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This article narrows the gap between physical sensing systems that measure physical signals and social sensing systems that measure information signals by (i) defining a novel algorithm for extracting information signals (building on results from text embedding) and (ii) showing that it increases the accuracy of truth discovery--the separation of true information from false/manipulated one. The work is applied in the context of separating true and false facts on social media, such as Twitter and Reddit, where users post predominantly short microblogs. The new algorithm decides how to aggregate the signal across words in the microblog for purposes of clustering the miscroblogs in the latent information signal space, where it is easier to separate true and false posts. Although previous literature extensively studied the problem of short text embedding/representation, this article improves previous work in three important respects: (1) Our work constitutes unsupervised truth discovery, requiring no labeled input or prior training. (2) We propose a new distance metric for efficient short text similarity estimation, we call Semantic Subset Matching, that improves our ability to meaningfully cluster microblog posts in the latent information signal space. (3)We introduce an iterative framework that jointly improves miscroblog clustering and truth discovery. The evaluation shows that the approach improves the accuracy of truth-discovery by 6.3%, 2.5%, and 3.8% (constituting a 38.9%, 14.2%, and 18.7% reduction in error, respectively) in three real Twitter data traces. [ABSTRACT FROM AUTHOR]

This article presents a tool for uncovering bugs due to interactive complexity in networked sensing applications. Such bugs are not localized to one component that is faulty, but rather result from complex and unexpected interactions between multiple often individually nonfaulty components. Moreover, the manifestations of these bugs are often not repeatable, making them particularly hard to find, as the particular sequence of events that invokes the bug may not be easy to reconstruct. Because of the distributed nature of failure scenarios, our tool looks for sequences of events that may be responsible for faulty behavior, as opposed to localized bugs such as a bad pointer in a module. We identified several challenges in applying discriminative sequence mining for root cause analysis when the system fails to perform as expected and presented our solutions to those challenges. We also present two alternative schemes, namely, two-stage mining and the progressive discriminative sequence mining to address the scalability challenge. An extensible framework is developed where a front-end collects runtime data logs of the system being debugged and an offline back-end uses frequent discriminative pattern mining to uncover likely causes of failure. We provided several case studies where we applied our tool successfully to troubleshoot the cause of the problem. We uncovered a kernel-level race condition bug in the LiteOS operating system and a protocol design bug in the directed diffusion protocol. We also presented a case study of debugging a multichannel MAC protocol that was found to exhibit corner cases of poor performance (worse than single-channel MAC). The tool helped to uncover event sequences that lead to a highly degraded mode of operation. Fixing the problem significantly improved the performance of the protocol. We also evaluated the extensions presented in this article. Finally, we provided a detailed analysis of tool overhead in terms ofmemory requirements and impact on the running application. [ABSTRACT FROM AUTHOR]

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme-Cluster-based Private Data Aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme- Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. [ABSTRACT FROM AUTHOR]

This article presents the design, implementation, and evaluation of EnviroMic, a low-cost experimental prototype of a novel distributed acoustic monitoring, storage, and trace retrieval system designed for disconnected operation. Our intended use of acoustic monitoring is to study animal populations in the wild. Since a permanent connection to the outside world is not assumed and due to the relatively large size of audio traces, the system must optimally exploit available resources such as energy and network storage capacity. Towards that end, we design, prototype, and evaluate distributed algorithms for coordinating acoustic recording tasks, reducing redundancy of data stored by nearby sensors, filtering out silence, and balancing storage utilization in the network. For experimentation purposes, we implement EnviroMic on a TinyOS-based platform and systematically evaluate its performance through both indoor testbed experiments and an outdoor deployment. Results demonstrate up to a four-fold improvement in effective storage capacity of the network compared to uncoordinated recording. [ABSTRACT FROM AUTHOR]