An In-Network Querying Framework for Wireless Sensor Networks

Demirbaş, Murat; Lu, Xuming; Singla, Puneet

doi:10.1109/tpds.2008.217

Cited by 39 publications

(24 citation statements)

References 27 publications

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“…The parent nodes combine this coming data with their own data and transmit to their parent nodes and so on until the data reaches the gateway. This approach that consists to process the data inside the sensor nodes themselves is called innetwork processing and it reduces the amount and size of transmitted data and the latency [38]. According to the scope of this work, an illustration of a distributed database on WSN may be seen in Fig.…”

Section: Background As In Traditional Database Systemmentioning

confidence: 99%

“…The in-network processing technique [38], [40], [41] generally includes the different types of op done on the server, for inst sensor nodes themselves. It is generally used, as its name indicated, to process sensing values inside the network nodes so as to filter and reduce the huge and needless data.…”

Section: A In-network Processingmentioning

confidence: 99%

“…3. Aggregation steps of sensor readings during an epoch using interval-based communication [38]. processing in this latform, they use a clustered approach.…”

Section: Aggregation Based Techniquesmentioning

confidence: 99%

“…In fact, in wireless sensor network areas, due to generally harsh environments of d han the all a ased wireless sensor networks, three suite of energye the un e energy co lsive noise. T a the complexities of or to efficiently extract th is performed by minimizing the activities of o eployment and the resource limitation, sensor nodes are always subject to random failures [38]. For these reasons, in [74] the authors propose a distributed energy-efficient replica placement for increasing data availability and prolonging the network lifetime.…”

Section: Approximation Based Techniquesmentioning

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: Background As In Traditional Database Systemmentioning

confidence: 99%

Section: A In-network Processingmentioning

confidence: 99%

“…3. Aggregation steps of sensor readings during an epoch using interval-based communication [38]. processing in this latform, they use a clustered approach.…”

Section: Aggregation Based Techniquesmentioning

confidence: 99%

Section: Approximation Based Techniquesmentioning

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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“…Since the average path length cannot tell the efficiency of different models, we introduce a new metric named average stretch factor to measure distance sensitivity. Distance-sensitivity for a tracking implies that the cost of a tracking message should be at most a constant factor of the distance to the event of interest in the network [21]. Here the constant factor is what we call stretch factor, and its value can be achieved by calculating the ratio of the path length(in hops) to the distance(in hops) between the tracking node and the target.…”

Section: Network Protocols and Algorithmsmentioning

confidence: 99%

Model-based Tracking for Mobile Ad Hoc Networks

Demirbaş

2011

NPA

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We consider the problem of tracking of a mobile target node in a mobile ad hoc network (MANET) set-up. We find a Gradient model alone is usually not very efficient, whereas a precise Markov model which includes transition probabilities is too hard to achieve. We propose a generic tracking framework for online tracking applications, by integrating a Gradient model of the target's proximity and an online statistically estimated Markov model of the target's likely direction. We show PMBT achieves a short catching path with a high success rate. PMBT is a probabilistic online tracking algorithm that computes information utilities at each step, and then chooses the next step toward the target based on the maximum expected utility. Our algorithm avoids the need to maintain a tracking data structure (such as a hierarchical directory look-up structure) and the need to send periodic update messages about the target's location. Simulation results show, by taking a hybrid approach that integrates a gradient model and a Markov model, our algorithm significantly outperforms both gradient-based and Markov approaches alone.

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Probabilistic query generation and fuzzy c‐means clustering for energy‐efficient operation in wireless sensor networks

Kumar

Chaturvedi

2016

Int J Communication

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SUMMARYDepending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution-based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c-means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed.

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An In-Network Querying Framework for Wireless Sensor Networks

Cited by 39 publications

References 27 publications

Distributed Database Management Techniques for Wireless Sensor Networks

Distributed Database Management Techniques for Wireless Sensor Networks

Model-based Tracking for Mobile Ad Hoc Networks

Probabilistic query generation and fuzzy c‐means clustering for energy‐efficient operation in wireless sensor networks

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