Physical Sciences and Mathematics Commons^™

Unleashing The Power Of Internet Of Things And Blockchain: A Comprehensive Analysis And Future Directions, Abderahman Rejeb, Karim Rejeb, Andrea Appolloni, Sandeep Jagtap, Mohammad Iranmanesh, Salem Alghamdi, Yaser Alhasawi, Yasanur Kayikci

Research outputs 2022 to 2026

As the fusion of the Internet of Things (IoT) and blockchain technology advances, it is increasingly shaping diverse fields. The potential of this convergence to fortify security, enhance privacy, and streamline operations has ignited considerable academic interest, resulting in an impressive body of literature. However, there is a noticeable scarcity of studies employing Latent Dirichlet Allocation (LDA) to dissect and categorize this field. This review paper endeavours to bridge this gap by meticulously analysing a dataset of 4455 journal articles drawn solely from the Scopus database, cantered around IoT and blockchain applications. Utilizing LDA, we have extracted 14 distinct topics …

Energy-Aware Path Planning For Fixed-Wing Seaplane Uavs, Benjamin Atkinson Wolsieffer

Dartmouth College Master’s Theses

Fixed-wing unmanned aerial vehicles (UAVs) are commonly used for remote sensing applications over water bodies, such as monitoring water quality or tracking harmful algal blooms. However, there are some types of measurements that are difficult to accurately obtain from the air. In existing work, water samples have been collected in situ either by hand, with an unmanned surface vehicle (USV), or with a vertical takeoff and landing (VTOL) UAV such as a multirotor. We propose a path planner, landing control algorithm, and energy estimator that will allow a low-cost and energy efficient fixed-wing UAV to carry out a combined remote …

Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Publications and Presentations

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Exploring High Performance And Energy Efficient Graph Processing On Gpu, Robert P. Watling

Dissertations, Master's Theses and Master's Reports

Parallel graph processing is central to analytical computer science applications, and GPUs have proven to be an ideal platform for parallel graph processing. Existing GPU graph processing frameworks present performance improvements but often neglect two issues: the unpredictability of a given input graph and the energy consumption of the graph processing. Our prototype software, EEGraph (Energy Efficiency of Graph processing), is a flexible system consisting of several graph processing algorithms with configurable parameters for vertex update synchronization, vertex activation, and memory management along with a lightweight software-based GPU energy measurement scheme. We observe relationships between different configurations of our software, …

Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Datasets

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa

Theses and Dissertations

“Energy Trilemma” has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation’s capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the …

Recent Advances In Wearable Sensing Technologies, Alfredo J. Perez, Sherali Zeadally

Information Science Faculty Publications

Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus …

Recent Advances In Wearable Sensing Technologies, Alfredo J. Perez, Sherali Zeadally

Computer Science Faculty Publications

Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus …

Area-Delay Efficient Radix-4 8×8 Booth Multiplier For Dsp Applications, Subodh Singhal, Sujit Patel, Anurag Mahajan, Gaurav Saxena

Turkish Journal of Electrical Engineering and Computer Sciences

Booth multiplier is the key component in portable very large-scale integration (VSLI) systems enabled with signal and image processing applications. The area, delay, and energy are the major constraints in these systems. Therefore, in this paper, a detailed analysis of the state-of-the-art Booth multiplier architecture and its various internal units are presented to find the scope of optimization. Based on the finding of analysis, optimized new binary to 2's complement (B2C), Booth encoder-cum-selector type-1 and type-2, and partial product addition units are proposed. Furthermore, using these optimized units, an efficient parallel radix-4 8×8 Booth multiplier architecture is proposed. The simulation …

Neural Network Model Of Information Fusion For Coal Storage And Kinetic Energy Of Ball Mill, Bai Yan, He Fang

Journal of System Simulation

Abstract: A dynamic mathematical model of coal pulverizing system was analyzed. Simulation experiments on mill operation process were conducted by PFC3D software platform based on discrete element method. The associated data between different coal quality, coal storage and balls' motion were obtained under certain quantitative optimized operating parameters configuration. Neural network model of information fusion for coal storage and kinetic energy of ball mill was established by using an adaptive combination learning algorithm. Coal storage in mill cylinder was predicted from the energy point of view. The results indicate that there is a close relationship between coal storage, pulverizing efficiency …

