900 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
Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing
Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users\u27 sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies
Crowdsensing Application on Coalition Game Using GPS and IoT Parking in Smart Cities
This paper provides an overview of crowdsensing and some of its applications. Crowdsensing is a part of the collecting data situations also; it’s built on a data system on multiple customer interactions. Moreover, writing the general information of the smart cities can be used to boost to received number frequency to send messages. This work mentioned the Crowdsensing layers that describe Mobile crowdsensing. The article focuses on crowdsensing layers, developed an application in Coalition Game using crowdsensing in terms of GPS. In addition, this paper discussed the Mobile crowdsensing system and how important the cloud is in serving the wireless network, the Internet of things (IoT), and data collection. Furthermore, this research also has developed a smart crowdsensing parking system that will help by reducing the time-wasting users
Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN
Mobile devices are rapidly becoming the primary computing device in people's
lives. Application delivery platforms like Google Play, Apple App Store have
transformed mobile phones into intelligent computing devices by the means of
applications that can be downloaded and installed instantly. Many of these
applications take advantage of the plethora of sensors installed on the mobile
device to deliver enhanced user experience. The sensors on the smartphone
provide the opportunity to develop innovative mobile opportunistic sensing
applications in many sectors including healthcare, environmental monitoring and
transportation. In this paper, we present a collaborative mobile sensing
framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on
smartphones capturing and sharing sensed data between multiple distributed
applications and users. MOSDEN follows a component-based design philosophy
promoting reuse for easy and quick opportunistic sensing application
deployments. MOSDEN separates the application-specific processing from the
sensing, storing and sharing. MOSDEN is scalable and requires minimal
development effort from the application developer. We have implemented our
framework on Android-based mobile platforms and evaluate its performance to
validate the feasibility and efficiency of MOSDEN to operate collaboratively in
mobile opportunistic sensing applications. Experimental outcomes and lessons
learnt conclude the paper
Managing pervasive sensing campaigns via an experimentation-as-a-service platform for smart cities
The adoption of technologies like the IoT in urban environments, together with the intensive use of smartphones, is driving transformation towards smart cities. Under this perspective, Experimentation-as-a-Service within OrganiCity aims to create an experimental facility with technologies, services, and applications that simplify innovation within urban ecosystems. We discuss here tools that facilitate experimentation, implementing ways to organize, execute, and administer experimentation campaigns in a smart city context. We discuss the benefits of our framework, presenting some preliminary results. This is the first time such tools are paired with large-scale smart city infrastructures, enabling both city-scale experimentation and cross-site experimentation.This work was partially supported by the OrganiCity research project funded by the European Union, under the grant agreement No. 645198 of the Horizon 2020 research and innovation progra
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