165 research outputs found
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
Mobile CrowdSensing (MCS), through employing considerable workers to sense
and collect data in a participatory manner, has been recognized as a promising
paradigm for building many large-scale applications in a cost-effective way,
such as combating COVID-19. The recruitment of trustworthy and high-quality
workers is an important research issue for MCS. Previous studies assume that
the qualities of workers are known in advance, or the platform knows the
qualities of workers once it receives their collected data. In reality, to
reduce their costs and thus maximize revenue, many strategic workers do not
perform their sensing tasks honestly and report fake data to the platform. So,
it is very hard for the platform to evaluate the authenticity of the received
data. In this paper, an incentive mechanism named Semi-supervision based
Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve
the recruitment problem of multiple unknown and strategic workers in MCS.
First, we model the worker recruitment as a multi-armed bandit reverse auction
problem, and design an UCB-based algorithm to separate the exploration and
exploitation, considering the Sensing Rates (SRs) of recruited workers as the
gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL)
approach is proposed to quickly and accurately obtain the workers' SRs, which
consists of two phases, supervision and self-supervision. Last, SCMABA is
designed organically combining the SRs acquisition mechanism with multi-armed
bandit reverse auction, where supervised SR learning is used in the
exploration, and the self-supervised one is used in the exploitation. We prove
that our SCMABA achieves truthfulness and individual rationality. Additionally,
we exhibit outstanding performances of the SCMABA mechanism through in-depth
simulations of real-world data traces.Comment: 18 pages, 14 figure
Design and Implementation of a Scalable Crowdsensing Platform for Geospatial Data
In the recent years smart devices and small low-powered sensors are becoming ubiquitous and nowadays everything is connected altogether, which is a promising foundation
for crowdsensing of data related to various environmental and societal phenomena. Very often, such data is especially meaningful when related to time and location, which is
possible by already equipped GPS capabilities of modern smart devices. However, in order to gain knowledge from high-volume crowd-sensed data, it has to be collected
and stored in a central platform, where it can be processed and transformed for various use cases. Conventional approaches built around classical relational databases and
monolithic backends, that load and process the geospatial data on a per-request basis are not suitable for supporting the data requests of a large crowd willing to visualize
phenomena. The possibly millions of data points introduce challenges for calculation, data-transfer and visualization on smartphones with limited graphics performance. We have created an architectural design, which combines a cloud-native approach with Big Data concepts used in the Internet of Things. The architectural design can be used as a generic foundation to implement a scalable backend for a platform, that covers aspects important for crowdsensing, such as social- and incentive features, as well as a sophisticated stream processing concept to calculate incoming measurement data and store pre-aggregated results. The calculation is based on a global grid system to index geospatial data for efficient aggregation and building a hierarchical geospatial
relationship of averaged values, that can be directly used to rapidly and efficiently provide data on requests for visualization. We introduce the Noisemap project as an exemplary use case of such a platform and elaborate on certain requirements and challenges also related to frontend implementations. The goal of the project is to collect crowd-sensed noise measurements via smartphones and provide users information and a visualization of noise levels in their environment, which requires storing and processing in a central platform. A prototypic implementation for the measurement context of the Noisemap project is showing that the architectural design is indeed feasible to realize
A Novel Methodology for designing Policies in Mobile Crowdsensing Systems
Mobile crowdsensing is a people-centric sensing system based on users'
contributions and incentive mechanisms aim at stimulating them. In our work, we
have rethought the design of incentive mechanisms through a game-theoretic
methodology. Thus, we have introduced a multi-layer social sensing framework,
where humans as social sensors interact on multiple social layers and various
services. We have proposed to weigh these dynamic interactions by including the
concept of homophily and we have modelled the evolutionary dynamics of sensing
behaviours by defining a mathematical framework based on multiplex EGT,
quantifying the impact of homophily, network heterogeneity and various social
dilemmas. We have detected the configurations of social dilemmas and network
structures that lead to the emergence and sustainability of human cooperation.