Data Management Of Data Processing Framework In Green Data Center, Zhang Xiao, Gao Yuan, Xiaoliang Wang, Yiyong Ge, Haixiang Yang, Shupeng Wan

Journal of System Simulation

Abstract: Using renewable energy in data center is an environment-friendly way to solve the problem of high energy consumption of data center. Since renewable energy is variable, delaying the jobs which has no strict deadline i a widely used strategy to maximize the usage of renewable energy. Meanwhile, turning the idle servers off can further reduce energy consumption. If the data required by the jobs to be processed are unavailable, some servers in sleep state need to be reactivated to guarantee that the data required by the jobs are available. Such operation may lead to energy waste due to the …

Multi-Objective Dynamic Programming Algorithm Of Energy-Efficient Scheduling For Tow-Train, Xinyan Zhang, Yuqing Zhou

Journal of System Simulation

Abstract: To balance the performance and energy consumption of the mixed-model assembly lines effectively, a multi-objective energy-saving scheduling method for the tow-train is proposed. The energy-saving objective is introduced into the traditional material handling scheduling model for the tow-train and a multi-objective mixed integer programming model is constructed with two objective functions of minimizing the maximum line-side inventory and the total energy consumption. A forwards multi-objective dynamic programming based on the time window and dominance rules is presented to obtain the Pareto solutions: the definition for new states is given to obtain the Markov property, the time window and dominance …

Agenda, Shubha Tewari

Science and Engineering Saturday Seminars

Abstracts for six Science and Engineering Saturday Seminars.

Software-Defined Infrastructure For Iot-Based Energy Systems, Stephen Lee

Doctoral Dissertations

Internet of Things (IoT) devices are becoming an essential part of our everyday lives. These physical devices are connected to the internet and can measure or control the environment around us. Further, IoT devices are increasingly being used to monitor buildings, farms, health, and transportation. As these connected devices become more pervasive, these devices will generate vast amounts of data that can be used to gain insights and build intelligence into the system. At the same time, large-scale deployment of these devices will raise new challenges in efficiently managing and controlling them. In this thesis, I argue that the IoT …

Visualization And 3d Printing Of A 3d Solar Tracker Model Using Mayavi And Pov-Ray, Aditya Mehra

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

In this work, we have created a realistic model of a solar tracker using Mayavi: 3D scientific data visualization and plotting in Python, Enthought Canopy:a comprehensive Python analysis environment and Persistence of Vision Ray Tracer, or POV-Ray, a ray tracing program which generates photo-realistic images from a text-based scene description, a model of the solar tracker was also 3D printed.

Software Development For Home Energy Audits: Reducing Energy Consumption In Harrisonburg Through Technology, Brantley E. Gilbert

Senior Honors Projects, 2010-2019

Fossil fuels play a vital role in our daily lives. Oil, natural gas, and coal powers our cars, heats our homes and water, and are used by power companies to generate the massive amounts of electricity used every day by the United States. However, this reliance on a finite source of energy is not sustainable. Fossil fuels such as these are non-renewable resources whose production will eventually be unable to keep up with the rate of consumption. Furthermore, the extraction of the stored energy in these fuels through combustion releases harmful substances into the environment, including toxins and greenhouse gases …

Fine-Grained Appliance Usage And Energy Monitoring Through Mobile And Power-Line Sensing, Nirmalya Roy, Nilavra Pathak, Archan Misra

Research Collection School Of Computing and Information Systems

To promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide …

Comon+: A Cooperative Context Monitoring System For Multi-Device Personal Sensing Environments, Youngki Lee, Seungwoo Kang, Chulhong Min, Younghyun Ju, Inseok Hwang, Junehwa Song

