Localization of 3D WSN using Mamdani Sugano fuzzy weighted centriod approaches

Nanda, Monika; Kumar, Ashok; Kumar, Sandeep

doi:10.1109/sceecs.2012.6184812

Cited by 13 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of anchors involved in this method is controlled by use of an optimized threshold. A range free WCL for 3D WSN using Mamdani & Sugano Fuzzy Inference System (FIS) is presented in [20]. The method involves computing the edge weights from the RSSI values by making use of the Mamdani & Sugano inference system where 121 anchor node were employed.…”

Section: Comparison With Other Related Literaturementioning

confidence: 99%

Localization for Wireless Sensor Networks: A Neural Network Approach

Kumar¹,

Sharma²,

Vans³

2016

IJCNC

View full text Add to dashboard Cite

show abstract

Section: Comparison With Other Related Literaturementioning

confidence: 99%

Localization for Wireless Sensor Networks: A Neural Network Approach

Kumar¹,

Sharma²,

Vans³

2016

IJCNC

View full text Add to dashboard Cite

show abstract

“…The high-resolution range-independent localization (HiRLoc) [14] and modified HiRLoc [7] scheme used omni directional antennas. Recently, the use of artificial intelligence in WSNs [8][9][10]15] has been on the rise. The following paragraph briefly describes some of these algorithms.…”

Section: ⅱ Related Workmentioning

confidence: 99%

“…where as the other methods present the results from simulations. The proposed 12-12-2 feed-forward ANN gives a accuracy that is higher than the 2D-WCL [18] , which used 100 anchor nodes for localization and the Mamdani and Sugano FIS [9] system utilizing 121 anchor nodes. Using higher number of anchor nodes mean message exchange take place at the mobile node for localization, which directly results in an increased energy consumption thus resulting in reducing the battery life of the sensor node.…”

Section: Number Of Anchor Nodesmentioning

confidence: 99%

“…Node localization is essential for reporting the source of the events, supporting the group querying of sensors, routing, and for answering questions concerning the network coverage [4] . Over the years, various solutions to the problem of node localization have been reported by researchers [2,[5][6][7][8][9][10][11][12][13][14] . The advantage of knowing the location information of sensor nodes are that it can be used to identify the location of an event of interest and responded to in a timely and efficient manner.…”

Section: ⅰ Introductionmentioning

confidence: 99%

“…Layout of the experimental setup laboratory & Sugano Fuzzy Inference System (FIS) is presented in[9]. In this method, the edge weights are computed based on the RSSI values via the Mamdani & Sugano inference system having 121 anchor nodes.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks

Kumar¹,

Jeon²,

Lee³

2014

The Journal of Korea Information and Communications Society

View full text Add to dashboard Cite

BSTRACTOne of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position)

show abstract

A Low Power Consumption Locating Method of Wireless Sensor Network for Particle Impact Drilling Monitoring System

Li¹,

Yao

2019

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Localization of 3D WSN using Mamdani Sugano fuzzy weighted centriod approaches

Cited by 13 publications

References 4 publications

Localization for Wireless Sensor Networks: A Neural Network Approach

Localization for Wireless Sensor Networks: A Neural Network Approach

Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks

A Low Power Consumption Locating Method of Wireless Sensor Network for Particle Impact Drilling Monitoring System

Contact Info

Product

Resources

About