Conference Papers

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

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    Multiple aggregator multiple chain routing protocol for heterogeneous wireless sensor networks
    (2013) Harichandan, P.; Jaiswal, A.; Kumar, S.
    Wireless sensor nodes are deployed to gather useful information from the field but their constraint on battery power leads us to think about energy efficient routing protocols so that they can operate over longer periods of time. We study the advantages of having multiple chains in a network with each chain's topmost node (called the aggregator) collecting the data from the nodes beneath it and transmitting it to the sink. In the proposed scheme, a chain in each region works as PEGASIS. We also study how considering heterogeneity in the network can improve the lifetime of a network by a significant period. We assume that a fraction of the nodes in the network possess additional energy. We show by simulations that the introduction of heterogeneity into the network results in a greater lifetime, compared to those of the classical data aggregation schemes, with the duration increasing with the amount of additional energy considered. © 2013 IEEE.
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    Resource aware scheduling in Hadoop for heterogeneous workloads based on load estimation
    (2013) Kapil, B.S.; Kamath S․, S.S.
    Currently, most cloud based applications require large scale data processing capability. Data to be processed is growing at a rate much faster than available computing power. Hadoop is used to enable distributed processing on large clusters of commodity hardware. In large clusters, the workloads may be heterogeneous in nature, that is, I/O bound, CPU bound or network intensive jobs that demand different types of resources requirement so as to run simultaneously on large cluster. Hadoops job scheduling is based on FIFO where, parallelization based on types of job has not been taken into account for scheduling. In this paper, we propose a new scheduling algorithm for Hadoop based distributed system, based on the classification of workloads to assign a specific category to a particular cluster according to current load of the cluster. The proposed scheduler increases the performance of both CPU and I/O resources in a cluster under heterogeneous workloads, by approximately 12% when compared to Hadoops FIFO scheduler. © 2013 IEEE.
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    Analyzing the Heterogeneity in Public Transit Demand: Impact of Spatial and Temporal Attributes
    (Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Venkateswari, N.P.
    The usage of personal vehicles is causing several issues like traffic congestion, greenhouse gas emissions, and massive energy consumption. These issues can be alleviated by implementing a public bus transport system. But the irregular frequency of buses and longer waiting times are diminishing the attractiveness of public bus transportation in India. To implement an affordable and efficient public transport system, it is necessary to understand the heterogeneity of public transit demand under different conditions. Limited scholarly exploration on the impact of spatial and temporal characteristics on the heterogeneity of public transit demand under Indian conditions. This paper aims to measure public transit demand heterogeneity using the coefficient of variation of transit demand, which is defined as a ratio of standard deviation to mean. Spatial, temporal, and weather characteristics are considered to analyze their influence on the heterogeneity of public transit demand. The statistical variability analysis is performed using Electronic Ticketing Machine (ETM) data, weather data, and bus network data. The results indicate that the combined impact of weather conditions and the built environment has a stronger influence on the variability in Public Transit Demand (PTD) than each factor individually. Based on the analyses, it is recommended to change the service type to improve the transport system's efficiency. This study suggests the importance of incorporating spatial, temporal, and weather characteristics. This study can help stakeholders to optimize public transport networks and schedule service frequency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.