Research Collection School Of Computing and Information Systems

Continuous mobile sensing applications are emerging. Despite their usefulness, their real-world adoption has been slow. Many users are turned away by the drastic battery drain caused by continuous sensing and processing. In this paper, we propose CoMon+, a novel cooperative context monitoring system, which addresses the energy problem through opportunistic cooperation among nearby users. For effective cooperation, we develop a benefit-aware negotiation method to maximize the energy benefit of context sharing. CoMon+ employs heuristics to detect cooperators who are likely to remain in the vicinity for a long period of time, and the negotiation method automatically devises a cooperation plan …

Scheduling Instruction Effects For A Statically Pipelined Processor, F. Rasapour, G. Cook, G.-R. Uh

Gang-Ryung Uh

Statically pipelined processors have a fully exposed datapath where all portions of the pipeline are directly controlled by effects within an instruction, which simplifies hardware and enables a new level of compiler optimizations. This paper describes an effect scheduling strategy to aggressively compact instructions, which has a critical impact on code size and performance. Unique scheduling challenges include more frequent name dependences and fewer renaming opportunities due to static pipeline (SP) registers being dedicated for specific operations. We also realized the SP in a hardware implementation language (VHDL) to evaluate the real energy benefits. Despite the compiler challenges, we achieve …

Best Cities: Software User Guide, Stephanie Ohshita, C Fino-Chen, L Hong, N Khanna

Environmental Science

The Benchmarking and Energy-Saving Tool for Low Carbon Cities (BEST Cities) is a dynamic decision-making tool, designed to assist local policy makers and urban planners in prioritizing strategies for energy and carbon saving at the city level in China.

China’s 12^th Five-Year Plan (2011-2015) targets a reduction in carbon intensity of the economy (CO₂ emissions per unit of GDP) by 17%. In the "Low Carbon Development 2014-2015 energy saving action plan," the State Council calls for interim targets of more than 4% in 2014 and more than 3.5% in 2015. The …

Design, Application, And Power Performance Analyses Of A Micro Wind Turbine, Hayati̇ Mamur

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, design, implementation, and power performance analyses of a micro wind turbine (MWT) system are presented. An original permanent magnet synchronous generator (PMSG) that reduced cogging torque was employed as a generator in the MWT. A novel blade form offering better performance at low wind speeds was also utilized for the MWT blades. Power performance analyses of the MWT were carried out for different wind regimes by truck testing. Performance coefficient, cut-in, and cut-out of the MWT were determined as 27.7{\%}, 2.7 m/s, and 20 m/s at the end of the truck testing, respectively. Moreover, a new supervisory …

Model-Driven Analytics Of Energy Meter Data In Smart Homes, Sean K. Barker

Doctoral Dissertations

The proliferation of smart meter deployments has led to significant interest in analyzing home energy use as part of the emerging 'smart grid'. As buildings account for nearly 40% of society's energy use, data from smart meters provides significant opportunities for both utilities and consumers to optimize energy use, minimize waste, and provide insight into how modern homes and devices use energy. Meter data is often difficult to analyze, however, owing to the aggregation of many disparate and complex loads as well as relatively coarse measurement granularities. At utility scales, analysis is further complicated by the vast quantity of data, …

Mechatronics In Electrical Efficiency And Environmental Impact, Sherif Hyseni

UBT International Conference

This paper focuses on the mechatronic technology development, and its influent on electrical efficiency with a direct result in cost and environment. Considering the well-known European Union standard “20-20-20” and the possibility and responsibility of Kosovo in this field, the role of energy efficiency has great impact for future directions. This paper refers to a new technology solution for heating that is available on the local market, called “DAIKIN” by analyzing the functionality of this device and its promise to save up to 40% of the heating costs. Implementation of “DAIKIN” technology in state institutions in Prishtina, and also the …

Hp-Daemon: HIgh PErformance DIstributed ADaptive ENergy-Efficient MAtrix-MultiplicatiOn, Li Tan, Longxiang Chen, Zizhong Chen, Ziliang Zong, Rong Ge, Dong Li

