134,586 research outputs found
Performance assessment of real-time data management on wireless sensor networks
Technological advances in recent years have allowed the maturity of Wireless Sensor Networks
(WSNs), which aim at performing environmental monitoring and data collection. This sort of
network is composed of hundreds, thousands or probably even millions of tiny smart computers
known as wireless sensor nodes, which may be battery powered, equipped with sensors, a radio
transceiver, a Central Processing Unit (CPU) and some memory. However due to the small size and
the requirements of low-cost nodes, these sensor node resources such as processing power, storage
and especially energy are very limited.
Once the sensors perform their measurements from the environment, the problem of data
storing and querying arises. In fact, the sensors have restricted storage capacity and the on-going
interaction between sensors and environment results huge amounts of data. Techniques for data
storage and query in WSN can be based on either external storage or local storage. The external
storage, called warehousing approach, is a centralized system on which the data gathered by the
sensors are periodically sent to a central database server where user queries are processed. The
local storage, in the other hand called distributed approach, exploits the capabilities of sensors
calculation and the sensors act as local databases. The data is stored in a central database server
and in the devices themselves, enabling one to query both.
The WSNs are used in a wide variety of applications, which may perform certain operations on
collected sensor data. However, for certain applications, such as real-time applications, the sensor
data must closely reflect the current state of the targeted environment. However, the environment
changes constantly and the data is collected in discreet moments of time. As such, the collected
data has a temporal validity, and as time advances, it becomes less accurate, until it does not
reflect the state of the environment any longer. Thus, these applications must query and analyze
the data in a bounded time in order to make decisions and to react efficiently, such as industrial
automation, aviation, sensors network, and so on. In this context, the design of efficient real-time
data management solutions is necessary to deal with both time constraints and energy consumption.
This thesis studies the real-time data management techniques for WSNs. It particularly it focuses
on the study of the challenges in handling real-time data storage and query for WSNs and on the
efficient real-time data management solutions for WSNs.
First, the main specifications of real-time data management are identified and the available
real-time data management solutions for WSNs in the literature are presented. Secondly, in order to
provide an energy-efficient real-time data management solution, the techniques used to manage
data and queries in WSNs based on the distributed paradigm are deeply studied. In fact, many
research works argue that the distributed approach is the most energy-efficient way of managing
data and queries in WSNs, instead of performing the warehousing. In addition, this approach can provide quasi real-time query processing because the most current data will be retrieved from the
network.
Thirdly, based on these two studies and considering the complexity of developing, testing, and
debugging this kind of complex system, a model for a simulation framework of the real-time
databases management on WSN that uses a distributed approach and its implementation are
proposed. This will help to explore various solutions of real-time database techniques on WSNs
before deployment for economizing money and time. Moreover, one may improve the proposed
model by adding the simulation of protocols or place part of this simulator on another available
simulator. For validating the model, a case study considering real-time constraints as well as energy
constraints is discussed.
Fourth, a new architecture that combines statistical modeling techniques with the distributed
approach and a query processing algorithm to optimize the real-time user query processing are
proposed. This combination allows performing a query processing algorithm based on admission
control that uses the error tolerance and the probabilistic confidence interval as admission
parameters. The experiments based on real world data sets as well as synthetic data sets
demonstrate that the proposed solution optimizes the real-time query processing to save more
energy while meeting low latency.Fundação para a Ciência e Tecnologi
Exploiting Context-Dependent Quality Metadata for Linked Data Source Selection
The traditional Web is evolving into the Web of Data which consists of huge collections
of structured data over poorly controlled distributed data sources. Live
queries are needed to get current information out of this global data space. In live
query processing, source selection deserves attention since it allows us to identify the
sources which might likely contain the relevant data. The thesis proposes a source
selection technique in the context of live query processing on Linked Open Data,
which takes into account the context of the request and the quality of data contained in
the sources to enhance the relevance (since the context enables a better interpretation
of the request) and the quality of the answers (which will be obtained by processing
the request on the selected sources). Specifically, the thesis proposes an extension of
the QTree indexing structure that had been proposed as a data summary to support
source selection based on source content, to take into account quality and contextual
information. With reference to a specific case study, the thesis also contributes an approach,
relying on the Luzzu framework, to assess the quality of a source with respect
to for a given context (according to different quality dimensions). An experimental
evaluation of the proposed techniques is also provide
Efficient Computation of Distance Sketches in Distributed Networks
Distance computation is one of the most fundamental primitives used in
communication networks. The cost of effectively and accurately computing
pairwise network distances can become prohibitive in large-scale networks such
as the Internet and Peer-to-Peer (P2P) networks. To negotiate the rising need
for very efficient distance computation, approximation techniques for numerous
variants of this question have recently received significant attention in the
literature. The goal is to preprocess the graph and store a small amount of
information such that whenever a query for any pairwise distance is issued, the
distance can be well approximated (i.e., with small stretch) very quickly in an
online fashion. Specifically, the pre-processing (usually) involves storing a
small sketch with each node, such that at query time only the sketches of the
concerned nodes need to be looked up to compute the approximate distance. In
this paper, we present the first theoretical study of distance sketches derived
from distance oracles in a distributed network. We first present a fast
distributed algorithm for computing approximate distance sketches, based on a
distributed implementation of the distance oracle scheme of [Thorup-Zwick, JACM
2005]. We also show how to modify this basic construction to achieve different
tradeoffs between the number of pairs for which the distance estimate is
accurate and other parameters. These tradeoffs can then be combined to give an
efficient construction of small sketches with provable average-case as well as
worst-case performance. Our algorithms use only small-sized messages and hence
are suitable for bandwidth-constrained networks, and can be used in various
networking applications such as topology discovery and construction, token
management, load balancing, monitoring overlays, and several other problems in
distributed algorithms.Comment: 18 page
Query management in a sensor environment
Traditional sensor network deployments consisted of fixed infrastructures and were relatively small in size. More and more, we see the deployment of ad-hoc sensor networks with heterogeneous devices on a larger scale, posing new challenges for device management and query processing. In this paper, we present our design and prototype implementation of XSense, an architecture supporting metadata and query services for an underlying large scale dynamic P2P sensor network. We cluster sensor devices into manageable groupings to optimise the query process and automatically locate appropriate clusters based on keyword abstraction from queries. We present experimental analysis to show the benefits of our approach and demonstrate improved query performance and scalability
Distributed resource discovery using a context sensitive infrastructure
Distributed Resource Discovery in a World Wide Web environment using full-text indices will never scale. The distinct properties of WWW information (volume, rate of change, topical diversity) limits the scaleability of traditional approaches to distributed Resource Discovery. An approach combining metadata clustering and query routing can, on the other hand, be proven to scale much better. This paper presents the Content-Sensitive Infrastructure, which is a design building on these results. We also present an analytical framework for comparing scaleability of different distribution strategies
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