4 research outputs found
Ranking in Distributed Uncertain Database Environments
Distributed data processing is a major field in nowadays applications. Many applications collect and process data from distributed nodes to gain overall results. Large amount of data transfer and network delay made data processing in a centralized manner a hard operation representing an important problem. A very common way to solve this problem is ranking queries. Ranking or top-k queries concentrate only on the highest ranked tuples according to user's interest. Another issue in most nowadays applications is data uncertainty. Many techniques were introduced for modeling, managing, and processing uncertain databases. Although these techniques were efficient, they didn't deal with distributed data uncertainty. This paper deals with both data uncertainty and distribution based on ranking queries. A novel framework is proposed for ranking distributed uncertain data. The framework has a suite of novel algorithms for ranking data and monitoring updates. These algorithms help in reducing the communication rounds used and amount of data transmitted while achieving efficient and effective ranking. Experimental results show that the proposed framework has a great impact in reducing communication cost compared to other techniques.DOI:http://dx.doi.org/10.11591/ijece.v4i4.592
Distributed Database Management Techniques for Wireless Sensor Networks
Authors and/or their employers shall have the right to post the accepted version of IEEE-copyrighted articles on their own
personal servers or the servers of their institutions or employers without permission from IEEE, provided that the posted version includes a prominently
displayed IEEE copyright notice and, when published, a full citation to the original IEEE publication, including a link to the article abstract in IEEE
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
Probabilistic Top-k Query Processing in Distributed Sensor Networks
In this paper, we propose the notion of sufficient set for distributed processing of probabilistic Top-k queries in cluster-based wireless sensor networks. Through the derivation of sufficient boundary, we show that data items ranked lower than sufficient boundary are not required for answering the probabilistic top-k queries, thus are subject to local pruning. Accordingly, we develop the sufficient set-based (SSB) algorithm for inter-cluster query processing. Experimental results show that the proposed algorithm reduces data transmissions significantly
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