Site Loader

Feasibility of Smart City Dashboards for Integrating Various Data Using Sensors

Ling Su Wynn, Muntaseer Alam, Rashik Tasin
School of Computing and IT
Taylor’s University
Selangor, Malaysia
[email protected], [email protected], [email protected]

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Abstract—The modern city incorporates a large amount of heterogeneous data, which are produced by diverse sources such as sensors, cameras and social networks; harnessing these data in a usable approach to provide a suitable view for the city’s numerous stakeholders. A smart city requires basic smart infrastructures such as, traffic sensors, thermal measurements, weather prediction equipments and so on. A smart city must be managed and planned well in order to handle problems such traffic congestions, air quality and energy supply. In this paper, we shall present a feasibility study on having a smart dashboard in order to govern and provide visual applications that empower these environmental and user-generated data in a meaningful way, appropriate for the future of Smart Dashboards.

Keywords—smart city, monitor, sensor, dashboard

I. INTRODUCTION
As of today more and more people are moving into cities and as it stands that population is well over half the current population. This number continues to increase exponentially. As this increases, more cities are being developed to become more complex and mega cities are running into the issue of sustainability of all their systems which is the main component to the operation and development in various areas.
Coincedently we can observe cities such as Tokyo and Seoul that are technology heavy have deployed a wide variety of sensors to monitor real-time situations of their cities. This type of performance however does not rely on the city’s endowment of networks, it relies heavily on the availability of knowledge and communication through social infrastructure.
This type of information data is crucial for the progress and growth of a city. Using this background, the idea of a smart city has been introduced many times as a strategic means to encircle modern production factors in a singular framework, This is to prove the up and coming importance of Information and Communication Technologies (ICT).
Currently there are numerous countries aiming to become smart cities not only for automation and seamless integration, but also to aid the understanding and planning of a city to improve in multiple sectors 2. This outlook provides evidence that there is a need for appropriate measures for urban assessment that is able to pinpoint the objective and smartness of a city 10. However, a smart city requires an intelligent computer network that is able to plan and organize decision-making activities to improve the citi’s performance 3.
There are a few Information and Communication Technology tools that have been used to improve the dynamics of cities. Although, most of them are aiming to monitor and manage from a single urban sector such as energy, land, and water 2. This goes to show that only a fragment of their resources are dedicated to the proposed monitoring and management tools.

II. RELATED RESEARCH
The ability to explore and discover a building or a city using data that has already been gathered is crucial for the development of a smart city. This can be seen as a common problem that a smart dashboard might face and various efforts have been made to this date to address the problem. Each city differs from one another and different approaches are needed to be implemented on each city. IBM came up with the idea of Intelligent Operations Center 4 which provides an exclusive dashboard to aid people and gain insight into various aspects of city management. Other efforts include the City Dashboard 5, which was a Dashboard made by the University College of London.
The dashboard is designed to provide a one stop look-out for citizens to have multiple views of cities around Great Britain. It collects official, observational and social media data into a single dashboard which is constantly refreshing and updating itself to maintain high level data. Suakanto presents a Smart City Dashboard for the city of Bandung in Indonesia that retrieves and summarizes the condition of the city in terms of traffic congestion, water supply, energy supply and air quality 6. Micheal Batty in 7 defines a smart city and how existing infrastructure is merged with Information Communication Technology (ICT) using new digital technologies.
There are some projects that are currently diving into the challenges and future opportunities of big data for smart cities and different levels, an example would be CityPulse 8, which is a European research team that aims to explore and stream large scale data to be analyzed and used for smart city application. Moreover, a team known as SmartNation9 has created a new smart city platform that enables greater pervasive connectivity and better awareness through data collections and interconnectivity of sensors.

