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Interdisciplinary Description of Complex Systems 18(3), 382-388, 2020
*Corresponding author,
: adylaszlo@nexttechnologies.hu; -;
*H- 2234 Maglód, Sugár út 44.
ARTIFICIAL INTELLIGENCE USAGE
OPPORTUNITIES IN SMART CITY
DATA MANAGEMENT
Luca F. Hudasi and László Ady*
NextTechnologies Ltd. Complex Systems Research Institute
Budapest, Hungary
DOI: 10.7906/indecs.18.3.8
Regular article
Received: 17 February 2020.
Accepted: 28 June 2020.
ABSTRACT
In our current study, we are aiming to explore data management methods in Smart City systems. In
data management, Artificial Intelligence can be used as well. Solutions for the usage of Artificial
Intelligence and integration into Smart City concept will be researched as well. Main motivation of
the study is to draw attention to one of the most important element of Smart Cities, to the flow of data.
Our study provides a possible solution for managing data and keep data up-to-date in such systems
with the usage of newest technology possibilities. While explaining the solution, we will give all the
necessary details about the data flow model between the Artificial Intelligence based system and
humans who are using the Smart City. For managing the dataflow, we would like to use Big Data
methods extended with other required methods. We are using the term of Big Data as a technology
maximizing computation power and algorithmic accuracy to gather, analyse, link, and compare large data
sets [1] connecting with Artificial Intelligence solutions.
KEY WORDS
smart city, artificial intelligence, data, management, innovation
CLASSIFICATION
ACM: 10010147.10010178
JEL: C8
Artificial intelligence usage opportunities in smart city data management
383
INTRODUCTION
Intelligent systems, such as Smart Cities are based on the flow of information [2]. Another
important aspect of a good smart city is to make good decisions. It is logical, that if a system
has bad or missing information, it cannot make good decisions [3].
‘The first step in a city becoming a “smart city” is collecting more and better data.’ [4] says John
Walker in his study. Therefore the following main areas are important to cover upon collecting data:
Develop an automata data collector system.
Develop a people triggered data collector system.
Develop a data sharing and correction system.
The following figure shows the flow of data based on collection and sharing:
Figure 1. Data flow of a smart city.
On figure 1, arrows show the direction of data flow. Although people triggered data and Automatic
data collection is mainly one directional, data sharing is not. Data Sharing part has many
subcomponent and the data flow in this case bidirectional since the participants not just providing but
getting data as well. In the following sections we discuss the details of each components.
DEVELOP AN AUTOMATA DATA COLLECTOR SYSTEM
Developing an automata data collector system covers the already well-known methods, such
as having traffic monitoring systems, automata government administration bodies (for voting,
for taxes, etc.), and an automata traffic-, weather forecast-, and energy distribution system.
To build fully automated systems using big data, it is a requirement to have a built-out sensor
network such as camera, temperature measurers, motion detectors and GPS based devices [5].
It does not mean these systems do not require a supervisor, but they can operate in an independent
way and processing data coming from sensors. Many of these systems are already existing in
testing or in live format, for example in Singapore, London, Barcelona, etc. [4, 6]. It is
important to use the experiences from these cities to build a more reliable one.
DEVELOP A PEOPLE TRIGGERED DATA COLLECTOR SYSTEM
People triggered data collectors are Intelligent Systems where the information is coming from
an active human input. Such as if an inhabitant visit an authorized system (through internet)
and report or handle an administrative hace they need. For example request a credit, rent a
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car, report a misdemeanour activity, etc. Here, the smart system has to be prepared to be able
to serve the customer’s need and make fair and clear decisions. This is the most dangerous
form of Data Collection because the final decision is made by an Artificial Intelligence (AI)
using a learnt scheme. The discrimination factor is too high, and there are already case
studies for detecting and removing discriminative part from the software [7]. ‘Some
companies curtailed their customers’ credit if charges appeared for counselling, because
depression and marital strife were signs of potential job loss or expensive litigation’ [8] says
Racher O’Dwyer and this was just one example from the many. The question which should
be considered is: is it ethical and legal to allow such kind of discrimination? If the answer is
no, then the information system should be prepared to prevent this, or supervised for such
actions. Connecting psychology and big data in the field of allowance is a new immature area
which needs further studies and testing before essential decisions are made based on the
result. Therefore, we strongly recommend to use a bipolar system in this case: first, the AI
based system make the decisions using the source from collected Big Data, then a human
supervisor should overview the output with the factors used in the decision, and validate or
decline it. Using this ‘two steps verification’ looks longer, but it is not so much. Collecting data
would be still the responsibility of the automata system and this is the most time-wasting part
of the process. Educated human supervising would correct and develop the AI to make better
decisions in the future. Once the system works measurably stable and ethical, the supervising
work can be decreased.