Moreover, we have defined and evaluated local and global Nash equilibrium
points by including the concepts of homophily and heterogeneity. We have
analytically defined and measured novel statistical measures of social honesty,
QoI and users' behavioural reputation scores based on the evolutionary
dynamics. We have defined the Decision Support System and a novel incentive
mechanism by operating on the policies in terms of users' reputation scores,
that also incorporate users' behaviours other than quality and quantity of
contributions. Experimentally, we have considered the Waze dataset on vehicular
traffic monitoring application and derived the disbursement of incentives
comparing our method with baselines. Results demonstrate that our methodology,
which also includes the local (microscopic) spatio-temporal distribution of
behaviours, is able to better discriminate users' behaviours. This multi-scale
characterisation of users represents a novel research direction and paves the
way for novel policies on mobile crowdsensing systems
Enabling Dynamic Crowdsensing through Models@Runtime
The complexity of applications in the mobile crowdsensing domain is due to factors such as interoperability among heterogeneous devices, recruiting of devices, collection of data from these devices, and adaptation of application operation in dynamic environments. This paper introduces a platform based on models at runtime (M@RT) for the development of the mobile crowdsensing functionality of applications. The platform supports model-based creation and processing of queries that target a distributed and dynamic set of sensor-capable devices. The paper also presents the results of an evaluation that shows the impact of runtime model processing on the performance of applications in mobile crowdsensing scenarios.Keywords: participatory sensing, models at runtime, model execution engine, mobile computing
Crowd-sensing our Smart Cities: a Platform for Noise Monitoring and Acoustic Urban Planning
Environmental pollution and the corresponding control measurements put in place to tackle it play a significant role in determining the actual quality of life in modern cities. Amongst the several pollutant that have to be faced on a daily basis, urban noise represent one of the most widely known for its already ascertained health-related issues. However, no systematic noise management and control activities are performed in the majority of European cities due to a series of limiting factors (e.g., expensive monitoring equipment, few available technician, scarce awareness of the problem in city managers). The recent advances in the Smart City model, which is being progressively adopted in many cities, nowadays offer multiple possibilities to improve the effectiveness in this area. The Mobile Crowd Sensing paradigm allows collecting data streams from smartphone built-in sensors on large geographical scales at no cost and without involving expert data captors, provided that an adequate IT infrastructure has been implemented to manage properly the gathered measurements. In this paper, we present an improved version of a MCS-based platform, named City Soundscape, which allows exploiting any Android-based device as a portable acoustic monitoring station and that offers city managers an effective and straightforward tool for planning Noise Reduction Interventions (NRIs) within their cities. The platform also now offers a new logical microservices architecture
Succeeding with Smart People Initiatives: Difficulties and Preconditions for Smart City Initiatives that Target Citizens
Smart City is a paradigm for the development of urban spaces through the implementation of state-of-the-art ICT. There are two main approaches when developing Smart Cities: top-down and bottom-up. Based on the bottom-up approach, the concepts of Smart People and Smart Communities have emerged as dimensions of the Smart City, advocating for the engagement of citizens in Smart People initiatives. The aim of this research is both to find the types of Smart People initiatives and to identify their difficulties and preconditions for success. However, such initiatives that aim to (1) leverage the citizens intellectually and (2) use citizens as a source of input for ideas and innovation, are understudied. Therefore, this research proposes a concentrated framework of Smart People initiatives from an extensive literature review. On one hand, this framework contributes with a common ground and vocabulary that facilitates the dialogue within and between practitioners and academia. On the other hand, the identification of difficulties and preconditions guides the academia and practitioners in how to successfully account for citizens in the Smart City. From the literature review and the conduct of case studies of five European cities, participation came out as the key difficulty across both types of Smart People initiatives and cases, closely followed by awareness, motivation and complexity
A privacy-aware crowd management system for smart cities and smart buildings
