Weizheng Wang, Junwei Zhang, Qing Wang, Marco Zúñiga
Localization based on visible light is gaining significant attention. But most existing studies rely on a key requirement: the object of interest needs to carry an optical receiver (camera or photodiode). We remove this requirement and investigate the possibility of achieving accurate localization in a passive manner, that is, without requiring objects to carry any optical receiver. To achieve this goal, we exploit the reflective surfaces of objects and the unique propagation properties of LED luminaires. We present geometric models, a testbed implementation, and empirical evaluations to showcase the opportunities and challenges posed by this new type of localization. Overall, we show that our method can track with high accuracy (few centimeters) a subset of an object's trajectory and it can also identify passively the object's ID.
{"title":"Leveraging Smart Lights for Passive Localization","authors":"Weizheng Wang, Junwei Zhang, Qing Wang, Marco Zúñiga","doi":"10.1109/MASS.2018.00049","DOIUrl":"https://doi.org/10.1109/MASS.2018.00049","url":null,"abstract":"Localization based on visible light is gaining significant attention. But most existing studies rely on a key requirement: the object of interest needs to carry an optical receiver (camera or photodiode). We remove this requirement and investigate the possibility of achieving accurate localization in a passive manner, that is, without requiring objects to carry any optical receiver. To achieve this goal, we exploit the reflective surfaces of objects and the unique propagation properties of LED luminaires. We present geometric models, a testbed implementation, and empirical evaluations to showcase the opportunities and challenges posed by this new type of localization. Overall, we show that our method can track with high accuracy (few centimeters) a subset of an object's trajectory and it can also identify passively the object's ID.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of appropriate participants from the huge user pool to perform the tasks. However, the capabilities of individual participants are usually unknown by the selection mechanism, which leads to the most challenging issue of participant selection. While online learning techniques can be used to learn the participant's capability, the diverse expertise of each individual makes a single capability metric is not sufficient. To address the multi-expertise of participants, in this paper we introduce a new self-learning architecture which leverages the historical performing records of participants to learn the different capabilities (both sensing probability and time delay) of participants. Formulating the participant selection problem as a combinational multi-armed bandit problem, we present an online participant selection algorithm with both performance guarantee and bounded regret. Extensive simulations with a real-world mobile dataset demonstrate the efficiency of the proposed solution.
{"title":"Multi-expertise Aware Participant Selection in Mobile Crowd Sensing via Online Learning","authors":"Hanshang Li, Ting Li, Fan Li, Song Yang, Yu Wang","doi":"10.1109/MASS.2018.00067","DOIUrl":"https://doi.org/10.1109/MASS.2018.00067","url":null,"abstract":"With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of appropriate participants from the huge user pool to perform the tasks. However, the capabilities of individual participants are usually unknown by the selection mechanism, which leads to the most challenging issue of participant selection. While online learning techniques can be used to learn the participant's capability, the diverse expertise of each individual makes a single capability metric is not sufficient. To address the multi-expertise of participants, in this paper we introduce a new self-learning architecture which leverages the historical performing records of participants to learn the different capabilities (both sensing probability and time delay) of participants. Formulating the participant selection problem as a combinational multi-armed bandit problem, we present an online participant selection algorithm with both performance guarantee and bounded regret. Extensive simulations with a real-world mobile dataset demonstrate the efficiency of the proposed solution.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126120428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Victoria G. Crawford, Alan Kuhnle, M. A. Alim, M. Thai
Previous approaches to caching for Device-to-Device (D2D) communication cache popular files during off-peak hours. Since the popularity of content may evolve quickly or be unavailable in advance, we propose a flexible approach to cellular device caching where files are cached or uncached dynamically as file popularity evolves Dynamic caching motivates a space-efficient optimization problem Minimum File Placement (MFP), which is to cache a single file in the least amount of cache space to ensure a specified cache hit rate. In order to estimate the future cache hit rate, we use historical heterogeneous contact and request patterns of the devices. We present a bicriteria greedy algorithm for MFP and incorporate this algorithm into a dynamic approach to caching from a library of files with evolving popularity distribution. In an extensive experimental evaluation, we analyze the effectiveness of our approach to mobile device caching and demonstrate its advantages over other static contact-pattern-aware caching and alternative dynamic approaches.
