Person:
Sasidhar, P S Kalyan

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Name

P S Kalyan Sasidhar

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Faculty

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079-68261560

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Specialization

Mobile and Pervasive Computing which include Wireless Sensor Networks, Mobile Phone Based Sensing

Abstract

Biography

Kalyan Sasidhar is currently an Associate Professor. Prior to this, he worked as assistant professor in the Wireless Networks and Applications group at Amrita Vishwa Vidhyapeetham (Amrita University, Kerala). He was a postdoctoral researcher at Nanyang Technological University after completing his PhD in Computer Science from University of North Texas in 2011. He has 25 plus publications in top journals and international conferences with citations of over 650 and hIndex of 11.

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2017 - 201912020 - 20255

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Now showing 1 - 6 of 6
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    A survey on Crowd Behavior Analysis through indoor localization
    (Taylor & Francis, 07-05-2025) Panja, Ayan Kumar; Sasidhar, P S Kalyan; Royand, Moumita; Chowdhury, Chandreyee; DA-IICT, Gandhinagar
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    Assessing Mobile Usage, Physical Activity and Sleep Through Smartphone Sensing: A Digital Phenotype Study
    (Springer, 13-06-2022) Vaghela, Maitri; Sasidhar, P S Kalyan; Parikh, Alka; Wagani, Rekha; DA-IICT, Gandhinagar; Vaghela, Maitri (201721005)
    The article asks if our �categorising impulse� restrains us from thinking of and problematising questions of �agency� in the self-description and assertion of tribal identities. Why is it that the state or the non-tribal outsider remains indifferent to the deconstruction of the statist category of the �vulnerable�? Could the discourse of the �changeless de- historicised� Andaman islander suit the �non- tribal� status quo in the Islands?
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    Smartphone Mediated Tracking and Analysis of Sleep Patterns in Indian College Students
    (Springer, 01-03-2023) Vaghela, Maitri; Sasidhar, P S Kalyan; DA-IICT, Gandhinagar; Vaghela, Maitri (201721005)
    Sleep is one of the essential bio-makers for human health. Poor sleep is associated with reduced cognitive performance. With most smartphone users in India being college students, the focus is now on exploring smartphone usage�s impact on students� sleep. Umpteen news articles in India have reported binge-watching, social media use during the night, and other mobile phone-related interruptions as causes of improper sleep and irregular sleep patterns. However, such studies may involve bias while self-reporting and are limited to a one-time exercise. To understand the reality, we need to accurately quantify the sleep duration, patterns, mobile usage before and after bedtime, number and duration of interruptions. In this first-of-its-kind study in India, we infer novel insights into the sleep patterns of a cohort of 40 college students. We implement a mobile sensing-based approach for the study by installing a custom-developed mobile app on all phones. We extract sleep activity and infer the sleep duration, bed-in and wake-up times, and interruption duration from the sensor data collected from the phone�s built-in sensors. The study brings about new insights into college student sleep patterns and, interestingly, shows that students have a regular sleep cycle and good sleep quality. Only one-fourth of the students revealed irregular sleep patterns, and we did not observe any mobile-related interruptions 30 min past bedtime.
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    Users' Experiences of a Web-Based Suicide Prevention Program for College Students: A Mixed Methods Approach
    (Brill, 14-02-2023) Shinde, Freyana; Wagani, Rekha; Sasidhar, P S Kalyan; DA-IICT, Gandhinagar
    The current pandemic has remarkably increased the dependence on digital interfaces for mental health. This dependency calls for a strong need to check the efficacy of such digital platforms under various contexts. An evaluation study was conducted with participants in an online web-based suicide prevention intervention program named Happetite. The program was found to be not only accessible, but also provided appropriate interventions by processing the inputs from users.
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    Scheduling Computing Tasks on Smartphones: Comparative Case Studies of Metaheuristic Algorithms on Real World Applications
    (Springer, 18-03-2025) Tripathi, Pramod; Sasidhar, P S Kalyan; Mistry, Harsh; Shah, Vyom; DA-IICT, Gandhinagar
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    Big Data for Context Aware Computing – Perspectives and Challenges
    (Elsevier, 01-12-2017) Sasidhar, P S Kalyan; Vasilakos, Athanasios V; DA-IICT, Gandhinagar
    Big data has arrived. Myriad applications, systems generate data of humongous volumes, variety and velocity which traditional computing systems and databases are unable to manage. The proliferation of sensors in every possible device is also becoming one of the major generators of Big data. Of particular interest in this article is how�context aware computing�systems which derive context from data and act accordingly, deal with such huge amounts of data. Big industry players namely Google, Yahoo, and Amazon are already developing�context aware applications�using user data from emails, chat messages, browsing and shopping histories etc. For instance, Gmail reminds us of our flight schedule by understanding flight booking related content in our emails. Similarly, Amazon understands user preference and recommends items of interest to shop and so on. In this paper, we survey context aware computing systems from a Big data perspective. We first propose a taxonomy of existing work on the basis of sensing platforms and then discuss the latest developments in this field of Big data context aware systems focusing on how such systems deal with various Big data challenges. We conclude the paper with an insight on�open research�issues involving designing and developing context aware Big data generating systems.