Crowdsensing

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The term crowdsensing refers to sharing data collected by sensing devices with the aim to measure a phenomena of common interest. (Wikipedia.org)






Conferences related to Crowdsensing

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2018 19th IEEE International Conference on Mobile Data Management (MDM)

The conference aims to attract original research contributions in the interesection of mobile computing and data management. Topics of interest include, but not limited to:- Mobile Cloud Computing and Data Management - Data Management for Internet of Things (IoT) and Sensor Systems- Data Management for Augmented Reality Systems- Data Management for Intelligent Transportation Systems, Smart Spaces- Mobile Crowd-Sourcing and Crowd-Sensing- Mobile Data Analytics- Behavioural/Activity Sensing and Analytics- Mobile Location-Based Social Networks- Mobile Recommendation Systems- Context-aware Computing for Intelligent Mobile Services- Middleware and Tools for Mobile and Pervasive Computing- Theoretical Foundations of Data-intensive Mobile Computing- Data Stream Processing in Mobile/Sensor Networks- Indexing, Optimisation and Query Processing for Moving Objects/Users- Security and Privacy in Mobile Systems

  • 2017 18th IEEE International Conference on Mobile Data Management (MDM)

    Mobile computing and data management

  • 2016 17th IEEE International Conference on Mobile Data Management (MDM)

    The Mobile Data Management series of conferences first debuted in December 1999. Since inception, it has established itself as a prestigious forum to exchange innovative and significant research results in mobile data management. Comprising both research and industry tracks, it serves as an important bridge between academic researchers and industry researchers. Along with the presentations of research publications, it also serves as a meeting place for technical demonstrations (Demos), workshops, panel discussions as well as PhD forum and Industrial forum to cater PhD students and industrial developers.The conference focuses on research contributions in data management in mobile, ubiquitous and pervasive computing.

  • 2015 16th IEEE International Conference on Mobile Data Management (MDM)

    The MDM series of conferences, since its debut in December 1999, has established itself as a prestigious forum for the exchange of innovative and significant research results in mobile data management. The term mobile in MDM has been used from the very beginning in a broad sense to encompass all aspects of mobility related to wireless, portable and tiny devices. The conference provides unique opportunities for researchers, engineers, practitioners, developers, and users to explore new ideas, techniques, and tools, and to exchange experiences.

  • 2014 15th IEEE International Conference on Mobile Data Management (MDM)

    The MDM series of conferences, since its debut in December 1999, has established itself as a prestigious forum for the exchange of innovative and significant research results in mobile data management. The term mobile in MDM has been used from the very beginning in a broad sense to encompass all aspects of mobility

  • 2013 14th IEEE International Conference on Mobile Data Management (MDM)

    The MDM series of conferences, since its debut in December 1999, has established itself as a prestigious forum for the exchange of innovative and significant research results in mobile data management. The term mobile in MDM has been used from the very beginning in a broad sense to encompass all aspects of mobility - aspects related to wireless, portable and tiny devices. The conference provides unique opportunities for researchers, engineers, practitioners, developers, and users to explore new ideas, techniques, and tools, and to exchange experiences.

  • 2012 13th IEEE International Conference on Mobile Data Management (MDM)

    The MDM series of conferences, since its debut in December 1999, has established itself as a prestigious forum for the exchange of innovative and significant research results in mobile data management. The term mobile in MDM has been used from the very beginning in a broad sense to encompass all aspects of mobility - aspects related to wireless, portable and tiny devices. The conference provides unique opportunities for researchers, engineers, practitioners, developers, and users to explore new ideas, techniques, and tools, and to exchange experiences.

  • 2011 12th IEEE International Conference on Mobile Data Management (MDM)

    The MDM series of conferences, since its debut in December 1999, has established itself as a prestigious forum for the exchange of innovative and significant research results in mobile data management. The term mobile in MDM has been used from the very beginning in a broad sense to encompass all aspects of mobility - aspects related to wireless, portable and tiny devices. The conference provides unique opportunities for researchers, engineers, practitioners, developers, and users to explore new ideas.

  • 2010 11th International Conference on Mobile Data Management (MDM)

    The annual MDM conference is a leading international forum that focuses on data management for mobile, ubiquitous, and pervasive computing. It brings together a wide range of researchers, practitioners, and users to explore scientific and industrial challenges that arise in the areas of data management and mobile computing.

