A Collaborative Mobile Crowdsensing System for Smart Cities

Nowadays words like Smart City, Internet of Things, Environmental Awareness surround us with the growing interest of Computer Science and Engineering communities. Services supporting these paradigms are definitely based on large amounts of sensed data, which, once obtained and gathered, need to be analyzed in order to build maps, infer patterns, extract useful information. Everything is done in order to achieve a better quality of life. Traditional sensing techniques, like Wired or Wireless Sensor Network, need an intensive usage of distributed sensors to acquire real-world conditions. We propose SenSquare, a Crowdsensing approach based on smartphones and a central coordination server for time-and-space homogeneous data collecting. SenSquare relies on technologies such as CoAP lightweight protocol, Geofencing and the Military Grid Reference System

Revolutionising the quality of life: the role of real-time sensing in smart cities

To further evolve urban quality of life, this paper explores the potential of crowdsensing and crowdsourcing in the context of smart cities. To aid urban planners and residents in understanding the nuances of day-to-day urban dynamics, we actively pursue the improvement of data visualisation tools that can adapt to changing conditions. An architecture was created and implemented that ensures secure and easy connectivity between various sources, such as a network of Internet of Things (IoT) devices, to merge with crowdsensing data and use them efficiently. In addition, we expanded the scope of our study to include the development of mobile and online applications, emphasizing the integration of autonomous and geo-surveillance. The main findings highlight the importance of sensor data in urban knowledge. Their incorporation via Tepresentational State Transfer (REST) Application Programming Interface (APIs) improves data access and informed decision-making, and dynamic data visualisation provides better insights. The geofencing of the application encourages community participation in urban planning and resource allocation, supporting sustainable urban innovation.This work was supported by FCT-Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER-000086. Rui Miranda was supported by grant no. UMINHO/BID/2021/137; Carlos Alves was supported by grant nos. 2022.12629.BD and UMINHO/BID/2021/134; Regina Sousa was supported by grant no. UMINHO/BID/2021/136; António Chaves was supported by grant no. UMINHO/BID/2021/135; Larissa Montenegro was supported by grant no. UMINHO/BID/2022/53

A Context-Aware Mobile-Based System for Crime Prevention and Emergencies

Crime is a global issue that arises as a consequence of social problems in society such as poverty and densely population due to urbanization. In large cities, governments have applied technology to support crime prevention. In this paper, a mobile-based system is proposed to increase knowledge and public awareness which may reduce the risk of crimes. A context-aware system, namely, geo-fence, is used to enable virtual fences around crime hotspots. Crime hotspots are determined using crime histories and crowd-sourced data provided by citizens. As citizens enter crime hotspots, they would be alerted and provided information. Meanwhile, if they find or experience crime, they are able to report and label the location of the crime

User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

Harnessing location-based services for effective citizen observatories

The essence of a city is its citizens and communities. A city’s infrastructure and associated services play a vital role in citizens' day-to-day living and their overall quality of life. Traditionally, services are deployed in a top-down approach where authorities, councils and public bodies take a reactive approach to address community needs and concerns. In this paper, we propose our ‘Citizen Observatory’ approach to enable citizens to take a proactive role in the management of their local communities and environment by supporting their engagement in the decision-making process. We discuss how to empower citizens and communities to engage with and assist authorities to establish a more informed understanding of residents’ needs and the status of their local environments. Through the WeSenseIt project, we employ a location-based crowdsourcing and communication strategy to develop a resilient, efficient and collaborative information ecosystem for decision-making in urban and rural areas

A flexible framework for assessing the quality of crowdsourced data

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Crowdsourcing as a means of data collection has produced previously unavailable data assets and enriched existing ones, but its quality can be highly variable. This presents several challenges to potential end users that are concerned with the validation and quality assurance of the data collected. Being able to quantify the uncertainty, define and measure the different quality elements associated with crowdsourced data, and introduce means for dynamically assessing and improving it is the focus of this paper. We argue that the required quality assurance and quality control is dependent on the studied domain, the style of crowdsourcing and the goals of the study. We describe a framework for qualifying geolocated data collected from non-authoritative sources that enables assessment for specific case studies by creating a workflow supported by an ontological description of a range of choices. The top levels of this ontology describe seven pillars of quality checks and assessments that present a range of techniques to qualify, improve or reject data. Our generic operational framework allows for extension of this ontology to specific applied domains. This will facilitate quality assurance in real-time or for post-processing to validate data and produce quality metadata. It enables a system that dynamically optimises the usability value of the data captured. A case study illustrates this framework

Evaluating Sensor Data in the Context of Mobile Crowdsensing

With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach