Crowdcloud: A Crowdsourced System for Cloud Infrastructure

The widespread adoption of truly portable, smart devices and Do-It-Yourself computing platforms by the general public has enabled the rise of new network and system paradigms. This abundance of wellconnected, well-equipped, affordable devices, when combined with crowdsourcing methods, enables the development of systems with the aid of the crowd. In this work, we introduce the paradigm of Crowdsourced Systems, systems whose constituent infrastructure, or a significant part of it, is pooled from the general public by following crowdsourcing methodologies. We discuss the particular distinctive characteristics they carry and also provide their “canonical” architecture. We exemplify the paradigm by also introducing Crowdcloud, a crowdsourced cloud infrastructure where crowd members can act both as cloud service providers and cloud service clients. We discuss its characteristic properties and also provide its functional architecture. The concepts introduced in this work underpin recent advances in the areas of mobile edge/fog computing and co-designed/cocreated systems

A Survey on Smartphone-Based Crowdsensing Solutions

© 2016 Willian Zamora et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.[EN] In recent years, the widespread adoption of mobile phones, combined with the ever-increasing number of sensors that smartphones are equipped with, greatly simplified the generalized adoption of crowdsensing solutions by reducing hardware requirements and costs to a minimum. These factors have led to an outstanding growth of crowdsensing proposals from both academia and industry. In this paper, we provide a survey of smartphone-based crowdsensing solutions that have emerged in the past few years, focusing on 64 works published in top-ranked journals and conferences. To properly analyze these previous works, we first define a reference framework based on how we classify the different proposals under study. The results of our survey evidence that there is still much heterogeneity in terms of technologies adopted and deployment approaches, although modular designs at both client and server elements seem to be dominant. Also, the preferred client platform is Android, while server platforms are typically web-based, and client-server communications mostly rely on XML or JSON over HTTP. The main detected pitfall concerns the performance evaluation of the different proposals, which typically fail to make a scalability analysis despite being critical issue when targeting very large communities of users.This work was partially supported by the Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014, Spain, under Grant TEC2014-52690-R, the "Universidad Laica Eloy Alfaro de Manabi-ULEAM," and the "Programa de Becas SENESCYT de la Republica del Ecuador."Zamora-Mero, WJ.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2016). A Survey on Smartphone-Based Crowdsensing Solutions. Mobile Information Systems. 2016:1-26. https://doi.org/10.1155/2016/9681842S126201

A crowdsensing method for water resource monitoring in smart communities

Crowdsensing aims to empower a large group of individuals to collect large amounts of data using their mobile devices, with the goal of sharing the collected data. Existing crowdsensing studies do not consider all the activities and methods of the crowdsensing process and the key success factors related to the process. Nor do they investigate the profile and behaviour of potential participants. The aim of this study was to design a crowdsensing method for water resource monitoring in smart communities. This study opted for an exploratory study using the Engaged Scholarship approach, which allows the study of complex real-world problems based on the different perspectives of key stakeholders. The proposed Crowdsensing Method considers the social, technical and programme design components. The study proposes a programme design for the Crowdsensing Methodwhich is crowdsensing ReferenceFrameworkthat includes Crowdsensing Processwith key success factors and guidelines that should be considered in each phase of the process. The method also uses the Theory of Planned Behaviour (TPB) to investigate citizens’intention to participate in crowdsensing for water resource monitoring and explores their attitudes, norms and perceived behavioural control on these intentions. Understanding the profiles of potential participants can assist with designing crowdsensing systems with appropriate incentive mechanisms to achieve adequate user participation and good service quality. A survey was conducted to validate the theoretical TB model in a real-world context. Regression and correlation analyses demonstrated that the attitudes, norms and perceived behavioural control can be used to predict participants’ intention to participate in crowdsensing for water resource monitoring. The survey results assisted with the development of an Incentive Mechanism as part of the Crowdsensing Method. This mechanism incorporates recruitment and incentive policies, as well as guidelines derived from the literature review and extant system analysis. The policies, called the OverSensepolicies, provide guidance for recruitment and rewarding of participants using the popular Stackelberg technique. The policies were evaluated using simulation experiments with a data set provided by the case study, the Nelson Mandela Bay Municipality. The results of the simulation experiments illustrated that the OverSenserecruitmentpolicycan reduce the computing resources required for the recruitment of participants and that the recruitment policy performs better than random or naïve recruitment policies. The proposed Crowdsensing Method was evaluated using an ecosystem of success factors for mobile-based interventions identified in the literature and the Crowdsensing Method adhered to a majority (90%) of the success factors. This study also contributes information systems design theory by proposing several sets of guidelines for crowdsensing projects and the development of crowdsensing systems. This study fulfils an identified need to study the applicability of crowdsensing for water resource monitoring and explores how a crowdsensing method can create a smart community

