2,932 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
A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures
This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
Distributed Database Management Techniques for Wireless Sensor Networks
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Xplore. Authors shall not post the final, published versions of their papers.In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.This work was partially supported by the Instituto de Telcomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Ministerio de Ciencia e Innovacion, through the Plan Nacional de I+D+i 2008-2011 in the Subprograma de Proyectos de Investigacion Fundamental, project TEC2011-27516, by the Polytechnic University of Valencia, though the PAID-05-12 multidisciplinary projects, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Diallo, O.; Rodrigues, JJPC.; Sene, M.; Lloret, J. (2013). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems. PP(99):1-17. https://doi.org/10.1109/TPDS.2013.207S117PP9
Power efficiency through tuple ranking in wireless sensor network monitoring
In this paper, we present an innovative framework for efficiently monitoring Wireless Sensor Networks (WSNs). Our framework, coined KSpot, utilizes a novel top-k query processing algorithm we developed, in conjunction with the concept of in-network views, in order to minimize the cost of query execution. For ease of exposition, consider a set of sensors acquiring data from their environment at a given time instance. The generated information can conceptually be thought as a horizontally fragmented base relation R. Furthermore, the results to a user-defined query Q, registered at some sink point,
can conceptually be thought as a view V . Maintaining consistency between V and R is very expensive in terms of communication and energy. Thus, KSpot focuses on a subset V′ (⊆ V ) that unveils only the k highest-ranked answers
at the sink, for some user defined parameter k. To illustrate the efficiency of our framework, we have implemented a real
system in nesC, which combines the traditional advantages of declarative acquisition frameworks, like TinyDB, with the ideas presented in this work. Extensive real-world testing and experimentation with traces from University of California-Berkeley, the University of Washington and Intel Research Berkeley, show that KSpot provides an up to 66% of energy savings compared to TinyDB, minimizes both the size and number of packets transmitted over the network (up to 77%), and prolongs the longevity of a WSN deployment to new scales
A Holistic Approach in Embedded System Development
We present pState, a tool for developing "complex" embedded systems by
integrating validation into the design process. The goal is to reduce
validation time. To this end, qualitative and quantitative properties are
specified in system models expressed as pCharts, an extended version of
hierarchical state machines. These properties are specified in an intuitive way
such that they can be written by engineers who are domain experts, without
needing to be familiar with temporal logic. From the system model, executable
code that preserves the verified properties is generated. The design is
documented on the model and the documentation is passed as comments into the
generated code. On the series of examples we illustrate how models and
properties are specified using pState.Comment: In Proceedings F-IDE 2015, arXiv:1508.0338
Mobile Databases: a Selection of Open Issues and Research Directions
International audienceThis paper reports on the main results of a specific action on mobile databases conducted by CNRS in France from October 2001 to December 2002. The objective of this action was to review the state of progress in mobile databases and identify major research directions for the French database community. Rather than provide a survey of all important issues in mobile databases, this paper gives an outline of the directions in which the action participants are now engaged, namely: copy synchronization in disconnected computing, mobile transactions, database embedded in ultra-light devices, data confidentiality, P2P dissemination models and middleware adaptability
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