Matching Items (26)
Description
Energy consumption of the data centers worldwide is rapidly growing fueled by ever-increasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. This dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level. We devise optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and cooling system power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.
ContributorsAbbasi, Zahra (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Shrivastava, Aviral (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2014
Description
Embedded Networked Systems (ENS) consist of various devices, which are embedded into physical objects (e.g., home appliances, vehicles, buidlings, people). With rapid advances in processing and networking technologies, these devices can be fully connected and pervasive in the environment. The devices can interact with the physical world, collaborate to share resources, and provide context-aware services. This dissertation focuses on collaboration in ENS to provide smart services. However, there are several challenges because the system must be - scalable to a huge number of devices; robust against noise, loss and failure; and secure despite communicating with strangers. To address these challenges, first, the dissertation focuses on designing a mobile gateway called Mobile Edge Computing Device (MECD) for Ubiquitous Sensor Networks (USN), a type of ENS. In order to reduce communication overhead with the server, an MECD is designed to provide local and distributed management of a network and data associated with a moving object (e.g., a person, car, pet). Furthermore, it supports collaboration with neighboring MECDs. The MECD is developed and tested for monitoring containers during shipment from Singapore to Taiwan and reachability to the remote server was a problem because of variance in connectivity (caused by high temperature variance) and high interference. The unreachability problem is addressed by using a mesh networking approach for collaboration of MECDs in sending data to a server. A hierarchical architecture is proposed in this regard to provide multi-level collaboration using dynamic mesh networks of MECDs at one layer. The mesh network is evaluated for an intelligent container scenario and results show complete connectivity with the server for temperature range from 25°C to 65°C. Finally, the authentication of mobile and pervasive devices in ENS for secure collaboration is investigated. This is a challenging problem because mutually unknown devices must be verified without knowledge of each other's identity. A self-organizing region-based authentication technique is proposed that uses environmental sound to autonomously verify if two devices are within the same region. The experimental results show sound could accurately authenticate devices within a small region.
ContributorsKim, Su-jin (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Dasgupta, Partha (Committee member) / Davulcu, Hasan (Committee member) / Lee, Yann-Hang (Committee member) / Arizona State University (Publisher)
Created2010
Description
Widespread adoption of smartphone based Mobile Medical Apps (MMAs) is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive data. Thus it is necessary to have evidences of their trustworthiness i.e. maintaining privacy of health data, long term operation of wearable sensors and ensuring no harm to the user before actual marketing. Traditionally, clinical studies are used to validate the trustworthiness of medical systems. However, they can take long time and could potentially harm the user. Such evidences can be generated using simulations and mathematical analysis. These methods involve estimating the MMA interactions with human physiology. However, the nonlinear nature of human physiology makes the estimation challenging.
This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes:
• Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment.
• Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology.
• Automatic code generation for MMAs: Investigating methods for automatically
• Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.
This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes:
• Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment.
• Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology.
• Automatic code generation for MMAs: Investigating methods for automatically
• Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.
ContributorsBagade, Priyanka (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Wu, Carole-Jean (Committee member) / Doupe, Adam (Committee member) / Zhang, Yi (Committee member) / Arizona State University (Publisher)
Created2015
Description
In recent years, deep learning systems have outperformed traditional machine learning systems in most domains. There has been a lot of research recently in the field of hand gesture recognition using wearable sensors due to the numerous advantages these systems have over vision-based ones. However, due to the lack of extensive datasets and the nature of the Inertial Measurement Unit (IMU) data, there are difficulties in applying deep learning techniques to them. Although many machine learning models have good accuracy, most of them assume that training data is available for every user while other works that do not require user data have lower accuracies. MirrorGen is a technique which uses wearable sensor data and generates synthetic videos using hand movements and it mitigates the traditional challenges of vision based recognition such as occlusion, lighting restrictions, lack of viewpoint variations, and environmental noise. In addition, MirrorGen allows for user-independent recognition involving minimal human effort during data collection. It also helps leverage the advances in vision-based recognition by using various techniques like optical flow extraction, 3D convolution. Projecting the orientation (IMU) information to a video helps in gaining position information of the hands. To validate these claims, we perform entropy analysis on various configurations such as raw data, stick model, hand model and real video. Human hand model is found to have an optimal entropy that helps in achieving user independent recognition. It also serves as a pervasive option as opposed to a video-based recognition. The average user independent recognition accuracy of 99.03% was achieved for a sign language dataset with 59 different users, 20 different signs with 20 repetitions each for a total of 23k training instances. Moreover, synthetic videos can be used to augment real videos to improve recognition accuracy.
