Citizen-centric data services for smarter cities

Smart Cities use Information and Communication Technologies (ICT) to manage more efficiently the resources and services offered by a city and to make them more approachable to all its stakeholders (citizens, companies and public administration). In contrast to the view of big corporations promoting holistic “smart city in a box” solutions, this work proposes that smarter cities can be achieved by combining already available infrastructure, i.e., Open Government Data and sensor networks deployed in cities, with the citizens’ active contributions towards city knowledge by means of their smartphones and the apps executed in them. In addition, this work introduces the main characteristics of the IES Cities platform, whose goal is to ease the generation of citizen-centric apps that exploit urban data in different domains. The proposed vision is achieved by providing a common access mechanism to the heterogeneous data sources offered by the city, which reduces the complexity of accessing the city’s data whilst bringing citizens closely to a prosumer (double consumer and producer) role and allowing to integrate legacy data into the cities’ data ecosystem.     

Towards a data‐driven IoT software architecture for smart city utilities

The Internet of things (IoT) is emerging as the next big wave of digital presence for billions of devices on the Internet. Smart cities are a practical manifestation of IoT, with the goal of efficient, reliable, and safe delivery of city utilities like water, power, and transport to residents, through their intelligent management. A data‐driven IoT software platform is essential for realizing manageable and sustainable smart utilities and for novel applications to be developed upon them. Here, we propose such service‐oriented software architecture to address 2 key operational activities in a smart utility: the IoT fabric for resource management and the data and application platform for decision‐making. Our design uses Open Web standards and evolving network protocols, cloud and edge resources, and streaming big data platforms. We motivate our design requirements using the smart water management domain; some of these requirements are unique to developing nations. We also validate the architecture within a campus‐scale IoT testbed at the Indian Institute of Science, Bangalore and present our experiences. Our architecture is scalable to a township or city while also generalizable to other smart utility domains. Our experiences serve as a template for other similar efforts, particularly in emerging markets and highlight the gaps and opportunities for a data‐driven IoT software architecture for smart cities.     

Big data and smart cities: a public sector organizational learning perspective

Public sector organizations (city authorities) have begun to explore ways to exploit big data to provide smarter solutions for cities. The way organizations learn to use new forms of technology has been widely researched. However, many public sector organisations have found themselves in new territory in trying to deploy and integrate this new form of technology (big data) to another fast moving and relatively new concept (smart city). This paper is a cross-sectional scoping study—from two UK smart city initiatives—on the learning processes experienced by elite (top management) stakeholders in the advent and adoption of these two novel concepts. The findings are an experiential narrative account on learning to exploit big data to address issues by developing solutions through smart city initiatives. The findings revealed a set of moves in relation to the exploration and exploitation of big data through smart city initiatives: (a) knowledge finding; (b) knowledge reframing; (c) inter-organization collaborations and (d) ex-post evaluations. Even though this is a time-sensitive scoping study it gives an account on a current state-of-play on the use of big data in public sector organizations for creating smarter cities. This study has implications for practitioners in the smart city domain and contributes to academia by operationalizing and adapting Crossan et al’s (Acad Manag Rev 24(3): 522–537, 1999) 4I model on organizational learning.     

How “OPTIMUS” is a city in terms of energy optimization? e-SCEAF: A web based decision support tool for local authorities

Nowadays cities tend to become “Smarter”, usually disregarding the issues of energy efficiency and sustainability. Therefore, optimizing energy use in a city remains a challenge and respective decision support systems are important to guide local authorities toward that direction. This paper provides a holistic approach presenting a Smart City Energy Assessment Framework (SCEAF) along with a specific web based decision support tool, the so-called e-SCEAF, which can provide local authorities with fruitful results for assessing the energy behavior and performance of their city. The tool merges heterogeneous information, such as clearly quantifiable energy related indicators, the related city policy context performance and the integration of smart infrastructure. This multi-source information fusion is based on the 2-tuple linguistic representation model of Herrera and Martínez. This particular model has been widely used in decision problems and was mainly selected due to the fact that it provides linguistic results that are accurate and easy to understand by the cities’ local authorities. The performance, usefulness and effectiveness of the SCEAF framework and the e-SCEAF tool are tested on a real life application in three different cities, Savona (Italy), Sant Cugat del Vallès (Spain) and Zaanstad (The Netherlands). In this respect, the role of fusion methods and algorithms for merging multiple information will be evaluated in a “real life environment”.     

