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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | TRAFFIC PREDICTION AND FAST UPLINK FOR HIDDEN MARKOV IOT MODELS |
| Authors Name: | Charly S , Sathyabalaji N |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_205184
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| Published Paper Id: | IJNRD2309118 |
| Published In: | Volume 8 Issue 9, September-2023 |
| DOI: | |
| Abstract: | In this work, I present a novel traffic predictionand fast uplink (FU) framework for IoT networks controlledby binary Markovian events. First, I apply the forward algorithm with hidden Markov models (HMMs) in order to schedulethe available resources to the devices with maximum likelihood activation probabilities via the FU grant. In addition, I evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next,we formulate a fairness optimization problem to minimize the Age of Information (AoI) while keeping the regret as minimumas possible. Finally, I propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) inareal-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms out perform baseline models, suchas time-division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and AoI. |
| Keywords: | Hidden Markov models, Active learning, wearable computing, machine learning, activity recognition, memory retention, cognitive factors, server monitoring. Time-division multiple access, Random-access, fast uplink, Age of Information |
| Cite Article: | " TRAFFIC PREDICTION AND FAST UPLINK FOR HIDDEN MARKOV IOT MODELS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 9, page no.b162-b173, September-2023, Available :http://www.ijnrd.org/papers/IJNRD2309118.pdf |
| Downloads: | 000118764 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2309118 Registration ID: 205184 Published In: Volume 8 Issue 9, September-2023 DOI (Digital Object Identifier): Page No: b162-b173 Country: Erode, Tamilnadu, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2309118 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2309118 |
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