智能城市手册：软件服务与赛博基础设施

smart-cities

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"Handbook of Smart Cities" 是Springer在2018年出版的一本专著，由Muthucumaru Maheswaran和Elarbi Badidi编辑，旨在探讨智能城市的研究项目和关键问题。这本书面向通信系统、计算机科学和数据科学领域的研究人员、智能城市技术开发者以及研究生，涵盖了智能城市规模的赛博物理系统的各个方面。本书包含14个章节，由研究智能城市不同方面的学者撰写。内容深入到软件服务和赛博基础设施等核心领域，为读者提供了智能城市的全面视角。书中可能讨论了如下知识点： 1. **智能城市定义与概念**：智能城市是运用信息技术、物联网、大数据和人工智能等先进技术，提升城市管理、服务和居民生活质量的城市形态。 2. **赛博物理系统（CPS）**：赛博物理系统是物理世界与数字世界的融合，它通过传感器、网络和控制系统实现对城市基础设施的实时监控和智能管理。 3. **软件服务**：在智能城市中，软件服务扮演着关键角色，如云平台、API接口、应用程序等，它们为城市提供高效的数据处理和信息服务。 4. **数据科学应用**：通过对城市产生的大量数据进行分析，可以发现模式、趋势，帮助决策者优化资源分配，改进公共服务。 5. **通信系统**：5G、物联网(IoT)、无线网络等通信技术是智能城市的基础，确保信息的快速传输和设备间的无缝连接。 6. **可持续发展与环保**：智能城市的建设强调环境保护和可持续性，如绿色能源、智能交通系统以减少碳排放。 7. **智慧城市治理**：通过数据驱动的决策支持系统，提升城市规划、交通管理、公共安全等领域的治理效率。 8. **居民参与**：智能城市设计也考虑了居民参与，通过公众平台收集反馈，促进社区参与和市民满意度。 9. **安全与隐私**：在利用数据的同时，必须确保数据安全和公民隐私，防止数据泄露和滥用。 10. **未来展望**：书中可能还涉及了智能城市的未来发展趋势，如边缘计算、人工智能在城市管理中的深化应用等。此书不仅是学术研究的宝贵资源，也是实践者理解智能城市复杂性的指南，有助于推动相关领域的发展和创新。通过深入阅读，读者将能全面了解智能城市的最新进展和挑战，为实际工作提供理论支持和实践参考。

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Internet of Things (IoT) Infrastructures for Smart Cities 7

Table 1 Notable examples of smart city applications around the world

Category City Application Instruments Outcome/impact

Smart mobility Barcelona [11] Parking

Notiﬁcation, public transport

monitoring

Wireless parking sensors,

GPS sensors

Encourages public transportation and

improves customer experience

Beijing [12] Smart trafﬁc management HD cameras,

Trafﬁc ﬂow detectors, trafﬁc

signals

Reduces congestion by 60% and doubled

road capacity

Santander[13, 14] Smart parking management Parking sensor, wireless

communications

Experimental application to notify drivers

of available parking to reduce commuting

Smart sustainability Amsterdam[15–18] Smart

Lighting, smart public

service

Light sensors, wireless

electricity meters, wireless

smart bins

Reduces energy consumption cost by 130

billion euro

Reduces the CO2 emission by 62% in

2 years

Padova [7] Smart

Lighting, environmental

monitoring

Wireless temperature,

humidity, light, benzene

sensors

Proof-of-concept implementation to

observe light intensity and weather

conditions

Las Vegas [20] Smart water leak detection Wireless acoustic sensors Enables preventive maintenance and

reduces water loss to leakage

SmartLiving New York [21] Smart information kiosks Smart screen kiosks with

video, video capabilities and

sensors

Delivers information and advertisements

on-demand to citizens

Environment monitoring and surveillance

Seoul [22] Smart safety GPS monitor,

CCTV cameras

Enhances security for children and

vulnerable citizens

Medellin [23] Smart public safety HD cameras Enables coordinated and enhanced

emergency response

8 Q. Le-Dang and T. Le-Ngoc

tional smart services while others begin pilot projects. In these initiatives, it is clear