ContributorsRamesh, Arun Srivatsa (Author) / Gupta, Sandeep K S (Thesis advisor) / Banerjee, Ayan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
Description
A medical control system, a real-time controller, uses a predictive model of human physiology for estimation and controlling of drug concentration in the human body. Artificial Pancreas (AP) is an example of the control system which regulates blood glucose in T1D patients. The predictive model in the control system such as Bergman Minimal Model (BMM) is based on physiological modeling technique which separates the body into the number of anatomical compartments and each compartment's effect on body system is determined by their physiological parameters. These models are less accurate due to unaccounted physiological factors effecting target values. Estimation of a large number of physiological parameters through optimization algorithm is computationally expensive and stuck in local minima. This work evaluates a machine learning(ML) framework which has an ML model guided through physiological models. A support vector regression model guided through modified BMM is implemented for estimation of blood glucose levels. Physical activity and Endogenous glucose production are key factors that contribute in the increased hypoglycemia events thus, this work modifies Bergman Minimal Model ( Bergman et al. 1981) for more accurate estimation of blood glucose levels. Results show that the SVR outperformed BMM by 0.164 average RMSE for 7 different patients in the free-living scenario. This computationally inexpensive data driven model can potentially learn parameters more accurately with time. In conclusion, advised prediction model is promising in modeling the physiology elements in living systems.
ContributorsAgrawal, Anurag (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Banerjee, Ayan (Committee member) / Kudva, Yogish (Committee member) / Arizona State University (Publisher)
Created2017
Description
Artificial Intelligence is quickly growing to be an influential part of our daily lives. Due to this, we believe it is important to analyze how cultural perceptions can influence how we interact and develop technology<br/>We decided to focus on India due to its large economic stature, cultural influence, and influence on the technology industry.
ContributorsBabbepalli Venkata, Sai Sandilya (Co-author) / Raka, Khyati (Co-author) / Banerjee, Ayan (Thesis director) / Finn, Edward (Thesis director) / Fortunato, Joseph (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Artificial Intelligence is quickly growing to be an influential part of our daily lives. Due to this, we believe it is important to analyze how cultural perceptions can influence how we interact and develop technology. We decided to focus on India due to its large economic stature, cultural influence, and influence on the technology industry.
ContributorsRaka, Khyati Pravin (Co-author) / Babbepalli Venkata, Sai Sandilya (Co-author) / Finn, Edward (Thesis director) / Banerjee, Ayan (Thesis director) / Fortunato, Joseph (Committee member) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Arizona State course enrollment regularly reaches triple digits. Despite the large enrollment numbers, the level of communication among students remain relatively low. Students often create Discord servers to keep in touch with classmates, but this requires each individual student to track down the invite link. The purpose of this project is to create an inviting chat service for students with minimal barriers of entry. This website, https://gibbl.io, offers a chat room for every class at ASU, making it simple for students to maintain communication.
