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The IEEE India Council, the IEEE Gujarat Section and the Organizing Committee of INDICON 2019 invite researchers in India to present their original work in the following three Focused Session at INDICON 2019 (13–15 December 2019) at Marwadi University, Rajkot.

All submissions will go through a peer review process if they do not contain plagiarized material (including self-plagiarism, find IEEE Policy on Plagiarism) and have not been submitted to any other conference at the same time (multiple submissions, find IEEE Policy on Multiple Submission). Conference Organizers will strictly follow IEEE guidelines on such matters.

Authors of selected papers will be invited to present their work at the Focussed session of the INDICON 2019, in one of the two categories, lecture (oral) and poster, once they register under author category for the conference.

Submission Guidelines

All relevant information may be found at

In case you face any difficulty at any point during the submission process, please feel free to write to the TPC Chairs at [email protected]


Only such papers will be published in the Proceedings of the Conference that meet IEEE's quality standards.

IEEE reserves the right not to publish any proceedings that do not meet these standards.

All accepted papers must be submitted finally by the camera-ready paper submission date incorporating the recommendations of the reviewers, and presented in person, at the conference by one of the authors.

Important Dates

Focused session paper submission closes 20 September 2019
Paper Accept/Reject notification 15 October 2019
Camera-ready paper submission 31 October 2019
Early Bird registration closes 10 November 2019

Track 16: Focused Session - Sensing of Heavy Metal Ions

Co-Chairs: Prof. Madhusudan Singh, IIT Delhi & Prof. Bhaskar Mitra, IIT Delhi

Heavy metal sensing is of tremendous importance as trace quantities in water, soil and food can have severe detrimental effects to human health and agriculture. We invite contributions to this focus session describing new materials for sensing, novel devices, low cost circuits, characterization and calibration techniques, reliability and long term testing of sensors. We also welcome talks from researches working on field tests, describing their experiences in field deployment and calibration of sensors.

Track 17: MEMS and Nanotechnologies for Healthcare and Diagnostics

Chair: Dr. Ajay Agrawal, CEERI Pilani

With "Technologies for Point-of-care and early Diagnostics" as its theme, this focused session aims to foster interaction between industry and academia and looks forward to covering research, development and applications in the areas belonging to Engineering and Physical sciences. INDICON 2019 invites original research papers (not being considered for publication elsewhere) in standard IEEE conference template describing new theoretical and/or experimental research results in one of the following fifteen topics/areas (but are not limited to):

INDICON 2019 invites original research papers (not being considered for publication elsewhere) in standard IEEE conference template describing new theoretical and/or experimental research results in one of the following fifteen topics/areas (but are not limited to):

  • Micro/ Nano Electro-Mechanical Systems (MEMS/ NEMS)
  • Nano-material based Devices and Systems
  • Bio-polymers and their applications in Healthcare
  • Paper based Sensor Diagnostics
  • Sensors, Actuators and Systems for Healthcare
  • Non-invasive Diagnostics
  • Micro/ Nano Fluidics for Healthcare/ Diagnostics
  • Lab-on-a-Chip and systems-on-a-chip
  • New Bio-markers
  • Surface functionalization protocols
  • Smart sensors with AI Applications
  • Flexible and Wearable Sensors for Healthcare Applications
  • IoT Sensors for Healthcare Applications
  • Advanced and low-cost Sensor Packaging
  • Point-of-Care Diagnostics

  • Track 18: IoT Data Analytics and Code Tamper-proofing

    Chair: Prof. Chittaranjan Hota, BITS Pilani

    Data analytics has taken the business world by surprise. Nowadays, enhancing business productivity and performance greatly depends on the collection of data from various sources and analysing it. In today’s Internet of Things (IoT) era, smartphones, electronic gadgets, household appliances, motor vehicles etc. are easily becoming sensor nodes measuring environmental parameters and generating huge amounts of user interaction data. IoT is trying to revolutionize applications like healthcare, agriculture, transportation, environmental monitoring etc.

    IoT data analytics examines all data including contextual and metadata sourced from sensors. Data analytics involves techniques to collect, store, filter, analyse, and manage data. Analysing this data using Machine learning and Artificial intelligence techniques provide actionable insights that drive digital transformations. These insights can be applied, for example, to implement smart cars, to implement route optimizations to maximize fleet uptime with predictive maintenance, etc. Processing of data will be easier at these IoT nodes as these devices will be able to self-learn newer patterns, hence improving the response time.

    Modern applications typically run on these embedded devices (IoT devices) that are vulnerable to attacks. Hence, application developers need to build applications that are secure or robust regardless of the device environment. More specifically, adversaries may tamper with the devices, intervene in their communication channels, to instrument the data gathering and overall operation to their own interest. One can develop techniques to prevent such attacks, by building tamper-proof codes that render an application resistant to analysis, change or manipulation by a hacker; by building code obfuscation techniques where an application developer can hide sensitive data on those IoT devices.

    This focused session will cover, but are not limited to the following topics:
  • Integrating IoT data with external data sources
  • IoT data analytics
  • Predictive analytics on IoT data streams
  • IoT application orchestration
  • Federated learning on IoT and edge devices
  • Distributed ML on columnar IoT workloads
  • Explainable AI on IoT real data traces
  • Deep learning techniques to identify anomalies in IoT data streams
  • Code tamper-proofing on IoT devices
  • Obfuscation techniques on IoT data