Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Data aggregation

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    Motion tracking and data aggregation in distributed sensor networks using mobile agents
    (2005) Shivakumar, S.; Vinaykumar, K.
    In this paper a distributed, energy aware and collaborative approach for motion tracking and data aggregation in distributed sensor networks is proposed based on mobile agents. Proposed approach is simulated using network simulator (NS2) and the results are compared with that of the client-server model approach based on energy consumed for tracking and data aggregation. Results show that mobile agent based performs very much better than that of client server approach.
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    Data Aggregation of Tweets and Topic Modelling Based on the Twitter Dataset
    (Association for Computing Machinery, 2021) Srinivasan, V.; Chandrasekaran, K.
    Twitter is one of the most popular online social networks. It has a relatively simple data model and an intuitive API to access Twitter data. This makes it easy to collect social data and analyse the patterns of online behaviour. Twitter has an impactful presence among politicians, entrepreneurs, news agencies, public figures, and this makes it a crucial playground for social discussion. The topics discussed on Twitter often lead to or are the cause of social events. Therefore, a lot of information can be inferred from Twitter data. This can be used by NGOs, government agencies or policymakers to develop meaningful understanding and respond to the emerging trends. In this project, I will discuss a method to aggregate tweets related to Elon Musk and Tesla from Twitter servers using the Twitter API in the form of a web crawler. The data obtained from the web crawler will be combined with a ready-made dataset containing similar information, and the datasets will be merged together. After collecting relevant tweet information, I will perform topic modelling using Latent Dirichlet Allocation (LDA) on his tweets to find out the most common topics tweeted by Elon Musk. © 2021 ACM.
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    Data Format Heterogeneity in IoT-Based Ambient Assisted Living: A Survey
    (Springer Science and Business Media Deutschland GmbH, 2023) Sandeep, M.; Khatri, S.; Chandavarkar, B.R.
    Ambient Assisted Living (AAL) has become a significant component of the lives of the elderly in the present decade, allowing them to live independently by assisting their daily activities with automation. Different sensors from various manufacturers with proprietary data formats to detect environmental changes and monitor a person’s health metrics. These data formats are the root cause of the data Heterogeneity issue in AAL and, in turn, contribute to data interoperability challenges. In this paper, we have presented a survey on currently available state-of-the-art solutions to address data heterogeneity challenges in AAL and made a comparative study of suggested methods to overcome the data interoperability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.