7.2 Smart City Surveillance
7.2.1 Concepts
Before giving a clear definition of smart cities, let us look at some statistical numbers. According to the statistics of United Nations World Urbanization Prospects report [2], in 2014, 54% of the total population of our world have been concentrated in urban areas and this number will be 66% in 2050. Among a number of critical issues introduced by the fast urbanization progress, urban transportation may highlight the impact. The Centre for Economics and Business Research [3]
reported that a 50% rise of direct or indirect annual monetary loss caused by traffic congestion will be witnessed in the next 16 years in United States if no further measures are taken for current situations. Besides the impressive money loss, traffic accidents leading to fatal consequences are of significance as well. According to the report from World Health Organization [4], in 2015, the total number of worldwide road accident deaths becomes 7.25 million per year. However, the big money loss or accidental deaths caused by traffic accidents are merely the tip of an iceberg of the issues that cities are confronted with. Therefore, all these urban issues are propelling researchers to seek innovative and effective solutions.
In recent years, the number of smart devices increases drastically and digital sensors are deployed ubiquitously. The smart infrastructures and advanced com-munication techniques lead to the concept of IoT and enable the development of smart cities. Smart devices, from personal laptops to smartphones, to even smaller embedded sensors, are not only acting as data collectors, but also collaboratively share real-time monitoring data with each other. With densely deployed smart sensors, the pulse of cities is digitally recorded in real time. Leveraging the cloud computing paradigm, big urban data will be processed to unveil underlying patterns of cities, which will significantly improve the decision-making process for city operators or urban planners.
While there are plenty of reported research efforts dealing with the issues in the domain of smart cities, there is still not a consensus definition of smart cities.
According to a definition of smart cities as provided in [5] that suggests a holistic vision, a smart city is a system integration of technological infrastructure that relies on advanced data processing with the goals of making city governance more
efficient, citizens happier, business more prosperous, and the environment more sustainable.
According to the above definition, the core of smart cities is the urban residents.
The advanced data acquisition and processing techniques are the essential parts.
Figure7.1illustrates a typical architecture for a smart city.
As shown in Fig.7.1, the smart city architecture consists of four layers. The bottom layer is the data sensing layer. As a result of increasing number and cate-gory of smart devices, various types of urban data become available at the network edge for further analysis. Apart from conventional data collected from deployed digital sensors, residents as end users are sharing their personal data through social media or other approaches. Hence, a variety of urban data including trajectories, texts, photos, and videos make the concept of smart cities feasible by extracting city activity patterns leveraging the big data. The second layer of the smart cities architecture is the data storage layer. After various urban data are collected, they will be stored at varying locations using different formats. There are a lot of research efforts in this domain addressing the data management issues at this layer.
Through these approaches, urban data can be stored either at the edge of network or at the centralized data centers to assist efficient information fusion. In other words, benefiting from IoT platform, smart devices at the data sensing layer can both be the data producers and data storage depots. Another option is to send the collected data to remote cloud data centers for storage. This approach potentially causes higher communication network bandwidth consumption. The third layer is the data mining
Fig. 7.1 A four-layer smart city architecture
layer where the urban data is processed in data centers or by decentralized devices.
A wide variety of advanced data mining techniques such as machine learning are utilized in this layer to recognize urban activity patterns for different service pur-poses. The top layer is the domain application layer. Analytical outcomes resulting from data mining layer are applied to provide residents better services in various areas, including but not limited to smart grid, smart transportations, smart medicare, smart home, smart surveillance, and more. All these layers work together to improve the living qualities of people in the smart cities.
The right part of Fig.7.1depicts the computing paradigms applicable in smart cities. End users at the network edge are data providers as well as the data con-sumers. A new computing paradigm named fog computing is inserted between end users and cloud layer. Fog computing is inspired by the concept of IoT. Smart devices with computing capability at the edge can serve as fog computing nodes.
Besides data sensing, part of data storage and data mining tasks can be migrated to this layer despite cloud centers conduct the same jobs. A more detailed introduction to fog computing and a comparison between cloud and fog paradigm is presented in the next section.
Smart city surveillance is one of the smart city application domains aiming at discovering, locating and dealing with anomalies occurred in urban environments.
Tremendous urban data within certain time and space scale not only make it fea-sible for urban operators to achieve a comprehensive understanding of the urban activity patterns over a certain period, but also provide the opportunities to rec-ognize anomaly situations, especially in dealing with emergencies. The timely detection of the occurrence of anomaly is not trivial and it may result in serious consequences.
Situational awareness (SAW) is essential to smart surveillance and it requires urban planners to maintain a holistic understanding of the cities [6]. Moreover, SAW entails efficient information fusion of diverse formats of urban data. For example, if afire alarm suddenly breaks out, decisions regarding how to deal with this emergency should be different based on weather, surroundings and what is burning, because burning chemical materials could lead to fatal results. Obviously, in smart cities, especially in dealing with emergent situations, a fast and compre-hensive SAW is mandatory for decision-making to avoid fatal consequences.
Therefore, quick detection of anomalies, awareness of situations, and precise decision-making are essential to make smart cities sustainable.
Anomalous events in smart cities may cover a wide range, from individually abnormal activities, like suspicious unauthorized payment different from regular credit card activities in records, to citywide anomalies such as driving violations or unexpected events of city power systems. In Sect.7.2.2, current research progress about smart city surveillance is introduced.