PCDM‐SN are developed and compared against the five baseline existing strategies namely IoT‐P, Edge‐P, Cloud‐P, Random‐P, and Probabilistic‐P. These nine strategies are again developed using MEDICI with the same parameters of PCDM. Finally, all the approaches using PCDM and MEDICI are compared against each other for four different applications. From the simulation results, it is inferred that every application has unique approach performing better in terms of response time, total task execution, energy consumption of device, and total energy consumption of applications.

Joint task offloading and resource allocation in mobile edge computing with energy harvesting

Mobile edge computing (MEC) is considered to be a promising technique to enhance the computation capability and reduce the energy consumption of smart mobile devices (SMDs) in the sixth-generation (6G) networks. With the huge increase of SMDs, many applications of SMDs can be interrupted due to the limited energy supply. Combining MEC and energy harvesting (EH) can help solve this issue, where computation-intensive tasks can be offloaded to edge servers and the SMDs can also be charged during the offloading. In this work, we aim to minimize the total energy consumption subject to the service latency requirement by jointly optimizing the task offloading ratio and resource allocation (including time switching (TS) factor, uplink transmission power of SMDs, downlink transmission power of eNodeB, computation resources of SMDs and MEC server). Compared with the previous studies, the task uplink transmission time, MEC computation time and the computation results downloading time are all considered in this problem. Since the problem is non-convex, we first reformulate it, and then decompose it into two subproblems, i.e., joint uplink and downlink transmission time optimization subproblem (JUDTT-OP) and joint task offloading ratio and TS factor optimization subproblem (JTORTSF-OP). By solving the two subproblems, a joint task offloading and resource allocation with EH (JTORAEH) algorithm is proposed to solve the considered problem. Simulation results show that compared with other benchmark methods, the proposed JTORAEH algorithm can achieve a better performance in terms of the total energy consumption.

Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing.

Advancement in technology has led to an increase in data. Consequently, techniques such as deep learning and artificial intelligence which are used in deciphering data are increasingly becoming popular. Further, advancement in technology does increase user expectations on devices, including consumer interfaces such as mobile apps, virtual environments, or popular software systems. As a result, power from the battery is consumed fast as it is used in providing high definition display as well as in charging the sensors of the devices. Low latency requires more power consumption in certain conditions. Cloud computing improves the computational difficulties of smart devices with offloading. By optimizing the device's parameters to make it easier to find optimal decisions for offloading tasks, using a metaheuristic algorithm to transfer the data or offload the task, cloud computing makes it easier. In cloud servers, we offload the tasks and limit their resources by simulating them in a virtual environment. Then we check resource parameters and compare them using metaheuristic algorithms. When comparing the default algorithm FCFS to ACO or PSO, we find that PSO has less battery or makespan time compared to FCFS or ACO. The energy consumption of devices is reduced if their resources are offloaded, so we compare the results of metaheuristic algorithms to find less battery usage or makespan time, resulting in the PSO increasing battery life or making the system more efficient.

Experimental investigation on a cooling cum desalination system using a modified mechanical heat pump

An experimental study on a mechanical heat pump unit incorporated with a humidification-dehumidification system for desalination and cooling is presented. The experimental prototype is developed by modifying the condenser of a mechanical heat pump unit into an evaporative condenser. The evaporative condenser is used as a humidifier, and the evaporator as a dehumidifier, of the humidification-dehumidification desalination system. The proposed unit is evaluated with combinations of air and saline water flow rates, and also evaluated by means of total desalination output, cooling output, gained output ratio and energy utilization factor. At fixed air flow rate, increasing saline water flow rate decreases the desalination output, and increases the cooling output, gained output ratio and energy utilization factor, respectively. The highest fresh water yield and gained output ratio are 2.94 LPH and 1.30 respectively. The highest cooling output and energy utilization factor are 4.39 kWth and 4.12, respectively. The total urban heat island mitigation at the operating condition of 200 m3.h−1 and 380 LPH is 5.25 kWth. Compared with similar vapor compression refrigeration operated humidification dehumidification desalination units, the present study produces more fresh water yield and cooling output with fewer energy consuming devices and heat exchangers.

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network.

Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices.