Another important point of data collection is to collect quality data [4], otherwise the
information the system’s decisions are based on is corrupt or missing, therefore the decisions
will be similarly wrong. To achieve this, our proposed solution is to include the inhabitants of
the city to clarify data.
One area where inhabitants can participate is the social-, public administration improvements.
The system would be capable of filling out data and do pre-tasks for the inhabitants (for example:
doing the tax, requesting for new social cards when the existing ones are going out-of-date,
providing public utility usages, making renting, other billing tasks, etc.) but the citizen would
have the opportunity to monitor the decisions, and correct them if needed. Next time the system
would learn from the mistakes and from the habits of the people, and would make better decisions.
Another are would be for extra comfort services, where people would voluntarily provide
information for the system which then can help them to take away tasks from their shoulders,
such as organizing trips, ordering and delivering food, other supplies, or appointments with
doctors or similar. With more up-to-date corrections of the information and decisions the
system is operating with are made, the better intelligent services could be provided. It is
always very important to leave an opportunity to supervise the decisions the AI is doing in
the place of people, to avoid discrimination and bad decisions (detailed above).
DEVELOP A DATA SHARING AND CORRECTION SYSTEM
To be able to cooperate with citizens in the development of data and decision-making, it is
important to make the information – the smart city collects – as transparent as possible. People
should see the base information of some bad decisions to be able to help to correct them.
While the smart city would provide transparent decision making for the citizens, it is critical
to guard the sensitive data. Big data collection is always a hazard factor. More information
the system provide, more value it represents and it will be more interesting for non-ethical
parties. Therefore, any data which can be provided to third party, should be depersonalized
carefully. To manage the proper depersonalization, is a key factor of the data flow. Creation
of standards document for depersonalization of Smart City data is a requirement.
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The main industry, which is collecting and using data is the advertisement industry. It can
also produce a great income for the city by using the data collected, but the depersonalization
should be act in the process here as well. People should been informed that the collected data
are provided and generating income, but the system should also keep people’s trust while do
so. The first aim of a smart city is always to provide a better and easier life for people, get rid
of discrimination and unethical decisions. Ethical and correct advertising is the part of this,
but it should not lead to people exposed to direct marketing harassments which can lead to
people leaving social media platforms and losing trust [9, 10].
There is another part of the industry, which is dealing with data: A company can provide
other useful data for the smart city and in exchange get data as well which is important for the
company. This can be a government level company as well which is collecting data and
provide the result as a service for the people.
STRATEGY TO PROCESS DATA
After discussing the aspects of collecting and managing quality data, the next important step
is to have the intelligent system make good decisions. We emphasized the need for
supervising decisions till they become trustable in the aspects of avoiding discrimination and
improvements. We offered the idea to include citizens in the supervising work as well. Also
mentioned to use up data and experience collected from other smart cities. These ideas can
help maintaining data decisions. Although we have to know that a good system beside having
the correct data and algorithms also needs to process data quick and effectively. A good
strategy to process data is taking the following steps: data filtering, pre-processing, processing
and decision support. Figure 2 shows the processing stages with their dependencies.
Figure 2. Logical architecture of data processing steps.
DATA FILTERING
The aim of data filtering is to collect data which the system really needs during the
processing, to minimize the amount to store and process. An existing solution to filter out too
many data (mainly when the system has limitations of processing quickly) is CERN’s
solution. CERN had a problem of bypassing all the measured data to their servers. With
Intel’s help, they developed a specific FPGA which help to filter out the relevant data before
bypassing it to the servers. This way, they minimized the load on the centre [11].
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PRE-PROCESSING
It is a good speeding strategy to execute pre-processing tasks as close at the point of data
collection as possible. This means that the stage when the data comes in the system, stored
procedures executed immediately and decode the input such as voice to text, picture to sort
and identify, etc. Sorting (mainly of pictures) is an effective way because dedicated devices
are already a stage of the sorting. As an example, a parking lot could have a dedicated camera
to recognize empty spaces and another dedicated one for recognizing unauthorized parking.
For pre-processing the pictures, the area reservation database needs to be presented at the
moment the picture is catched and pre-processed by the camera system. This is the stage
when the data depersonalization should be performed as well in case it is needed.
PROCESSING
Processing has many types how it can be performed. These types can be individually used or
together as well. These types are the following:
1st type: Stored procedures. In this context, stored procedure means a logically separated unit
of functions for performing a specific task. The sorting component will decide which stored
procedure can be executed on the data by sorting it to categories. These procedures need to be
written, but on the contrary it takes less processor time while they are running on production.
2nd type: AI. During the processing, an AI module will decide which AI based stored
procedure can process the data,. These AI based stored procedures will only get pre-
processed data. Raw data will be pre-processed by the sorting unit. These AI units (stored
procedures) are looking for relations in data.
3rd type: Data mining. Using the opportunities of data mining, the system can learn models
from big data to predict problems. After detecting upcoming problems, there is a possibility
to make decisions to prevent them and create a safer environment. ‘Difficulties that need to
be addressed during data mining include data gathering, data labelling, data and model integration,
and model evaluation’ [12]. Data gathering and data labelling can happen in the pre-processing
stage, data and model integration, and model evaluation should happen in the processing stage.
4th type: Manual. Manual actions needed In cases the system can not recognize and process a
certain data from the pre-processing stage (because it is not prepared for it). The system will
display the details on a graphical interface and will ask a human to decide the next steps, such
as sorting the data into one of the existing categories. This type of processing is more like an
extend method for “error handling” together with other AI based solutions.
DECISION SUPPORT
This stage of data processing is responsible to make automated decisions or help the human
decision making.
Result after the processing stage is stored in a database. The decision supporting unit has the
knowledge of the connections between result types and actions. Let’s take an example: There
is a processing result that contains a picture that a car took a parking place. The connected
action is to decrease the number of free spaces. The digital table in front of the parking lot
will change and display the new data with the amount of free parking places. Or let’s take a
more complex case example when the camera system detects that a big container occupied
the public space near a building. The first action will be to check the permission of taking the
place at the related authorities. In case there is no permission, the second action to execute is
to create a draft report for the police (or related authority) and put the case up to a human
supervisor to accept or decline. It is important to make this case half-automated with human
Artificial intelligence usage opportunities in smart city data management
387
supervising, because making a punishment should not be full automated. The decision
support unit has to contain a set of rules about which action can be done in which case: for
example, at a heating system there could be a rule that after switching off the gas unit, it
cannot be turned back in the next 5 minutes (safety period of the gas unit to chill down). If an
automated decision would be made to turn on the heating because it is too cold within this 5
minutes, this rule would write it over. All the decisions which were made, should be logged
for possible investigations and later improvements.
PROTECTION OF DATA FLOW
Finally, since smart cities are operating with sensitive data, it is also a part of data
management to save the data from being stolen, unauthorised modifications and destruction.
Beside using the well-known defensive solutions – since the system is based on artificial
intelligence and there are couple of paradigms available of normal behaviours – we can use
these resources to add another level of defence for the system’s protection. We propose to
build an alarm system, which – by monitoring the information flow – can alert about
disharmonious data detections. Which means, if the data flowing through the system does not
follow a continuously measured norm, it could be considered that the data was manipulated.
This prevention method can be considered as a “software” type prevention. Physical prevention
means that important data is allowed to travel on a way that is theoretically impossible to
interfered or read by malicious bodies without immediate detection. In practise, this means
the usage of optical cables where the network can detect any interference if the specifications
of the light change. Using IEC 62443 standard is highly advised [13]. As a conclusion, we
suggest to consider using the mentioned techniques and processes from this article when new
smart cities are designed and build, and also for existing smart cities to develop.
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