246,676 research outputs found
DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams
Similarity matching and join of time series data streams has gained a lot of
relevance in today's world that has large streaming data. This process finds
wide scale application in the areas of location tracking, sensor networks,
object positioning and monitoring to name a few. However, as the size of the
data stream increases, the cost involved to retain all the data in order to aid
the process of similarity matching also increases. We develop a novel framework
to addresses the following objectives. Firstly, Dimension reduction is
performed in the preprocessing stage, where large stream data is segmented and
reduced into a compact representation such that it retains all the crucial
information by a technique called Multi-level Segment Means (MSM). This reduces
the space complexity associated with the storage of large time-series data
streams. Secondly, it incorporates effective Similarity Matching technique to
analyze if the new data objects are symmetric to the existing data stream. And
finally, the Pruning Technique that filters out the pseudo data object pairs
and join only the relevant pairs. The computational cost for MSM is O(l*ni) and
the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction
Factor. We have performed exhaustive experimental trials to show that the
proposed framework is both efficient and competent in comparison with earlier
works.Comment: 20 pages,8 figures, 6 Table
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
200
Energy harvesting from electrospun piezoelectric nanowires for structural health monitoring of a cable-stayed bridge
Wireless monitoring could greatly impact the fields of structural health assessment and infrastructure asset management, but some technological challenges pose unsolved issues toward its reliable use in continuous large-scale applications. Among the others, it is worth highlighting that power supply by means of batteries is usually implemented within wireless sensor networks, even though it causes practical concerns that heavily prevent the development of efficient monitoring systems for large structures and infrastructures. Conversely, scavenging ambient energy can alleviate or eventually eliminate the problem of electrical supply by batteries, a strategy that has emerged in recent years as a promising technological solution for bridges. Within this framework, the present work proposes to harvest ambient-induced vibrations of bridge structures using a new class of piezoelectric textiles. The considered case study is an existing cable-stayed bridge located in Italy along the high-speed road that connects Rome and Naples, for which a recent monitoring campaign has allowed to record the dynamic responses of deck and cables. In order to enhance the electric energy that can be converted from wind- and traffic-induced bridge vibrations, the energy harvester exploits a piezoelectric nanogenerator built using arrays of piezoelectric electrospun nanofibers. Particularly, several fiber arrangements are studied at the nano/micro-scale leading to different macro constitutive laws and different electric energy output. A computational study is performed to demonstrate that such nanogenerator is able to provide higher energy levels from recorded dynamic loading time histories than a standard piezoelectric energy harvester. The feasibility of this piezoelectric nanogenerator for bridge monitoring applications is finally discussed
A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things
The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE
Analysis and Extraction of Tempo-Spatial Events in an Efficient Archival CDN with Emphasis on Telegram
This paper presents an efficient archival framework for exploring and
tracking cyberspace large-scale data called Tempo-Spatial Content Delivery
Network (TS-CDN). Social media data streams are renewing in time and spatial
dimensions. Various types of websites and social networks (i.e., channels,
groups, pages, etc.) are considered spatial in cyberspace. Accurate analysis
entails encompassing the bulk of data. In TS-CDN by applying the hash function
on big data an efficient content delivery network is created. Using hash
function rebuffs data redundancy and leads to conclude unique data archive in
large-scale. This framework based on entered query allows for apparent
monitoring and exploring data in tempo-spatial dimension based on TF-IDF score.
Also by conformance from i18n standard, the Unicode problem has been dissolved.
For evaluation of TS-CDN framework, a dataset from Telegram news channels from
March 23, 2020 (1399-01-01), to September 21, 2020 (1399-06-31) on topics
including Coronavirus (COVID-19), vaccine, school reopening, flood, earthquake,
justice shares, petroleum, and quarantine exploited. By applying hash on
Telegram dataset in the mentioned time interval, a significant reduction in
media files such as 39.8% for videos (from 79.5 GB to 47.8 GB), and 10% for
images (from 4 GB to 3.6 GB) occurred. TS-CDN infrastructure in a web-based
approach has been presented as a service-oriented system. Experiments conducted
on enormous time series data, including different spatial dimensions (i.e.,
Khabare Fouri, Khabarhaye Fouri, Akhbare Rouze Iran, and Akhbare Rasmi Telegram
news channels), demonstrate the efficiency and applicability of the implemented
TS-CDN framework
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