{"title":"Space-Efficient and Dynamic Caching for D2D Networks of Heterogeneous Users","authors":"Victoria G. Crawford, Alan Kuhnle, M. A. Alim, M. Thai","doi":"10.1109/MASS.2018.00054","DOIUrl":"https://doi.org/10.1109/MASS.2018.00054","url":null,"abstract":"Previous approaches to caching for Device-to-Device (D2D) communication cache popular files during off-peak hours. Since the popularity of content may evolve quickly or be unavailable in advance, we propose a flexible approach to cellular device caching where files are cached or uncached dynamically as file popularity evolves Dynamic caching motivates a space-efficient optimization problem Minimum File Placement (MFP), which is to cache a single file in the least amount of cache space to ensure a specified cache hit rate. In order to estimate the future cache hit rate, we use historical heterogeneous contact and request patterns of the devices. We present a bicriteria greedy algorithm for MFP and incorporate this algorithm into a dynamic approach to caching from a library of files with evolving popularity distribution. In an extensive experimental evaluation, we analyze the effectiveness of our approach to mobile device caching and demonstrate its advantages over other static contact-pattern-aware caching and alternative dynamic approaches.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"142 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129270263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingao Xu, Zheng Yang, Hengjie Chen, Yunhao Liu, Xiancun Zhou, Jianbo Li, N. Lane
Indoor localization gains increasingly attentions in the era of Internet of Things. Among various technologies, WiFi-based systems that leverage Received Signal Strengths (RSSs) as location fingerprints become the mainstream solutions. However, RSS fingerprints suffer from critical drawbacks of spatial ambiguity and temporal instability that root in multipath effects and environmental dynamics, which degrade the performance of these systems and therefore impede their wide deployment in real world. Pioneering works overcome these limitations at the costs of ubiquity as they mostly resort to additional information or extra user constraints. In this paper, we present the design and implementation of MatLoc, an indoor localization system purely based on WiFi fingerprints, which jointly mitigates spatial ambiguity and temporal instability and derives reliable performance without impairing the ubiquity. The key idea is to embrace the spatial awareness of RSS values in a novel form of RSS Spatial Gradient (RSG) matrix for enhanced WiFi fingerprints. We devise techniques for the representation, construction, and comparison of the proposed fingerprint form, and integrate them all in a practical system, which follows the classical fingerprinting framework and requires no more inputs than any previous RSS fingerprint based systems. Extensive experiments in different environments demonstrate that MatLoc significantly improves the accuracy in both localization and tracking scenarios by about 30% to 50% compared with five state-of-the-art approaches.
{"title":"Embracing Spatial Awareness for Reliable WiFi-Based Indoor Location Systems","authors":"Jingao Xu, Zheng Yang, Hengjie Chen, Yunhao Liu, Xiancun Zhou, Jianbo Li, N. Lane","doi":"10.1109/MASS.2018.00050","DOIUrl":"https://doi.org/10.1109/MASS.2018.00050","url":null,"abstract":"Indoor localization gains increasingly attentions in the era of Internet of Things. Among various technologies, WiFi-based systems that leverage Received Signal Strengths (RSSs) as location fingerprints become the mainstream solutions. However, RSS fingerprints suffer from critical drawbacks of spatial ambiguity and temporal instability that root in multipath effects and environmental dynamics, which degrade the performance of these systems and therefore impede their wide deployment in real world. Pioneering works overcome these limitations at the costs of ubiquity as they mostly resort to additional information or extra user constraints. In this paper, we present the design and implementation of MatLoc, an indoor localization system purely based on WiFi fingerprints, which jointly mitigates spatial ambiguity and temporal instability and derives reliable performance without impairing the ubiquity. The key idea is to embrace the spatial awareness of RSS values in a novel form of RSS Spatial Gradient (RSG) matrix for enhanced WiFi fingerprints. We devise techniques for the representation, construction, and comparison of the proposed fingerprint form, and integrate them all in a practical system, which follows the classical fingerprinting framework and requires no more inputs than any previous RSS fingerprint based systems. Extensive experiments in different environments demonstrate that MatLoc significantly improves the accuracy in both localization and tracking scenarios by about 30% to 50% compared with five state-of-the-art approaches.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128480035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a secure lightweight and thing-centered IoT communication system based on MQTT, SecT, in which a device/thing authenticates users. Compared with a server-centered IoT system in which a cloud server authenticates users, a thing-centered system preserves user privacy since the cloud server is primarily a relay between things and users and does not store or see user data in plaintext. The contributions of this work are three-fold. First, we explicitly identify critical functionalities in bootstrapping a thing and design secure pairing and binding strategies. Second, we design a strategy of end-to-end encrypted communication between users and things for the sake of user privacy and even the server cannot see the communication content in plaintext. Third, we design a strong authentication system that can defeat known device scanning attack, brute force attack and device spoofing attack against IoT. We implemented a prototype of SecT on a $10 Raspberry Pi Zero W and performed extensive experiments to validate its performance. The experiment results show that SecT is both cost-effective and practical. Although we design SecT for the smart home application, it can be easily extended to other IoT application domains.
{"title":"SecT: A Lightweight Secure Thing-Centered IoT Communication System","authors":"Chao Gao, Z. Ling, Biao Chen, Xinwen Fu, Wei Zhao","doi":"10.1109/MASS.2018.00018","DOIUrl":"https://doi.org/10.1109/MASS.2018.00018","url":null,"abstract":"In this paper, we propose a secure lightweight and thing-centered IoT communication system based on MQTT, SecT, in which a device/thing authenticates users. Compared with a server-centered IoT system in which a cloud server authenticates users, a thing-centered system preserves user privacy since the cloud server is primarily a relay between things and users and does not store or see user data in plaintext. The contributions of this work are three-fold. First, we explicitly identify critical functionalities in bootstrapping a thing and design secure pairing and binding strategies. Second, we design a strategy of end-to-end encrypted communication between users and things for the sake of user privacy and even the server cannot see the communication content in plaintext. Third, we design a strong authentication system that can defeat known device scanning attack, brute force attack and device spoofing attack against IoT. We implemented a prototype of SecT on a $10 Raspberry Pi Zero W and performed extensive experiments to validate its performance. The experiment results show that SecT is both cost-effective and practical. Although we design SecT for the smart home application, it can be easily extended to other IoT application domains.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127268521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xunpeng Rao, Panlong Yang, Haipeng Dai, Hao Zhou, Tao Wu, Xiaoyu Wang
In this work, we study the mobile charger scheduling problem for multi-node charging with deadline constraints. In that, we aim at scheduling the charger to maximize the effective charging utility in dealing with the mismatch between time and spatial constraints. The local charging spots selection and globe traveling path should be jointly optimized, which is APX-hard. Nevertheless, our problem becomes much more complex with deadline constraints. To handle aforementioned challenges, we combine the spatial and temporal relevancy into a bipartite graph, and incorporate the multi-charging strategy instead of serving nodes strictly by the non-soft charging demands. We formulate the effective charging utility maximization problem into a monotone submodular function maximization subjected to a partition matroid constraint, and propose a simple but effective 1/2-approximation greedy algorithm. The results show that our scheme outperforms Early Deadline First (EDF) by 37.5%.
{"title":"Multi-node Mobile Charging Scheduling with Deadline Constraints","authors":"Xunpeng Rao, Panlong Yang, Haipeng Dai, Hao Zhou, Tao Wu, Xiaoyu Wang","doi":"10.1109/MASS.2018.00030","DOIUrl":"https://doi.org/10.1109/MASS.2018.00030","url":null,"abstract":"In this work, we study the mobile charger scheduling problem for multi-node charging with deadline constraints. In that, we aim at scheduling the charger to maximize the effective charging utility in dealing with the mismatch between time and spatial constraints. The local charging spots selection and globe traveling path should be jointly optimized, which is APX-hard. Nevertheless, our problem becomes much more complex with deadline constraints. To handle aforementioned challenges, we combine the spatial and temporal relevancy into a bipartite graph, and incorporate the multi-charging strategy instead of serving nodes strictly by the non-soft charging demands. We formulate the effective charging utility maximization problem into a monotone submodular function maximization subjected to a partition matroid constraint, and propose a simple but effective 1/2-approximation greedy algorithm. The results show that our scheme outperforms Early Deadline First (EDF) by 37.5%.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hejun Xiao, Dongxin Liu, Fan Wu, L. Kong, Guihai Chen
Exploring and utilizing indoor white spaces (vacant VHF and UHF TV channels) have been recognized as an effective way to satisfy the rapid growth of the radio frequency (RF) demand. Although a few methods of exploring indoor white spaces have been proposed in recent years, they only focus on the exploration of the current indoor white spaces. However, due to the dynamic nature of the spectrum and the time delay in the process of exploration, users often cannot get accurate white space information in time, resulting in issues, such as spectrum utilization conflicts or inadequate white space utilization. To solve the problem, in this paper, we first perform an indoor TV spectrum measurement to study how the spectrum state changes over time and the spatio-temporal-spectral correlation of spectrum. Then, we propose a real-time aCcurate indoOR whiTe spacE predictioN mechanism, called CORTEN. CORTEN can predict the white space distribution for various time spans with high accuracy. Furthermore, we build a prototype of CORTEN and evaluate its performance based on the real-world measured data. The evaluation results show that CORTEN can predict accurately 38.7% more indoor white spaces with 51.3% less false alarms compared with the baseline approach when predicting the white spaces one hour ahead.