  • 2008 9th International Conference on Mobile Data Management (MDM)

  • 2007 International Conference on Mobile Data Management (MDM)

  • 2006 International Conference on Mobile Data Management (MDM)

  • 2004 IEEE International Conference on Mobile Data Management (MDM)



Periodicals related to Crowdsensing

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Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


Communications Surveys & Tutorials, IEEE

Each tutorial reviews currents communications topics in network management and computer and wireless communications. Available tutorials, which are 2.5 to 5 hours in length contains the original visuals and voice-over by the presenter. IEEE Communications Surveys & Tutorials features two distinct types of articles: original articles and reprints. The original articles are exclusively written for IEEE Communications Surveys & Tutorials ...


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


Industrial Electronics, IEEE Transactions on

Theory and applications of industrial electronics and control instrumentation science and engineering, including microprocessor control systems, high-power controls, process control, programmable controllers, numerical and program control systems, flow meters, and identification systems.


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Most published Xplore authors for Crowdsensing

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Xplore Articles related to Crowdsensing

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Secure mobile crowdsensing based on deep learning

China Communications, 2018

To improve the quality of multimedia services and stimulate secure sensing in Internet of Things applications, such as healthcare and traffic monitoring, mobile crowdsensing (MCS) systems must address security threats such as jamming, spoofing and faked sensing attacks during both sensing and information exchange processes in large-scale dynamic and heterogeneous networks. In this article, we investigate secure mobile crowdsensing and ...


Vita: A Crowdsensing-Oriented Mobile Cyber-Physical System

IEEE Transactions on Emerging Topics in Computing, 2013

As a prominent subcategory of cyber-physical systems, mobile cyber-physical systems could take advantage of widely used mobile devices, such as smartphones, as a convenient and economical platform that facilitates sophisticated and ubiquitous mobile sensing applications between humans and the surrounding physical world. This paper presents Vita, a novel mobile cyber-physical system for crowdsensing applications, which enables mobile users to perform ...


Anchor-Assisted and Vote-Based Trustworthiness Assurance in Smart City Crowdsensing

IEEE Access, 2016

Smart city sensing calls for crowdsensing via mobile devices that are equipped with various built-in sensors. As incentivizing users to participate in distributed sensing is still an open research issue, the trustworthiness of crowdsensed data is expected to be a grand challenge if this cloud-inspired recruitment of sensing services is to be adopted. Recent research proposes reputation-based user recruitment models ...


Exploiting Social Trust Assisted Reciprocity (STAR) Toward Utility-Optimal Socially-Aware Crowdsensing

IEEE Transactions on Signal and Information Processing over Networks, 2015

Mobile crowdsensing takes advantage of pervasive mobile devices to collect and process data for a variety of applications (e.g., traffic monitoring and spectrum sensing). In this study, a socially-aware crowdsensing system is advocated in which a cloud-based platform incentivizes mobile users to participate in sensing tasks by leveraging social trust among users, upon receiving sensing requests. For this system, social ...


Toward Optimum Crowdsensing Coverage With Guaranteed Performance

IEEE Sensors Journal, 2016

Mobile crowdsensing networks have emerged to show elegant data collection capability in loosely cooperative network. However, in the sense of coverage quality, marginal works have considered the efficient (less participants) and effective (more coverage) designs for mobile crowdsensing network. We investigate the optimal coverage problem in distributed crowdsensing networks. In that, the sensing quality and the information delivery are jointly ...


More Xplore Articles

Educational Resources on Crowdsensing

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IEEE.tv Videos

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IEEE-USA E-Books

  • Secure mobile crowdsensing based on deep learning

    To improve the quality of multimedia services and stimulate secure sensing in Internet of Things applications, such as healthcare and traffic monitoring, mobile crowdsensing (MCS) systems must address security threats such as jamming, spoofing and faked sensing attacks during both sensing and information exchange processes in large-scale dynamic and heterogeneous networks. In this article, we investigate secure mobile crowdsensing and present ways to use deep learning (DL) methods, such as stacked autoencoder, deep neural networks, convolutional neural networks, and deep reinforcement learning, to improve approaches to MCS security, including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS. We discuss the performance gain of these DL-based approaches compared to traditional security schemes and identify the challenges that must be addressed to implement these approaches in practical MCS systems.

