Probabilistic Path Selection in Mobile Wireless Sensor Networks for Stochastic Events Detection

Zhang, Xiwei; Jia, Yu

doi:10.1007/978-3-642-23641-9_7

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…In i+1 round, the higher probability could be given to the links of good performance [6], thus, the selected probability of links of worse performance would be decreased. Compared with ESLA, BSLA improves convergence-speed by adjusting selected probability of links in time; BLSA can get more ideal estimated value in the case of small samples [4]. Thus Section 3 will design two algorithms based on Bayesian estimation.…”

Section: Preliminariesmentioning

confidence: 99%

“…Therefore according to the actual performance of WSN, estimating the quality of links and selecting the high-quality link will become an important basic research [3]. The empirical-algorithm has widely been used to select the network links, but the empirical-algorithm needs lots of data packages as testing samples [4]. Then this algorithm would consume more energy of nodes, while the nodes of WSN are battery-powered, and the energy is limited.…”

Section: Introductionmentioning

confidence: 99%

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Wireless Sensor Network Link Selection Algorithm with Bayesian Technique

Luo

Ren

2014

AMR

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In wireless sensor networks, traditional link selection algorithm needs lots of data packages as testing samples, but the nodes of WSN are battery-powered, so the energy is extremely limited. To overcome this shortcoming, the aim of this paper is to propose three new link selection algorithms based the concept of Bayesian approach. Simulation results demonstrate that the three algorithms based on Bayesian approach have a higher success rate than empirical-algorithm by about 10 percent in selecting the highest quality link with the case of small samples. Among them, BSLA-EB has a good adaptability and it can get better experimental results.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Network Link Selection Algorithm with Bayesian Technique

Luo

Ren

2014

AMR

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“…After i round, BLSA uses the new samples to modify the estimated value of the link quality. In i+1 round, the better link will be given higher selected probability, and the selected probability of poor link will become smaller [6]. Compared with TLSA, BLSA can obtain more ideal estimated value in the case of small samples.…”

Section: Preliminariesmentioning

confidence: 99%

“…Theorem 1 Supposed that the parameter θ is a random variable, and it is regarded as the success rate (packet received rate) of a link [6]. We assume the prior distribution of θ is uniform distribution in (0, 1) defined in Eq.…”

Section: New Bayesian Algorithm Of Link Selection 1)new Bayesian Algo...mentioning

confidence: 99%

A New Bayesian Algorithm for Wireless Sensor Network Link Selection

Luo

Ren

2014

AMM

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In wireless sensor network, there are several links from one node to another, selecting the high quality link from the links is an important basic research. As a general algorithm, the typical link selection algorithm is usually used in selecting the high quality link, but the typical link selection algorithm needs lots of data packages as the testing samples. But the energy of a wireless sensor network is limited; therefore, the typical link selection algorithm is not suitable for wireless sensor network. The paper puts forward two new Bayesian link selection algorithms. Simulation results showed that the news algorithms have better performance than typical link selection algorithm in the case of small samples.

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Probabilistic Path Selection in Mobile Wireless Sensor Networks for Stochastic Events Detection

Cited by 2 publications

References 13 publications

Wireless Sensor Network Link Selection Algorithm with Bayesian Technique

Wireless Sensor Network Link Selection Algorithm with Bayesian Technique

A New Bayesian Algorithm for Wireless Sensor Network Link Selection

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