Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/5125
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dc.contributor.authorChatterjee, Rituparna-
dc.contributor.authorRoy, Ipsita-
dc.date.accessioned2025-03-18T10:56:08Z-
dc.date.available2025-03-18T10:56:08Z-
dc.date.issued2025-03-
dc.identifier.citation59th Annual Conference of The Indian Econometric Society (TIES), Banaras Hindu University (BHU),Varanasi, 3-5 March 2025en_US
dc.identifier.urihttp://hdl.handle.net/2080/5125-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThe rapid advancements in wireless communication result in demand for low-latency and energy-efficient services. Mobile Edge Computing (MEC) offers a promising solution by providing computational resources closer to end users, thus enhancing user experience and reducing the energy consumption of mobile devices. This paper uses a novel simulation environment to deploy and evaluate the Edge-Intelligent Service Placement Algorithm (EISPA). The parameter setting used in implementation demonstrates significant energy savings for EISPA, which reduces energy consumption by 41.07% compared to Gale-Shapley Stable Marriage Matching (GSSMM). The comparative analysis also highlights EISPA’s ability to handle dynamic service demands effectively. Furthermore, the results reveal the robustness of EISPA in adapting to varying network conditions, ensuring reliable service placement decisions.en_US
dc.subjectEdge intelligenceen_US
dc.subjectservice placementen_US
dc.subjectsensor networksen_US
dc.subject5G-and-beyonden_US
dc.subjectIndustrial Internet of Things (IIoT)en_US
dc.titleWhat Determines University-Industry Collaboration? Evidence from R&D Intensive Firms in Indiaen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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