Mathematics, Statistics and Computer Science Faculty Research and Publications

The demands of improving energy efficiency for high performance scientific applications arise crucially nowadays. Software-controlled hardware solutions directed by Dynamic Voltage and Frequency Scaling (DVFS) have shown their effectiveness extensively. Although DVFS is beneficial to green computing, introducing DVFS itself can incur non-negligible overhead, if there exist a large number of frequency switches issued by DVFS. In this paper, we propose a strategy to achieve the optimal energy savings for distributed matrix multiplication via algorithmically trading more computation and communication at a time adaptively with user-specified memory costs for less DVFS switches, which saves 7.5% more energy on average than …

Tesla: An Extended Study Of An Energy-Saving Agent That Leverages Schedule Flexibility, Jun Young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Milind Tambe, Burcin Becerik-Gerber

Research Collection School Of Computing and Information Systems

This paper presents transformative energy-saving schedule-leveraging agent (TESLA), an agent for optimizing energy usage in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. This paper provides four key contributions: (i) online scheduling algorithms, which are at the heart of TESLA, to solve a stochastic mixed integer linear program for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; (iii) an extensive analysis on energy savings achieved by TESLA; and (iv) surveys of real …

Tesla: An Energy-Saving Agent That Leverages Schedule Flexibility, Jun Young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Burcin Becerik-Gerber, Milind Tambe

Research Collection School Of Computing and Information Systems

This innovative application paper presents TESLA, an agent-based application for optimizing the energy use in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. TESLA provides three key contributions: (i) three online scheduling algorithms that consider flexibility of people’s preferences for energyefficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; and (iii) surveys of real users that indicate that TESLA’s assumptions exist in practice. TESLA was evaluated on data of over 110,000 meetings held …

Software Techniques To Reduce The Energy Consumption Of Low-Power Devices At The Limits Of Digital Abstractions, Mastooreh Salajegheh

Open Access Dissertations

My thesis explores the effectiveness of software techniques that bend digital abstractions in order to allow embedded systems to do more with less energy. Recent years have witnessed a proliferation of low-power embedded devices with power ranges of few milliwatts to microwatts. The capabilities and size of the embedded systems continue to improve dramatically; however, improvements in battery density and energy harvesting have failed to mimic a Moore's law. Thus, energy remains a formidable bottleneck for low-power embedded systems.

Instead of trying to create hardware with ideal energy proportionality, my dissertation evaluates how to use unconventional and probabilistic computing that …

Congestion Avoidance Energy Efficient Mac Protocol For Wireless Sensor Networks, Alexander Sundling

Boise State University Theses and Dissertations

Wireless Sensor Network (WSNs) are generally energy-constrained and resource-constrained. When multiple simultaneous events occur in densely deployed WSNs, nodes near the base station can become congested, decreasing the network performance. Additionally, multiple nodes may sense an event leading to spatially-correlated contention, further increasing congestion. In order to mitigate the effects of congestion near the base station, an energy-efficient Media Access Control (MAC) protocol that can handle multiple simultaneous events and spatially-correlated contention is needed. Energy efficiency is important and can be achieved using duty cycles but they could degrade the network performance in terms of latency. Existing protocols either provide …

Mobicon: Mobile Context Monitoring Platform: Incorporating Context-Awareness To Smartphone-Centric Personal Sensor Networks, Youngki Lee, Younghyun Ju, Chuihong Min, Jihun Yu, Junehwa Song

Research Collection School Of Computing and Information Systems

In this demonstration, we will show MobiCon, a context monitoring platform; it runs over smartphones and sensor OSs, and facilitates development and deployment of everyday context-aware applications. For many years, lots of research efforts have been made in building low-cost, yet effective sensor networks for various application domains such as structural health monitoring of bridges, disaster recovery, automated ventilation of buildings. Integration of sensors into smartphones and the advent of wearable devices open a new opportunity for mobile applications to leverage in-situ user contexts such as his/her location, activity, social relationship, health status. In recent studies of mobile and pervasive …

Comon: Cooperative Ambience Monitoring Platform With Continuity And Benefit Awareness, Youngki Lee, Younghyun Ju, Chulhong Min, Seungwoo Kang, Inseok Hwang, Junehwa Song

Research Collection School Of Computing and Information Systems

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) …