A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> Noise pollution is a major problem in cities around the world. The current methods to assess it neglect to represent the real exposure experienced by the citizens themselves, and therefore could lead to wrong conclusions and a biased representations. In this paper we present a novel approach to monitor noise pollution involving the general public. Using their mobile phones as noise sensors, we provide a low cost solution for the citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. Our prototype, called NoiseTube, can be found online [1]. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> Temperature in an urban area exhibits a complicated pattern due to complexity of infrastructure. Despite geographical proximity, structures of a group of buildings and streets affect changes in temperature. To investigate the pattern of fine-grained distribution of temperature, we installed a densely distributed sensor network called UScan. In this paper, we describe the system architecture of UScan as well as experience learned from installing 200 sensors in downtown Tokyo. The field experiment of UScan system operated for two months to collect long-term urban temperature data. To analyze the collected data in an efficient manner, we propose a lightweight clustering methodology to study the correlation between the pattern of temperature and various environmental factors including the amount of sunshine, the width of streets, and the existence of trees. The analysis reveals meaningful results and asserts the necessity of fine-grained deployment of sensors in an urban area. <s> BIB003 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> The advances in mobile computing and social networking services enable people to probe the dynamics of a city. In this paper, we address the problem of detecting and describing traffic anomalies using crowd sensing with two forms of data, human mobility and social media. Traffic anomalies are caused by accidents, control, protests, sport events, celebrations, disasters and other events. Unlike existing traffic-anomaly-detection methods, we identify anomalies according to drivers' routing behavior on an urban road network. Here, a detected anomaly is represented by a sub-graph of a road network where drivers' routing behaviors significantly differ from their original patterns. We then try to describe the detected anomaly by mining representative terms from the social media that people posted when the anomaly happened. The system for detecting such traffic anomalies can benefit both drivers and transportation authorities, e.g., by notifying drivers approaching an anomaly and suggesting alternative routes, as well as supporting traffic jam diagnosis and dispersal. We evaluate our system with a GPS trajectory dataset generated by over 30,000 taxicabs over a period of 3 months in Beijing, and a dataset of tweets collected from WeiBo, a Twitter-like social site in China. The results demonstrate the effectiveness and efficiency of our system. <s> BIB004 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> We present a technique for estimating crowd density by using a mobile phone to scan the environment for Bluetooth devices. The paper builds on previous work directed to use Bluetooth scans to analyze social context and extends it with more advanced features, leveraging collaboration between close by devices, and the use of relative features that do not directly depend on the absolute number of devices in the environment. The method is evaluated on a data set from an experiment at the public viewing event in Kaiserslautern during the European soccer championship showing over 75% recognition accuracy on seven discrete classes. <s> BIB005 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services. <s> BIB006 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality. <s> BIB007 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> The purpose of this article is to summarize the current state of understanding the smart city concept and to present a proposed communication platform for the development of city services. The first part of the article is an introduction and definition of a smart city concept. This introduction gives an overview of various aspects - city services, smart infrastructure and facilities, using information and communication technologies, interconnection, feedback, and electronic and digital applications. The next part addresses individual challenges for the planning, development, and operation of cities. New solutions allow for use of different data on cities and meet the request for better city services. An overview of smart cities applications and services is given in the next section of the article. The deployment, implementation and approval of innovative internet based services and applications have to be made in order to permit facing the challenges of advanced cities. An overview of some aspects related to the technological solution of services is discussed in the summary section. <s> BIB008 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> In recent times, emergencies such as the 2013 flood in mid Europe have clearly shown that besides the professional emergency services and authorities, citizens get a more and more active role in crisis response work. They organize themselves and coordinate private relief activities. Those activities can be found in (physical) groups of affected local citizens, but also within (digital) social media groups. To detect and use this civil potential by professional emergency services, approaches are needed that support the instructing of citizens and coordinating of their actions to avoid needless duplications or conflicts. In this paper we present a concept, based on a mobile crowd sensing approach, which was designed as well as implemented as the system prototype CrowdMonitor and facilitates the monitoring of physical and digital activities of and the assignment of specific tasks to citizens. <s> BIB009 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users' mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users' privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces. <s> BIB010 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience. <s> BIB011 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> Smart city sensing calls for crowdsensing via mobile devices that are equipped with various built-in sensors. As incentivizing users to participate in distributed sensing is still an open research issue, the trustworthiness of crowdsensed data is expected to be a grand challenge if this cloud-inspired recruitment of sensing services is to be adopted. Recent research proposes reputation-based user recruitment models for crowdsensing; however, there is no standard way of identifying adversaries in smart city crowdsensing. This paper adopts previously proposed vote-based approaches, and presents a thorough performance study of vote-based trustworthiness with trusted entities that are basically a subset of the participating smartphone users. Those entities are called trustworthy anchors of the crowdsensing system. Thus, an anchor user is fully trustworthy and is fully capable of voting for the trustworthiness of other users, who participate in sensing of the same set of phenomena. Besides the anchors, the reputations of regular users are determined based on vote-based (distributed) reputation. We present a detailed performance study of the anchor-based trustworthiness assurance in smart city crowdsensing through simulations, and compare it with the purely vote-based trustworthiness approach without anchors, and a reputation-unaware crowdsensing approach, where user reputations are discarded. Through simulation findings, we aim at providing specifications regarding the impact of anchor and adversary populations on crowdsensing and user utilities under various environmental settings. We show that significant improvement can be achieved in terms of usefulness and trustworthiness of the crowdsensed data if the size of the anchor population is set properly. <s> BIB012 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy. <s> BIB013 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators and stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about $1\sim 2m$ and $5\sim 9^\circ$ , while the hallway connectivity and connection areas between stories are 100 percent correct. <s> BIB014 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. SMART CITY <s> The Internet of Things is a novel cutting edge technology that proffers to connect a plethora of digital devices endowed with several sensing, actuation, and computing capabilities with the Internet, thus offering manifold new services in the context of a smart city. The appealing IoT services and big data analytics are enabling smart city initiatives all over the world. These services are transforming cities by improving infrastructure and transportation systems, reducing traffic congestion, providing waste management, and improving the quality of human life. In this article, we devise a taxonomy to best bring forth a generic overview of the IoT paradigm for smart cities, integrated ICT, network types, possible opportunities and major requirements. Moreover, an overview of the up-to-date efforts from standard bodies is presented. Later, we give an overview of existing open source IoT platforms for realizing smart city applications followed by several exemplary case studies. In addition, we summarize the latest synergies and initiatives worldwide taken to promote IoT in the context of smart cities. Finally, we highlight several challenges in order to give future research directions. <s> BIB015
A smart city aims to increase the quality of life in the city by making it more convenient for the residents to find information of interest and providing such information in a way that is easily understood BIB012 . To form a smart city, several interconnected systems are necessary to provide the required services (healthcare, infrastructure, environmental, social netwroking) based on intelligent technologies. According to Navigant Research report on Smart Cities [109] the worldwide smart city market is expected to grow at $88.7 billion dollars by 2025 from $36.8 billion in 2016. The report shows that this market was escalating from the cooperation interconnection of five key industries and service sectors: water, buildings, energy, mobility, and government. The report shows that the worldwide revenue forecasts for smart city technologies, divided by industry and region spread through 2025. The report also studied the significant market drivers and challenges correlated to smart cities, key business models used to fund smart city projects, and the competitive landscape. However, for a numeral of technical, financial, and political barriers the Smart City market has indeed started taking off. For realization of smart city using Internet of Things (IoT) the research in BIB006 proposed a unified framework includes a complete urban information system. The system components vary from the sensors level and network support through data management and Cloud based consolidation of concerned systems and services, and form a transformative part of the current cyber-physical system. A taxonomy has been devised in BIB015 to bring the best summary of the IoT paradigm for smart cities, network types, and possible openings and major requirements. This research also presented the up-to-date efforts in this filed as well as focused on the current open source IoT platforms for recognizing smart city applications. Smart city emphasizes the collaboration between the government and the society in several fields that affects the citizen in his daily life such as, economy, mobility, environment, and governance. The idea of a smart city can offer high-quality services to the population to reduce the operational cost through information and communication technology BIB007 . A smart city needs to effectively use public resources and enhance the quality of services, while decreasing the public administration operational cost. Smart cities need to consider people daily life conditions, environment protection, safety, and city services as well as industrial and commercial activities. All of the proposed solutions should use collected data and a way to meet the demand for high quality services BIB008 . The city services are registered and reported through collected geo-content, such as waste disposal, damage of car parking, road condition, and traffic lights. Since the purpose of smart cities is enhancing the citizens' quality of life, the role of people in a smart city framework through participatory sensing and MCS is of utmost importance. For example, Smart City sensing is an application that can sense, report, review, and discuss local problems through social media and participatory sensing BIB010 . Mobile crowd-sensing becomes a significant part of any smart city by leveraging the national mobile services to monitor the city provided services. MCS applications include monitoring the city noise BIB001 , BIB002 , traffic congestion BIB004 , emergency incidents BIB009 , weather BIB003 , population density BIB005 , and even detecting earthquakes  . Mainly, these applications depend on mobile sensors available with users or installed in vehicles, however, the user's privacy will be at risk at such locations and times. Nowadays, smartphones with powerful embedded sensors have facilitated new applications such as real-time road-traffic monitoring, air and noise pollution, crime control, and wildlife monitoring through pervasive Internet connectivity. As a case study, TreSight BIB013 is an example of smart city big data application that uses data analytics and Internet of Things (IoT). Authors used the concept of smart and connected communities for a community to live in the present, plan for the future, and remember the past by highlighting MCS as the most important IoT technology. TreSight was proposed to develop the smart tourism and sustainable cultural heritage in the city of Trento, Italy. Gao et al. BIB014 the research work proposes a system called Jigsaw that reconstructs a floor plan by integrating data crowdsensed from mobile users such as place of image capturing, accelerometer, and gyroscope data. This data is integrated to figure accurate indoor floor plans that increase localization performance. Zhang et al. BIB011 proposed a selfcontained indoor navigation system (GROPING) isolated from any infrastructure support. GROPING utilizes MCS to build floor maps without the need for digitized maps of individual places. GROPING was able to deliver adequate accuracy for localization and provided smooth navigation through 20 participants in each floor in a big shopping mall.