By Julián García Barbosa, Sales Executive at Telefónica
21 May 2014: Boosting citizen security, cutting waste management, anticipating traffic jams and analysing citizen sentiment: just a few examples of what Big Data could do to catapult our cities into the future.
A UN report shows that 70% of the global population will live in urban areas by 2050. In spite of occupying only 2% of the Earth, these cities are responsible for 70% of global energy consumption and greenhouse gas emissions.
Cities will need to take on the big problems of managing scarce resources and providing critical public services such as security, transportation, energy and water.
Hence, ICT solutions are critical tools and Big Data is set to become, without doubt, the cornerstone of next-generation cities.
We’re now facing a data explosion due to the abundance of personal interactions through social networks. And because of the existence of millions of M2M devices, it is the metropolitan cities where we see the largest concentration of people and sensors.
Besides the massive volume of data cities need to cope with, this data is also of a great range and variety. More than 80% of data is unstructured in the form of videos, tweets, GPS coordinates, Excel files and emails which means that decisions need to be made at high velocity.
These are the three Vs for any Big Data project.
But how can cities leverage the use of Big Data technology to become really smart?
First, we must identify the issues that need to be remedied, and involve all city council departments. In the case of smart cities, we need to collect, process, share, store and analyse a vast amount of data coming from multiple different sources in order to turn Big Data into information, and this information into powerful insights. These insights will play an important part in improving the decisions made by city leaders.
Here are a few examples of how Big Data technology could improve our cities:
- Public Safety: We could improve the efficiency of police and fire services by capturing and correlating all the data coming from different systems installed in the city including surveillance cameras, emergency vehicle GPS tracking and fire and smoke sensors.
- Urban transportation: Through real time data capture and the management of signals from video cameras and magnetic sensors installed in the road network, GPS systems could be used to track the location of public buses. Equally, social media monitoring systems could enable us to flag a protest organised on social networks and therefore facilitate the management of potential traffic jams by changing bus routes, modifying traffic light sequences and delivering information to drivers via mobile apps indicating approximate driving times and giving alternative routes.
- Water management: By analysing the data coming from metering systems, pressure or PH sensors installed in water supply networks and videocameras situated in water treatment plants it would be possible to optimise water management detecting leaks, reducing water consumption and mitigating sewer overflow.
- Energy management: With all the data coming from smart electric meters installed in customer’s homes as well as meteorological open data platforms it would be possible to optimise energy production, depending on demand, which would help us to maximise the integration of renewable energy resources like wind and solar energy.
- Urban waste management: By gathering data in real time from sensors that detect the container filling level and comparing to the historical data and usage trends it would be possible to forecast the ideal time for emptying each individual container and optimise waste collection routes.
- Public Sentiment Analysis: By analysing social media networks and blogs and then using Big Data technologies, cities would be able to measure public opinion on key issues and services such as public transportation, waste management or public safety allowing them to prioritise and shape policy.
M2M and IoT solutions are an absolutely fundamental element of Smart City projects. Installing thousands of sensors in public buildings (HVAC, lighting, security), energy management systems (smart meters, turbines, generators, batteries), transportation platforms (vehicles, lights, signage) and security systems (ambulances, video cameras, smoke detectors) allows us to use all of that data which is transmitted to a central server where it can be correlated and analysed with other sources of data turning it into meaningful information.
Cloud computing technology is also an intrinsic part of Big Data and Smart City projects. Here are some drivers for the cloud adoption of Big Data:
- Cost Reduction: Big Data environments require a cluster of servers to support the processing of large volumes, high velocity and varied formats of data and the cloud pay-per-use model will be financially advantageous.
- Rapid provisioning/time to market: Big Data environments can be easily scaled up or down based on the processing requirements and the provisioning of the cloud servers could be done in real time.
- Flexibility/scalability: Big Data analysis in smart city environments requires huge computing power for a brief amount of time and servers need to be provisioned in minutes. This kind of scalability and flexibility can only be achieved with cloud technologies avoiding the required investments in very expensive IT infrastructure by simply paying for the consumed computing resources on an hourly basis.
In a nutshell, if cities want to be smarter, more competitive and sustainable they need to leverage Big Data, together with M2M and Cloud Computing technologies in order to achieve their goals.
It won’t be an easy path, but it will most definitely be worthwhile.