A Smart City View

Ankur Thareja and Subrata Bhattacharya elaborate upon Transcendental Coalescence in the Age of IoT and Analytics.

  • Client

    Johnson Controls (India Engineering Centre)

  • Services

    To unify all the elements and make them work together towards the betterment of its citizens via Transcendental Coalescence.

  • Technologies

    IoT and Analytics.

  • Dates




In a previous article “How IoT and Big Data are transforming Green Buildings into Living Buildings” we talked about:

  • How IoT combined with ubiquitous computing provides enormous data and information
  • Data is used to forecast, predict, and optimise operations and needs
  • Concept of “Device Autonomy”, i.e., self-aware, self-regulated and optimised transforms.


In this article, we would like to project this concept to more generic business environment (a Smart City) and reflect on:

  • A use case (value creation and capture) based approach for complex business landscape
  • User centric needs and interactions.


Before we start deliberating on this topic let’s first qualify some definitions:

  • Transcendental: As per Oxford dictionary, mathematically it is something that is not capable of being produced by the mathematical operations; in philosophical terms, it is something beyond physical realm
  • Coalescence: Coalescence as a process of “joining or merging of elements to form one mass or whole”
  • Transcendental Coalescence: Is more than mashing-up of technologies and use cases owned by separate business/industry verticals. It is the fusion of people, processes and technology to provide most intuitive and optimised business outcome.
  • Smart City: A City comprises of multiple and complex interdependent systems. The word ‘Smart’ by itself is subject to interpretation and there are multiple definitions of Smart cities. For our reference we will define this as “A smart city uses information and communication technology (ICT) to enhance its livability, workability, and sustainability” [Smart Cities Council 2014]


Smart City use case of “Transcendental Coalescence”


The United Nations estimates that between 2015 and 2050 the world population will increase by 32%, i.e., from 7.2 to 9.7 billion inhabitants, while the urban population will increase by 63% from 3.9 to 6.3 billion inhabitants. This extraordinary growth in urbanisation is already putting and will continue to put a great amount of stress on cities in providing basic services to citizens. We will need cities and megacities, thus spurring the need for more housing, offices, hospitals, and schools along with connected and efficient public infrastructure and amenities.


Both, rapid urbanisation and unplanned growth pose significant challenges for cities by the way of increase in demand for natural resources. Currently, even though the world’s cities occupy just 2% of the earth’s land, they account for up to 80% of energy consumption and 75% of carbon dioxide emissions. Hence, we need to find smart ways to better use our natural resources and better serve the aspirations of the urban population so that cities grow sustainably.


The word ‘Smart’ by itself, as mentioned above, is subject to interpretation and there are multiple definitions of Smart cities that exist in public domain. A smart city is beyond the aggregation of smart buildings.


The aim of a smart city is to unify all the elements and make them work together towards the betterment of its citizens via Transcendental Coalescence. This is achieved by a conglomeration of multiple use cases in action across its citizen diaspora; in a bid to increase productivity, safety, and security while creating an experience of sustainable urban living.


Figure 1: Smart city ecosystem


As explained in Figure 1, there are multiple systems and subsystems in a smart city eco-system, viz.,

  • Waste the disposal and management
  • Traffic management, transport systems, and clean vehicle with associated infrastructure
  • Water management
  • Clean power & energy management
  • Physical security, cybersecurity, and surveillance and an overall threat modeling of a city
  • E-governance (transport, utilities, healthcare, safety & security), etc., and
  • Others.


Each of these aspects is an area of specialisation applied to the building, precincts, municipality and finally to the city level. Thanks to the continuous evolution of technology, there is a set of the common underlying theme from a design perspective that helps make the ‘Smart City’ from concept to reality.


The first step towards the design is the rigorous and meticulous definition of personas and their use cases. The problem statement and/or use cases need to be identified and solutions need to be crafted accordingly; keeping scalability in mind too. Technology helps to bring the solutions together, but solutions cannot be fitted to a problem. The design considerations of retrofitting Singapore as a smart city vis-à-vis Jaipur (a proposed smart city in India) are very different. Some of examples are:


  • The smart city initiative of Kansas City, Mo., involves smart streetlights, interactive kiosks; data visualisation app, data for available parking spaces, traffic flow and pedestrian hotspots
  • Singapore uses sensors and IoT-enabled cameras to monitor the cleanliness of public spaces, crowd density and the movement of locally registered vehicles. Its smart technologies help companies and residents monitor energy use, waste production and water use in real time.


