Toward adaptive query processing in wireless sensor networks

Brayner, Ângelo; Lopes, Aretusa; Meira, Diorgens; Vasconcelos, Ricardo; Menezes, Ronaldo

doi:10.1016/j.sigpro.2007.05.017

Cited by 19 publications

(20 citation statements)

References 10 publications

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“…The authors in [56] proposed a set of algorithms to minimize the by considering a real time scenario where the raw data gathered fr To these ends, the authors of [57], [58] p at is useful because the system should adapt to c overall energy consumption of the sensor nodes om the source nodes must be aggregated and transmitted to the sink within a specified latency constraint. This work is particularly important for applications requiring a prompt delivery of the information to the sink.…”

Section: Aggregation Based Techniquesmentioning

confidence: 99%

“…Besides performing in-network processing, to increase the network lifetime and the accuracy of data and queries, the ADAGA [57], [58]system adapts the collecting and sending activities of the devices according respectively to the remaining memory and energy of sensors. The main idea is to dynamically adapt values for Sense interval clause that specifies the interval between consecutive data collections and values for Send interval clause that specifies the interval between consecutive sending of packets.…”

Section: Cross-layer Optimizationmentioning

confidence: 99%

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Distributed Database Management Techniques for Wireless Sensor Networks

Diallo

Rodrigues

Sène

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In sensor networks, the large amount of data nsor reWireless Se composed of a large nu activities are sometimes not negligible in energy consumption 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.

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Section: Aggregation Based Techniquesmentioning

confidence: 99%

Section: Cross-layer Optimizationmentioning

confidence: 99%

Distributed Database Management Techniques for Wireless Sensor Networks

Diallo

Rodrigues

Sène

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

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“…The sensor nodes are modeled as Abstract Data Types (ADT) with functional interfaces providing access to data encapsulation. Finally is presented the SNQL that is more than one data management system so that is a language created for the networks of smart sensors (SNQL: Sensor Network Query Language) [26]. This language provides support for the consultations in a sensor network.…”

Section: Data Management System To Wssnmentioning

confidence: 99%

Wireless Smart Sensors Networks, systems, trends and its impact in environmental monitoring

Herrera-Quintero

Macia-Perez

Ramos-Morillo

et al. 2009

2009 IEEE Latin-American Conference on Communications

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Abstract-Wireless Smart Sensor Networks (WSSN) offers a new paradigm regards to the way that the traditional monitoring systems have been designed, all this happens due to the impactgenerating by technology in society. The deployment of Information and Communication Technologies or ICT allow us now rely with small devices that are able to integrate into a "simple element" the potential of computing, communication and monitoring systems and from here starts to move to these minidevices, the ability of intelligence that ensure the deployment of new systems focused in environmental monitoring. In this paper we present a survey of technologies employed to wireless smart sensor networks, their management systems as well as their trends and finally we presents the design and implementation of an application for monitoring environmental variables using the WSSN and virtual instrumentation on Smartphone.

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“…This can be achieved by introducing database query processing (QP) and innetwork aggregation techniques into WSN. While the distributed query engines proposed in [3,7] are representatives of the former, ADAGA [3,4] and TinyDB [7] relate to two effective mechanisms based on in-network aggregation. An alternative way that also shows to be worth in reducing the number of messages exchanged across WSN is more machine learning (ML) oriented and follows the idea of inducing a model directly from the collected data in order to characterize the normal profile of values associated with the sensed phenomena [8,10].…”

Section: Introductionmentioning

confidence: 99%

Dealing with application requirements and energy consumption in wireless sensor networks

Brayner

Coelho

Souza

2010

Proceedings of the 2010 ACM Symposium on Applied Computing

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This paper presents an approach for adapting query processing in wireless sensor networks (WSN) based on the notions of quality of query services (QoQS) and novelty detection (ND). While the former concept captures the idea of possibly having different queries serviced in different ways by the same WSN, the second relates to a machine learning technique embedded in the WSN components that allows them to modify their query processing behavior in a dynamic fashion. This approach aims at the intelligent consumption of the limited resources available in these networks while still trying to deliver the data quality as expected by their client applications. In this context, four classes of QoQS (CoQoS) have been specified having in mind distinct levels of requirements in terms of accuracy and temporal behavior of the sensed data. Moreover, a new ND-based algorithm, named as AdaQuali (after ADAptive QUALIty control for query processing in WSN), is introduced in detail as a way to control the sensor node activities through the adjustment of their rates of data collection and transmission. For validation purposes, experiments with a simulation prototype have been conducted over real data, and the results achieved so far point to gains in terms of energy consumption reduction that vary from 1.73% to 42.99% for different CoQoS.

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Toward adaptive query processing in wireless sensor networks

Cited by 19 publications

References 10 publications

Distributed Database Management Techniques for Wireless Sensor Networks

Distributed Database Management Techniques for Wireless Sensor Networks

Wireless Smart Sensors Networks, systems, trends and its impact in environmental monitoring

Dealing with application requirements and energy consumption in wireless sensor networks

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