Management of IOT Open Data Projects in Smart Cities

Cezary Orlowski

• Publisher: Academic Press
• Year: 2020
• Print ISBN: 9780128187791
• eBook ISBN: 9780128187807
• Topic: Social Sciences
• Subtopic: Sociology
• Index: Social Sciences

Management of IoT Open Data Projects in Smart Cities demonstrates a key project management methodology for the implementation of Smart Cities projects: Principles and Regulations for Smart Cities (PaRSC). This methodology adopts a basis in classic Scrum soft management methods with carefully considered expansions. These include design principals for high-level architecture design and recommendations for design at the level of project teams. This approach enables the deployment of rule-based linguistic models for IoT project management, supporting the design of high-level architecture and providing rules for Scrum Smart Cities team.After reading this book, the reader will have a thorough grounding in IoT nodes and methods of their design, the acquisition and use of open data, and the use of project management methods to collect open data and build business models based on them.- Presents a unified method for smart urban interventions based on the adjustment of Scrum to the complexity of smart city projects- Establishes a key model for intelligent systems verification in Smart Cities projects- Demonstrates how practitioners can gain from the adoption of rule-based linguistic models

Chapter 1

Smart Cities and open data

Abstract

This chapter is devoted to Smart Cities and the importance of open data for their development. We, therefore, address the complexity of the Smart Cities environment for generating, collecting, and using open data from Internet of Things devices. The characteristics for the development of both Smart Cities and open data are discussed. We also present sample projects, whose product was embodied via open data. On this basis, we determine the business and legal conditions of Smart Cities for the acquisition, processing, and use of open data. In this way we intend to demonstrate the complexity of open data project management processes and to demonstrate the need to analyze existing managerial methods.

Keywords

Smart Cities; open data; Internet of Things networks; business models; software project management

1.1 Smart Cities as an environment for generating open data

Thinking about Smart Cities, it should be mentioned that currently more than 50% of the world’s population lives in cities, and by 2030 this population will increase from 3.5 to 4.7 billion [1]. This means that districts, housing estates, and clusters are necessary for the city, as every new city is actually a system of systems, and we can, therefore, say that cities coexist in cities in a way of self-inclusiveness and self-similarity. It worthies noting that 50% of the global gross domestic product (GDP) is generated by a number of 600 cities. By 2025, 40% of global GDP growth will be generated by medium-sized cities linked with emerging markets. The demographic trend will increase, and in 2050, the world’s population will reach 9 billion. This means that the expansion of many megacities will result from population growth. In addition, it should be mentioned that cities consume two-thirds of the world’s energy, 60% of drinking water and 70% of CO₂ emissions. In parallel their population is aging: population over 65 years will increase threefold by 2050 [2].

Transforming a city into a Smart City requires undertaking significant reforms. In addition, using the example of Barcelona [3], the Smart City initiative should actively generate intelligent ideas in an open environment. These ideas should support the construction of urban clusters as well as (digital) open data, while engaging citizens in the process of creating products and services via Internet of Things (IoT) projects. It is relatively difficult to talk about managing IoT projects in Smart Cities unless we address the specificity of these cities in detail. For this reason, we initially present and discuss different in scale Smart City definitions [3]. In [4], Smart Cities are cities where information technology (IT) is used to provide greater service efficiency for citizens to monitor and optimize existing city infrastructure and related city services. In parallel, these technologies are used to increase cooperation between various business entities as well as to encourage the use of innovative business models in both the private and public sectors. In another approach ([5]) the Smart City definition has a strategic dimension. It indicates that Smart Cities is the result of creative knowledge-based strategies to improve the socioeconomic, ecological, and logistic competitiveness of the city. The use of these creative knowledge-based strategies enables the combination of human capital (e.g., a qualified workforce), infrastructure capital (e.g., modern communication devices), social capital (e.g., open network connections), and entrepreneurial capital (e.g., risk-taking business). This definition can also be seen as an effect of the analysis of the four theories of Smart Cities development presented by Neuroni et al. [6].

