In urban fields, Mobile Wireless Sensor Networks (MWSNs) become ubiquitous. Accurate GPS positioning for sensors is a fundamental problem for MWSNs. To solve this problem, this paper proposes a ...Crowdsourcing-Aided Positioning scheme, which takes an ideal situation and a more realistic situation into account. In the ideal situation, all participants are considered accurate. Then, two optimization objectives are addressed for the efficient Crowdsourcing-Aided Positioning task. Their utility functions are proven to be submodular and a greedy algorithm is given to solve them. In the more realistic situation, randomly selected participants cannot guarantee the accuracy of the data. We propose a data-accuracy-calibration-based participant selection framework to solve this dilemma. Through data accuracy calibration, participants gain their data accuracy and reliability with the help of wireless sensor networks. First, we design three kinds of data accuracy calibration methods based on probabilistic models. Then, we propose a Truthful-Data-Driven Participant Selection problem, which tends to raise the data accuracy and reliability. The optimization problem is proved to be NP-hard and its optimization function has submodular property. We give a greedy algorithm with <inline-formula> <tex-math notation="LaTeX">1-\frac {1}{e} </tex-math></inline-formula> approximation ratio to solve this problem. Simulation experiments are conducted to validate the algorithmic effectiveness at last.
The problem of estimating event truths from conflicting agent opinions in a social network is investigated. An autoencoder learns the complex relationships between event truths, agent reliabilities ...and agent observations. A Bayesian network model is proposed to guide the learning process by modeling the relationship of the autoencoder's outputs with different variables. At the same time, it also models the social relationships between agents in the network. The proposed approach is unsupervised and is applicable when ground truth labels of events are unavailable. A variational inference method is used to jointly estimate the hidden variables in the Bayesian network and the parameters in the autoencoder. Experiments on three real datasets demonstrate that our proposed approach is competitive with, and in most cases better than, several state-of-the-art benchmark methods.
Two goals of network science are to (i) uncover fundamental properties of phenomena modeled as networks, and to (ii) explore novel use of networks as models for a diverse range of systems and ...phenomena in order to improve our understanding of such systems and phenomena. This paper advances the latter direction by casting credibility estimation in social sensing applications as a network science problem, and by presenting a network model that helps understand the fundamental accuracy trade-offs of a credibility estimator. Social sensing refers to data collection scenarios, where observations are collected from (possibly unvetted) human sources. We call such observations claims to emphasize that we do not know whether or not they are factually correct. Predictable, scalable and robust estimation of both source reliability and claim correctness, given neither in advance, becomes a key challenge given the unvetted nature of sources and lack of means to verify their claims. In a previous conference publication, we proposed a maximum likelihood approach to jointly estimate both source reliability and claim correctness. We also derived confidence bounds to quantify the accuracy of such estimation. In this paper, we cast credibility estimation as a network science problem and offer systematic sensitivity analysis of the optimal estimator to understand its fundamental accuracy trade-offs as a function of an underlying network topology that describes key problem space parameters. It enables assured social sensing, where not only source reliability and claim correctness are estimated, but also the accuracy of such estimates is correctly predicted for the problem at hand.
The truth discovery approach can effectively obtain ground truth from the conflict sensing data provided by multiple participants, but privacy leakages restrict the enthusiasm of users for ...participating in the MCS, ESPPTD:an efficient slicing-based privacy-preserving truth discovery is proposed, which updates user weights and evaluation object truth values through secure iterations. ESPPTD first divides users into several clusters according to their location and number. The nodes in each cluster divide the sensing observation data into two parts according to the Extended Euclidean algorithm, and one part of the data is sent to any other node in the cluster. Secondly, the node mixes the received slice data with the remaining slice data and sends them to the cluster head node to ensure the privacy. Finally, the cluster head merges the slice data of all nodes in the cluster and uploads it to the server, and the node completes the weight update according to the aggregation result broadcast by the server, and the server cannot obtain the sensing observation data and weight of a single user. The truth evaluation is performed in the same way. In addition, in order to further reduce the user’s communication overhead and enhance the practicability of the system, this paper proposes an improved slicing-based privacy-preserving truth discovery in mobile crowd sensing based on ESPPTD, which eliminates slices. The forwarding requirement reduces the amount of computation and communication while ensuring data privacy. Experimental results show that ESPPTD can identify true and reliable data information while protecting data privacy.
In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data ...sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects from various sources is essential and urgent. In this paper, we aim to deliver a comprehensive and exhaustive survey on truth discovery problems from the perspectives of concepts, methods, applications, and opportunities. We first systematically review and compare problems from objects, sources, and observations. Based on these problem properties, different methods are analyzed and compared in depth from observation with single or multiple values, independent or dependent sources, static or dynamic sources, and supervised or unsupervised learning, followed by the surveyed applications in various scenarios. For future studies in truth discovery fields, we summarize the code sources and datasets used in above methods. Finally, we point out the potential challenges and opportunities on truth discovery, with the goal of shedding light and promoting further investigation in this area.
In this paper, we explore data poisoning attacks and their defenses in local differential privacy (LDP)-based crowdsensing systems. First, we construct data poisoning attacks launched by corrupted ...workers to subvert crowdsensing results by tampering information reported. Specifically, the attacks are formulated as a bi-level optimization problem where attackers strive to conceal their malicious behavior by delicately exploiting noise perturbation introduced by LDP protocols. In this way, the attacks can not be detected, even with the weight-based truth discovery methods. Due to the NP-hard nature of the bi-level problem, we decompose it into upper-level and lower-level sub-problems and employ the augmented Lagrangian method to iteratively solve them, ultimately identifying optimal attack strategies. Second, we propose corresponding countermeasures to defend against the attacks. The countermeasures are formulated as a minimization problem, with the objective of minimizing disruptions caused by attacks through the identification and removal of corrupted workers from crowdsensing systems. To solve the problem, we utilize a differential evolution algorithm instead of gradient-based methods since the objective function of the problem is not differentiable. Extensive experiments on real-world datasets are conducted to evaluate the performance of the proposed attacks and defenses. The evaluation results demonstrate that LDP perturbation indeed facilitates the success of data poisoning attacks, and the proposed defenses can accurately distinguish malicious behaviors disguised.
The truth discovery problem involves identifying the truth associated with objects from observations, which is crucial in health recommendation systems. While many methods effectively calculate the ...truth from multiple data sources, few address the challenges posed by dynamically generated data. In dynamic environments, data continually arrive and require processing in time due to the limited memory of health monitoring devices. In this paper, we propose a novel approach to discovering truths for health recommendation systems that uses time-series analysis methods to mine the evolutionary patterns of truths and account for potential similarities between objects. Our approach efficiently manages dynamic data without compromising estimation accuracy. We evaluate the performance of our proposed framework on two real-world datasets and a synthetic dataset, demonstrating its superiority over state-of-the-art methods in terms of estimation accuracy and efficiency. This approach has great potential to advance the development of more accurate and personalized health recommendation systems.
With the rapid development of embedded smart devices, a new data collection paradigm, mobile crowd-sensing (MCS), has been proposed. MCS allows individuals from the crowd to act as sensors and ...contribute their observation data. However, existing MCS systems are mostly based on third-party platforms, and there is no guarantee that a center is completely credible. In addition, security and privacy issues should not be ignored. During MCS' execution, the participants' various information and truth value are usually exposed, and the computation related to data privacy cannot be verified. In this paper, we integrate the blockchain into the MCS scenario to design a blockchain based privacy-preserving quality control mechanism, which prevents data from being tampered with, and denied, ensuring that the reward is distributed fairly. In the new system, we propose a privacy preserving participant selection scheme and the result can be verified (i.e., security against malicious node) without any third-party arbiter. Finally, considering the issues with sensing data privacy and efficiency in the truth discovery process, we propose a new privacy-aware crowdsensing design with iterative truth discovery based on rational secure multi-party computation. The experimental results show that compared to the prior result, the proposed solutions are highly practical and facilitate quality control without violating the participant's privacy.
In the era of big data, information can be collected from many sources. Unfortunately, the information provided by the multiple sources on the same object is usually conflicting. In light of this ...challenge, truth discovery has emerged and used in many applications. The advantage of truth discovery is that it incorporates source reliabilities to infer object truths. Many existing methods for truth discovery are proposed with many traits. However, most of them ignore the characteristic of object correlations in data and focus on static data only. Object correlations exist in many applications. In this work, we propose a probabilistic truth discovery model that considers not only source reliability but also object correlations. This is especially useful when objects only claimed by few sources, which is common for many real applications. Furthermore, an incremental truth discovery method that considers object correlations is also developed when data provided by multiple sources arrives sequentially. Truth can be inferred dynamically without revisiting historical data, and temporal correlation is considered for truth inference. The experiments on both real-world and synthetic datasets demonstrate that the proposed methods perform better than the existing truth discovery methods.
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