III. PRIVACY AND SECURITY CONCERNS
Local governments in most of the world are in the middle of technological and economic developments and a significant fraction of it comes with the label of ‘smart cities’. In such a project, extensive monitoring and steering of city maintenance, water quality, air, energy usage, mobility, visitor movements, neighbourhood sentiment, and much more are enabled using ICT-infused infrastructures. Naturally, such processes use and produce huge amounts of data. For instance, the traffic authority monitors about 22,000 vehicle movements every morning In the Dutch city of Rotterdam, while the regional environment agency produces over 175,000 observations per year from hourly data about air quality detected through sensors across greater Rotterdam 10.
This extent of data gathering also extends to other sectors such as policing, public sentiment monitoring and crowd control making the amount of data even more elaborate, as they would often be combined to produce joint indicators of safety, economic vitality and overall city well-being. Most of this data is made public by the local government often, to maintain transparency as well as further open-ended research and development.
Hence leading to the penultimate questions about who should have legitimate access to the data, how should the privacy framework for the data be as well as which data should be open for public usage. Resulting in blurred lines between the legal and social concepts of a citizen’s ‘right to privacy’ and the challenge of cyber security and the benefits of the Smart City 11.
For these reasons, Several national and international organisations have identified privacy as a key policy, regulatory and legislation challenge of the 21st century 12 as research on this is diverse and contradictory in terms of theory, methods and outcome. Some of the defining factors for these are the types of data collected, purpose of collection, as well as the authority collecting the data.
For a Smart City project such as a dashboard, the local government is the key data partner. Hence the extent to what local citizens trust their local government to handle their personal data comes up as an important variable. Even though there is ample evidence of local government being unreliable in being a trustworthy partner for handling personal data in the past 13, it often seen in opinion polls that people tend to trust their local government significantly more than their respective national ones, for instance 72% of US citizens trust in their local government compared to the 18% in favor of their national government 14, while in the UK, 79% of the citizens have higher trust in their local government versus 11% for the national government 15.
Urban infrastructure such as water distribution, electricity supply, buildings and streets face several intentional and accidental security threats in their specific cyber-physical components and systems such as cameras, communication systems, building management systems and transport management systems.
The data collected can be classified in terms of its type and purpose in order to predict which forms of data collection are surrounded with more public concern regarding privacy and security. These can be referred to as,

Personal data collected for service purpose

Includes all kinds of traditional data collected by the city about its citizens such as civil status (birth, marital state, and death), housing, elections, work etc. The privacy challenge for this is likely to be moderate because this kind of data collection has been traditionally carried out in ways and forms for city management for a very long time.

Impersonal data collected for surveillance purposes

Includes the data used for surveillance and control purposes that is not directly connected to individuals such as monitoring traffic flows, streets, public transports and events through infrared video, CCTV and heat sensors among others, which will often be used in various Smart City systems. Since they are not data gathered individually from person to person this may have seemed insensitive enough to not raise concerns. However, such data can be analysed and enhanced through software like facial recognition to identify individuals in crowds added with location profiling to identify their households making the data quite sensitive.

It is said that in United State many NGOs and civic organisations have voiced their concern over the predictive software and biased algorithms, the citizens are strongly against the idea of it predicting the profile of a certain individual based on nonsensical stigmas16.

Personal data collected for surveillance purposes

Revolves around all policing data, from minor violations like parking tickets and stop and search to major criminal offences. Because of the high sensitivity and personal nature of such data when used for surveillance and control purposes, different governments approach this differently. For instance, the mayor of the French city Nice won the Big Brother Award in 2008 for a rather controversial decision to install the most pervasive and expensive video surveillance system in France while the city of Dresden received the award in 2012 for logging and tracing mobile phone traffic during a massive anti-Nazi demonstration. At the same time, the EU General Data Protection Regulation sets strict rules for the legitimate usage of personal data, offers a stronger position to citizens to control their data and imposes high fines on data abuse, for which the data processor will be held responsible 10.

Impersonal data collected for service purposes

This involves the big chunk of data collected for smart city systems that are about things and not about people. Systems such as monitoring systems for air, noise and water quality, energy systems tailored to real time usage and waste management systems are good examples that use data from collected in this manner. This also includes all data made available to the public through open data portals, which city public health policies make use of in identifying areas with poor air quality or noise pollution and their correlation with particular health diseases 17.
The combination of impersonal data with service purposes makes this type of data collection significantly of less concern in terms of security and privacy. However some minor concerns due to increasingly detailed methods of profiling being able to identify individuals from such anonymous data does exist.
As it can be derived from this, people’s privacy and security concerns for data can range from hardly any (impersonal data for service purpose) to extremely high (personal data for surveillance purpose). Based on these classifications it can be noted that the way people perceive particular data collection for smart city systems is based on not only the type of data being collected but also the justification of the purpose for its collection and the ways and forms in which they are to be used.

IV. CHALLENGES OF INTEGRATING SMART DASHBOARD
Buildings are a combination of complex IoT system that is able to understand and cooperate with technologies and economic trends and challenges, there is currently an influx of IoT systems and using those systems we are able to analyze and offer insight into the trends and challenges of IoT in general.