  • Vita: A Crowdsensing-Oriented Mobile Cyber-Physical System

    As a prominent subcategory of cyber-physical systems, mobile cyber-physical systems could take advantage of widely used mobile devices, such as smartphones, as a convenient and economical platform that facilitates sophisticated and ubiquitous mobile sensing applications between humans and the surrounding physical world. This paper presents Vita, a novel mobile cyber-physical system for crowdsensing applications, which enables mobile users to perform mobile crowdsensing tasks in an efficient manner through mobile devices. Vita provides a flexible and universal architecture across mobile devices and cloud computing platforms by integrating the service- oriented architecture with resource optimization mechanism for crowdsensing, with extensive supports to application developers and end users. The customized platform of Vita enables intelligent deployments of tasks between humans in the physical world, and dynamic collaborations of services between mobile devices and cloud computing platform during run-time of mobile devices with service failure handling support. Our practical experiments show that Vita performs its tasks efficiently with a low computation and communication overhead on mobile devices, and eases the development of multiple mobile crowdsensing applications and services. In addition, we present a mobile crowdsensing application, Smart City, developed on Vita to demonstrate the functionalities and practical usage of Vita.

  • Anchor-Assisted and Vote-Based Trustworthiness Assurance in Smart City Crowdsensing

    Smart city sensing calls for crowdsensing via mobile devices that are equipped with various built-in sensors. As incentivizing users to participate in distributed sensing is still an open research issue, the trustworthiness of crowdsensed data is expected to be a grand challenge if this cloud-inspired recruitment of sensing services is to be adopted. Recent research proposes reputation-based user recruitment models for crowdsensing; however, there is no standard way of identifying adversaries in smart city crowdsensing. This paper adopts previously proposed vote-based approaches, and presents a thorough performance study of vote-based trustworthiness with trusted entities that are basically a subset of the participating smartphone users. Those entities are called trustworthy anchors of the crowdsensing system. Thus, an anchor user is fully trustworthy and is fully capable of voting for the trustworthiness of other users, who participate in sensing of the same set of phenomena. Besides the anchors, the reputations of regular users are determined based on vote-based (distributed) reputation. We present a detailed performance study of the anchor-based trustworthiness assurance in smart city crowdsensing through simulations, and compare it with the purely vote-based trustworthiness approach without anchors, and a reputation-unaware crowdsensing approach, where user reputations are discarded. Through simulation findings, we aim at providing specifications regarding the impact of anchor and adversary populations on crowdsensing and user utilities under various environmental settings. We show that significant improvement can be achieved in terms of usefulness and trustworthiness of the crowdsensed data if the size of the anchor population is set properly.

  • Exploiting Social Trust Assisted Reciprocity (STAR) Toward Utility-Optimal Socially-Aware Crowdsensing

    Mobile crowdsensing takes advantage of pervasive mobile devices to collect and process data for a variety of applications (e.g., traffic monitoring and spectrum sensing). In this study, a socially-aware crowdsensing system is advocated in which a cloud-based platform incentivizes mobile users to participate in sensing tasks by leveraging social trust among users, upon receiving sensing requests. For this system, social trust assisted reciprocity (STAR), a synergistic marriage of social trust and reciprocity, is exploited to design an incentive mechanism that stimulates users' participation. Given the social trust structure among users, the efficacy of STAR for satisfying users' sensing requests is thoroughly investigated. Specifically, it is first shown that all requests can be satisfied if and only if sufficient social credit can be “transferred” from users who request more sensing service than they can provide to users who can provide more than they request. Then utility maximization for sensing services under STAR is investigated, and it is shown that it reduces to maximizing the utility of a circulation flow in the combined social graph and request graph. Accordingly, an algorithm that iteratively cancels a cycle of positive weight in the residual graph is developed, which computes the optimal solution efficiently, for both cases of divisible and indivisible sensing service. Extensive simulation results corroborate that STAR can significantly outperform the mechanisms using social trust only or reciprocity only.

  • Toward Optimum Crowdsensing Coverage With Guaranteed Performance

    Mobile crowdsensing networks have emerged to show elegant data collection capability in loosely cooperative network. However, in the sense of coverage quality, marginal works have considered the efficient (less participants) and effective (more coverage) designs for mobile crowdsensing network. We investigate the optimal coverage problem in distributed crowdsensing networks. In that, the sensing quality and the information delivery are jointly considered. Different from the conventional coverage problem, ours only select a subset of mobile users, so as to maximize the crowdsensing coverage with limited budget. We formulate our concerns as an optimal crowdsensing coverage problem, and prove its NP-completeness. In tackling this difficulty, we also prove the submodular property in our problem. Leveraging the favorable property in submodular optimization, we present the greedy algorithm with approxima√ tion ratio O( √k), where k is the number of selected users. Such that the information delivery and sensing coverage ratio could be guaranteed. Finally, we make extensive evaluations for the proposed scheme, with trace- driven tests. Evaluation results show that the proposed scheme could outperform the random selection by 2× with a random walk model, and over 3× with real trace data, in terms of crowdsensing coverage. Besides, the proposed scheme achieves near optimal solution comparing with the bruteforce search results.