A Snapshot of the Frontiers of Client Selection in Federated Learning

Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model's parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as a strategy to limit the number of communicating parties at every step of the process. Since the early na\"{i}ve random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.Comment: 17 pages, 3 figures, 1 appendix, submitted to TML

Crowdsensing solutions for urban pollution monitoring using smartphones

La contaminación ambiental es uno de los principales problemas que afecta a nuestro planeta. El crecimiento industrial y los aglomerados urbanos, entre otros, están contribuyendo a que dicho problema se diversifique y se cronifique. La presencia de contaminantes ambientales en niveles elevados afecta la salud humana, siendo la calidad del aire y los niveles de ruido ejemplos de factores que pueden causar efectos negativos en las personas tanto psicológicamente como fisiológicamente. Sin embargo, la ubiquidad de los microcomputadores, y el aumento de los sensores incorporados en nuestros smartphones, han hecho posible la aparición de nuevas estrategias para medir dicha contaminación. Así, el Mobile Crowdsensing se ha convertido en un nuevo paradigma mediante el cual los teléfonos inteligentes emergen como tecnología habilitadora, y cuya adopción generalizada proporciona un enorme potencial para su crecimiento, permitiendo operar a gran escala, y con unos costes asumibles para la sociedad. A través del crowdsensing, los teléfonos inteligentes pueden convertirse en unidades de detección flexibles y multiuso que, a través de los sensores integrados en dichos dispositivos, o combinados con nuevos sensores, permiten monitorizar regiones de interés con una buena granularidad tanto espacial como temporal. En esta tesis nos centramos en el diseño de soluciones de crowdsensing usando smartphones donde abordamos problemas de contaminación ambiental, específicamente del ruido y de la contaminación del aire. Con este objetivo, se estudian, en primer lugar, las propuestas de crowdsensing que han surgido en los últimos años. Los resultados de nuestro estudio demuestran que todavía hay mucha heterogeneidad en términos de tecnologías utilizadas y métodos de implementación, aunque los diseños modulares en el cliente y en el servidor parecen ser dominantes. Con respecto a la contaminación del aire, proponemos una arquitectura que permita medir la contaminación del aire, concretamente del ozono, dentro de entornos urbanos. Nuestra propuesta utiliza smartphones como centro de la arquitectura, siendo estos dispositivos los encargados de leer los datos de un sensor móvil externo, y de luego enviar dichos datos a un servidor central para su procesamiento y tratamiento. Los resultados obtenidos demuestran que la orientación del sensor y el período de muestreo, dentro de ciertos límites, tienen muy poca influencia en los datos capturados. Con respecto a la contaminación acústica, proponemos una arquitectura para medir los niveles de ruido en entornos urbanos basada en crowdsensing, y cuya característica principal es que no requiere intervención del usuario. En esta tesis detallamos aspectos tales como la calibración de los smartphones, la calidad de las medidas obtenidas, el instante de muestreo, el diseño del servidor, y la interacción cliente-servidor. Además, hemos validado nuestra solución en escenarios reales para demostrar el potencial de la solución alcanzada. Los resultados experimentales muestran que, con nuestra propuesta, es posible medir niveles de ruido en diferentes zonas urbanas o rurales con un grado de precisión comparable al de los dispositivos profesionales, todo ello sin requerir intervención del usuario, y con un consumo reducido en cuanto a recursos del sistema. En general, las diferentes contribuciones de esta tesis doctoral ofrecen un punto de partida para nuevos desarrollos, ofreciendo estrategias de calibración y algoritmos eficientes de cara a realizar medidas representativas. Además, una importante