Smart Cities, Big Data and IoT
INDIA’S smart city project is well underway and according to data shared by the government, almost all the 60 cities earmarked as ‘smart cities’ are at various stages of implementing key projects. However the only city in India that has begun to be built up grounds-up as a smart city is Amaravati – the new capital of Andhra Pradesh (AP). The AP government is working closely with a clutch of public and private Japanese agencies to enable this. From smart traffic to smart energy and smart disaster management, the Amaravati administration is leaving no stone unturned to ensure that their city comes as India’s first integrated and end-to-end smart city.
While European and US cities lead the list among the 180 cities surveyed, in Asia four cities dominate the rankings – Seoul, Tokyo, Singapore and Taipei. In India, Delhi, Mumbai, Kolkata and Chennai make the cut in the rankings but are at the bottom of the list.
Smart Cities = Smart Organizations = Smart Data
The basic premise of a smart city is improved delivery of public services, conservation of resources and better citizen-government interaction.It’s a no-brainer that smart city projects will focus on exploiting the ‘smartness of things’ through a vast and complex network of sensors using IoT technology integrated with the analytics-
of-things (AoT). Clearly this will not be achievable by city governments alone and will require the involvement of citizens and partners and will also warrant the seamless collaboration among participating departments and organisations.
Let me illustrate this with a smart-transportation example. As smart cities incorporate or introduce smart transportation, they will also need to ensure higher levels of efficiency that will incorporate smart management and maintenance programs incorporating data-enabled functionality. So, for example big data, IoT and AoT will come together to ensure a cost-effective, condition-based and predictive maintenance model to ensure higher levels of service – as compared to existing models that involve reactive maintenance. Data enabled systems will therefore help predict when and where a problem is likely to occur, so that it can be addressed before anything unexpected happens. To give an example, the Spanish train operator Renfe uses Teradata’s customer Siemens’ high-speed Velaro E train, key components of which are continually monitored by Siemens. A train developing abnormal patterns is dispatched for an inspection service to prevent failure on the track. This has resulted in only one of 2,300 journeys being noticeably delayed, and only by five minutes. This allows Siemens to provide data monetized services to their customers. So if there is a delay of over 15 minutes all passengers are reimbursed fully.
To improve the quality of public services and facilities, surveillance and security are rudimentary requirements in today’s mega cities. These entirely depend on utilization of data and information, its processing and analysis. Data generated and captured by smart devices and utilized in public utilities such as water departments, electricity boards and public transportation must be analyzed quickly and efficiently and relayed on through feedback information systems – all this can be achieved by leveraging data analytics.
Take another example of Brazilian Federal District Water Utility. The Utility needed to improve meter management at the individual level. It had a database of information from all customers, including water consumption, bill control, real state records, revenues and demand on the water meter system and using this they built the profile portfolio of customers and their detailed usage. Each water meter had an average lifetime of five years; if the device was defective or tampered with, the measured consumption read inaccurately low, a result of fraud. ‘Ghost’ bills, or the practice of some consumers was studied and suspected fraud was stymied with action to save money. All these data-driven changes hiked business income, leading to 6 percent revenue growth for the year. Thus, the Utility gained the money for more investments in new meters, resources, equipment, not something easily possible without data analytics.
Similarly, power utilities can improve their financial performance and streamline maintenance costs, extending from where power is generated right up to the meter at a customer’s home or office. Using data spawned by the IoT devices, utilities can program grid efficiency, enhance quality of power, decide on load reduction or to direct power better. This, by using realtime, real world data and analytics.
An efficient addition of IoT and monitoring devices is also integral for public security. Real-time processing of data from devices such as surveillance cameras in public places, ATMs, and vulnerable areas can discourage crime. Individual alarm systems in homes can be linked to police stations or medical centers automatically.
In the Indian context, the smart city programme is still at a nascent state. There are many issues to be addressed.That the smart city program needs to contend with is whether the city administration has the adequate infrastructure to handle the sheer volume of data that will be generated by the city each day. Secondly, it’s about having the capability of managing it and drawing relevant inferences from it to manage the infrastructure. Third, it’s about avoiding a disconnect between the companies that build new technologies (the technologists) and the officials who manage cities (urbanists). There are enough instances of smart city managers treating the city too much like a machinewhose only task was to become more efficient, without stopping to think about whether or not that efficiency mattered to the everyday lives of people.
The smart city is therefore much more than a sum of its parts. It needs to be integrally connected to smart organisations – to enable a city to become fully optimised and highly efficient in managing its resources.