  • Jump-start crowdsensing: A three-layer incentive framework for mobile crowdsensing

    In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia. Recently, plenty of mobile crowdsensing applications come forth, such as indoor positioning, environment monitoring, transportation, and so on. However, most existing mobile crowdsensing systems lack of vast user bases, and thus urgently need appropriate incentive mechanisms to attract mobile users to guarantee the service quality. In this paper, we propose to incorporate sensing platform and social network applications, which already have large user bases to build a three-layer network model. Thus, we can publicize the sensing platform promptly in large scale, and provide longterm guarantee of data sources. Based on a three-layer network model, we design incentive mechanisms for both intermediaries and the crowdsensing platform, and provide a solution to cope with the problem of user overlapping among intermediaries. We indicate the properties of our proposed incentive mechanisms, including incentive compatibility, individual rationality, and efficiency.

  • ParticipAct: A Large-Scale Crowdsensing Platform

    In recent years, the widespread availability of sensor-provided smartphones has enabled the possibility of harvesting large quantities of data in urban areas exploiting user devices, so enabling the so-called crowdsensing that allows to realize complex applications impossible without the involvement of the research community. While many efforts have been made to improve specific techniques - spanning from signal processing to the assignment of data collection campaigns to users, and to the entire data processing - to the best of our knowledge, there are no active experiments aimed to explore the challenging issues raised by the management of large-scale crowdsensing campaigns as real-world experiments. This paper presents the ParticipAct platform and its ParticipAct living lab, an ongoing experiment at the University of Bologna that involves 170 students for one year in several crowdsensing campaigns that can access passively smartphone sensors and also prompt for user active collaboration. In this paper, we describe the guidelines behind the design of ParticipAct, its features, its architecture, and report quantitative results that assess and confirm the feasibility, obtained via intelligent coordination and management of crowdsensing campaigns.

  • Demo Abstract: Walkway Discovery from Large Scale Crowdsensing

    Existing digital maps mainly focus on motorways and miss many walkways facilitating people's daily mobility especially for pedestrians. Based on in- depth analysis of massive human mobility trajectories collected from the National Science Experiment (NSE) in Singapore, we propose a system, discovering walkways from the large scale crowdsensing mobility data. In this demo, we show the new-found walkways discovered by our system in Singapore through a custom visualization platform.

  • Mobile crowdsensing game in vehicular networks

    Vehicular crowdsensing takes advantage of the mobility of vehicles to provide location-based services in large-scale areas. In this paper, we analyze vehicular crowdsensing and formulate the interactions between a crowdsensing server and a number of vehicles equipped with sensors in the area of interest as a vehicular crowdsensing game. Each participant vehicle chooses its sensing strategy based on the sensing and transmission costs, and the expected payment by the server, while the server determines its payment policy according to the number and accuracy of the sensing reports. A reinforcement learning based crowdsensing strategy is proposed for vehicular networks, with incomplete system parameters such as the sensing costs of the other vehicles. The server and vehicles achieve their optimal payment and sensing strategies by learning via trials, respectively. Simulation results have verified the efficiency of the proposed mobile crowdsensing systems, showing that the average utilities of the vehicles and the server can be improved and converged to the optimal values in fast speed. Vehicles with less sensing costs are motivated to upload more accurate sensing data.

  • Privacy-Preserving Incentive Mechanisms for Mobile Crowdsensing

    Ubiquitous smartphones with various built-in sensors have boosted development of many crowdsensing applications in recent years. To achieve good performance with this class of applications, a large number of participating users with sensed data are needed. Two important considerations for improving user participation in crowdsensing are the incentive mechanism for motivating users to contribute their sensing capabilities, and the techniques for protecting user privacy. In this article, we survey the works that address these issues by integrating privacy techniques with incentive mechanisms. We also advocate two future research directions to enhance incentive mechanisms for mobile crowdsensing.



Standards related to Crowdsensing

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