The first theory assumes that Smart Cities are a combination of intelligent machines and modernly managed organizations. This theory refers to works on intelligent machines and the use of two processes: automation and the generation of new information and knowledge. Both processes allow generating knowledge to translate the city’s clients’ requirements into structures of programming languages and automatic data acquisition from IoT devices. Knowledge of these processes allows managers to build new business models of organizations operating as part of the structure of Smart Cities.

The second theory assumes the importance of partnerships between city offices, communities, enterprises, research institutions, etc., as part of projects that drive innovation for Smart City. This theory criticized the construction of strategies focused on the city as one entity. It was pointed out in this theory that the citizens’ initiatives are seen as the city’s wisdom and are the result of the synergy of their citizens.

The third theory assumes that Smart City learns by acquiring knowledge from city networks and using metrics and return processes of these networks. According to Ref. [7], urban and institutional networks were created to exchange knowledge as well as best practices concerning urban development. Examples of such activities are “Smart Cities” (www.Smartcities.info) and “European Smart Cities” (www.smart-cities.eu). It was found that Smart Cities can also build feedback loops for relearning and adaptation. Knowledge about learning processes and adaptation of cities makes it possible to assess their development status. This assessment is possible based on defined performance indicators in line with the city’s development goals.

The fourth theory indicates that Smart Cities are cities that invest in their future. They are aware of their human, social, and physical resources. These cities also invest in technologies that translate into the development of the city’s functions for economic, social, and environmental benefits. As part of this theory, it was assumed that Smart Cities are created from resources or perspective business. As part of this theory, the importance of developing human and social capital as well as traditional (transport) and modern IT infrastructure was emphasized. It also indicates the need to create new processes for the city and its entities with the special role of the municipal office [5].

In turn [8] pointed to the importance of investing in “smart” applications in Smart City for building new business models. This report explained that smart initiatives are those that pay attention to economic efficiency and perceive return on investment. This report assumes that under these initiatives, economic performance and the long-term sustainability of cities can be an important factor in the conceptualization and adaptation of cities.

These definitions are the result of a multipoint view of Smart Cities as a complex system for which the implementation of joint projects of city offices, IT companies, and citizens (in this case, clients of Smart City projects) is of key importance. In these projects, citizens’ requirements form the basis for their implementation. These requirements are operational (in accordance with the Marsal-Llacuna definition [4]) as well as strategic (in accordance with the Kourtit and Nijkamp definition [5]). The implementation of these projects is not only the fulfillment of citizens’ requirements but also the effect of the implementation of the Smart Cities concept by the City Hall.

The concepts of Smart Cities development are presented next. The first of these is the most known concept of the Vienna University of Technology, also called the area concept [9]. As part of this concept, it was assumed that the development of the city toward a Smart City is the development of six specific such areas. In these areas, development indicators for city processes are determined.

On this basis the six areas (dimensions) of Smart Cities are specified as follows [9].

• • People (Smart People)—The city’s development is seen through the prism of the qualifications and competences of the citizens of their creativity and openness to changes.
• • Economy (Smart Economy)—In this area components such as the innovativeness of organizations and enterprises, their entrepreneurship, and openness to cooperation were analyzed.
• • Management (Smart Governance)—In this area the participation of city stakeholders in the city’s decision-making processes was analyzed.
• • Mobility (Smart Mobility)—This area includes components such as the availability of public transport and information an...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Introduction
• Chapter 1. Smart Cities and open data
• Chapter 2. Architectures and reference models in designing Internet of Things systems
• Chapter 3. Specification of architecture layers and reference models of Internet of Things systems
• Chapter 4. Project management methods for open data projects in Smart Cities
• Chapter 5. Layer-regulatory model project management
• Chapter 6. Verification of the Layers and Rules Model for Smart Cities
• Chapter 7. Conclusions
• Index