Experienced Software Research Engineer | Ph.D. Researcher
ORCID ID: https://orcid.org/0000-0002-2261-5722
Mohit is working as a software research engineer with the Emerging Networks Lab (ENL) research unit of TSSG. He is also pursuing his Ph.D. in the Department of Computing and Mathematics at the ENL research unit in TSSG, Waterford Institute of Technology, Ireland. He joined in 2015 as a Masters student, and has since been working as a part of the Science Foundation Ireland (SFI) funded CONNECT research centre. During his master studies, he worked on SFI-CONNECT-IBM project “SmartHerd”, which focused on the welfare and monitoring of dairy cows, and the early detection of lameness in cattle with the help of technology. He is also working on the follow on use-case project MELD(Machine Learning for Early Lameness Detection) of SmartHerd, which is funded through the IoF2020 project under the European Union’s Horizon 2020 research and innovation programme.
He is also currently working on a CISCO funded project that aims to develop a framework for dynamic task sharing and distributed decomposed data analytics in fog enabled IoT deployments with cloud support. His current research interests include Fog and Cloud Computing, Internet of Things (IoT), Distributed Systems, and Distributed Data Analytics. His research focuses on leveraging fog computing for data analytics in IoT deployments, and in particular decomposing data analytics and machine learning programs for fog enabled IoT systems towards effective resource and service management to support and meet the requirements for real-time IoT analytics.
Bachelor’s Degree in Computer Science and Engineering from The LNM Institute of Information Technology, Jaipur, India in 2015. He has also previously interned with ArcelorMittal, Kazakhstan, and McAfee, India. During SmartHerd project, he has also been on research exchange with Innovation Exchange, IBM Research Labs, Ireland.
- SmartHerd (SFI-IBM-CONNECT, 2015-2018)
- MELD(Machine Learning for Early Lameness Detection) (MELD-IoF2020, H2020, 2019-2020)
- Fog Assisted Middleware Support for IoT Applications (CISCO Research Gift Fund, 2019-2020)
- Dynamic and Intelligent Allocation of Fog Resources to IoT Applications (NGI Explorers Fellowship, EU-US Collaboration, H2020, 2019-2020)
- Finalist in AI Awards 2019 under the category of Best Application of AI in an Academic Research Body for SmartHerd project .
- Finalist in Make an Impact Competition 2019 for the SmartHerd project.
- Listed in top 5 projects among those submitted in IEEE ComSoc Student Competition 2019 for the SmartHerd project.
- Listed in top 10 projects among those submitted in IEEE ComSoc Student Competition 2018 for the SmartHerd project entered under the title of Connected Cows.
Research Awards and Grants
- M. Taneja, J. Byabazaire, N. Jalodia, A. Davy, C. Olariu, and P. Malone, “Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle,” Computers and Electronics in Agriculture, vol. 171, p.105286, 2020. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S016816991931840X
- M. Taneja, N. Jalodia, P. Malone, J. Byabazaire, A. Davy and C. Olariu, “Connected Cows: Utilizing Fog and Cloud Analytics toward Data-Driven Decisions for Smart Dairy Farming,” in IEEE Internet of Things Magazine, vol. 2, no. 4, pp. 32-37, December 2019. https://ieeexplore.ieee.org/document/8982746
- M. Taneja, N. Jalodia and A. Davy, “Distributed Decomposed Data Analytics in Fog Enabled IoT Deployments,” in IEEE Access, vol. 7, pp. 40969-40981, 2019. doi: 10.1109/ACCESS.2019.2907808. https://ieeexplore.ieee.org/document/8675283
- M. Taneja, N. Jalodia, J. Byabazaire, A. Davy, and C. Olariu,“Smartherd management: A microservices-based fog computing assisted iot platform towards data-driven smart dairy farming,” in Software: Practice and Experience, vol. 49, no. 7, pp. 1055-1078, 2019. doi: 10.1002/spe.2704. https://onlinelibrary.wiley.com/doi/full/10.1002/spe.270
- Byabazaire, C. Olariu, M. Taneja, and A. Davy, “Lameness detection as a service: Application of machine learning to an internet of cattle,” in 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC), Jan 2019, pp. 1–6. URL: https://ieeexplore.ieee.org/document/8651681
- M. Taneja, J. Byabazaire, A. Davy and C. Olariu, “Fog Assisted Application Support for Animal Behaviour Analysis and Health Monitoring in Dairy Farming,” 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 2018, pp. 819-824. doi: 10.1109/WF-IoT.2018.8355141. URL https://ieeexplore.ieee.org/abstract/document/8355141
- M. Taneja, N. Jalodia, and A. Davy, “Towards Decomposed Data Analytics in Fog Enabled IoT Deployments – IEEE Internet of Things Newsletter,” September 2018, [Online]. Available: https://iot.ieee.org/newsletter/september-2018/towardsdecomposed-data-analytics-in-fog-enabled-iot-deployments.html
- M. Taneja and A. Davy, “Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm,” 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, 2017, pp. 1222-1228. doi: 10.23919/INM.2017.7987464. URL: https://ieeexplore.ieee.org/document/7987464
- M. Taneja and A. Davy, “Poster Abstract: Resource Aware Placement of Data Stream Analytics Operators on Fog Infrastructure for Internet of Things Applications,” 2016 IEEE/ACM Symposium on Edge Computing (SEC), Washington, DC, 2016, pp. 113-114. doi: 10.1109/SEC.2016.44. URL: https://ieeexplore.ieee.org/document/7774693
- M. Taneja, Alan Davy, Resource Aware Placement of Data Analytics Platform in Fog Computing, In Procedia Computer Science, Volume 97, 2016, Pages 153-156, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2016.08.295. URL : http://www.sciencedirect.com/science/article/pii/S1877050916321111
- Somani, A. Johri, M. Taneja, U. Pyne, M.S. Gaur and D. Sanghi, ” DARAC: DDoS Mitigation using DDoS Aware Resource Allocation in Cloud,” 2015 11th International Conference On Information Systems Security (ICISS), Kolkata, 2015, pp 263-282, Lecture Notes in Computer Science, vol 9478. Springer, Cham Print ISBN 978-3-319-26960-3, Online ISBN 978-3-319-26961-0https://doi.org/10.1007/978-3-319-269610_16. URL: https://link.springer.com/chapter/10.1007/978-3-319-26961-0_16