{"title":"CORTEN: A Real-Time Accurate Indoor White Space Prediction Mechanism","authors":"Hejun Xiao, Dongxin Liu, Fan Wu, L. Kong, Guihai Chen","doi":"10.1109/MASS.2018.00065","DOIUrl":"https://doi.org/10.1109/MASS.2018.00065","url":null,"abstract":"Exploring and utilizing indoor white spaces (vacant VHF and UHF TV channels) have been recognized as an effective way to satisfy the rapid growth of the radio frequency (RF) demand. Although a few methods of exploring indoor white spaces have been proposed in recent years, they only focus on the exploration of the current indoor white spaces. However, due to the dynamic nature of the spectrum and the time delay in the process of exploration, users often cannot get accurate white space information in time, resulting in issues, such as spectrum utilization conflicts or inadequate white space utilization. To solve the problem, in this paper, we first perform an indoor TV spectrum measurement to study how the spectrum state changes over time and the spatio-temporal-spectral correlation of spectrum. Then, we propose a real-time aCcurate indoOR whiTe spacE predictioN mechanism, called CORTEN. CORTEN can predict the white space distribution for various time spans with high accuracy. Furthermore, we build a prototype of CORTEN and evaluate its performance based on the real-world measured data. The evaluation results show that CORTEN can predict accurately 38.7% more indoor white spaces with 51.3% less false alarms compared with the baseline approach when predicting the white spaces one hour ahead.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126086234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Welcome Message from IWSSC 2018 Chairs","authors":"","doi":"10.1109/mass.2018.00011","DOIUrl":"https://doi.org/10.1109/mass.2018.00011","url":null,"abstract":"","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132587639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Title Page i","authors":"","doi":"10.1109/mass.2018.00001","DOIUrl":"https://doi.org/10.1109/mass.2018.00001","url":null,"abstract":"","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121778518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhuoqian Li, Shuo Yang, Fan Wu, Xiaofeng Gao, Guihai Chen
Mobile crowdsensing has become a novel and effective way to collect sensing data of people's surrounding environment. Among the data collected from multiple contributors, inconsistency often occurs due to noise, different sensor precision, or contributors' heterogeneous sensing behaviors. To tackle the data inconsistency, the problem of truth discovery has been widely studied to jointly infer the underlying ground truths and the contributors' data qualities. Existing truth discovery algorithms are based on the aggregation of large amounts of data so as to generate accurate estimations. However, in mobile crowdsensing, the collected data are usually sparsely distributed among a large sensing area, where each point of interest (PoI) may receive only a few sensing reports. In this case, traditional truth discovery algorithms may not provide an accurate truth estimation for each PoI. To tackle this challenge, in this paper, we propose an effective truth discovery method, namely Holmes, which takes advantage of the spatial correlations of the monitored phenomena by reusing each contributor's data for multiple nearby PoIs. We also take the issue of long-tail data phenomenon into the estimation of contributors' data quality levels, and proposed Holmes-LT. We further propose Holmes-OL to address the online streaming data scenarios. We evaluate the performance of our proposed algorithms on both real and synthetic datasets. The evaluation results demonstrate that our algorithms achieve significant performance improvements in terms of estimation accuracy over the existing truth discovery algorithms.
{"title":"Holmes: Tackling Data Sparsity for Truth Discovery in Location-Aware Mobile Crowdsensing","authors":"Zhuoqian Li, Shuo Yang, Fan Wu, Xiaofeng Gao, Guihai Chen","doi":"10.1109/MASS.2018.00066","DOIUrl":"https://doi.org/10.1109/MASS.2018.00066","url":null,"abstract":"Mobile crowdsensing has become a novel and effective way to collect sensing data of people's surrounding environment. Among the data collected from multiple contributors, inconsistency often occurs due to noise, different sensor precision, or contributors' heterogeneous sensing behaviors. To tackle the data inconsistency, the problem of truth discovery has been widely studied to jointly infer the underlying ground truths and the contributors' data qualities. Existing truth discovery algorithms are based on the aggregation of large amounts of data so as to generate accurate estimations. However, in mobile crowdsensing, the collected data are usually sparsely distributed among a large sensing area, where each point of interest (PoI) may receive only a few sensing reports. In this case, traditional truth discovery algorithms may not provide an accurate truth estimation for each PoI. To tackle this challenge, in this paper, we propose an effective truth discovery method, namely Holmes, which takes advantage of the spatial correlations of the monitored phenomena by reusing each contributor's data for multiple nearby PoIs. We also take the issue of long-tail data phenomenon into the estimation of contributors' data quality levels, and proposed Holmes-LT. We further propose Holmes-OL to address the online streaming data scenarios. We evaluate the performance of our proposed algorithms on both real and synthetic datasets. The evaluation results demonstrate that our algorithms achieve significant performance improvements in terms of estimation accuracy over the existing truth discovery algorithms.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124770925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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