Analyzing Data Prediction in Wireless Sensor Networks

Blaß, Erik-Oliver; Horneber, Jens; Zitterbart, Martina

doi:10.1109/vetecs.2008.30

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…Any interested node (a node that wants to receive the data and does not have it) asks for it by sending a query. For instance, in SPIN (Sensor Protocols for Information via Negotiation) [38], any data is described by a descriptor named meta-data which is unique and shorter than the actual data. However, SPIN data forwarding cannot guarantee the delivery of data.…”

Section: Optimized Floodingmentioning

confidence: 99%

“…Mainly, two approaches were proposed for interest dissemination. The first is SPIN [38] where any node advertises the availability of data and waits for requests from interested nodes. The second is Directed Diffusion (DD) [20] in which sinks broadcast an interest message to sensors, only interested nodes reply with a gradient message.…”

Section: A Data Centric Protocolsmentioning

confidence: 99%

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A survey on energy efficient techniques in wireless sensor networks

Soua

Minet

2011

2011 4th Joint IFIP Wireless and Mobile Networking Conference (WMNC 2011)

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To cite this version:Abstract-The myriad of potential applications supported by wireless sensor networks (WSNs) has generated much interest from the research community. Various applications range from small size low industrial monitoring to large scale energy constrained environmental monitoring. In all cases, an operational network is required to fulfill the application missions. In addition, energy consumption of nodes is a great challenge in order to maximize network lifetime. Unlike other networks, it can be hazardous, very expensive or even impossible to charge or replace exhausted batteries due to the hostile nature of environment.Researchers are invited to design energy efficient protocols while achieving the desired network operations. This paper focuses on different techniques to reduce the consumption of the limited energy budget of sensor nodes. After having identified the reasons of energy waste in WSNs, we classify energy efficient techniques into five classes, namely data reduction, control reduction, energy efficient routing, duty cycling and topology control. We then detail each of them, presenting subdivisions and giving many examples. We conclude by a recapitulative table.

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Section: Optimized Floodingmentioning

confidence: 99%

Section: A Data Centric Protocolsmentioning

confidence: 99%

A survey on energy efficient techniques in wireless sensor networks

Soua

Minet

2011

2011 4th Joint IFIP Wireless and Mobile Networking Conference (WMNC 2011)

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“…Distributed data processing could be used for any local decision making like power saving or routing in a wireless sensor network to increase the overall performance [8] [9]. Thus, accuracy and energy efficiency of the chosen data processing technique are taken into consideration [10].…”

Section: Related Workmentioning

confidence: 99%

Advanced Bio-inspired Plausibility Checking in a Wireless Sensor Network Using Neuro-immune Systems: Autonomous Fault Diagnosis in an Intelligent Transportation System

Jabbari

Lang

2010

2010 Fourth International Conference on Sensor Technologies and Applications

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Recent developments in wireless sensing technology lead to implement advanced algorithms for distributed data processing in various applications; intelligent transportation system is one of the main applications of the advanced networked sensing technology to monitor the environmental conditions for controlling the quality of the products. To ensure the desired performance of a wireless sensor network, the reliability of the network records needs to be evaluated using an efficient data processing algorithm. In this paper, a new application of a bio-inspired technique is introduced for autonomous plausibility checking in a wireless sensor network; at first, an optimized Neuro-immune system is introduced and developed to predict the sensor records; then, performance of the proposed Neuro-immune system is compared with a neural network approximation mechanism. A secondary algorithm evaluates the sensor records to check the plausibility of the records in the wireless sensor network. The proposed data processing algorithm could serve in various applications of wireless sensor networks.Keywords-wireless sensor network; distributed data processing; artificial immune system; artificial neural network.

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“…A soft computing technique could be developed to predict and classify the sensory records to perform the autonomous data processing. Estimating the records of the nodes has a crucial role in data processing [6,7,8]. Then, the predicted data is compared to the actual data to be classified either as plausible or implausible [5,9].…”

Section: Related Workmentioning

confidence: 99%

Application of bio-inspired data processing in intelligent transportation systems

Jabbari

Kreowski

Lang

2010

2010 5th IEEE International Conference Intelligent Systems

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Abstract-There has been a growing interest in using the soft computing techniques for distributed data processing in monitoring systems during last years; wireless sensor network is an example which can be utilized to record and process the environmental conditions in transportation systems. In this paper, by using knowledge based soft computing techniques, the reliability of the sensory records is evaluated autonomously in an intelligent transportation system; for this purpose, a new combinational architecture is applied including a bio-inspired local/global data approximation as well as a knowledge based data classification mechanism. The approximation mechanism is established on a least squares mechanism for global data prediction and a radial basis function network including immunological center selection to perform the local data approximations. To classify the records, a new probabilistic neural network including immune system features is developed. The proposed data processing technique is applicable for various intelligent data processing purposes.

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Analyzing Data Prediction in Wireless Sensor Networks

Cited by 10 publications

References 3 publications

A survey on energy efficient techniques in wireless sensor networks

A survey on energy efficient techniques in wireless sensor networks

Advanced Bio-inspired Plausibility Checking in a Wireless Sensor Network Using Neuro-immune Systems: Autonomous Fault Diagnosis in an Intelligent Transportation System

Application of bio-inspired data processing in intelligent transportation systems

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