Design of Probability Density Function Targeting Efficient Coverage in Wireless Sensor Networks

2018 ◽  
Vol 105 (1) ◽  
pp. 61-85 ◽  
Author(s):  
Richa Mishra ◽  
Rajeev K. Tripathi ◽  
Ajay K. Sharma
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Wireless Sensor

Localization in Wireless Sensor Networks in Beacon Drifting Scenarios

2012 ◽  
Vol 562-564 ◽  
pp. 1234-1239
Author(s):  
Ming Xia ◽  
Qing Zhang Chen ◽  
Yan Jin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Probability Density ◽  
Sensor Nodes ◽  
Density Model ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Scenarios Simulation ◽  
Simulation Results

The beacon drifting problem occurs when the beacon nodes move accidentally after deployment. In this occasion, the localization results of sensor nodes in the network will be greatly affected and become inaccurate. In this paper, we present a localization algorithm in wireless sensor networks in beacon drifting scenarios. The algorithm first uses a probability density model to calculate the location reliability of each node, and in localization it will dynamically choose nodes with highest location reliabilities as beacon nodes to improve localization accuracy in beacon drifting scenarios. Simulation results show that the proposed algorithm achieves its design goals.

A novel mobile agent-based distributed evidential expectation maximization algorithm for uncertain sensor networks

2020 ◽  
pp. 014233122096958
Author(s):  
Mohiyeddin Mozaffari ◽  
Behrouz Safarinejadian ◽  
Mokhtar Shasadeghi
Keyword(s):  
Sensor Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Convergence Analysis ◽  
Density Function ◽  
Mobile Agent ◽  
Expectation Maximization ◽  
Convergence Criterion ◽  
Sufficient Statistics ◽  
Agent Based

In this paper, a novel mobile agent-based distributed evidential expectation maximization (MADEEM) algorithm is presented for sensor networks. The proposed algorithm is used for probability density function estimation and data clustering in the presence of uncertainties in sensor measurements. It is assumed that the sensor measurements are statistically modeled by a common Gaussian mixture model. The proposed algorithm maximizes a new generalized likelihood criterion in an iterative and distributed manner. For this purpose, mobile agents compute some local sufficient statistics by using local measurements of each sensor node. After the local computations, the global sufficient statistics are updated. At the end of iterations, the parameters of the probability density function are updated by using the global sufficient statistics. The mentioned process will be continued until the convergence criterion is satisfied. Convergence analysis of the proposed algorithm is also presented in this paper. After the convergence analysis, the simulation results show the promising performance of the proposed algorithm. Finally, the last part of the paper is devoted to the concluding remarks.

scholarly journals Localization Algorithm Based on Maximum a Posteriori in Wireless Sensor Networks

2011 ◽  
Vol 8 (1) ◽  
pp. 260302 ◽  
Author(s):  
Kezhong Lu ◽  
Xiaohua Xiang ◽  
Dian Zhang ◽  
Rui Mao ◽  
Yuhong Feng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Least Squares ◽  
Probability Density ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Maximum A Posteriori ◽  
Localization Error ◽  
A Posteriori ◽  
Localization Algorithm

Many applications and protocols in wireless sensor networks need to know the locations of sensor nodes. A low-cost method to localize sensor nodes is to use received signal strength indication (RSSI) ranging technique together with the least-squares trilateration. However, the average localization error of this method is large due to the large ranging error of RSSI ranging technique. To reduce the average localization error, we propose a localization algorithm based on maximum a posteriori. This algorithm uses the Baye's formula to deduce the probability density of each sensor node's distribution in the target region from RSSI values. Then, each sensor node takes the point with the maximum probability density as its estimated location. Through simulation studies, we show that this algorithm outperforms the least-squares trilateration with respect to the average localization error.

Principles of Wireless Sensor Networks

2012 ◽  
Author(s):  
Mohammad S. Obaidat ◽  
Sudip Misra
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor
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