ventaja de nuestra propuesta es que puede ser implementada de forma directa tanto en instituciones públicas como no gubernamentales en poco tiempo, ya que utiliza tecnología accesible y soluciones basadas en código abierto.La contaminació ambiental és un dels principals problemes que afecten el nostre planeta. El creixement industrial i els aglomerats urbans, entre altres, estan contribuint al fet que aquest problema es diversifique i es cronifique. La presència de contaminants ambientals en nivells elevats afecta la salut humana, sent la qualitat de l'aire i els nivells de soroll exemples de factors que poden causar efectes negatius en les persones, tant psicològicament com fisiològicament. No obstant això, la ubiqüitat de les microcomputadores i l'augment dels sensors incorporats als nostres telèfons intel·ligents han fet possible l'aparició de noves estratègies per a mesurar aquesta contaminació. Així, el mobile crowdsensing s'ha convertit en un nou paradigma mitjançant el qual els telèfons intel·ligents emergeixen com a tecnologia habilitadora, i l'adopció generalitzada d'aquest proporciona un enorme potencial per al seu creixement, ja que permet operar a gran escala i amb uns costos assumibles per a la societat. A través del crowdsensing, els telèfons intel·ligents poden convertir-se en unitats de detecció flexibles i multiús que, a través dels sensors integrats en els esmentats dispositius, o combinats amb nous sensors, permeten monitoritzar regions d'interès amb una bona granularitat, tant espacial com temporal. En aquesta tesi ens centrem en el disseny de solucions de crowdsensing usant telèfons intel·ligents, on abordem problemes de contaminació ambiental, específicament del soroll i de la contaminació de l'aire. Amb aquest objectiu, s'estudien, en primer lloc, les propostes de crowdsensing que han sorgit en els últims anys. Els resultats del nostre estudi demostren que encara hi ha molta heterogeneïtat en termes de tecnologies utilitzades i mètodes d'implementació, encara que els dissenys modulars en el client i en el servidor semblen ser dominants. Pel que fa a la contaminació de l'aire, proposem una arquitectura que permeta mesurar la contaminació d'aquest, concretament de l'ozó, dins d'entorns urbans. La nostra proposta utilitza telèfons intel·ligents com a centre de l'arquitectura, sent aquests dispositius els encarregats de llegir les dades d'un sensor mòbil extern, i d'enviar després aquestes dades a un servidor central per al seu processament i tractament. Els resultats obtinguts demostren que l'orientació del sensor i el període de mostratge, dins de certs límits, tenen molt poca influència en les dades capturades. Pel que fa a la contaminació acústica, proposem una arquitectura per a mesurar els nivells de soroll en entorns urbans basada en crowdsensing, i la característica principal de la qual és que no requereix intervenció de la persona usuària. En aquesta tesi detallem aspectes com ara el calibratge dels telèfons intel·ligents, la qualitat de les mesures obtingudes, l'instant de mostratge, el disseny del servidor i la interacció client-servidor. A més, hem validat la nostra solució en escenaris reals per a demostrar el potencial de la solució assolida. Els resultats experimentals mostren que, amb la nostra proposta, és possible mesurar nivells de soroll en diferents zones urbanes o rurals amb un grau de precisió comparable al dels dispositius professionals, tot això sense requerir intervenció de l'usuari o usuària, i amb un consum reduït quant a recursos del sistema. En general, les diferents contribucions d'aquesta tesi doctoral ofereixen un punt de partida per a nous desenvolupaments, i ofereixen estratègies de calibratge i algorismes eficients amb vista a realitzar mesures representatives. A més, un important avantatge de la nostra proposta és que pot ser implementada de forma directa tant en institucions públiques com no governamentals en poc de temps, ja que utilitza tecnologia accessible i solucions basades en el codi obert.Environmental pollution is