A novel communication system approach for a Smart City based on the human nervous system

The massive introduction of internet, architect of the Internet of Thingsparadigm, was expected to facilitate the spread of people out of the urban spaces. However, the reality is that increasingly people are moving to cities, which generates many challenges for the cities of tomorrow. In this context, the Smart Cityconcept emerges, where Information and Communication Technologies have a key role. This situation has revealed an important issue that needs to be addressed: the lack of an ubiquitous communication architecture able to deal with the expected requirements of a SC. To this end, a wide review of the existing communication frameworks and city services have been performed, which showed that existing proposals are classical fixed ad-hocsolutions for very specific problems with no resilience and common thread. By rethinking the nature of cities and considering them as living organisms, it is possible to relate the nervous systems with the communication architecture of a city. Therefore, this work proposes a novel communication architecture, ubiquitous and resilient, inspired in the human nervous system by the definition of Smart Gateways, able to satisfy the needs of a real SCand adaptable to the growing and specific requirements of every single city. Additionally, the research also includes the simulation of the communication channel with different technologies.     

DynamiCITY: Revealing city dynamics from citizens social media broadcasts

Social media and mobile devices have revolutionized the way people communicate and share information in various contexts, such as in cities. In today’s “smart” cities, massive amounts of multiple forms of geolocated content is generated daily in social media, out of which knowledge for social interactions and urban dynamics can be derived. This work addresses the problem of detecting urban social activity patterns and interactions, by modeling cities into “dynamic areas”, i.e., coherent geographic areas shaped through social activities. Social media users provide the information on such social activities and interactions in cases when they are on the move around the city neighborhoods. The proposed approach models city places as feature vectors which represent users visiting patterns (social activity), the time of observed visits (temporal activity), and the context of functionality of visited places category. To uncover the dynamics of city areas, a clustering approach is proposed which considers the derived feature vectors to group people’s activities with respect to location, time, and context. The proposed methodology has been implemented on the DynamiCITY platform which demonstrates neighborhood analytics via a Web interface that allows end-users to explore neighborhoods dynamics and gain insights for city cross-neighborhood patterns and inter-relationships.     

Green Bear - A LoRaWAN-based Human-in-the-Loop case-study for sustainable cities

Citizens are a crucial factor in the sustainability of the cities because they are one of the pillars for balancing their environment. Smart city projects can offer citizens an opportunity to understand how they contribute to their city’s sustainability. Furthermore, an interactive smart city system can create synergies that benefit both the citizens and the city itself. This paper presents the Green Bear smart city platform, an IoT Human-in-the-Loop system that uses LoRaWAN nodes to gather information on city green spaces, bike lanes, and recycling depots. A user-centered mobile application that allows for active user participation, feedback, and incentivization is used to close the loop with humans. This system allows citizens to evaluate their participation in aspects that improve the city’s sustainability through a gamification scheme, obtaining points for different activities in the city’s public spaces and personal activities to improve their quality of life. This solution is being implemented in the city of Coimbra, Portugal. After an overview of the system, the paper describes each main system module. The Green Bear prototype was subject to functional and technological assessment, and the results are presented and discussed.     

Elastic Smart Contracts in Blockchains

In this paper, we deal with questions related to blockchains in complex Internet of Things (IoT)-based ecosystems. Such ecosystems are typically composed of IoT devices, edge devices, cloud computing software services, as well as people, who are decision makers in scenarios such as smart cities. Many decisions related to analytics can be based on data coming from IoT sensors, software services, and people. However, they are typically based on different levels of abstraction and granularity. This poses a number of challenges when multiple blockchains are used together with smart contracts. This work proposes to apply our concept of elasticity to smart contracts and thereby enabling analytics in and between multiple blockchains in the context of IoT. We propose a reference architecture for Elastic Smart Contracts and evaluate the approach in a smart city scenario, discussing the benefits in terms of performance and self-adaptability of our solution.      

边缘计算和云计算在智慧城市建设中的应用

文章主要对边缘计算、云计算的定义和特点及智慧城市的概念进行简单阐释,结合城市建设情况,分析边缘计算和云计算在智慧城市建设中应用的必要性,并提出二者的具体应用,希望充分挖掘边缘、云计算技术的优势,在二者相互补充的过程中实现智慧城市的建设,为实际智慧城市建设工作提供参考依据。     

Heuristics-based mediation for building smart architectures at run-time

Smart architectures are increasingly being used in current software development. Smart user interfaces, smart homes, or smart buildings are becoming common examples in the new era of smart cities. Software architectures usually related to these domains need to be adapted and reconfigured at run-time, for example, to provide new services, react to user interaction, or due to changes decided from the business logic of the application. Component-based techniques are a suitable way to carry out this kind of adaptation, as dynamic reconfiguration operations can be applied to the architecture. In this paper, we address run-time generation of component-based applications, taking the abstract definitions of their architecture as a reference, in addition to a set of available components. The process calculates the best configuration of components from the abstract definition by applying a trading approach based on an adapted A* algorithm. This algorithm uses heuristics based on syntactic and semantic information obtained from the component definitions. A case study related to mashup user interfaces formed by coarse-grained components is also explained. In short, the results show the usefulness of heuristics and suitable execution times for building the best configurations.     