Diversified Instrumentation
The digitization of buildings initiated early to enable automatic climate control which during the late 70s with the start of automating a central system such as boilers, chillers and air handling units. These automated systems are mostly usually completed using simple program logic controllers to which all sensors and actuators were individually wired The development of field-bus in late 80s networks enabled the devices to connect to one centralised network which in turn simplifies the installation.
It is surprising that the integration of building into the internet is still at its infancy, and that most building are still operating in isolation and detached from a network. This is due to the fact that most operations for buildings have focused on the controls of the systems and from a control perspective, provides no benefits to the connecting buildings.
Sensors are changing in two ways: (1) the increasing affordability of devices that play a roles in instrumentation of legacy buildings with thousands of sensors down to individual workplace level. (2) Data created by the system is able to create new ways of optimizing building operations from comfortable to demanding driven facility maintenance.
This in turn will lead to a substantial increase of diverse devices such as multi-sensors,mobile devices, and trackers in our environment. This will create a large integration problem of devices into the infrastructure.

Big Data Analytics
Data is collected from a multitude of sources and useful data is crucial for a smart city. These data are mostly collected from IoT testbeds, video cameras, social networks and other open sourced sharing center. However the biggest challenge of this is to clean the data in which there are different formats and different data sources
Ever since the second millennia, management systems have been implemented and by taking the percentage of the analysis of central system is it safe to say that it have successfully increase energy saving and it by analyzing the data and information that was made available the patterns and behaviour of building consumption have helped the supply and demand of energy to the occupant side.
Although in 19 it was shown that energy consumption was not in their priority during that era and because of that the design of the energy management system was poorly designed and not optimized. What they did was to use rule based systems that are difficult to manage and individual adaptation of buildings and rooms where energy is mostly consumed.
This lead to the initiation of big data that are difficult to be analysed using old technology and outdated approaches. Machine learning is well suited to learning from large amounts of data and provide relevant information that will be able to increase the operation of performance. These advanced analytics are available according to 20 however they also require major efforts of highly skilled data scientist in configurations to bring out the full potential 18.

IV. SUSTAINABILITY
Smart cities are an endeavour to make cities more efficient, liveable and sustainable. In turn, the infrastructure of a smart city must rely on the need for real time data sustainability. This essentially includes effective management of the raw data collected from smart city embedded and non-embedded Internet of Things (IoT) sensors traversing through the entire ecosystem of the smart city applied with analytics and reasoning.
The raw data that is collected from the sensors does not come in a format which we can use directly and require data cleaning and further classification to provide an optimum data collection. Various alternatives are mapped to fetch data values and schema to be translated into XML tags. Then transformation process picks needed information from tagged file into JSON documents and RDF triples. After passing through all the procedures from collection to classification and then to transformation, data is brought into a sustainable form, capable of being analysed and linked together for further processing 21.
This form is based on the data transformation model used by the particular authority in charge of development and maintenance of the respective Smart City project. The benefits are then classified in different sustainable development dimensions such as environment, economy, energy, living, etc. and for each of these one or more indicators are identified to evaluate benefits in a quantitative or at least a qualitative way, ensuring that all the dimensions of sustainability are taken into account 22.
Sustainability is also a key purpose why smart city projects came to be in the first place. Cities consume 75% of our energy resources and produce 80% of our carbon-based emissions that are harming the environment while 70% of the world’s population is expected to reside in cities in less than 40 years 23. To diminish the impact of cities on the environment it is vital to encourage and endorse intelligent and effective technology deployment to integrate infrastructures 22.
Hence, we can state that in order to create a more sustainable city life, Smart City projects are undertaken and they in turn come with their own Sustainability challenges.

IV. CONCLUSION
In the modern urbanized world, attractiveness and liveability of the city are the result of many factors such as location and geography, climate, accessibility, business opportunities and governance. Every system defines a set of objects that function interoperably as well as co-operably with one another. Likely, urban city systems comprise of a large and complex mechanism consisting of buildings, infrastructures, energy and other resources as parts of it. The city system services define the underlying structure and necessary components for allowing the urban structure to function properly 24. In this retrospect, Smart Cities can provide tremendous opportunities for livable future cities, not only through the use of technology and web-based data but also by increasing the flexibility and operability of the urban environment through greater efficiency and innovative developments such as the Smart Dashboards that have been discussed in this paper. As it has also been shown in this paper, the significantly larger part of the world’s resources are being used in cities making the need to have more sustainable and ‘Smarter’ ways to manage these resources in the urbanized world even more crucial.