ContributorsGarrett, Zachary (Author) / Miller, Phillip (Thesis director) / Banerjee, Ayan (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
With the advent of new advanced analysis tools and access to related published data, it is getting more difficult for data owners to suppress private information from published data while still providing useful information. This dual problem of providing useful, accurate information and protecting it at the same time has been challenging, especially in healthcare. The data owners lack an automated resource that provides layers of protection on a published dataset with validated statistical values for usability. Differential privacy (DP) has gained a lot of attention in the past few years as a solution to the above-mentioned dual problem. DP is defined as a statistical anonymity model that can protect the data from adversarial observation while still providing intended usage. This dissertation introduces a novel DP protection mechanism called Inexact Data Cloning (IDC), which simultaneously protects and preserves information in published data while conveying source data intent. IDC preserves the privacy of the records by converting the raw data records into clonesets. The clonesets then pass through a classifier that removes potential compromising clonesets, filtering only good inexact cloneset. The mechanism of IDC is dependent on a set of privacy protection metrics called differential privacy protection metrics (DPPM), which represents the overall protection level. IDC uses two novel performance values, differential privacy protection score (DPPS) and clone classifier selection percentage (CCSP), to estimate the privacy level of protected data. In support of using IDC as a viable data security product, a software tool chain prototype, differential privacy protection architecture (DPPA), was developed to utilize the IDC. DPPA used the engineering security mechanism of IDC. DPPA is a hub which facilitates a market for data DP security mechanisms. DPPA works by incorporating standalone IDC mechanisms and provides automation, IDC protected published datasets and statistically verified IDC dataset diagnostic report. DPPA is currently doing functional, and operational benchmark processes that quantifies the DP protection of a given published dataset. The DPPA tool was recently used to test a couple of health datasets. The test results further validate the IDC mechanism as being feasible.
Contributorsthomas, zelpha (Author) / Bliss, Daniel W (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Banerjee, Ayan (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2023
Description
Power systems are transforming into more complex and stressed systems each day. These stressed conditions could lead to a slow decline in the power grid's voltage profile and sometimes lead to a partial or total blackout. This phenomenon can be identified by either solving a power flow problem or using measurement-based real-time monitoring algorithms. The first part of this thesis focuses on proposing a robust power flow algorithm for ill-conditioned systems. While preserving the stable nature of the fixed point (FP) method, a novel distributed FP equation is proposed to calculate the voltage at each bus. The proposed algorithm's performance is compared with existing methods, showing that the proposed method can correctly find the solutions when other methods cannot work due to high condition number matrices. It is also empirically shown that the FP algorithm is more robust to bad initialization points. The second part of this thesis focuses on identifying the voltage instability phenomenon using real-time monitoring algorithms. This work proposes a novel distributed measurement-based monitoring technique called voltage stability index (VSI). With the help of PMUs and communication of voltage phasors between neighboring buses, the processors embedded at each bus in the smart grid perform simultaneous online computations of VSI. VSI enables real-time identification of the system's critical bus with minimal communication infrastructure. Its benefits include interpretability, fast computation, and low sensitivity to noisy measurements. Furthermore, this work proposes the ``local static-voltage stability index" (LS-VSI) that removes the minimal communication requirement in VSI by requiring only one PMU at the bus of interest. LS-VSI also solves the issue of Thevenin equivalent parameter estimation in the presence of noisy measurements. Unlike VSI, LS-VSI incorporates the ZIP load models and load tap changers (LTCs) and successfully identifies the bifurcation point considering ZIP loads' impact on voltage stability. Both VSI and LS-VSI are useful to monitor the voltage stability margins in real-time using the PMU measurements from the field. However, they cannot indicate the onset of voltage emergency situations. The proposed LD-VSI uses the dynamic measurements of the power system to identify the onset of a voltage emergency situation with an alarm. Compared to existing methods, it is shown that it is more robust to PMU measurement noise and can also identify the voltage collapse point while the existing methods have issues with the same.
ContributorsGuddanti, Kishan Prudhvi (Author) / Weng, Yang (Thesis advisor) / Banerjee, Ayan (Committee member) / Zhang, Baosen (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2021