Professor Marimuthu Palaniswami

Research Interests

  • Biomedical Instrumentation, Signal and Image Processing (Time Frequency Analysis, Machine Learning, Active Shape Models)
  • Biomedical signal processing
  • Cardiovascular signal processing and modelling, Human gait analysis, Medical Instrumentation (Machine learning, Biomedical Electronics)
  • Control and Optimization of Communication Networks (Internet Traffic Control, Congestion Control, Quality of Service)
  • Event Detection (Pattern Recognition, Machine Learning)
  • Image Processing
  • Machine learning (support vector machines)
  • Signal Processing
  • Signal and Image Processing, Computer Vision (Video Analysis, Surveillance)
  • Sliding Mode Control (Nonlinear Systems)
  • Smart City Research (Internet of Things)
  • Smart City Research (Wireless Sensor Networks, Internet of Things)
  • Smart City, Cyber-Physical System (Wireless Sensor Networks, Internet of Things)
  • Smart grid (Security)
  • Wireless Sensor Neteworks (Computer Network Monitoring, Environmental Monitoring)
  • Wireless sensor networks (Communications)
  • Wireless sensor networks (Security)

Personal webpage


Marimuthu Palaniswami is a Fellow of the Institute of Electrical and Electronic Engineering (IEEE) and an internationally recognised expert in Internet of Things (IoT), Sensor Networks, Automated Learning, and Computational Intelligence in large-scale complex systems. He is a named Distinguished Lecturer of the IEEE Computational Intelligence Society over the period 2013-2015.

He obtained his Ph.D from the University of Newcastle, Australia and is currently the Professor in the Department of Electrical and Electronic Engineering. He has a demonstrated track record in leading large research initiatives. In particular, he has been the Founder and Director of the ARC Research Network ISSNIP (Information, Signals, Sensor Networks and Information Processing), which has become an internationally recognised constellation of researchers, partner universities and industry organisations in the area of sensor networks.

He has built many large-scale projects by bringing together teams of chief investigators in related areas such as the Distributed Sensor Networks Project funded by former Department of Education, Science and Technology (DEST), the SEMAT project of the Queensland government’s Smart State Program and IMOS-GBROOS for the Great Barrier Reef. He was the co-director of the Centre of Excellence for Networked Decision Systems (CENDS) funded by the Defence Science and Technology Organisation.

He is also a co-founder of the European Centred IoT forum. He served as a general Chair for over 10 IEEE Sponsored International conferences with a focus on Sensor Networks and Internet of Things (IoT). As a Chief Investigator, he was funded by various government agencies for a number of IoT projects covering Infrastructure, Smart City, Healthcare, Transport, BigData Analytics and Smart Governance. His international IoT funding for smart city covers projects such as European Union’s FP7 Smart Santander, SocioTal, and OrganiCity.

His research has also focused on translational aspects of his research and he has a fantastic track record in working with diverse industry sectors – from defence to environment, from telecom to biomedical and from health to local government domains. His extensive publication and citation record is a clear testament to his technical leadership spanning Internet flow control, cloud computing, computational tools for analytics, image processing, sensor network architectures, sensor data fusion, autonomous tracking, sensor network security and control engineering. He has published well over 400 scientific papers including books and edited volumes in related topics in IEEE Transactions on IoT Journal, Cybernetics, Fuzzy Systems, Neural Networks, Power Systems, Communications Magazine, Computational Intelligence Magazine, Information and Forensic Society, Mobile Computing, Automatica Control; ACM Transactions on Sensor Networks; Pattern Recognition, Computer Vision and Image Understanding (CVIU); Automatica; IEEE Journal of Biomedical and Health Informatics (JBHI), PLoS ONE, Frontiers in Physiology, Medical and Biological Engineering and Computing, and others.

He has been recognised and awarded (in 2007, 2008 and 2010) for his substantial knowledge transfer to industry and the community. Many of these knowledge transfers were using deployable technologies for research outcomes, e.g. DIISR-ISL Project on Distributed Sensor Networks. His research continues to deliver in Healthcare (e.g. CSIRO and Microsoft Research Diagnostic tools project) and on the environment (e.g. Great Barrier Reef monitoring) which is testimony to the capacity of his research in providing benefits to society.

He served as a member of the US NSF proposals panel, steering committee member for EU IoT projects and advisory panel member for Centre of Excellence on IoT projects. He has contributed immensely to creating IoT startups, keen to push the impact of IoT further on industry and society.

Recent Publications

  1. Kumar D, Bezdek J, Rajasegarar S, Leckie C, Palaniswami M. A visual-numeric approach to clustering and anomaly detection for trajectory data. VISUAL COMPUTER. Springer. 2017, Vol. 33, Issue 3.
  2. Li J-W, Li S-N, Zhang Y, Gu T, Law YW, Yang Z, Zhou X, Palaniswami M. An Analytical Model for Coding-Based Reprogramming Protocols in Lossy Wireless Sensor Networks. IEEE TRANSACTIONS ON COMPUTERS. IEEE - Institute of Electrical and Electronic Engineers. 2017, Vol. 66, Issue 1.
  3. Krishnavilas Udhayakumar R, Karmakar C, Palaniswami M. Approximate entropy profile: a novel approach to comprehend irregularity of short-term HRV signal. NONLINEAR DYNAMICS. Springer. 2017, Vol. 88, Issue 2.
  4. Desai N, Sridhara Rao A, Palaniswami P, Thyagarajan D, Palaniswami M. Arytenoid cartilage feature point detection using laryngeal 3D CT images in Parkinson's disease. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Institute of Electrical and Electronics Engineers. 2017.
  5. Marzbanrad F, Khandoker A, Kimura Y, Palaniswami M, Clifford GD. Assessment of Fetal Development Using Cardiac Valve Intervals. FRONTIERS IN PHYSIOLOGY. Frontiers Research Foundation. 2017, Vol. 8, Issue MAY.
  6. Rathore P, Bezdek J, Monazam Erfani S, Rajasegarar S, Palaniswami M. Ensemble Fuzzy Clustering using Cumulative Aggregation on Random Projections. IEEE Transactions on Fuzzy Systems. IEE Institute of Electronic Engineers. 2017.
  7. Lyu L, Jin J, Rajasegarar S, He X, Palaniswami M. Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering. IEEE INTERNET OF THINGS JOURNAL. IEEE - Institute of Electrical and Electronic Engineers. 2017, Vol. 4, Issue 5.
  8. Fahiman F, Bezdek J, Monazam Erfani S, Palaniswami M, Leckie C. Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms. IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers. 2017.
  9. Fahiman F, Monazam Erfani S, Rajasegarar S, Palaniswami M, Leckie C. Improving load forecasting based on deep learning and K-shape clustering. Proceedings of the International Joint Conference on Neural Networks. 2017, Vol. 2017-May.
  10. Rathore P, Kumar D, Rajasegarar S, Palaniswami M. Maximum Entropy-Based Auto Drift Correction Using High-and Low-Precision Sensors. ACM TRANSACTIONS ON SENSOR NETWORKS. Association for Computing Machinery (ACM). 2017, Vol. 13, Issue 3.
  11. Motin M, Karmakar C, Palaniswami M. Modified thresholding technique of MMSPCA for extracting respiratory activity from short length PPG signal. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Institute of Electrical and Electronics Engineers. 2017.
  12. Desai N, Seghouane A, Palaniswami M. Multisubject fMRI Data Analysis via Two Dimensional Multi-set Canonical Correlation Analysis. 2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017). IEEE. 2017.
  13. Jin J, Palaniswami M, Yuan D, Dong Y-N, Moessner K. Priority Service Provisioning and Max-Min Fairness: A Utility-Based Flow Control Approach. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT. Springer. 2017, Vol. 25, Issue 2.
  14. Lyu L, Law Y, Jin J, Palaniswami M. Privacy-preserving aggregation of smart metering via transformation and encryption. Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems. 2017.
  15. Lyu L, He X, Law Y, Palaniswami M. Privacy-preserving collaborative deep learning with application to human activity recognition. International Conference on Information and Knowledge Management, Proceedings. 2017, Vol. Part F131841.

Marimuthu Palaniswami

Level: 03 Room: 3.02
Electrical and Electronic Engineering, Parkville
University of Melbourne
3010 Australia

T: +61 3 83446710

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile