IEEE Electrification Magazine - June 2018 - 76
TablE 1. Data sources in smart cities.
Data Sources
Domain
Sensor Based
Application Based
Electricity
Supervisory control and data acquisition
(SCADA) voltage/current transformer, smart
meter, phasor-measurement unit, and so on
Load prediction, solar/wind prediction, energy
management system, distribution management
system, market, along with others
Water
Source/district/residential water meters,
water pipe/tower/well, among others
Water leak detection, water-pressure optimization,
as well as others
Gas and Oil
SCADA, drill monitor, pressure/flow/temperature sensors, and more
Drilling control system, leak detection, gas compression automation, and more
Transportation
Positioning/motion-tracking sensors, accelerometer, inclinometer, compass, and so on
Traffic monitoring, fleet management, traffic-signal
control, among others
Human Activity
Cellphone, social media, energy-use record,
fitness sensor, web browser, biosensors, as
well as others
Telecare, early detection of disorders, rehabilitation, infection-spread analysis, and more
Security
Surveillance camera, motion/infrared detector, fire sensor, smoke sensor, along with
others
Smart home/building security system, privacy
preserving, and so on
Others
Municipal, environmental, health care, weather,
and more
Concept
Study
Data
Collection
Data
Processing
Data
Distribution
Data
Archiving
Data
Discovery
Data
Analysis
Figure 2. The data lifecycle model.
interdomain and intradomain applications. therefore,
cybersecurity becomes an essential and emerging concern
for implementing many advanced smart city applications.
traditional industrial infrastructures (e.g., electricity,
gas, oil, along with others) normally use dedicated communication networks that are isolated from the public
internet. Hence, the cybersecurity issues induced by
76
I EEE E l e c t r i f i c a t i on M a gaz ine / J UN E 2018
outside factors can be sufficiently prevented. to avoid
inside penetration, some cybersecurity strategies, such as
access control, encryption, firewalls, and antivirus software, are employed to defend, detect, and mitigate cyberthreats that are launched from inside. However, these
prevention practices cannot guarantee sufficient protection because they cannot catch up to the fast pace of malicious malware development. once a system flaw is found,
malicious malware and extensive variants will be developed and launched to numerous industrial systems subject to the flaw. unfortunately, any corresponding patch
development is always activated after the flaw has been
identified. in many cases, some system flaws remain
undiscovered for years. What is even worse is that most
existing industrial control networks still use open standard protocols (e.g., modBus) that can be easily accessed,
intercepted, and manipulated once a malicious attacker
penetrates the intranet.
smart city cybersecurity can only be enhanced through a
systematic framework that can fundamentally change the
landscape of existing passive cyberdefense strategies and
fully consider the interdependency of multiple infrastructures. recently, machine-learning technologies have been
used for cybersecurity enhancement. instead of capturing
signatures from data streams, machine-learning technologies identify and block suspicious communication activities
after being trained by a large volume of data. another promising element that can potentially enhance cybersecurity in
smart cities in terms of information sharing and exchanging
is blockchain technology. instead of accessing data from a
centralized database, which is vulnerable to cyberattacks,
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