Impacts of low citizen awareness and usage in smart city services: the case of London’s smart parking system

Smart city applications and services are increasingly considered as strategic means to cope with emerging global challenges such as climate change, pollution, the ageing population, and energy shortage. In particular, smart parking is a type of smart services used to improve traffic congestion and pollution within cities. Nevertheless, although smart city services are driven by advanced information technologies, their success is highly dependent on user engagement, which is historically problematic. This paper presents and discusses the results of a case study on the smart parking service in London. A questionnaire (involved a total of 212 local drivers) was adopted as the main data collection method. This was complemented by the collection and analysis of 470 online user comments left for the service. The results showed that London’s smart parking service may potentially help each driver to save an average of £68 (62.2 l) on petrol annually and reduce CO₂ emissions by 238.14 kg per car per year. At city level, a smart parking system could help London save £183.6 million worth of petrol per year and reduce its annual CO₂ emissions by 642,978 tons. However, public awareness, actual usage, and user satisfaction of this smart service are currently very low. These present substantial barriers to realise the potential economic and environmental benefits of the service. This paper concluded that further to the very technological efforts, local authorities and service providers need to make a stronger endeavour to enhance public engagement and user satisfaction towards smart services, in order to realise the promises of such solutions.     

A Game-theoretic approach to analyze interacting actors in GRL goal models

Goal-oriented requirements engineering aims to capture desired goals and strategies of relevant stakeholders during early requirements engineering stages, using goal models. Goal-oriented modeling techniques support the analysis of system requirements (especially non-functional ones) from an operationalization perspective, through the evaluation of alternative design options. However, conflicts and undesirable interactions between requirements produced from goals are inevitable, especially as stakeholders often aim for different objectives. In this paper, we propose an approach based on game theory and the Goal-oriented Requirement Language (GRL) to reconcile interacting stakeholders (captured as GRL actors), leading to reasonable trade-offs. This approach consists in building a payoff bimatrix that considers all actor’s valid GRL strategies, and computing its Nash equilibrium. Furthermore, we use two optimization techniques to reduce the size of the payoff bimatrix, hence reducing the computational cost of the Nash equilibrium. The approach goes beyond existing work by supporting nonzero-sum games, multiple alternatives, and inter-actor dependencies. We demonstrate the applicability of our game-theoretic modeling and analysis approach using a running example and two GRL models from the literature, with positive results on feasibility and applicability, including performance results.

     

Recommender systems for smart cities

Among other conceptualizations, smart cities have been defined as functional urban areas articulated by the use of Information and Communication Technologies (ICT) and modern infrastructures to face city problems in efficient and sustainable ways. Within ICT, recommender systems are strong tools that filter relevant information, upgrading the relations between stakeholders in the polity and civil society, and assisting in decision making tasks through technological platforms. There are scientific articles covering recommendation approaches in smart city applications, and there are recommendation solutions implemented in real world smart city initiatives. However, to the best of our knowledge, there is not a comprehensive review of the state of the art on recommender systems for smart cities. For this reason, in this paper we present a taxonomy of smart city features, dimensions, actions and goals, and, according to these variables, we survey the existing literature on recommender systems. As a result of our survey, we do not only identify and analyze main research trends, but also show current opportunities and challenges where personalized recommendations could be exploited as solutions for citizens, firms and public administrations.     

Analysis and assessment of a knowledge based smart city architecture providing service APIs

The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs.Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented.     

A decision support system for surveillance of smart cities via a novel aggregation operator on intuitionistic fuzzy sets

In recent times, terror attacks are becoming one of the most important issues of defense section for almost all the countries, especially for smart cities. Sometimes countries have to spend a lot of money and man power to protect and servile the cities, which is a challenging task for the smart cities to rely on technologies rather than man power for surveillance and protection. In this paper, a fuzzy multi criteria decision support system is utilized to prioritize the parts of a smart city which may lie under potential threat of terror attacks. For this purpose, a new aggregation operation on Intuitionistic fuzzy sets has been proposed. In addition, a case study on a smart city has been carried out which showcase the applicability of the proposed methodology.