References
1 A. Caragliu, C. del Bo, P. Nijkamp, “Smart cities in Europe”, Proc. 3rd Centre. Europ. Conf. Regional Science, Oct. 2009.
2 M. Batty, K.W. Axhausen, et al., “Smart cities of the future”, Eur. Phys. J., vol. 214, no. 1, pp. 481-518, 2012
3 Carli R., Deidda P., Dotoli M., Pellegrino R., “An urban control center for the energy governance of a smart city”, 19th IEEE International Conference on Emerging Technologies and Factory Automation, Barcelona, Spain, September 16-19, 2014.
4 IBM Intelligent Operation Center – http://www03.ibm.com/software/products/en/intelligent-operationscenter/

5 CityDashBoard project – http://citydashboard.org/london/

6 Suakanto, S., Supangkat, S.H., Suhardi, Saragih, R. 2013. Smart city dashboard for integrating various data of sensor networks. ICT for Smart Society (ICISS), 2013 International Conference on , vol., no., pp.1,5, 13-14 June 2013

7 Batty, M., Axhausen, K., Fosca, G., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G. and Portugali, Y. Oct 2012. Smart Cities of the Future. UCL Working Paper, ISSN 1467-1298.

8 (2014) The citypulse project. Online. Available: http://www. ict-citypulse.eu

9 (2014) Singapore smart nation platform. Online. Available: http: //www.ida.gov.sg/Infocomm-Landscape/Smart-Nation-Vision

10 Carli R., Dotoli M., Pellegrino R., Ranieri L., “Measuring and managing the smartness of cities: A framework for classifying performance indicators”, Proc. IEEE Conf. Systems, Man and Cybernetics, pp.1288- 1293, 13-16 Oct. 2013
10 L. van Zoonen, “Privacy concerns in smart cities”, Government Information Quarterly, 2016.
11 Elmaghraby, A. and Losavio, M. (2014). Cyber security challenges in Smart Cities: Safety, security and privacy. Journal of Advanced Research, 5(4), pp.491-497.
12 Camenisch, J., Fischer-Hübner, S., & Rannenberg, K. (Eds.). (2011). Privacy and identity management for life. New York/Heidelberg: Springer Press.
13BCG (2013). The value of our digital identity. Liberty global policy series Boston Consultancy Group.
14 Thompson, N., Ravindran, R., & Nicosia, S. (2015). Government data does not mean data governance: Lessons learned from a public sector application audit. Government Information Quarterly, 32(3), 316–322.
15 Goldsmith, R. (2015). Why trusts in local governments should be even higher than it is. Governing, the states and localities, August 18. http://www.telegraph.co.uk/news/health/news/11655777/Nearly-1million-patients-could-be-having-confidentialdata-shared-against-their-wishes.html
16 Walker, D. (2013). Good people trust local government, but councils need to trust people. The Guardian, March 1. http://www.theguardian.com/local-government-network/2013/mar/01/people-trust-local-government
17 Koss, K. K. (2015). Leveraging predictive policing algorithms to restore fourth amendment protections in high-crime areas in a post-Wardlow world. Chicago-Kent Law Review, 90(1), 301–334.

18 A. Bhattacharya, J. Ploennigs, and D. Culler, “Short paper: Analyzing
metadata schemas for buildings: The good, the bad, and the ugly,” in
BuildSys, Nov. 2015, pp. 33–34.

19 R. Galvin, “Making the rebound effect more useful for performance
evaluation of thermal retrofits of existing homes: defining the energy
savings deficit and the energy performance gap,” Energy and buildings,
vol. 69, pp. 515–524, 2014.
20 J. Ortiz, “A platform architecture for sensor data processing and verification
in buildings,” Ph.D. dissertation, EECS Dep. Univ. of California,
Berkeley, 2013
21 Elmaghraby, A. and Losavio, M. (2014). Cyber security challenges in Smart Cities: Safety, security and privacy. Journal of Advanced Research, 5(4), pp.491-497.
22 Girardi, P. and Temporelli, A. (2017). Smartainability: A Methodology for Assessing the Sustainability of the Smart City. Energy Procedia, 111, pp.810-816.
23 Charbel Aoun, “The smart city cornerstone: Urban efficiency” (2013). Available: http://www.digital21.gov.hk/eng/relatedDoc/download/2013/079%20SchneiderElectric%20(Annex).pdf
24 Sofeska, E. (2017). Understanding the Livability in a City Through Smart Solutions and Urban Planning Toward Developing Sustainable Livable Future of the City of Skopje. Procedia Environmental Sciences, 37, pp.442-453.

Post Author: admin

x

Hi!
I'm Jessica!

Would you like to get a custom essay? How about receiving a customized one?

Check it out