Selection of technology elements while building the smart city ecosystem and/or its components is critical. A smart city will be dealing with a very large amount of data (in petabytes) that could be structured, or text records of human and machine or video surveillance feeds or even social media feeds. All this data is interdependent and needs to be connected and interpreted in the right context for a pro-active and meaningful action. Knowledge of technology components, the data flow understanding, and allied analytics is a common lever across the systems and subsystems. Further appreciation for ‘obsolescence’, be it electronic hardware or software components in the ever-demanding need of citizens, is necessary to ensure there is a right trade-off between “future proofing” and “implementation success”.


Technology components for Transcendental Coalescence

Let’s look at few of these technology components. Complete Transcendental Coalescence will require an ecosystem consisting of three main technology components:

  • Data Collection
  • Cloud, data platform and analytics, and
  • Apps and Visualisation.


Figure 2: Unified Reference Architecture for Transcendental Coalescence


Let’s discuss each enabler in detail.

1. Data Collection: This can be subdivided into

  1. Data Sources: Legacy – Sensors, controllers, BMS; Social Media; World Wide Web; Electronic Data bases; etc.
  2. Data Ingestion: In today’s era where everything is claimed to be IoT device or IoT enabled, determining the data collection/ingestion strategy is a critical step in designing for a legacy/retrofit environment (Transition systems: till all the end devices are IoT compatible). This will define how easy or difficult it will be for the software applications in the cloud being able to process data from the ingestion point. The absence of a clearly defined and widely accepted industry data transfer protocols/mechanisms, broken value chain, and unclear data ownership and use issues make this job even difficult.


Optimised data transfer protocols like RESTful API, CoAP, HTTP(s), AMQP, MQTT play a major role here. While the smart edge devices in the network are expected to use these, in most cases gateway playss an important role in converting data into IoT defined protocols. Gateway works as a bridge between the non-IP legacy systems or non- IoT traditional systems and protocols with lightweight cloud services protocols for onward processing/analysis of data.


Further, the data is not necessarily a real-time or a time series data in nature. There are and could be contextual or unstructured data feed as well, e.g., asset maintenance history records, social media content etc. to be used as part of the system.


  1. Edge computing (Edge Gateway): Edge computing/gateway is a method of optimising cloud computing systems by performing data processing at the edge of the network near the source of the data. This reduces the communications bandwidth needed between end devices and the central data processing units (either in servers or in the cloud) by performing analytics and knowledge generation at or near the source of the data. Edge devices are classical “Things” in an IoT world that are defined as computing devices having embedded intelligence to perform a pre-defined action based on input condition or input from other connect smart things or from remote services. Smart meter, smart sensors/actuators, etc., are just a few examples of edge devices.


Edge computing ensures dependable operation of the IoT ecosystem even when the connectivity is intermittent. Edge application services significantly decrease the volumes of data that must be moved, the consequent traffic, and the distance the data must travel, thereby reducing transmission costs, shrinking latency, and improving quality of service (QoS). It is always desired to have as many as edge devices/intelligence on the edge while designing a smart city subsystem and its components.


2. Cloud, data platform & analytics

The success of data strategy and various systems interconnectivity/interoperability depends on the data platform. While there is a generic guideline, mostly it depends on the software architecture of the data platform of the provider. The generic checkpoints remain:

  • Data type compatibility (Data Ingestion methodology and support): Time series events, unstructured text, image, video streams, pdf or scanned documents
  • Distributed architecture having microservices implementation for autonomously distributed computing at the edge level
  • Big Data storage and processing system e.g. a Hadoop file system
  • On the analytics side, the data platform should have the capability to
  • Rule ( mathematical expressions based on if, then else and simple equations) based analytic decision approach
  • Ability to carry out statistical analysis
  • Perform (reinforced/deep) learning. Based on statistical data, the system by itself recognises the pattern based on historical data and takes appropriate action automatically (AI)
  • Able to manages complicated workflows


3. Mobility, Apps and Visualisation

Until recentlly, every buniess problem was looked upon as technology and cost saving problem. End user and his needs and experince was mostly neglected. Technologies and use case approach need to bring up the experience for the personas in the ecosystem. The user interface for the citizen with the smart city needs to be:

  1. Engaging: Social, creative and collaborative
  2. Empowering:Providing a choice and flexibility; sense of control, and
  3. Sense of fulfillment: This is directly related to a happy citizen and hence a happy city.


Figure 3:  Three typical categories of mobility & apps.


