scholarly journals SmartFix: Indoor Locating Optimization Algorithm for Energy-Constrained Wearable Devices

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoliang Wang ◽  
Ke Xu ◽  
Ziwei Li
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Power Efficiency ◽  
Indoor Localization ◽  
Wearable Devices ◽  
2Nd Generation ◽  
Smart Watch ◽  
Locating Error ◽  
Motion Features ◽  
Locating System

Indoor localization technology based on Wi-Fi has long been a hot research topic in the past decade. Despite numerous solutions, new challenges have arisen along with the trend of smart home and wearable computing. For example, power efficiency needs to be significantly improved for resource-constrained wearable devices, such as smart watch and wristband. For a Wi-Fi-based locating system, most of the energy consumption can be attributed to real-time radio scan; however, simply reducing radio data collection will cause a serious loss of locating accuracy because of unstable Wi-Fi signals. In this paper, we present SmartFix, an optimization algorithm for indoor locating based on Wi-Fi RSS. SmartFix utilizes user motion features, extracts characteristic value from history trajectory, and corrects deviation caused by unstable Wi-Fi signals. We implemented a prototype of SmartFix both on Moto 360 2nd-generation Smartwatch and on HTC One Smartphone. We conducted experiments both in a large open area and in an office hall. Experiment results demonstrate that average locating error is less than 2 meters for more than 80% cases, and energy consumption is only 30% of Wi-Fi fingerprinting method under the same experiment circumstances.

A Hybrid Swarm Optimization Algorithm For Reducing Energy Consumption In The Cloud

2018 ◽  
Vol 11 ◽  
Author(s):  
Mohammed Mostafa Abdulghafoor ◽  
Raed Abdulkareem Hasan ◽  
Zeyad Hussein Salih ◽  
Hayder Ali Nemah Alshara ◽  
Nicolae Tapus
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Reducing Energy

scholarly journals Minimization of energy consumption by building shape optimization using an improved Manta-Ray Foraging Optimization algorithm

2021 ◽  
Vol 7 ◽  
pp. 1068-1078
Author(s):  
Jiaying Feng ◽  
Xiaoguang Luo ◽  
Mingzhe Gao ◽  
Adnan Abbas ◽  
Yi-Peng Xu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Shape Optimization ◽  
Optimization Algorithm ◽  
Building Shape ◽  
Manta Ray

Control-Oriented Modelling and Optimal Adaptive Control of Parallel Compressors

2021 ◽  
Author(s):  
Ayman Ismail Al Zawaideh ◽  
Khalifa Hassan Al Hosani ◽  
Igor Boiko ◽  
Abdulla AlQassab ◽  
Ibrahim Khan
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Dynamic Control ◽  
Operating Conditions ◽  
High Rate ◽  
Total Energy Consumption ◽  
Process Industries ◽  
Gas Processing ◽  
Processing Plants ◽  
Efficient Power

Abstract Compressors are widely used to transport gas offshore and onshore. Oil rigs and gas processing plants have several compressors operating either alone, in parallel or in trains. Hence, compressors must be controlled optimally to insure a high rate of production, and efficient power consumption. The aim of this paper is to provide a control algorithm to optimize the compressors operation in parallel in process industries, to minimize energy consumption in variable operating conditions. A dynamic control-oriented model of the compression system has been developed. The optimization algorithm is tested on an experimental prototype having two compressors connected in parallel. The developed optimization algorithm resulted in a better performance and a reduction of the total energy consumption compared to an equal load sharing scheme.

scholarly journals A New Approach for Real Time Train Energy Efficiency Optimization

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2660 ◽  
Author(s):  
Agostinho Rocha ◽  
Armando Araújo ◽  
Adriano Carvalho ◽  
João Sepulveda
Keyword(s):  
Energy Efficiency ◽  
Simulated Annealing ◽  
Energy Consumption ◽  
Real Time ◽  
Optimization Algorithm ◽  
Normal Operation ◽  
Speed Profile ◽  
Railway Systems ◽  
Optimal Speed ◽  
Advisory System

Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development.

Energy Harvesting for Smart Shoes: A Real Life Application

2013 ◽  
Author(s):  
Emanuele Frontoni ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Andrea Gatto
Keyword(s):  
Energy Harvesting ◽  
Power Efficiency ◽  
Indoor Localization ◽  
Electrical Power ◽  
Real Life ◽  
Reducing Power ◽  
Electrical Energy ◽  
General Purpose ◽  
Technical Developments ◽  
Microprocessor Technology

Advanced technical developments have increased the efficiency of devices in capturing trace amounts of energy from the environment (such as from human movements) and transforming them into electrical energy (e.g., to instantly charge mobile devices). In addition, advancements in microprocessor technology have increased power efficiency, effectively reducing power consumption requirements. In combination, these developments have sparked interest in the engineering community to develop more and more applications that utilize energy harvesting for power. The approach here described aims to designing and manufacturing an innovative easy-to-use and general-purpose device for energy harvesting in general purpose shoes. The novelty of this device is the integration of polymer and ceramic piezomaterials accomplished by injection molding. In this spirit, this paper examines different devices that can be built into a shoe, (where excess energy is readily harvested) and used for generating electrical power while walking. A Main purpose is the development of an indoor localization system embedded in shoes that periodically broadcasts a digital RFID as the bearer walks. Results are encouraging and real life test are conducted on the first series of prototypes.

scholarly journals Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study (Preprint)

2018 ◽  
Author(s):  
Junqing Xie ◽  
Dong Wen ◽  
Lizhong Liang ◽  
Yuxi Jia ◽  
Li Gao ◽  
...  
Keyword(s):  
Heart Rate ◽  
Energy Consumption ◽  
Sleep Duration ◽  
Measurement Accuracy ◽  
Health Indicators ◽  
Wearable Devices ◽  
Health Related ◽  
Fitness Tracking ◽  
Activity State ◽  
Fitness Trackers

BACKGROUND Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. OBJECTIVE The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. METHODS A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). RESULTS Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). CONCLUSIONS At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.

scholarly journals Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2822 ◽  
Author(s):  
Laura García ◽  
Lorena Parra ◽  
Jose Jimenez ◽  
Jaime Lloret
Keyword(s):  
Energy Consumption ◽  
Monitoring System ◽  
Low Cost ◽  
Wearable Devices ◽  
Initial Energy ◽  
Low Energy ◽  
Non Invasive ◽  
Energy Sensor ◽  
Physical Wellbeing ◽  
Evaluation Module

Determining and improving the wellbeing of people is one of the priorities of the OECD countries. Nowadays many sensors allow monitoring different parameters in regard to the wellbeing of people. These sensors can be deployed in smartphones, clothes or accessories like watches. Many studies have been performed on wearable devices that monitor certain aspects of the health of people, especially for specific diseases. In this paper, we propose a non-invasive low-cost and low-energy physical wellbeing monitoring system that provides a wellness score based on the obtained data. We present the architecture of the system and the disposition of the sensors on the sock. The algorithm of the system is presented as well. The wellness threshold evaluation module allows determining if the monitored parameter is within healthy ranges. The message forwarding module allows decreasing the energy consumption of the system by detecting the presence of alerts or changes in the data. Finally, a simulation was performed in order to determine the energy consumption of the system. Results show that our algorithm allows saving 44.9% of the initial energy in 10,000 min for healthy people.

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