Cities are growing at a dizzying pace and they require improved methods to manage crowded areas. Crowd management stands for the decisions and actions taken to supervise and control densely populated spaces and it involves multiple challenges, from recognition and assessment to application of actions tailored to the current situation. To that end, Wi-Fi-based monitoring systems have emerged as a cost-effective solution for the former one. The key challenge that they impose is the requirement to handle large datasets and provide results in near real-time basis. However, traditional big data and event processing approaches have important shortcomings while dealing with crowd management information. In this paper, we describe a novel system architecture for real-time crowd recognition for smart cities and smart buildings that can be easily replicated. The described system proposes a privacy-aware platform that enables the application of artificial intelligence mechanisms to assess crowds' behavior in buildings employing sensed Wi-Fi traces. Furthermore, the present paper shows the implementation of the system in two buildings, an airport and a market, as well as the results of applying a set of classification algorithms to provide crowd management information.This work was supported in part by the Spanish Government (MINECO) by means of the Project Future Internet Enabled Resilient CitiEs (FIERCE) under Grant RTI2018-093475-A-I00, and in part by the European Union’s Horizon 2020 Programme through the European project Federated CPS Digital Innovation Hubs for the Smart Anything Everywhere Initiative (FED4SAE) under Grant 761708
Infrastructure-less D2D Communications through Opportunistic Networks
MenciĂłn Internacional en el tĂtulo de doctorIn recent years, we have experienced several social media blackouts, which have
shown how much our daily experiences depend on high-quality communication services.
Blackouts have occurred because of technical problems, natural disasters, hacker attacks
or even due to deliberate censorship actions undertaken by governments. In all cases,
the spontaneous reaction of people consisted in finding alternative channels and media so
as to reach out to their contacts and partake their experiences. Thus, it has clearly
emerged that infrastructured networks—and cellular networks in particular—are well
engineered and have been extremely successful so far, although other paradigms should
be explored to connect people. The most promising of today’s alternative paradigms
is Device-to-Device (D2D) because it allows for building networks almost freely, and
because 5G standards are (for the first time) seriously addressing the possibility of using
D2D communications.
In this dissertation I look at opportunistic D2D networking, possibly operating in an
infrastructure-less environment, and I investigate several schemes through modeling and
simulation, deriving metrics that characterize their performance. In particular, I consider
variations of the Floating Content (FC) paradigm, that was previously proposed in the
technical literature.
Using FC, it is possible to probabilistically store information over a given restricted
local area of interest, by opportunistically spreading it to mobile users while in the area.
In more detail, a piece of information which is injected in the area by delivering it to one
or more of the mobile users, is opportunistically exchanged among mobile users whenever
they come in proximity of one another, progressively reaching most (ideally all) users in
the area and thus making the information dwell in the area of interest, like in a sort of
distributed storage.
While previous works on FC almost exclusively concentrated on the communication
component, in this dissertation I look at the storage and computing components of FC,
as well as its capability of transferring information from one area of interest to another.
I first present background work, including a brief review of my Master Thesis activity,
devoted to the design, implementation and validation of a smartphone opportunistic
information sharing application. The goal of the app was to collect experimental data that permitted a detailed analysis of the occurring events, and a careful assessment of
the performance of opportunistic information sharing services. Through experiments, I
showed that many key assumptions commonly adopted in analytical and simulation works
do not hold with current technologies. I also showed that the high density of devices and
the enforcement of long transmission ranges for links at the edge might counter-intuitively
impair performance.
The insight obtained during my Master Thesis work was extremely useful to devise
smart operating procedures for the opportunistic D2D communications considered in this
dissertation. In the core of this dissertation, initially I propose and study a set of schemes
to explore and combine different information dissemination paradigms along with real
users mobility and predictions focused on the smart diffusion of content over disjoint
areas of interest. To analyze the viability of such schemes, I have implemented a Python
simulator to evaluate the average availability and lifetime of a piece of information, as
well as storage usage and network utilization metrics. Comparing the performance of
these predictive schemes with state-of-the-art approaches, results demonstrate the need
for smart usage of communication opportunities and storage. The proposed algorithms
allow for an important reduction in network activity by decreasing the number of data
exchanges by up to 92%, requiring the use of up to 50% less of on-device storage,
while guaranteeing the dissemination of information with performance similar to legacy
epidemic dissemination protocols.
In a second step, I have worked on the analysis of the storage capacity of probabilistic
distributed storage systems, developing a simple yet powerful information theoretical
analysis based on a mean field model of opportunistic information exchange. I have
also extended the previous simulator to compare the numerical results generated by the
analytical model to the predictions of realistic simulations under different setups, showing
in this way the accuracy of the analytical approach, and characterizing the properties of
the system storage capacity.
I conclude from analysis and simulated results that when the density of contents seeded
in a floating system is larger than the maximum amount which can be sustained by the
system in steady state, the mean content availability decreases, and the stored information
saturates due to the effects of resource contention. With the presence of static nodes, in
a system with infinite host memory and at the mean field limit, there is no upper bound
to the amount of injected contents which a floating system can sustain. However, as with
no static nodes, by increasing the injected information, the amount of stored information
eventually reaches a saturation value which corresponds to the injected information at
which the mean amount of time spent exchanging content during a contact is equal to
the mean duration of a contact.
As a final step of my dissertation, I have also explored by simulation the computing
and learning capabilities of an infrastructure-less opportunistic communication, storage and computing system, considering an environment that hosts a distributed Machine
Learning (ML) paradigm that uses observations collected in the area over which the FC
system operates to infer properties of the area. Results show that the ML system can
operate in two regimes, depending on the load of the FC scheme. At low FC load, the ML
system in each node operates on observations collected by all users and opportunistically
shared among nodes. At high FC load, especially when the data to be opportunistically
exchanged becomes too large to be transmitted during the average contact time between
nodes, the ML system can only exploit the observations endogenous to each user, which
are much less numerous. As a result, I conclude that such setups are adequate to support
general instances of distributed ML algorithms with continuous learning, only under the
condition of low to medium loads of the FC system. While the load of the FC system
induces a sort of phase transition on the ML system performance, the effect of computing
load is more progressive. When the computing capacity is not sufficient to train all
observations, some will be skipped, and performance progressively declines.
In summary, with respect to traditional studies of the FC opportunistic information
diffusion paradigm, which only look at the communication component over one area of
interest, I have considered three types of extensions by looking at the performance of FC:
over several disjoint areas of interest;
in terms of information storage capacity;
in terms of computing capacity that supports distributed learning.
The three topics are treated respectively in Chapters 3 to 5.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en IngenierĂa Telemática por la Universidad Carlos III de MadridPresidente: Claudio Ettori Casetti.- Secretario: Antonio de la Oliva Delgado.- Vocal: Christoph Somme
GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing
[EN] Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, these measurements were limited to occasional sampling made by specialized companies, that mainly focus on major roads. In this paper, we propose an alternative approach to this problem based on crowdsensing. Our proposed architecture empowers participating citizens by allowing them to seamlessly, and based on their context, sample the noise in their surrounding environment. This allows us to provide a global and detailed view of noise levels around the city, including places traditionally not monitored due to poor accessibility, even while using their vehicles. In the paper, we detail how the different relevant issues in our architecture, i.e., smartphone calibration, measurement adequacy, server design, and clientÂżserver interaction, were solved, and we have validated them in real scenarios to illustrate the potential of the solution achieved.This work was partially supported by Valencia's Traffic Management Department, 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, and the "Universidad Laica Eloy Alfaro de Manabi, and the Programa de Becas SENESCYT" de la Republica del Ecuador.Zamora-Mero, WJ.; Vera, E.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2018). GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing. Sensors. 18(8):1-25. https://doi.org/10.3390/s18082596S12518
- …