You cannot escape mention of Smart Cities today because the biggest technology giants, the IBMs, Cisco’s and Seimens’ of the world are at the forefront of the news flow. This is hardly surprising considering the potential market opportunity – the UK Government estimated that the worldwide spend on building and running smart cities would reach $ 400 Billion per year by 2020. We had written previously about how Smart Cities were, essentially, being built around a big data driven strategy. At this market size, that’s a mega big data play – so where is big data likely to have the maximum impact in the smart city of the future?
At its core the idea of the smart city is straight forward – the objective is to give those who plan for and manage the city timely information about various parameters that they care about. The people who matter should then be able to leverage this information to take the right decisions and ensure that those decisions are implemented accurately and speedily – effectively get services to the citizens who need them exactly when they need them.
This is one of those use-cases that is spoken of often. Popular examples abound of RFID tagged vehicles being monitored by smart systems that are then able to generate information about traffic patterns. Planners can plan for and act to clear traffic congestion by rerouting incoming loads. In addition today, the location-awareness built into the smartphones of the drivers and pedestrians add a new dimension since smartphones can consume information as well as relay it. A powerful example of how “intelligently” smart machines can put this information to use is provided by Google’s sensor-laden driver-less cars. Information generated by this combination of sensors and smartphones is being put to use effectively to locate and manage scarce resources like empty parking slots (only when you’ve driven around a lot for 20 minutes can you realise just how scarce this resource is!). There is also a powerful case for planning the flow of public transport and providing information on routes and schedules to citizens based on real-time information and intelligent predictions on passenger loads.
Utility / Energy Management
Energy and the commodities that utilities deal with like Water are scarce resources by any definition. Apart from being expensive to produce, distribute and manage there is the environmental impact of using them injudiciously. In that context, big data techniques and technologies have an important role to play. Big data and analytics can help first in helping plan for peak loads and demand variations across physical locations and times of the day so that resources are provisioned appropriately. Then there is the applicability of analytics to help make real-time adjustments to the distribution of energy based on actual demand on the ground. Data from sensors and analysis of other inputs like smartphone traffic and network usage can be analysed to give a clear picture of where the people are and appropriate decisions can be taken. For example in places where footfalls are low streetlights could be dimmed or switched off to conserve energy. There is tremendous interest in the development of Smart Grids that provide real-time information about loads, usage fluctuation and other parameters. This data can be leveraged for better decision making regarding generation and distribution.
This is just the beginning – big data and analytics potentially can impact security and law order by helping track “at risk” citizens or helping law enforcement agencies create profiles of potential offenders or victims of crime. There is a slew of potential applications in other areas that can impact everyday life in the smart city of tomorrow including waste disposal, pollution control and overall service delivery and management. That is not to say that challenges do not exist – today anecdotal evidence seems to be thicker on the ground than real evidence of functional smart cities. Chances are when smart cities do become the norm these will be the areas where the maximum impact will be felt.