HB-ReTriple: Mobile rendering optimization based on efficient history reusing

UI rendering on smartphones needs powerful CPU and GPU to meet user-perceived smoothness, and it contributes a significant portion of energy consumption of the battery-driven device. Improving energy efficiency of rendering process without degrading user experience has attracted research attention in both academia and industry. In this work, we propose an energy-efficient rendering optimization framework, called HB-ReTriple, based on reusing the rendering results of the unchanged Views across adjacent frames. This reusing can effectively reduce the amount of computation spent on UI rendering, thereby saving considerable energy. We implement our framework on a real Android mobile platform, and the experiments show that HB-ReTriple achieves an average speedup of 2.89x in terms of frames-per-second (FPS) and an energy saving of 38.2% per-frame.

Joint Optimization Framework for Minimization of Device Energy Consumption in Transmission Rate Constrained UAV-Assisted IoT Network

Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be an alternative option for a scenario where Internet of Things (IoT) devices consume high energy to achieve the required data rate when they are far away from the terrestrial base station (BS). Therefore, this article has proposed an energy-efficient UAV-assisted IoT network where a low-altitude quad-rotor UAV provides mobile data collection service from static IoT devices. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, devices association, and respectively, transmit power allocation at every time slot while ensuring that every device should achieve a given data rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt a reinforcement learning (RL)-based solution methodology that effectively decouples it into three individual optimization subproblems. The formulated optimization problem has transformed into a Markov decision process (MDP) where the UAV learns its trajectory according to its current state and corresponding action for maximizing the generated reward under the current policy. Finally, we conceive state–action–reward–state–action, a low complexity iterative algorithm for updating the current policy of UAV, that achieves an excellent computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on optimal UAV trajectory. The proposed methodology reduces the total energy consumption of all devices by 6.91%, 8.48%, and 9.94% in 80, 100, and 120 available time slots of UAV, respectively, compared to the particle swarm optimization (PSO) algorithm.

Energy Consumption Forecasting for the Digital-Twin Model of the Building

The aim of the paper is to propose a new approach to forecast the energy consumption for the next day using the unique data obtained from a digital twin model of a building. In the research, we tested which of the chosen forecasting methods and which set of input data gave the best results. We tested naive methods, linear regression, LSTM and the Prophet method. We found that the Prophet model using information about the total energy consumption and real data about the energy consumption of the top 10 energy-consuming devices gave the best forecast of energy consumption for the following day. In this paper, we also presented a methodology of using decision trees and a unique set of conditional attributes to understand the errors made by the forecast model. This methodology was also proposed to reduce the number of monitored devices. The research that is described in this article was carried out in the context of a project that deals with the development of a digital twin model of a building.

Clustering Method of Mobile Cloud Computing According to Technical Characteristics of Cloudlets

The rapid increase in the number of mobile phones and IoT devices connected to the network reduces the bandwidth of the Internet communication channel, and as a result, delays occur in the delivery of data processed in remote clouds. Edge computing systems (cloudlet, fog computing, etc.) are used to eliminate resource shortages, energy consumption, and communication channel delays in mobile devices. Edge computing systems place processing devices (computers) close to users. Cloudlet-based mobile cloud computing is widely used to reduce delays in communication channels and energy consumption in mobile devices. Selection of the most suitable cloudlet allowing users to run applications fast in cloud is still a considerable problem. This paper proposes a strategy for the selection of high-performance cloudlets providing fast solutions, considering the complexity of application (file type). It offers a method for cloudlet selection out of large number of cloudlets with different technical capabilities providing faster processing of user application. The timing of user applications in cloudlets with different technical capabilities (operating frequency, number of cores, volume of RAM, etc.) also varies. The proposed method provides faster solution for the user application. User applications are grouped by type of application, and a set of cloudlets are clustered by the number of groups. Clustering is performed first by the parameters corresponding to the operating frequency of the cloudlets, then by the number of cores and the volume of RAM. The proposed method reduces energy consumption of mobile devices by providing faster processing of applications. Thus, the proposed strategy provides an energy consumption reduction on mobile devices, faster processing of results and decrease of network delays.

Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT

The Internet of Things (IoT) has subsequently been applied to a variety of sectors, including smart grids, farming, weather prediction, power generation, wastewater treatment, and so on. So if the Internet of Things has enormous promise in a wide range of applications, there still are certain areas where it may be improved. Designers had focused our present research on reducing the energy consumption of devices in IoT networks, which will result in a longer network lifetime. The far more suitable Cluster Head (CH) throughout the IoT system is determined in this study to optimize energy consumption. Whale Optimization Algorithm (WOA) with Evolutionary Algorithm (EA) is indeed a mixed meta-heuristic algorithm used during the suggested study. Various quantifiable metrics, including the variety of adult nodes, workload, temperatures, remaining energy, and a target value, were utilized IoT network groups. The suggested method then is contrasted to several cutting-edge optimization techniques, including the Artificial Bee Colony method, Neural Network, Adapted Gravity Simulated annealing. The findings show that the suggested hybrid method outperforms conventional methods.

An Effective Edge-Intelligent Service Placement Technology for 5G-and-Beyond Industrial IoT

With the rapid development of wireless communication, traditional cloud computing cannot fully support low-latency services, especially in sensor networks. Mobile edge computing (MEC) can improve the quality of experience of end users and save the energy consumption of mobile end devices by providing computing resources and storage space. However, it may cause discontinuity of services if these mobile end devices roam around different MEC servers’ areas. To solve the aforementioned problem, in this article, we propose an effective edge-intelligent service placement algorithm (EISPA), which transforms the service placement problem into finding a globally optimal solution via nature-inspired particle swarm optimization (PSO). Moreover, we use a shrinkage factor and combine it with the simulated annealing (SA) algorithm to adjust the position of particles in our algorithm, which aims to avoid falling into an optimal local solution to a certain extent. Performance analysis results show that the EISPA is approaching the optimal enumeration collaborative computation offloading algorithm, and system cost under energy constraints is 83.6%, 20.4%, and 20.3% lower than that in Only Local, Finding the Nearest Edge, and the genetic SA-based PSO algorithms, respectively, which proves that the EISPA has better performance.

Human activity recognition via wearable devices using enhanced ternary weight convolutional neural network

Human activity recognition (HAR) has been known as an active area for more than a decade, and there are still crucial aspects that are intended as challenging problems. Providing detailed and appropriate information about the activities and behaviors of people is one of the most important fields in ubiquitous computing. There are numerous applications in this field, among which healthcare, security, and entertainment scenarios can be listed. Human activity recognition can be carried out with the assistance of smartphone sensors such as accelerometers and gyroscopes or images captured from webcams. Today, the application of deep neural networks in this domain has received much attention and has led to more accurate and effective results compared to traditional techniques. The deep neural network performs arithmetic operations on a various number of hidden layers. In this article, a new approach called HAR-CT is proposed to enhance the accuracy of human activity recognition in various classes by adopting a convolutional neural network (CNN). Subsequently, an optimization technique using the TWN model is also suggested to reduce the complexity of the deep neural network approach that decreases the energy consumption of mobile devices. To this end, the float precision weights of the convolutional neural network are quantized and converted into ternary weights, while the decline in the accuracy is very low compared to the initial deep neural network. The evaluation results of both networks demonstrate that the proposed methods outperform the recently published approaches in human activity recognition.

Security and Makespan Trade-Off Strategy in Fog-Enabled IoT Networks

Fog computing relieves the Internet of Things (IoT) from a heavy burden of workload computation, consequently minimizes workload makespan, and reduces the considerable energy consumption of IoT devices. However, the fog-enabled IoT poses a risk to data security due to the intrinsic open and distributed structure of the fog. Hence, it is imperative to search for an optimal scheduling strategy on fog nodes to minimize workload makespan, while guaranteeing high data security. In this study, we investigate the trade-off between security and makespan and propose a multiobjective optimization model to optimize them simultaneously. To achieve this goal, we design an evolutionary algorithm together with two effective measures to handle large-scale partitionable workloads: a conflict-free multi-installment scheduling (ConfMIS) strategy as well as a security criterion based on Eigenvector centrality measure. Experimental results show that the proposed algorithm can generate a set of widely spread and uniformly distributed solutions in the decision space. Among these representative solutions, system designers/decision-makers can choose their favorite one based on specific IoT application demands.

Low-Cost HEM with Arduino and Zigbee Technologies in the Energy Sector in Colombia

Since no solutions have been proposed in Colombia that seek to reduce the consumption of electricity at the residential level, this paper describes the design and implementation of a simple prototype of a low-cost home energy management system (HEMS). The objective of this platform is to monitor the energy consumption of typical household devices so that users can access the consumption of each device separately and then establish the strategy that allows them to reduce energy consumption at home. In order to demonstrate that our system is viable, the system has been evaluated by measuring weekly energy consumption with the on-line and off-line HEMS using a test bench with typical household devices in a Sincelejo typical household. The evaluation has shown that with the installation of this HEMS, consumption is reduced by 27%. This shows that it is possible to achieve a good reduction percentage with a low-cost system.