Browsing by Subject "energy consumption"

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    Energy Management using Industrial Internet of Things (IIoT) and ISO 50001
    (Indiana Chamber of Commerce, 2022-07-26) Razban, Ali; Mechanical Engineering, School of Engineering and Technology
    The manufacturers and businesses are under increasing pressure to reduce their energy consumption due to increase in energy cost and growing concern regarding global warming effect on our environment. Energy management is one of the fastest and most cost-effective ways to save money, cut greenhouse gas pollution and help businesses/ manufacturers to improve their energy efficiency. Proper energy management program reduces energy consumption, improve energy efficiency, reduces utility bills and improves profit. This can be achieved by having a proper energy management program in place which would not only improves the energy efficiency and it would also make the efficiency sustainable. “ISO 50001” can help the businesses/ manufacturers to reduce their energy consumption and achieve continual operational improvement. Measuring and considering the full array of benefits provided by energy efficiency is crucial to ensuring that all cost-effective efficiency resources are captured. New emerging technologies, such as Industrial Internet of Things (IIoT), propel the advancement of production process monitoring into real-time. An area where IIoT plays a major role is in the monitoring of energy consumption. Smart meters and sensors, which form the backbone of IIoT technology, provide awareness of energy consumption patterns by collecting real-time energy consumption data. As the data size amasses, a new word was coined – Industrial Big Data (IBD). With the help from IIoT and cheap data storage, IBD become available in almost every aspect of production process, including in energy management.
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    Intelligent Device Selection in Federated Edge Learning with Energy Efficiency
    (2021-12) Peng, Cheng; Hu, Qin; Kang, Kyubyung; Zou, Xukai
    Due to the increasing demand from mobile devices for the real-time response of cloud computing services, federated edge learning (FEL) emerges as a new computing paradigm, which utilizes edge devices to achieve efficient machine learning while protecting their data privacy. Implementing efficient FEL suffers from the challenges of devices' limited computing and communication resources, as well as unevenly distributed datasets, which inspires several existing research focusing on device selection to optimize time consumption and data diversity. However, these studies fail to consider the energy consumption of edge devices given their limited power supply, which can seriously affect the cost-efficiency of FEL with unexpected device dropouts. To fill this gap, we propose a device selection model capturing both energy consumption and data diversity optimization, under the constraints of time consumption and training data amount. Then we solve the optimization problem by reformulating the original model and designing a novel algorithm, named E2DS, to reduce the time complexity greatly. By comparing with two classical FEL schemes, we validate the superiority of our proposed device selection mechanism for FEL with extensive experimental results. Furthermore, for each device in a real FEL environment, it is the fact that multiple tasks will occupy the CPU at the same time, so the frequency of the CPU used for training fluctuates all the time, which may lead to large errors in computing energy consumption. To solve this problem, we deploy reinforcement learning to learn the frequency so as to approach real value. And compared to increasing data diversity, we consider a more direct way to improve the convergence speed using loss values. Then we formulate the optimization problem that minimizes the energy consumption and maximizes the loss values to select the appropriate set of devices. After reformulating the problem, we design a new algorithm FCE2DS as the solution to have better performance on convergence speed and accuracy. Finally, we compare the performance of this proposed scheme with the previous scheme and the traditional scheme to verify the improvement of the proposed scheme in multiple aspects.