     

Smart city image landscape design based on wireless sensors

Smart City is a city environment that advanced sensors combine to manage the city's assets the information technology virtually. Data they have collected, because it is the foundation of these services, the sensor network is an integral part of the smart city. Wireless sensors are to be deployed in several different locations, therefore a relatively inexpensive ability to transmit remote data to reduce the power consumption and maintenance costs; the small built-in sensor is incredibly essential. For each sensor, since it prerequisites for complex information, and Smart City needs is rugged, the integrated sensor system is the concept of Smart City in development. The most important thing is the smart city's side, and the reverse in urban design the plan is to reveal the importance of the role of landscape design. The purpose of this article is, from the point of view of urban design, find outlines the concept of Smart City, highlighting important touchpoint of smart urban landscape design that leads to strong principles of smart, relations and the role of urban design, planning and creativity it is to be sustainable development, city to allow for efficient urban development and beautiful urban landscape.     

Quality and the efficiency of data in “Smart-Cities”

As a new form of sustainable development, the concept “Smart Cities” knows a large expansion during the recent years. It represents an urban model, refers to all alternative approaches to metropolitan ICTs case to enhance quality and performance of urban service for better interaction between citizens and government. However, the smart cities based on distributed and autonomous information infrastructure contains millions of information sources that will be expected more than 50 billion devices connected by using IoT or other similar technologies in 2020. In Information Technology, we often need to process and reason with information coming from various sources (sensors, experts, models). Information is almost always tainted with various kinds of imperfection: imprecision, uncertainty, ambiguity, we need a theoretical framework general enough to allow for the representation, propagation and combination of all kinds of imperfect information. The theory of belief functions is one such Framework. Real-time data generated from autonomous and distributed sources can contain all sorts of imperfections regarding on the quality of data e.g. imprecision, uncertainty, ignorance and/or incompleteness. Any imperfection in data within smart city can have an adverse effect over the performance of urban services and decision making. In this context, we address in this article the problem of imperfection in smart city data. We will focus on handling imperfection during the process of information retrieval and data integration and we will create an evidential database by using the evidence theory in order to improve the efficiency of smart city. The expected outcomes from this paper are (1) to focus on handling imperfection during the process of information retrieval and data integration (2) to create an evidential database by using the evidence theory in order to improve the efficiency of smart city. As experimentation we present a special case of modeling imperfect data in the field of Healthcare. An evidential database will be built which will contain all the perfect and imperfect data. These data come from several Heterogeneous sources in a context of Smart Cities. Imperfect aspects in the evidential database expressed by the theory of beliefs that will present in this paper.     

以数据为中心的智慧城市研究综述

在城市信息化浪潮与数据科学崛起的共同推动下,智慧城市开始在全球范围内成为未来城市发展的新理念与新实践.大数据、数据活化、数据挖掘等数据管理、应用与分析技术在智慧城市建设当中具有核心作用.站在信息科学的视角之上,围绕以数据为中心这一主题,对当前智慧城市研究工作的最新动态进行了综述.梳理了当前智慧城市相关研究中广泛采用的城市数据类型及其特点,并从相关研究工作和技术与研究特点两个大的方面对该领域的研究工作现状进行了介绍.其中相关研究涵盖了技术体系研究、数据驱动的智能交通、城市计算技术和城市人类活动的统计力学等方面.而技术与研究特点的介绍包括核心技术与理论,以及领域研究的学科交叉、城市数据为中心、区域特性等方面.最后对该研究领域未来可能的发展方向进行了总结和展望.     

A novel Bluetooth low energy based system for spatial exploration in smart cities

Smart Cities are employing information and communication technologies in the quest for sustainable economic development and the fostering of new forms of collective life. They facilitate connections between citizens and organizations that are of paramount importance for their long-term sustainability. As cities become more complex and their communities more dispersed, questions such as ‘where can I find …’ are increasingly pertinent. In this paper, we introduce NomaBlue, a new vision of spatial recognition in smart cities, the proposed system is based on an intelligent nomadic data collection and users' collaboration using smart Bluetooth technology. We demonstrate using two case-studies that our approach is capable of proposing an efficient spatial recognition service while supporting a range of users’ constraints, our system is disconnected from the internet, it can operate in any indoor/outdoor area, it doesn't require pre-defined geographic databases and uses a new concept of nomadic data collection and sharing to speed-up the circulating information in smart cities.