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A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

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Frontiers of Computer Science 2023年第4期17卷 127-139页

作者： Jian AN Siyuan WU Xiaolin GUI Xin HE Xuejun ZHANG School of Computer Science and Technology Shaanxi Province Key Laboratory of Computer NetworkXi’an Jiaotong UniversityXi’an 710049China School of Software Henan UniversityKaifeng 475001China School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive *** the limitation of calculation power,edge computing is introduced to release unnecessary data *** edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data *** involved in one task are required to cooperate to process the task *** privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and *** crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is *** mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality ***,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this ***(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in ***,the final result is calculated without revealing the privacy of *** on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.

关键词： crowdsensing edge computing devices blockchain quality assessment reinforcement learning

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FIMI： A Constant Frugal Incentive Mechanism for Time WindowCoverage in Mobile crowdsensing

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Journal of Computer Science & Technology 2017年第5期32卷 919-935页

作者： Jia Xu Jian-Ren Fu De-Jun Yang Li-Jie Xu Lei Wang Tao Li School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Department of Computer Science Colorado School of Mines Golden 80401 U.S.A.

Mobile crowdsensing has become an efficient paradigm for performing large-scale sensing tasks. An incentive mechanism is important for a mobile crowdsensing system to stimulate participants and to achieve good service quality. In this paper, we explore truthful incentive mechanisms that focus on minimizing the total payment for a novel scenario, where the platform needs the complete sensing data in a requested time window (RTW). We model this scenario as a reverse auction and design FIMI, a constant frugal incentive mechanism for time window coverage. FIMI consists of two phases, the candidate selection phase and the winner selection phase. In the candidate selection phase, it selects two most competitive disjoint feasible user sets. Afterwards, in the winner selection phase, it finds all the interchangeable user sets through a graph-theoretic approach. For every pair of such user sets, FIMI chooses one of them by the weighted cost. Further, we extend FIMI to the scenario where the RTW needs to be covered more than once. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve the properties of RTW feasibility (or RTW multi-coverage), computation efficiency, individual rationality, truthfulness, and constant frugality.

关键词： crowdsensing incentive mechanism constant frugality

A Blockchain Based Mobile crowdsensing Market

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China Communications 2019年第6期16卷 31-41页

作者： Xin Wei Yong Yan Wei Jiang Jing Shen Xuesong Qiu State Key Laboratory of Networking and Switching Technology of Beijing University of Posts and Telecommunications Beijing 100876China State Grid Zhejiang Electric Power Research Institute Hangzhou 310014China Internet department of State Grid Corporation of China Beijing 100031China State Grid Henan Electric Power Company Information&Telecommunication Company Zhengzhou 450000China

Mobile crowdsensing(MCS)is an emerging pattern which means task initiators attract mobile users sensing with their own devices by some *** could exploit idle resources in low cost,while it has lots of flaws,which impede its ***,isolations between different MCS systems leads to wastage of social ***’s more,current MCS always operate in a centralized way,which causes it vulnerable and *** is a promising technology which could supply a credible and transparent *** paper construct a blockchain based MCS market and design smart contract for its *** our design,platform breaks isolation by blockchain,task initiators and mobile users manage their tasks by smart contract and bargain price with distributed *** this way,resource could be exploited better,and the market could be more ***’s more,the paper analyzes Walrasian Equilibrium(WE)in the market,and details how to deploy MCS in *** results shows that Equilibrium could be found.

关键词： mobile crowdsensing incentive mechanism blockchain Walrasian Equilibrium

An Incentive Mechanism Model for crowdsensing with Distributed Storage in Smart Cities

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Computers, Materials & Continua 2023年第8期76卷 2355-2384页

作者： Jiaxing Wang Lanlan Rui Yang Yang Zhipeng Gao Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijingChina

crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data *** crowdsensing relies on user equipment to consume resources to obtain information,and the quality and distribution of user equipment are uneven,crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data,which affects the widespread use of *** paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain,such as immutability and *** architecture for constructing a crowdsensing incentive mechanism under distributed incentives is proposed.A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the *** users upload data,determine data quality according to the algorithm,update user reputation,and realize the selection of perceived *** process of screening data and updating reputation value is realized by smart contracts,which ensures that the information cannot be tampered with,thereby encouraging more users to *** of the simulation show that using two algorithms can well reflect data quality and screen out malicious *** the help of blockchain performance,the architecture and algorithm can achieve decentralized storage and tamper-proof information,which helps to motivate more users to participate in perception tasks and improve data quality.

关键词： crowdsensing incentive mechanism blockchain smart contract

A Binary Rating Protocol for crowdsensing Applications 36

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A Binary Rating Protocol for Crowdsensing Applications

From one to crowd： a survey on crowdsourcing-based wireless indoor localization

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作者： Jianfeng Lu Qing Dai Jianmin Han Hao Peng Yun Xin Department of Computer Science and Engineering Zhejiang Normal University

ISBN: (纸本)9781538629185

crowdsensing,as a novel method of data collecting,environment sensing and information providing,has been widely used in place of the traditional wired sensor networks and wireless sensor network to tackle real-world challenges by leveraging mobile ***,the practicality of crowdsensing can be tampered with antisocial behavior,such as free-riding,false-reporting and ***,incentive mechanisms are needed to compel self-interested users to contribute *** this paper,we propose a rating protocol by integrating the reputation and pricing schemes based on game theory for crowdsensing,in which each participant is labeled with a binary rating to represent its social status,and users are encouraged to contribute good behaviors to increase their ratings and indirectly receive higher *** quantifying the sufficient and necessary conditions under which users comply with the rating protocol in their self-interests to do so,we formulate the rating protocol design problem,and further verify the protocol *** rigorously analysis the optimal social welfare,as well as the optimal design *** proposed protocol design for crowdsensing applications can achieve social benefit optimization and prevent the whitewashing problem.

关键词： crowdsensing free-ride self-interested rating protocol game theory

来源： cnki会议评论

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Frontiers of Computer Science 2018年第3期12卷 423-450页

作者： Xiaolei ZHOU Tao CHEN Deke GUO Xiaoqiang TENG Bo YUAN Nanjing Telecommunication Technology Research Institute National University of Defense Technology Nanjing 210089 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha 410073 China

Wireless indoor localization has attracted growing research interest in the mobile computing community for the last decade. Various available indoor signals, including radio frequency, ambient, visual, and motion signals, are extensively exploited for location estimation in indoor environments. The physical measurements of these signals, however, are still limited by both the resolution of devices and the spatial-temporal variability of the signals. One type of noisy signal complemented by another type of signal can benefit the wireless indoor localization in many ways, since these signals are related in their physics and independent in noise. In this article, we survey the new trend of integrating multiple chaotic signals to facilitate the creation of a crowdsourced localization system. Specifically, we first present a three-layer framework for crowdsourcing-based indoor localization by integrating multiple signals, and illustrate the basic methodology for making use of the available signals. Next, we study the mainstream signals involved in indoor localization approaches in terms of their characteristics and typical usages. Furthermore, considering multiple different outputs from different signals, we present significant insights to integrate them together, to achieve localizability in different sce- narios.

关键词： Wireless indoor localization crowdsourcingsystem crowdsensing

Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

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Digital Communications and Networks 2024年第3期10卷 666-675页

作者： Lizong Zhang Yiming Wang Ke Yan Yi Su Nawaf Alharbe Shuxin Feng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Beijing Institute of Remote Sensing Equipment Beijing100039China Applied College Taibah UniversityMedina42353Saudi Arabia

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour *** applications have dramatically increased the diversity of IoT ***,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal *** recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial ***,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in *** this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing *** particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and *** experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

关键词： Behaviour recognition Motion features Attention mechanism Internet of things crowdsensing

Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

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Digital Communications and Networks 2023年第1期9卷 280-291页

作者： Rui Liu Jianping Pan Department of Computer Science University of VictoriaVictoriaBCV8P 5C2Canada

Air pollution has become a global concern for many *** crowdsensing systems make it possible to monitor air quality at a fine *** better utilize the sensory data with varying credibility,truth discovery frameworks are ***,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth *** the privacy of participant vehicles is also a crucial *** first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity *** further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data *** frameworks are more lightweight than the existing cryptography-based *** also evaluate the work with simulations and fully discuss the performance and possible extensions.

关键词： Privacy preserving Truth discovery crowdsensing Vehicular networks

Budget-Aware Dynamic Incentive Mechanism in SpatialCrowdsourcing

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Journal of Computer Science & Technology 2017年第5期32卷 890-904页

作者： Jia-Xu Liu Yu-Dian Ji Wei-Feng Lv Ke Xu State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Software Liaoning Technical University Huludao 125105 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China

The ubiquitous deployment of GPS-equipped devices and mobile networks has spurred the popularity of spatial crowdsourcing. Many spatial crowdsourcing tasks require crowd workers to collect data from different locations. Since workers tend to select locations nearby or align to their routines, data collected by workers are usually unevenly distributed across the region. To encourage workers to choose remote locations so as to avoid imbalanced data collection, we investigate the incentive mechanisms in spatial crowdsourcing. We propose a price adjustment function and two algorithms, namely DFBA (Dynamic Fixed Budget Allocation) and DABA (Dynamic Adjusted Budget Allocation), which utilize price leverage to mitigate the imbalanced data collection problem. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed incentive mechanisms are able to effectively balance the popularity of different locations.

关键词： incentive mechanism crowdsensing spatial crowdsourcing