Benefits and Challenges of Data Analytics in Smart Cities
Smart cities are composed of information and communication technologies to make better decisions and improve the quality of life. It leverages innovative technologies to enhance the quality and performance of urban services to reduce costs and resource consumption (Parliament of Australia, 2020). In addition, it helps to engage more effectively and actively with the citizens.
Real-time data and smart technology can be used to utilize infrastructure, energy, and services better. The smart city collects information and data about itself through sensors and other inter-connected systems which are communicated via wired or wireless networks. These data are processed and analyzed to understand current events and make a prediction about future events. Such a city can make the community more prosperous and sustainable. The major goals of smart cities are to improve the liveability and sustainability of cities, open public and private data sets to support citizen engagement, unlock innovation, create business opportunities through collaboration and smart city innovation ecosystem development (Parliament of Australia, 2020). In addition, it can contribute to the development of smart city standards and improve the regulations and use of smart technology.
The smart cities data is collected so that it can offer opportunities to make sense of it. There are various benefits of data and analytics to inform city planning, infrastructure investment, and improve the decision making of smart cities. The data is consistently collected and stored so that it can be used for data analytics. These data are in isolated information systems that are unstructured, poorly described, and not readable format (Parliament of Australia, 2020). According to the Barcelona Smart City, they collect more than 1,300,000 daily records in their databases from 1800 sensors (Sinaeepourfard, et al., 2015). In addition, the total number of generated data in a day in energy monitoring management is over 3 megabytes, noise monitoring management is 578 kilobytes, urban lab monitoring is 153 kilobytes, garbage collection management is 480 kilobytes and parking spots management is 615 kilobytes which add around 5 gigabytes of data (Sinaeepourfard, et al., 2015).
Smart cities generate a very high amount of information and will even be higher in the future. The data comes from various data sources such as mobile devices, cameras, or web devices in different formats. The smart city technologies such as smart energy, transportation, and health care can have a major change in people’s lifestyle. The architecture of a smart city can be organized into a sensing layer that generated data, a network layer that moves data around, a middleware layer that manages all collected data and prepares the data to be used, and an application layer to provide the smart services advantaging from this data.
There are various challenges in using data analytics in smart cities. Smart cities generate a huge amount of data which introduces the challenges of harnessing big data analytics. The volume of data is far beyond the size of traditional databases or data warehouses which required some other technology to handle huge data in petabytes or exabytes. The velocity of data is very high as the incoming data is at a higher rate which raises the issue of data aging, it is difficult to find the importance of data value. Similarly, the veracity of sensor data can lead to misleading analytics as it could lack the trustworthiness of the data. There is the potential issue of data privacy which may include sensitive information about citizens, government, and service providers (Osman, et al., 2015).
To overcome the issue of a huge amount of data, new technologies such as Hadoop, data lakes and Extract Transform Load (ETL) could be used to store information and build a data analytics pipeline. Vertical and horizontal scaling is required to deal with big data scalability issues. Data anonymization techniques such as k-anonymity can be used to prevent the exposure of sensitive and personal information about citizens, government, and service providers.
References
Osman, A. M., Elragal, A. & Bergvall-Kåreborn, . B., 2015. BIG DATA ANALYTICS AND SMART CITIES: A LOOSE OR TIGHT COUPLE?, Department of Computer Science, Electrical and Space Engineering.
Parliament of Australia, 2020. 9. Smart cities. [Online] Available at: https://www.aph.gov.au/Parliamentary_Business/Committees/House/ITC/DevelopmentofCities/Report/section?id=committees%2Freportrep%2F024151%2F25693 [Accessed 28 November 2020].
Sinaeepourfard, A., Garcia, J., Masip-Bruin, X. & Marín-Tordera , E., 2015. Estimating Smart City Sensors Data Generation.