that IoT technology plays an integral role in many applications of Smart City to

collect comprehensive data about the cities and their residents. While most designs

utilize dedicated sensors to collect speciﬁc data for their smart services, devices

producing multi-purpose data like cameras are becoming more common and often

seen in many applications across multiple domains, such as high-deﬁnition cameras

being used in both smart trafﬁc management and public safety applications. As

such, it is more efﬁcient in terms of both deployment and maintenance cost to reuse

generic data from these devices for various Smart City applications. Moreover, it

is also observed that wireless communication also gains its popularity as the glue,

connecting the low-power, low-capacity IoT devices to the core network.

3 Sensing Architectures for Smart Cities

Due to the infancy of Smart City technologies, many architectural models were

proposed in the literature. The proposals includes the 3-layer [24, 25], 5-layer [26–

28] and 7-layer [29, 30] architectural models. Basically, the more layers the model

has, the more separation of functionalities for the development but more complex

to understand and less correlated between the architecture model and the real IoT

components. To strike a balance between these two extremes, we propose a 4-

layer architecture based on the well-known IoTWF [29] and ITU-T architecture

[31] for IoT applications in general and Smart City in speciﬁc. We believe that this

architecture provides a good abstraction for understanding the Smart City system

as a whole as well as enough details for implementation. This IoT architecture for

Smart City is divided into four layers: the data acquisition and control layer, the

connectivity layer, the data management layer and the application layer as illustrated

in Fig. 2.

The data acquisition & control layer is where all data gathering in Smart

City takes place. Enormous amount of information is collected by various types

of IoT sensors. This layer also composes of actuators such as trafﬁc lights, street

panels to react according to some predeﬁned conﬁgurations or explicit directions

from the human in charge. In fact, the components of this layer vary the most

in type, capability, and quantity according to the requirements of Smart City

applications. RFID and sensor networks are considered the basic building blocks

of this layer [32], mainly due to the low cost hardware. In order to connect

constrained devices (without IP capability) to the upper layer, gateways are used

for protocol translations, security and management. Recently, new applications

like smart video surveillance and trafﬁc control become more common in Smart

Cities [33], as discussed in Chapter 11 “Management of Video Surveillance for

Smart Cities”. These applications demand sensing capability and data rate much

higher than that supported by sensor networks. These applications normally demand

real-time high-deﬁnition (HD) images from Internet protocol (IP) cameras, which

requires very high-speed and low-delay data transmission. To accommodate the new

10 Q. Le-Dang and T. Le-Ngoc

Serving as a backbone infrastructure, wired communication technologies such as

ﬁber optic networks are normally used at this layer due to their stability, large band-

width, and low delay characteristics. However, if the IoT components are mobile, for

instance, mounting on a service truck, wireless communication technologies need

to be used due to their ﬂexibility and mobility support. In these cases, depending on

the trafﬁc volume, different scenarios can be envisioned. First, for low bandwidth

IoT components (GPS, temperature sensors, etc.), LTE and LoRaWAN can be used.

LTE networks have wide coverage and higher bandwidth; however, LTE radios and

subscriptions are quite expensive so it is only suitable for a limited number of mobile

IoT devices. On the other hand, LoRaWAN networks offer wide coverage at low cost

but at narrow bandwidth. For large bandwidth IoT components (such as cameras

on road inspection vehicles), wireless communications are normally not adequate

or too expensive for real-time streaming and ofﬂine storage is required for latter

upload.

The data management layer is the central point that oversees the operation of

Smart City devices and data. This is where the collected data from the entire city is

stored and processed. It also provides interface to remotely monitor, conﬁgure, and

control IoT devices in the data acquisition layer. Usually, the logical components

of the management layer are separated into two subsystems: device management

(control plane) and data management (data plane). Device management system

provide facilities for collecting, storing status reports from deployed devices as

well as a uniform platform for controlling devices with different capabilities from

different vendors. Monitoring device availability, ﬁrmware update and reconﬁgu-

ration are some of the typical functionalities on this device management system.

