Using fuzzy logic for localization in mobile sensor networks: simulations and experiments

Dharne, A.G.; Lee, Jae Yong; Jayasuriya, Suhada

doi:10.1109/acc.2006.1656524

Cited by 18 publications

(4 citation statements)

References 7 publications

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“…In addition,the authors of [10] present an activity recognition architecture utilizing multi-node collaboration in order to produce a reliable recognition result from unreliable sensor data. Moreover, in [11] it is proved that fuzzy logic can be effectively used to enable location estimation in mobile robots, while they emphasize the reduced computational requirements and heuristic determination of combination rules. Nevertheless, none of the above papers present comparative evaluation metrics of the performance of the fuzzy algorithm, under critical networking conditions.…”

Section: Related Workmentioning

confidence: 99%

FLOGERA — A fuzzy logic event recognition algorithm in a WSN environment

Dima

Tsitsipis

Antonopoulos

et al. 2012

2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC)

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Wireless Sensors Networks (WSNs) emphasize the necessity of accurate event detection in realistic environments, while health monitoring comprise one of their most pronounced applications. Although respective fuzzy logic systems have been proposed based on vital and environmental sensors, there is a significant lack of investigation on the reliability of such systems when applied in error prone wireless environments. Therefore, this paper aims to address this need by porting an adequate fuzzy inference system (FIS) to a WSN simulation framework. The considered FIS has been validated in previous work and is implemented in a TelosB emulated mote, which evaluates the health status of a monitored person. Such implementation allows the thorough performance evaluation of the FIS under a wide range of communication conditions. Respective results and comparative evaluation against Matlab environment reveal strong dependencies to critical WSN network parameters.

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Section: Related Workmentioning

confidence: 99%

FLOGERA — A fuzzy logic event recognition algorithm in a WSN environment

Dima

Tsitsipis

Antonopoulos

et al. 2012

2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC)

View full text Add to dashboard Cite

show abstract

“…Hardware based 2. Probabilistic estimation based Dharne et al 18 employ fuzzy logic to replace rigorous probabilistic concepts with fuzzy heuristic rules for WSN localization. The system employs a grid-centered method, with a node's position decided by its degree of certainty that it is put at a particular spot on the grid.…”

Section: Localizationmentioning

confidence: 99%

“…A precise position estimate is required for energy‐aware routing and sensor event localization and coverage. There are dual techniques to deal with the localization issue in WSNs: Hardware based Probabilistic estimation based Dharne et al 18 employ fuzzy logic to replace rigorous probabilistic concepts with fuzzy heuristic rules for WSN localization. The system employs a grid‐centered method, with a node's position decided by its degree of certainty that it is put at a particular spot on the grid.…”

Section: Introductionmentioning

confidence: 99%

Hybrid localization scheme using K‐fold optimization with machine learning in WSN

Yadav

Sharma

Rishiwal

2022

Int J Communication

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Summary Node localization technology can identify and track nodes, making observing data more relevant; for example, information received at the sink node would be useless to the client if node localization data from the sensor region were not included. Localization is described as determining the location of unknown sensor nodes named destination nodes applying the recognized location of anchor nodes based on measurements such as time difference of occurrence, time of occurrence, angle of occurrence, triangulation, and maximum probability. The purpose of node localization is to assign coordinates points to all sensor nodes arbitrarily put in the monitoring region and have an unknown location. Localization of nodes is essential to account for the cause of events that help group sensor querying, routing, and network coverage. In this paper, data transmission among the nodes is done by comparing the received signal strength indicator (RSSI) value with the supervised learning value. If the RSSI value is less than the supervised learning value, the data transmission takes place; else, no transmission. This paper proposes a hybrid localization scheme that effectively uses K‐fold optimization with supervised learning and gives good results for distance error and RSSI/energy efficiency. The proposed scheme can effectively detect the optimal path for data transmission, node localization for the destination, and overall performance enhancement using threshold decision making.

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“…In addition, the authors of [15] present an activity recognition architecture utilizing multinode collaboration and fuzzy logic, in order to produce a reliable recognition result from unreliable sensor data. Moreover, in [16] it is proven that fuzzy logic can be effectively used to enable location estimation in mobile robots, while the authors emphasize the computational requirements reduction and heuristic determination of combination rules. Nevertheless, none of the above papers present evaluation results with respect to required energy for executing the classification system or comparative metrics of the performance and the sensitivity of the fuzzy algorithm, under critical networking Journal of Sensors conditions.…”

Section: Fuzzy Logic In Wsnsmentioning

confidence: 99%

On Event/Time Triggered and Distributed Analysis of a WSN System for Event Detection, Using Fuzzy Logic

Dima

Antonopoulos²,

Koubias³

2016

Journal of Sensors

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Event detection in realistic WSN environments is a critical research domain, while the environmental monitoring comprises one of its most pronounced applications. Although efforts related to the environmental applications have been presented in the current literature, there is a significant lack of investigation on the performance of such systems, when applied in wireless environments. Aiming at addressing this shortage, in this paper an advanced multimodal approach is followed based on fuzzy logic. The proposed fuzzy inference system (FIS) is implemented on TelosB motes and evaluates the probability of fire detection while aiming towards power conservation. Additionally to a straightforward centralized approach, a distributed implementation of the above FIS is also proposed, aiming towards network congestion reduction while optimally distributing the energy consumption among network nodes so as to maximize network lifetime. Moreover this work proposes an event based execution of the aforementioned FIS aiming to further reduce the computational as well as the communication cost, compared to a periodical time triggered FIS execution. As a final contribution, performance metrics acquired from all the proposed FIS implementation techniques are thoroughly compared and analyzed with respect to critical network conditions aiming to offer realistic evaluation and thus objective conclusions’ extraction.

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Using fuzzy logic for localization in mobile sensor networks: simulations and experiments

Cited by 18 publications

References 7 publications

FLOGERA — A fuzzy logic event recognition algorithm in a WSN environment

FLOGERA — A fuzzy logic event recognition algorithm in a WSN environment

Hybrid localization scheme using K‐fold optimization with machine learning in WSN

On Event/Time Triggered and Distributed Analysis of a WSN System for Event Detection, Using Fuzzy Logic

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Using fuzzy logic for localization in mobile sensor networks: simulations and experiments

Cited by 18 publications

References 7 publications

FLOGERA &#x2014; A fuzzy logic event recognition algorithm in a WSN environment

FLOGERA &#x2014; A fuzzy logic event recognition algorithm in a WSN environment

Hybrid localization scheme using K‐fold optimization with machine learning in WSN

On Event/Time Triggered and Distributed Analysis of a WSN System for Event Detection, Using Fuzzy Logic

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Product

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FLOGERA — A fuzzy logic event recognition algorithm in a WSN environment

FLOGERA — A fuzzy logic event recognition algorithm in a WSN environment