one of the main problems that affect our planet. Industrial growth and urban agglomerations, among others, are contributing to the diversification and chronification of this problem. The presence of environmental pollutants at high levels affect human health, with air quality and noise levels being examples of factors that can cause negative effects on people both psychologically and physiologically. Traditionally, environmental pollution is measured through monitoring centers, which are usually fixed and have a high cost. However, the ubiquity of microcomputers and the increase in the number of sensors embedded in our smartphones, have paved the way for the appearance of new strategies to measure such pollution. Thus, Mobile Crowdsensing has become a new paradigm through which smartphones emerge as an enabling technology, and whose widespread adoption provides enormous potential for growth, allowing large-scale operations, and with costs acceptable to our society. Through crowdsensing, smartphones can become flexible and multipurpose detection units that, through the sensors integrated into these devices, or combined with new sensors, allow monitoring regions of interest with good spatial and temporal granularity. In this thesis, we focus on the design of crowdsensing solutions using smartphones. We deal with environmental pollution problems, specifically noise and air pollution. With this objective, the crowdsensing proposals that have emerged in recent years are studied in the first place. The results of our study show that there is still a lot of heterogeneity in terms of technologies used and implementation methods, although modular designs at both client and server seem to be dominant. Concerning air pollution, we propose an architecture that allows measuring air pollution, specifically ozone, in urban environments. Our proposal uses smartphones as the center of the architecture, being these devices responsible for reading the data obtained by an external mobile sensor, and then sending such data to a central server for processing and analysis. In this proposal, several problems have been analyzed with regard to the orientation of the external sensor and the sampling time, and the proposed solution has been validated in real scenarios. The results obtained show that the orientation of the sensor and the sampling period, within certain limits, have very little influence on the captured data. Also, by comparing the heat maps generated by our solution with the data from the existing monitoring stations in the city of Valencia, we demonstrate that our approach is capable of providing greater data granularity. Concerning noise pollution, we propose an architecture to measure noise levels in urban environments based on crowdsensing, and whose main characteristic is that it does not require user intervention. In this thesis, we detail aspects such as the calibration of smartphones, the quality of the measurements obtained, the sampling instant, the server design, and the client-server interaction. Besides, we have validated our solution in real scenarios to demonstrate the potential of the proposed solution. Experimental results show that, with our proposal, it is possible to measure noise levels in different urban or rural areas with a degree of precision comparable to that of professional devices, all without requiring the intervention of the user, and with reduced consumption of system resources. In general, the different contributions of this doctoral thesis provide a starting point for new developments, offering efficient calibration strategies and algorithms to make representative measurements. Besides, a significant advantage of our proposal is that it can be implemented straightforwardly by both public and non-governmental institutions in a short time, as it relies on accessible technology and open source softwareZamora Mero, WJ. (2018). Crowdsensing solutions for urban pollution monitoring using smartphones [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/115483TESI