Data management system facilitates data collection, storage, and visualization from

all the IoT devices in the network. Since there are multiple devices from different

vendors, with different capabilities, sensing different environmental factors, data

should be translated and stored in a uniﬁed format for ease of access and usage

afterwards. As this layer is critical to the Smart City infrastructure, it must

be protected against threats from the Internet, such as via the use of ﬁrewalls.

Additionally, the management layer maintains access control over devices and

data to prevent misuse of information and services provided by Smart City IoT

infrastructure.

To facilitate these functionalities, two main approaches can be identiﬁed based

on the placement of the data management platform: On-premise and cloud-based

approach. In the On-premise approach, all the above functionalities are hosted

on local computing facilities such as a data center. In this case, the operational,

administration and maintenance of these facilities will be on the shoulder of the

data center’s operator. This approach offers the beneﬁts of performance and data

privacy. Since the data sink is placed close to its sources, the performance of

data archiving in terms of bandwidth and delay can be guaranteed easily. This is

especially advantageous for real-time data. In addition, since data is stored locally,

the operator has the complete control over the data, how to use it and its privacy

control. However, this approach can be costly to set up, maintain and scale up this

Internet of Things (IoT) Infrastructures for Smart Cities 11

back-end infrastructure for supporting the growing Smart City IoT network and

applications.

Cloud computing is a trendy solution which brings about the availability of

virtually unlimited storage, processing capacity and computing resources at low cost

that can be virtualized and leased to users on demand [34]. In cloud-based approach,

many of the management functionalities are designated to the cloud computing

facility, which is also referred as Cloud of Things [35] or CloudIoT [36]. In this

case, the Cloud abstracts away the complex hardware back-end server details that

would have to be carefully planned in the On-premise approach. To catch up with the

IoT integration and Smart City trends, many cloud providers such as Amazon [37],

IBM [38], and Microsoft [39] are racing to adopt their cloud computing platforms

for delivering various IoT services over the Internet.

The Application Layer is where the vast amount of collected data is processed

and analysed in order to extract useful information about the current state of the

Smart City. The applications can be provided in this layer are what make up the

attractiveness of IoT and Smart City. Depending on the analytic functions applied

on the data, the value added information could be as raw as counting the number

of vehicles passing an intersection or as complicated as the recommendations

for planning the city infrastructure for the next 10 years. Moreover, the readings

of the collected data could raise an alarm or warning on some critical events

and depending on the applied algorithms, these events could in turn trigger an

actuator to react quickly to the situation. Furthermore, each iteration of processing

produce more valuable information that may be applicable to other application

domains. The possibilities for IoT applications in Smart City, are virtually endless,

though they generally aim to achieve one of the following 3 purposes: presentation,

optimization, and prediction. Presentation enables human operators to observe and

comprehend a situation through data collected from IoT devices. These applications

can visualize incoming data in real-time or combine it with historical data to identify

trending patterns or suspicious activities. Optimization uses advanced analytics to

automatically optimize the operations and utilization of resources in Smart Cities.

These applications (such as smart lighting and waste management) have active

control over IoT components such as actuators, to adjust the system dynamically

in response to the observed conditions through the collected data. Prediction

applies statistical analysis to predict the likelihood of events in the future. These

applications normally incorporate historical data from many sources to determine

the prospect of important events such as trafﬁc jams, ﬂoods, power outage, etc.

As shown in Fig. 2, the presence of the data acquisition and control layer imposes

a signiﬁcant challenge to the implementation of Smart Cities. On one hand, since

the IoT networks may span a wide coverage area, the question of how to facilitate

the communication to and from this vast number of devices is a hard question. A

general answer to this question should be the use of wireless communications, but

how to choose which technology to use in a certain scenario is still not clear. To

answer this question, the next section will present a survey on enabling wireless

communication technologies for Smart Cities. On the other hand, as the IoT device

are exposed to the public, how to secure these devices is a critical issue. Unattended

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