63 research outputs found
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns.
Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas
Harnessing Context for Budget-Limited Crowdsensing with Massive Uncertain Workers
Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a
crowd of workers are recruited to perform sensing tasks collaboratively.
Although it has stimulated many applications, an open fundamental problem is
how to select among a massive number of workers to perform a given sensing task
under a limited budget. Nevertheless, due to the proliferation of smart devices
equipped with various sensors, it is very difficult to profile the workers in
terms of sensing ability. Although the uncertainties of the workers can be
addressed by standard Combinatorial Multi-Armed Bandit (CMAB) framework through
a trade-off between exploration and exploitation, we do not have sufficient
allowance to directly explore and exploit the workers under the limited budget.
Furthermore, since the sensor devices usually have quite limited resources, the
workers may have bounded capabilities to perform the sensing task for only few
times, which further restricts our opportunities to learn the uncertainty. To
address the above issues, we propose a Context-Aware Worker Selection (CAWS)
algorithm in this paper. By leveraging the correlation between the context
information of the workers and their sensing abilities, CAWS aims at maximizing
the expected total sensing revenue efficiently with both budget constraint and
capacity constraints respected, even when the number of the uncertain workers
are massive. The efficacy of CAWS can be verified by rigorous theoretical
analysis and extensive experiments
Mobile Crowd Sensing in Edge Computing Environment
abstract: The mobile crowdsensing (MCS) applications leverage the user data to derive useful information by data-driven evaluation of innovative user contexts and gathering of information at a high data rate. Such access to context-rich data can potentially enable computationally intensive crowd-sourcing applications such as tracking a missing person or capturing a highlight video of an event. Using snippets and pictures captured from multiple mobile phone cameras with specific contexts can improve the data acquired in such applications. These MCS applications require efficient processing and analysis to generate results in real time. A human user, mobile device and their interactions cause a change in context on the mobile device affecting the quality contextual data that is gathered. Usage of MCS data in real-time mobile applications is challenging due to the complex inter-relationship between: a) availability of context, context is available with the mobile phones and not with the cloud, b) cost of data transfer to remote cloud servers, both in terms of communication time and energy, and c) availability of local computational resources on the mobile phone, computation may lead to rapid battery drain or increased response time. The resource-constrained mobile devices need to offload some of their computation.
This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Is it crowdsourcing or crowdsensing? An analysis of human participation in digital platforms in the age of surveillance capitalism
This paper contributes to studies on the dark side of digitization by relying on the concept of surveillance capitalism to analyze the role of individuals in digital organizations in performing activities known as crowdsourcing. Even though there is a discourse of empowerment and mutual interest exchanges between organizations and individuals through crowdsourcing, the transformation of computer systems into the so-called 4.0 era or 4.0 industry seems to have altered their role in digital organizations as well. These individuals began to be analyzed from the data they produce, and no longer from their desires, thus approaching the sensors of these organizations. Using the case study method, we analyze the contents of the Netflix, Facebook and Google platform home pages, as well as their terms of service and privacy policies. The way users participate in these platforms is analyzed, as well as the way their data are exploited, and the reason why this continuous exploitation of data occurs. We argue that this exploration alienates the empowering and participatory concept of crowdsourcing and brings the passive concept of individuals closer together as sensors, or crowdsensing. This approach, instead of treating individuals as singular, quantifies and categorizes their uniqueness to meet the controlling longings of hegemonic organizational structures, limited by capitalist discourse, or surveillance capitalism
CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments
Smart cities take advantage of recent ICT developments to provide added value to existing public services and improve quality of life for the citizens. The Internet of Things (IoT) paradigm makes the Internet more pervasive where objects equipped with computing, storage and sensing capabilities are interconnected with communication technologies. Because of the widespread diffusion of IoT devices, applying the IoT paradigm to smart cities is an excellent solution to build sustainable Information and Communication Technology (ICT) platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques augments capabilities of these ICT platforms without additional costs. For proper operation, MCS systems require the contribution from a large number of participants. Simulations are therefore a candidate tool to assess the performance of MCS systems. In this paper, we illustrate the design of CrowdSenSim, a simulator for mobile crowdsensing. CrowdSenSim is designed specifically for realistic urban environments and smart cities services. We demonstrate the effectiveness of CrowdSenSim for the most popular MCS sensing paradigms (participatory and opportunistic) and we present its applicability using a smart public street lighting scenario
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