Challenges in developing Transcendental Coalesce (Smart City use case)

1. The need for an integrated approach

A city is made up of different infrastructure verticals forming a system of systems. However, such city infrastructure elements typically operate in silos. Smart cities need an integrated approach in order to harness the full potential of smart infrastructure. Integrated approaches are effective tools for capturing the dynamic relations between people, policies and environments. Some of the recent examples made in this direction:

  • New York City’s Mayor’s Office of Data Analytics (MODA), is developing a formal structure for reporting out cost savings and increased revenue achieved for the city, along with efficiencies that improve service quality and fairness.
  • Wellesley, Massachusetts has saved $132,000 in energy costs using data analytics. By managing and reporting regularly on energy use for each town building and benchmarking.
  • The city of San Diego, with its open data portal, predictive analytics, geospatial analysis, and performance management program, has achieved significant savings. Some of the projects are:
    • Lean Six Sigma methodology streamlined the 911 call answering and dispatch process to offload non-emergency calls, allowing true emergency calls to be answered faster.
    • Sensors on streetlights that allow automatic dimming and brightening of the lights, estimated to save $2.4 million annually on the city’s energy costs.

All the above are individually planned projects. More insights and increase in overall efficiencies can be obtained by combining them and correlating data. For example:

  • Use of 911 data in coordination with CCTV monitoring for added security and infrastructure measures such as obtaining information on public lights not dimmed in sensitive areas.
  • Building energy data and benchmarking can be used to develop Smart grid strategies for flattening the energy production line as well as reducing the T&D losses.


2. Monetisation Strategy: Infrastructure ownership and use

Integrated approach leads to other challenges being faced in the absence of universal accepted Cloud/Data connectivity architecture. At the pace with which new technologies and protocols are being designed and proposed for connectivity, there is an absence of standardisation for diverse system connectivity.


Initial cost of IoT is one of the hindrance; current business models do not provide mass proliferation of technologies. As the market matures cost, legal, sharing infrastructure business models will evolve to justify first cost and have pull through market.


3. Cybersecurity and privacy

While leveraging technology brings many benefits, it also creates significant exposure to security and privacy vulnerabilities which one need to be appraised of and strategy defined for mitigation.


As Sudhi Sinha (VP – Data Enabled Business, Johnson Controls) rightly pointed in his book “Building an Effective IoT Ecosystem for Your Business”; below are the must-have considerations while creating the cybersecurity strategy:

  • Understanding the threat vectors in the world of IoT; the path was taken in the IoT world to penetrate your device.
  • Recognising data privacy and ownership concerns
  • Learning about the various industry standards and available resources
  • Understanding popular and modern technologies and standards
  • Designing security policies and practices
  • Securing data and insights
  • Building risk management and compliance program
  • Hacking and auditing security and privacy effectiveness’
  • Managing attacks and communicating effectively


4. Lack of a smart city visionary and citizen partnerships

Finally, every challenge brings new opportunity. The challenge to deliver the experience of an urban quality life, brings a huge opportunity for Cloud, IoT, Mobility & Apps and IT & Data security industry.



Figure 4:  Smart City Market Size.


Businesses need to be proactive in adopting new technologies, innovate, make new products and put into action to address the use cases.


It is not important to have interest in the Smart city, it is critical to have a commitment towards it!!!



The views expressed in this paper are strictly of the author and does not necessarily represent the view of Johnson Controls.



  1. “Building an Effective IoT Ecosystem for Your Business” by Sudhi Sinha, Youngchoon Park – Data enabled business, Johnson Controls
  2. Workplace powered by Human experience – Research paper by JLL
  3. How we design and build a smart city and nation | Cheong Koon Hean | TEDxSingapore
  4. Discovering the True Value of City Data Experts


Johnson Controls (India Engineering Centre) 


Subrata Bhattacharya And Ankur Thareja

Subrata Bhattacharya is Managing Director – Johnson Controls (India Engineering Centre) and a business leader with 20 years of experience in Building Automation & Controls industry across building verticals. He has worked and led teams/businesses across the spectrum of new construction, building retrofit, service, performance contracting, remote monitoring/fault diagnostics, products and solution development. He is key patron for implementation of the Cloud and IoT strategy of Johnson Controls. Email: Ankur Thareja is Sr General Manager & Group Head – Data Enabled Business and Advanced Product Research with Johnson Controls – India Engineering Centre. He has a career graph spanning over 17 years in Building Controls, Energy Solutions, and Connected Services (Cloud based solutions). He is big proponent of IoT, Big Data, and Cloud based solutions. He is Certified Energy Auditor (CEA) from Bureau of Energy Efficiency (BEE), Certified Measurement and Verification Professional (CMVP) by Efficiency Valuation Organisation (EVO) and an Accredited Professional from Indian Green Building Congress (IGBC). Email: