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You searched for +publisher:"Santa Clara University" +contributor:("Behnam Dezfouli"). Showing records 1 – 3 of 3 total matches.

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1. Amirtharaj, Immanuel. Energy Measurement and Profiling of Internet of Things Devices.

Degree: MS, Computer Engineering, 2018, Santa Clara University

As technological improvements in hardware and software have grown in leaps and bounds, the presence of IoT devices has been increasing at a fast rate. Profiling and minimizing energy consumption on these devices remains to be an an essential step towards employing them in various application domains. Due to the large size and high cost of commercial energy measurement platforms, the research community has proposed alternative solutions that aim to be simple, accurate, and user friendly. However, these solutions are either costly, have a limited measurement range, or low accuracy. In addition, minimizing energy consumption in IoT devices is paramount to their wide deployment in various IoT scenarios. Energy saving methods such as duty-cycling aim to address this constraint by limiting the amount of time the device is powered on. This process needs to be optimized, as devices are now able to perform complex, but energy intensive tasks due to advancements in hardware. The contributions of this paper are two-fold. First we develop an energy measurement platform for IoT devices. This platform should be accurate, low-cost, easy to build, and configurable in order to scale to the high volume and varying requirements for IoT devices. The second contribution is improving the energy consumption on a Linux-based IoT device in a duty-cycled scenario. It is important to profile and optimize boot up time and shutdown time, and improve the way user applications are executed. EMPIOT is an accurate, low-cost, easy to build, and flexible power measurement platform. We present the hardware and software components that comprise EMPIOT and then study the effect of various design parameters on accuracy. In particular, we analyze the effect of driver, bus speed, input voltage, and buffering mechanisms on sampling rate, measurement accuracy, and processing demand. In addition to this, we also propose a novel calibration technique and report the calibration parameters under different settings. In order to demonstrate EMPIOT's scalability, we evaluate its performance against a ground truth on five different devices. Our results show that for very low-power devices that utilize 802.15.4 wireless standard, measurement error is less than 4%. In addition, we obtain less than 3% error for 802.11-based devices that generate short and high power spikes. The second contribution is the optimization the energy consumption of IoT devices in a duty cycled scenario by reducing boot up duration, shutdown duration, and user application duration. To this end, we study and improve the amount of time a Linux-based IoT device is powered on to accomplish its tasks. We analyze the processes of system boot up and shutdown on two platforms, the Raspberry Pi 3 and Raspberry Pi Zero Wireless, and enhance duty-cycling performance by identifying and disabling time consuming or unnecessary units initialized in the userspace. We also study whether SD card speed and SD card capacity utilization affect boot up duration and energy consumption. In… Advisors/Committee Members: Behnam Dezfouli.

Subjects/Keywords: Computer Engineering; Engineering

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APA (6th Edition):

Amirtharaj, I. (2018). Energy Measurement and Profiling of Internet of Things Devices. (Doctoral Dissertation). Santa Clara University. Retrieved from https://scholarcommons.scu.edu/cseng_mstr/5

Chicago Manual of Style (16th Edition):

Amirtharaj, Immanuel. “Energy Measurement and Profiling of Internet of Things Devices.” 2018. Doctoral Dissertation, Santa Clara University. Accessed April 01, 2020. https://scholarcommons.scu.edu/cseng_mstr/5.

MLA Handbook (7th Edition):

Amirtharaj, Immanuel. “Energy Measurement and Profiling of Internet of Things Devices.” 2018. Web. 01 Apr 2020.

Vancouver:

Amirtharaj I. Energy Measurement and Profiling of Internet of Things Devices. [Internet] [Doctoral dissertation]. Santa Clara University; 2018. [cited 2020 Apr 01]. Available from: https://scholarcommons.scu.edu/cseng_mstr/5.

Council of Science Editors:

Amirtharaj I. Energy Measurement and Profiling of Internet of Things Devices. [Doctoral Dissertation]. Santa Clara University; 2018. Available from: https://scholarcommons.scu.edu/cseng_mstr/5

2. Magid, Salma Abdel. Image Classification on IoT Edge Devices: Profiling and Modeling.

Degree: MS, Computer Engineering, 2018, Santa Clara University

With the advent of powerful, low-cost IoT systems, processing data closer to where the data originates, known as edge computing, has become an increasingly viable option. In addition to lowering the cost of networking infrastructures, edge computing reduces edge-cloud delay, which is essential for mission-critical applications. In this thesis, we show the feasibility and study the performance of image classification using IoT devices. Specifically, we explore the relationships between various factors of image classification algorithms that may affect energy consumption such as dataset size, image resolution, algorithm type, algorithm phase, and device hardware. Our experiments show a strong, positive linear relationship between three predictor variables, namely model complexity, image resolution, and dataset size, with respect to energy consumption. In addition, in order to provide a means of predicting the energy consumption of an edge device performing image classification, we investigate the usage of three machine learning algorithms using the data generated from our experiments. The performance as well as the trade offs for using linear regression, Gaussian process, and random forests are discussed and validated. Our results indicate that the random forest model outperforms the two former algorithms, with an R-squared value of 0.95 and 0.79 for two different validation datasets. Advisors/Committee Members: Behnam Dezfouli.

Subjects/Keywords: Computer Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Magid, S. A. (2018). Image Classification on IoT Edge Devices: Profiling and Modeling. (Masters Thesis). Santa Clara University. Retrieved from https://scholarcommons.scu.edu/cseng_mstr/9

Chicago Manual of Style (16th Edition):

Magid, Salma Abdel. “Image Classification on IoT Edge Devices: Profiling and Modeling.” 2018. Masters Thesis, Santa Clara University. Accessed April 01, 2020. https://scholarcommons.scu.edu/cseng_mstr/9.

MLA Handbook (7th Edition):

Magid, Salma Abdel. “Image Classification on IoT Edge Devices: Profiling and Modeling.” 2018. Web. 01 Apr 2020.

Vancouver:

Magid SA. Image Classification on IoT Edge Devices: Profiling and Modeling. [Internet] [Masters thesis]. Santa Clara University; 2018. [cited 2020 Apr 01]. Available from: https://scholarcommons.scu.edu/cseng_mstr/9.

Council of Science Editors:

Magid SA. Image Classification on IoT Edge Devices: Profiling and Modeling. [Masters Thesis]. Santa Clara University; 2018. Available from: https://scholarcommons.scu.edu/cseng_mstr/9

3. Tsao, Brandon. Analysis of the Duration and Energy Consumption of AES Algorithms on a Contiki-based IoT Device.

Degree: MS, 2019, Santa Clara University

With the growing prevalence of the Internet of Things, securing the sheer abundance of devices is critical. The current IoT and security landscapes lack empirical metrics on encryption algorithm implementations that are optimized for constrained devices, such as encryption/decryption duration and energy consumption. In this paper, we achieve two things. First, we survey for optimized implementations of symmetric encryption algorithms. Seconds, we study the performance of various symmetric encryption algorithms on a Contiki-based IoT device. This paper provides encryption and decryption durations and energy consumption results on three implementations of AES: TinyAES, B-Con’s AES, and Contiki’s own built-in AES. In our experiments, we found the algorithms specifically built for constrained devices used about 0.16 the energy and time to perform encryption and decryption when compared to algorithm implementation that weren’t optimized for constrained devices. Advisors/Committee Members: Behnam Dezfouli.

Subjects/Keywords: Computer Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tsao, B. (2019). Analysis of the Duration and Energy Consumption of AES Algorithms on a Contiki-based IoT Device. (Doctoral Dissertation). Santa Clara University. Retrieved from https://scholarcommons.scu.edu/cseng_mstr/13

Chicago Manual of Style (16th Edition):

Tsao, Brandon. “Analysis of the Duration and Energy Consumption of AES Algorithms on a Contiki-based IoT Device.” 2019. Doctoral Dissertation, Santa Clara University. Accessed April 01, 2020. https://scholarcommons.scu.edu/cseng_mstr/13.

MLA Handbook (7th Edition):

Tsao, Brandon. “Analysis of the Duration and Energy Consumption of AES Algorithms on a Contiki-based IoT Device.” 2019. Web. 01 Apr 2020.

Vancouver:

Tsao B. Analysis of the Duration and Energy Consumption of AES Algorithms on a Contiki-based IoT Device. [Internet] [Doctoral dissertation]. Santa Clara University; 2019. [cited 2020 Apr 01]. Available from: https://scholarcommons.scu.edu/cseng_mstr/13.

Council of Science Editors:

Tsao B. Analysis of the Duration and Energy Consumption of AES Algorithms on a Contiki-based IoT Device. [Doctoral Dissertation]. Santa Clara University; 2019. Available from: https://scholarcommons.scu.edu/cseng_mstr/13

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