M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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Subject: search.filters.subject.Smartphone

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    Smartphones as Computing Platforms: An all-in-one Mobile Application
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Trivedi, Jaineel Udayan; Sasidhar, Kalyan
    Advancements in technology have led to more intelligent computing systems, butserver-based computing presents itself with network delay and high energy consumptionleading to significant carbon footprints. A lag in computing the resultand rendering it on a user�s end device, such as a smartphone could lead to poorquality of experience or user experience. Edge computing and its variants suchas fog and cloudlet computing, are paradigms for processing data close to thesource. However, the computing platform still remains to be a high-end server.The pipeline of mobile (smartphone-based) sensing involves sensing, data preprocessing,feature extraction, model training, and testing. The majority of workimplemented only the sensing phase whereas the remaining were offloaded to aserver. However, smartphones have rich computing power in terms of octa-coreprocessors, faster clock speeds that are under-utilized. Few attempts have beenmade to utilize this computing power. Benchmark studies have shown that theperformance of smartphone processors equals an Intel i3 processor. Most importantly,these devices consumed 30 times lesser energy than traditional servers forthe same computing task.In this work, we design, develop and implement a mobile application wherethe entire pipeline is implemented on the phone. The application takes the collectedsensor data on which we implement machine learning algorithms to classifythe physical activity of a user. Performance results show that the app consumes350mAh amount of battery, with a CPU utilization of 13%. We also receivedan average user rating of 4.5/5 for user experience on the impact of positive interventionsthat our app automatically provides. This thesis provides a frameworkfor implementing applications on smartphones eliminating the need for offloading.
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    Empirical Study Of Smartphones As An Edge Device
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Shah, Vyom Hiteshkumar; Sasidhar, Kalyan
    Increased automation and intelligence in computer systems have revealed Cloudbasedcomputing constraints such as unpredictable latency in safety-critical andperformance-sensitive applications. Features of smartphones attract researchersmore towards using smartphones as edge computing devices because of the presenceof the sensors inbuilt and the computing powers of CPU cores. So a smartphoneis a combination of IoT and Edge computing devices.To overcome the usage of high-end computing devices at the edge layer, thisarticle proposes the idea of using a smartphone as an edge device for processingdata. Sensors or IoT devices generally send data to the edge device ratherthan directly sending it to the cloud for processing. So mainly, this article emphasizessolving the research question of whether smartphones can be used as edgedevices. So in this, a distributed smartphone system following master-slave architectureis proposed, which helps to distribute the computation power amongslaves. Word count, average of temperature data and indoor localization. Comparedto desktop PCs computation, master-slave utilizes CPU 75% more than juston single-device computation and on an average 50% faster than on computingon a single device. This motivates us to design an architecture that can utilizethe data from the cloud and perform the computation using the CPU cores of thesmartphone.
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    Application of smartphone sensing system for general and mental assessment of students
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2019) Upasani, Ashwini; Sasidhar, Kalyan P S
    Smartphone has become a vital part of a human being and it has been used as an analytic tool, due to its ability to provide a context-aware system, which enables applications to infer context from sensor data and these sensors are equipped in smartphones and used to infer human behavior such as activity, speech, sleep, mobility, etc. Inferring such behavior using passive sensing known pervasive sensing. Researchers have used the smartphone as a measurement tool to understand the behavior and psychology of college students. As a college life can be a transition period for any student with respect to psychology and their behavior. In this paper, a smartphone has been used as a platform for a context-aware system to assess human behavior such as activity, conversation, sleep, visited locations, application usage. Moreover, we have correlated these aspects of the behavior with mood and personality.
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    Application of smartphone sensing system for general and mental assessment of students
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2019) Shah, Dimple; Sasidhar, Kalyan P S
    Smartphone has become a vital part of a human being and it has been used as an analytic tool, due to its ability to provide a context-aware system, which enables applications to infer context from sensor data and these sensors are equipped in smartphones and used to infer human behavior such as activity, speech, sleep, mobility, etc. Inferring such behavior using passive sensing known pervasive sensing. Researchers have used the smartphone as a measurement tool to understand the behavior and psychology of college students. As a college life can be a transition period for any student with respect to psychology and their behavior. In this paper, a smartphone has been used as a platform for a context-aware system to assess human behavior such as activity, conversation, sleep, visited locations, application usage. Moreover, we have correlated these aspects of the behavior with mood and personality.
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    Personalized gait abnormality detection system
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Dhokai, Ronak; Sasidhar, Kalyan
    Gait refers to walking manner of a person, and it is also an indication of neurologicalhealth status of a person. Gait variability can occur due to factors like aging,injuries and diseases. If not notified or diagnosed at an early stage, this variabilityof gait could lead to lifetime abnormality.In this work, we have proposed a smartphone based solution for the task ofcapturing gait and performing abnormality detection on the sensed data. Usingthe built-in accelerometer, we collected walking data from 10 different users,which consisted of both normal and minor abnormalities. Features such as stridetime and stride length were extracted and the sudden changes in the walk weredetected by calculating the extent of deviation of these features between the walkdata. Individual user based threshold value of deviation was estimated and thedetection algorithm performance was evaluated for each of the 10 users.