An Open Architecture for Smart Cities

Sponsor and Collaborator: Siemens India

Time Frame: May 2015 — Apr2016

Status: Active

Smart cities are an upcoming area of growth in the region and provide a large gamut of technical challenges in the area of wide area distributed sensing and processing. A significant part of these challenges can be addressed by leveraging upcoming technologies in the fog computing area where edge devices not only collect data and provide control signals but perform local optimizations based on global optimization requirements. However, to implement rapid growth of smart campus/city requires applications. These applications may be on different devices, software, middleware etc. As a result, this heterogeneity is a huge challenge at present to integrate to available system, collect data etc. Hence, one needs to develop a platform agnostic to above complex heterogeneous environment.

This provides an opportunity for both Siemens and IIIT Bangalore to create an open platform to share data and expose relevant APIs using which smart applications can be built by anyone.

Inferencing in the Large: Semantic Integration of Open-Data Tables

Inferencing in the Large (ITL) is a research problem encompassing knowledge extraction, knowledge organisation and knowledge retrieval from open structured data, especially from the Indian Open Government Data.

With vast amounts of tabular data freely available under several Open-Data initiatives, consumption of information depends upon the perspectives of the consumer. These perspectives can be viewed as various contexts the data can be placed in and analysed. Extraction and Organisation of these contexts are non-trivial and we address the problem using semantic integration of open structured data. A collection of open datasets can map to similar contexts (themes) and a single table can map to different themes. ITL presents a model that semantically integrates and aggregates open data in a data mesh of applicable inter-related contexts. Sandesh 1.0 are Sandesh-RDF (v 2.0) are implementations of ITL using open government data from the Indian Open Government Data portal. We use the Linked Open Data (LOD) to associate semantics to the data. The MWF (Many Worlds on a Frame) knowledge framework has been implemented using RDF N-Quads to represent the knowledge extraction in Sandesh-RDF (v 2.0). Sandesh-RDF queries the knowledge graph created from the N-Quads which is the semantic representation of data from The previous version of Sandesh used the default SQLlite implementation of the MWF framework.


Sponsors and Collaborators: Horizon 2020, European Commission

Time Frame: Jan 2016 – Dec 2020

Status – Active

The REACH project aims to develop solution to avail the provision for high speed Internet access in rural India using unlicensed TV white space spectrum and designing the Geolocation database for it. With the wide increase of population and use of Internet in India, the efficient utilization and management of spectrum is needed. The utilization of TV white space spectrum is emerging as a best alternative to fulfill this need since there are many unused channel in TV spectrum due to migration from analog to digital transmission technology.

At IIIT-B, we are working on Distributed Algorithms for Spectrum Assignment for White Space Devices. Spectrum assignment for devices in white-space spectrum is challenging due to the fact that, white-space spectrum has temporal and spatial variations and is most often fragmented. We have created an autonomous agent model for spectrum assignment of white space devices at a given location. Each white space device (WSD) acts autonomously out of self-interest, choosing a strategy from its bag of strategies. It obtains a payoff based on its choice and choices made by all other agents. WSDs interact with each other using a central shared memory located at a “Master” device. Based on payoffs received by different strategies, WSDs evolve their strategic profile over time. This has the effect of “demographic changes” in the population. The system is said to have reached a state of equilibrium (or, in a state of evolutionary best-response) when the demographic profile stabilises. The system is trained on different load profiles to compute their respective evolutionary best responses.

Project Outcomes

  1. Chaitali Diwan. Autonomous Spectrum Assignment of White Space devices. MTech thesis. June 2016.
  2. Chaitali Diwan, Srinath Srinivasa, Bala Murali Krishna. Autonomous Spectrum Assignment of White Space Devices. Proceedings of the 12th EAI International Conference on Cognitive Radio Oriented Wireless Networks. Lisbon, Portugal. September 2017.
  3. Simulation dashboard for autonomous spectrum allocation algorithm

Other Relevant Links


Sandesh is Semantic Data Mesh for publishing of Knowledge aggregated from Indian Open Data. Open structured data is published by several agencies like World Health Organization (WHO), United Nations Organization (UNO), private firms, NGOs, governmental bodies etc. Government of India publishes open data on its data portal called To aggregate and integrate data from disparate datasets,  a framework called Many Worlds on a Frame (MWF) is proposed. The framework is partially implemented in software called RootSet on top of which, the module Sandesh is implemented.

Co-creation of a Center of Excellence in Big Data Engineering

This project aims to establish a collaboration between the International Institute of Information Technology (IIIT-B), with City University London, as the UK partner and Siemens Research, India, as the industry partner, to set up a centre of excellence in Big Data Engineering. With emerging trends like Web Science and the Internet of Things, expertise in Big Data is going to be in high demand in the future. As part of our initiatives to create a talent pool of research and engineering expertise, IIIT-B has collaborated with several partners in this area on specific projects. This project aims to consolidate our disparate activities in this area and create a Centre of Excellence in Big Data Engineering. The term “Big Data” is defined here to mean any kind of data management problem for which, conventional RDBMS based solutions are inadequate. The “Big” refers to not just the volume of data, but also challenges concerning variety, veracity and velocity of the data.


  • Dr. Srinath Srinivasa IIIT Bangalore
  • Prof Muttukrishnan Rajarajan, City University, Northampton Square , London,
    United Kingdom
  • Dr. Amarnath Bose, Siemens Technology and Service, Bangalore
  • Praseeda , Research Scholar, IIIT Bangalore
  • Raksha, Research Scholar, IIIT Bangalore
  • Anish, Mtech. Thesis Student, IIIT Bangalore


  • A two day workshop on Big Data on April 18th and 19th at IIIT Bangalore

Associated Projects




  • Srinivasa S., Pavagada Subbanarasimha R. (2018) Design of the Cogno Web Observatory for Characterizing Online Social Cognition. In: Anirban Mondal, Himanshu Gupta, Jaideep Srivastava, P.Krishna Reddy, D.V.L.N. Somayajulu. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science. Springer, Cham. (To appear)
  • Raksha Pavagada Subbanarasimha, Lokesh Todwal, Mamillapalli Rachana, Aditya Naidu, and Srinath Srinivasa. 2018. Mithya: A Framework For Identifying Opinion Drivers On Social Media. Demo at ACM IKDD Conference on Data Science and International Conference on Management of Data, Goa, India, Jan 2018 (CODS-COMAD 2018).
  • Anish Bhanushali, Raksha Pavagada Subbanarasimha, and Srinath Srinivasa. 2017. Identifying Opinion Drivers on Social Media. In On the Move to Meaningful Internet Systems. OTM 2017 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part II. Springer International Publishing, Cham, 242–253.