Conference Papers

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    Total Power Minimization Using Dual Power Assignment in Wireless Sensor Networks
    (Institute of Electrical and Electronics Engineers Inc., 2016) Sisodiya, N.; Shetty D, D.P.
    To minimize the energy consumption in a wireless sensor network (WSN) is very important task as the sensors are generally very small and have a power battery attached to it which cannot have very large power capacity. In a wireless sensor network any sensor node must be able to retrieve information from any other sensor node in the network, so the connectivity of the network is also very important concern. The range assignment problem in WSN is to assign transmission power levels to the nodes of a WSN such that some constraints like connectivity is satisfied. In practice it is usually impossible to assign arbitrary power levels to the sensor nodes in a wireless sensor network. Most sensors available in the market operate with discrete power levels. So in this paper we consider the sensor nodes with only two possible power levels assigned to them. Our aim is to minimize the total power consumption while maintaining the strong connectivity between the sensor nodes in the network. We present a nearly optimal heuristic algorithm and also the experimental results. Our SCDPA algorithm gives an average approximation of 1.51. We also present a heuristic algorithm to minimize total power using 3 power levels. © 2015 IEEE.
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    Analyzing Information Flow of Hashtag Networks during Elections using Sentiment Analysis and Graph Algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2022) Patra, C.; Shetty D, P.D.; Chakraborty, S.
    An exponential increase in the usage of social media across the world creates a lot of unstructured data and cross-communication between individuals. These platforms provides opportunity to the political parties to spread their word out. The information is spread using several hashtags in the form of user-generated tagging that facilitates cross-referencing of content. These hashtag-generated networks serve as a huge reservoir of data and if analyzed systematically can help in understanding the agenda-setting of each party and how successful or unsuccessful they are. This in turn helps in predicting the outcome of the election looking from the prism of social media. In the present study, a model is proposed by combining sentiment analysis and graph techniques to look into the trending hashtag networks propagated by political parties using Twitter. The sentiment analysis gives us a sense of inclination of each tweet and thereafter it's extrapolated onto the hashtag's user network to get insights as to how the information is diffusing and how one party propagates its favorable hashtag and how the others try to counter it. The major aim of the present work is to find out the intricacies that go on in the social media space before a major election. © 2022 IEEE.