VIVO: a Secure, Privacy-Preserving, and Real-Time Crowd-Sensing Framework for the Internet of Things

Smartphones are a key enabling technology in the Internet of Things (IoT) for gathering crowd-sensed data. However, collecting crowd-sensed data for research is not simple. Issues related to device heterogeneity, security, and privacy have prevented the rise of crowd-sensing platforms for scientific data collection. For this reason, we implemented VIVO, an open framework for gathering crowd-sensed Big Data for IoT services, where security and privacy are managed within the framework. VIVO introduces the enrolled crowd-sensing model, which allows the deployment of multiple simultaneous experiments on the mobile phones of volunteers. The collected data can be accessed both at the end of the experiment, as in traditional testbeds, as well as in real-time, as required by many Big Data applications. We present here the VIVO architecture, highlighting its advantages over existing solutions, and four relevant real-world applications running on top of VIVO

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

A public safety, participatory crowdsourcing smart city model for a developing country

Worldwide the population in cities is increasing. It is the responsibility of local government to provide public safety services in order to ensure the safety of their citizens and, yet, the local government often have inadequate resources to do this. ‘Smart Cities’ is a new and innovative concept that has emerged during the past few years and which involves using current infrastructure and resources more effectively and efficiently. One of the methods used to collect data in a smart city is participatory crowdsourcing but, in order to ensure effectiveness and efficiency, it is essential that a large amount of data be collected from the participants in such a project, who are generally citizens residing in the city. This study was conducted in the city of East London, which is part of the Buffalo City Metropolitan Municipality (BCMM). The study made use of a Design Science approach with a mixed method data collection method. The quantitative data collection comprised a questionnaire that was completed by 394 participants, while the qualitative data collection included a detailed literature review, conversational analysis and observations arising from the building of the crowdsourcing system prototype. The design artefact produced by this research is a model based on the literature, conversational analysis and the principles and concepts learnt from the prototype. Thus, this model represents what must be incorporated in the prototype to assist with the implementation of a public safety, participatory crowdsourcing smart city in a developing country. The model includes three areas ‒ the crowdsourcing system, the city (Buffalo City Metropolitan Municipality) and the citizens of East London. The crowdsourcing system incorporates factors of information security, specifically the CIA triad, and the usability of the crowdsourcing system. Usability includes characteristics such as the quality of the system and interface, as well as the usefulness of the public safety, participatory crowdsourcing system which was used to measure the confidence of the East London citizens in the system. Three steps were identified in the literature as being necessary for the implementation of a smart city project by a city. These steps include the planning, development and delivery of the smart city project. Finally, the trustworthiness of the public safety participatory crowdsourcing system is determined by the ability, reliability and benevolence of the system. These three characteristics were included in the citizen factor of the model

A public safety, participatory crowdsourcing smart city model for a developing country

Worldwide the population in cities is increasing. It is the responsibility of local government to provide public safety services in order to ensure the safety of their citizens and, yet, the local government often have inadequate resources to do this. ‘Smart Cities’ is a new and innovative concept that has emerged during the past few years and which involves using current infrastructure and resources more effectively and efficiently. One of the methods used to collect data in a smart city is participatory crowdsourcing but, in order to ensure effectiveness and efficiency, it is essential that a large amount of data be collected from the participants in such a project, who are generally citizens residing in the city. This study was conducted in the city of East London, which is part of the Buffalo City Metropolitan Municipality (BCMM). The study made use of a Design Science approach with a mixed method data collection method. The quantitative data collection comprised a questionnaire that was completed by 394 participants, while the qualitative data collection included a detailed literature review, conversational analysis and observations arising from the building of the crowdsourcing system prototype. The design artefact produced by this research is a model based on the literature, conversational analysis and the principles and concepts learnt from the prototype. Thus, this model represents what must be incorporated in the prototype to assist with the implementation of a public safety, participatory crowdsourcing smart city in a developing country. The model includes three areas ‒ the crowdsourcing system, the city (Buffalo City Metropolitan Municipality) and the citizens of East London. The crowdsourcing system incorporates factors of information security, specifically the CIA triad, and the usability of the crowdsourcing system. Usability includes characteristics such as the quality of the system and interface, as well as the usefulness of the public safety, participatory crowdsourcing system which was used to measure the confidence of the East London citizens in the system. Three steps were identified in the literature as being necessary for the implementation of a smart city project by a city. These steps include the planning, development and delivery of the smart city project. Finally, the trustworthiness of the public safety participatory crowdsourcing system is determined by the ability, reliability and benevolence of the system. These three characteristics were included in the citizen factor of the model