Professor Marimuthu Palaniswami

  • Room: Level: 03 Room: 3.02
  • Building: Electrical and Electronic Engineering
  • Campus: Parkville

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. Goudarzi M, Palaniswami M, Buyya R. A fog-driven dynamic resource allocation technique in ultra dense femtocell networks. Journal of Network and Computer Applications. Academic Press. 2019, Vol. 145. DOI: 10.1016/j.jnca.2019.102407
  2. Rathore P, Kumar D, Bezdek J, Rajasegarar S, Palaniswami M. A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. IEEE Computer Society. 2019, Vol. 31, Issue 4. DOI: 10.1109/TKDE.2018.2842191
  3. Rathore P, Kumar D, Rajasegarar S, Palaniswami M, Bezdek J. A Scalable Framework for Trajectory Prediction. IEEE Transactions on Intelligent Transportation Systems. IEEE - Institute of Electrical and Electronic Engineers. 2019, Vol. 20, Issue 10. DOI: 10.1109/TITS.2019.2899179
  4. Brown L, Karmakar C, Flynn M, Motin M, Palaniswami M, Celano CM, Huffman J, Bryant C. A Self-Compassion Group Intervention for Patients Living With Chronic Medical Illness: Treatment Development and Feasibility Study.. Prim Care Companion CNS Disord. 2019, Vol. 21, Issue 5. DOI: 10.4088/PCC.19m02470
  5. Nguyen TH, Huynh TTB, Nguyen XH, Palaniswami M. An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. INFORMATION SCIENCES. Elsevier. 2019, Vol. 488. DOI: 10.1016/j.ins.2019.02.059
  6. Rathore P, Ghafoori Z, Bezdek J, Palaniswami M, Leckie C. Approximating Dunn's Cluster Validity Indices for Partitions of Big Data. IEEE TRANSACTIONS ON CYBERNETICS. Institute of Electrical and Electronics Engineers. 2019, Vol. 49, Issue 5. DOI: 10.1109/TCYB.2018.2806886
  7. Kusmakar S, Karmakar CK, Yan B, O'Brien T, Muthuganapathy R, Palaniswami M. Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. IEEE - Institute of Electrical and Electronic Engineers. 2019, Vol. 66, Issue 2. DOI: 10.1109/TBME.2018.2845865
  8. Philip B, Alpcan T, Jin J, Palaniswami M. Distributed Real-Time IoT for Autonomous Vehicles. IEEE Transactions on Industrial Informatics. IEEE - Institute of Electrical and Electronic Engineers. 2019, Vol. 15, Issue 2. DOI: 10.1109/TII.2018.2877217
  9. Krishnavilas Udhayakumar R, Karmakar C, Palaniswami M. Multiscale entropy profiling to estimate complexity of heart rate dynamics. Physical Review E: covering statistical, nonlinear, biological, and soft matter physicsE. American Physical Society. 2019, Vol. 100, Issue 1. DOI: 10.1103/PhysRevE.100.012405
  10. Kusmakar S, Karmakar C, Yan B, Muthuganapathy R, Kwan P, O'Brien T, Palaniswami M. Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures. EPILEPSIA. Blackwell Science. 2019, Vol. 60, Issue 1. DOI: 10.1111/epi.14619
  11. Motin M, Karmakar C, Palaniswami M. PPG Derived Heart Rate Estimation During Intensive Physical Exercise. IEEE ACCESS. IEEE - Institute of Electrical and Electronic Engineers. 2019, Vol. 7. DOI: 10.1109/ACCESS.2019.2913148
  12. Motin M, Karmakar C, Palaniswami M. Selection of Empirical Mode Decomposition Techniques for Extracting Breathing Rate From PPG. IEEE SIGNAL PROCESSING LETTERS. IEEE - Institute of Electrical and Electronic Engineers. 2019, Vol. 26, Issue 4. DOI: 10.1109/LSP.2019.2900923
  13. Naganur VD, Kusmakar S, Chen Z, Palaniswami M, Kwan P, O'Brien T. The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non-epileptic seizures.. Epilepsia Open. 2019, Vol. 4, Issue 2. DOI: 10.1002/epi4.12327
  14. Desai N, Seghouane A, Palaniswami M. Algorithms for two dimensional multi set canonical correlation analysis. PATTERN RECOGNITION LETTERS. Elsevier Science. 2018, Vol. 111. DOI: 10.1016/j.patrec.2018.04.038
  15. Rathore P, Bezdek J, Kumar D, Rajasegarar S, Palaniswami M. Approximate Cluster Heat Maps of Large High-Dimensional Data. 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). I EEE Xplore. 2018. DOI: 10.1109/ICPR.2018.8545519

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