Transmission Reliability Evaluation for Wireless Sensor Networks

Zhu, Xiaoyan; Lü, Yang; Han, Jiuqiang; Shi, Lei

doi:10.1155/2016/1346079

Cited by 27 publications

(16 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Network of 100 nodes used in the simulation model in a 500 m x 500m region with sensor nodes varying from 0-100 nodes and with different traffic. The proposed work is compared with RFBCH [28] and LEACH [30].…”

Section: Simulation and Performance Analysismentioning

confidence: 99%

Multi-Level Multi-Constraint Multi-Priority Fuzzy Sensor Routing (M3L-C-PFSR)

Agarkhed¹,

Kadrolli²,

Patil³

2019

IJITEE

View full text Add to dashboard Cite

In the field of Wireless Sensor Network (WSN), real time applications, the interesting research emerged in the field of QoS routing. Networks must address resource constraints although providing an accurate guarantee of quality of service (PDR, throughput, energy, End-to-End delay).This document presents the routing protocol compatible with QoS, namely [M3 L-C-PFSR] Multilevel, Multi constraint Multi Priority Fuzzy Sensor Routing Protocol which gives priority for important packets of important applications and also ensures the PDR, delay and throughput. Applying a scheme of multi level fuzzy based efficient buffer management to limit packet loss due to overflow. It supports lower throughput for packets which are lower in priority and higher throughput will be given for real-time prioritized packets which are assigned privileged higher priority, thus reducing end-to-end delay. The proposed work performance will be appraised [M3 L-C-PFSR] in NS2. The results of the simulation depicts that the proposed protocol [M3 L-C-PFSR] effectively minimizes the losses of critical packet, with different traffic conditions, ensuring the reliability and required delivery of data.

show abstract

Section: Simulation and Performance Analysismentioning

confidence: 99%

Multi-Level Multi-Constraint Multi-Priority Fuzzy Sensor Routing (M3L-C-PFSR)

Agarkhed¹,

Kadrolli²,

Patil³

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…The effect of these factors on link quality varies significantly and rapidly in time and space [7] and hence, unlike SN-related factors, cannot be reduced to a constant probability of failure throughout WSN mission time. In [12], the authors consider the problem of evaluating the transmission reliability of cluster-based and mesh-based WSN deployments. They define transmission reliability as the ratio of the packets received by a destination node to the whole packets generated by the transmission for a given period of time.…”

Section: Related Work On Wireless Sensor Network Reliabilitymentioning

confidence: 99%

“…They present transmission reliability evaluation models for the uplink and downlink traffic based on the assumptions that SNs are not subject to any hardware failures and that SNs only fail when their initial battery energy is exhausted. Although the timedependent models presented in [12] can help assess the transmission reliability over time for a given routing strategy, they are limited by the assumption that SNs cannot fail due to random hardware failures unrelated to battery exhaustion. Also, it is not possible to use the study in [12] to calculate or estimate the reliability of the WSN over a given mission time since coverage functionality is not considered.…”

Section: Related Work On Wireless Sensor Network Reliabilitymentioning

confidence: 99%

“…Based on these assumptions, they derive the reliability of the sink's WVS using the universal generating function (UGF) method. Similar to the study in [12], the scope of the study in [13] does not include evaluating the reliability of the WSN as a whole over a given mission time, since it is restricted to evaluating the reliability of detecting a single target/phenomena based on a fraction of the SNs in the WSN.…”

Section: Related Work On Wireless Sensor Network Reliabilitymentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive wireless sensor network reliability metric for critical Internet of Things applications

Deif

Gadallah

2017

J Wireless Com Network

View full text Add to dashboard Cite

Evaluating the reliability of a wireless sensor network (WSN) deployment is a highly important task especially when the WSN is used for a critical Internet of Things (IoT) application. In this paper, we introduce a novel comprehensive reliability metric to evaluate the reliability of WSN deployments over their intended mission time. Unlike the existing studies on the topic, the proposed metric takes into account that sensor nodes (SNs) are multi-component systems that are subject to different component failures, namely, sensor, transceiver, processor, and battery failures. Consequently, SNs are modeled as three-mode (on, relay, and off) systems instead of the simplistic two-mode (on and off) model adopted in the existing studies. To calculate the proposed reliability metric in a computationally efficient manner, we develop a search algorithm which generates the complete path set of the given WSN deployment. Extensive experimental results demonstrate the use of the proposed metric in evaluating the reliability of several WSN deployments under different operating conditions. Results also demonstrate the computational efficiency of the developed search algorithm used for calculating the proposed metric and the significant effect of using the proposed three-mode SN model on the accuracy of the evaluated reliability.

show abstract

“…Suppose station S has sensors as S= {S1, S2 , Sn} during the time interval with the help of finding the reliability of sensors. Different Techniques to do so can be seen in Zhu, Lu, Han, & Shi (2016). These techniques are beyond the scope of this thesis.…”

Section: Big Data Reliability Of System Model and Examplementioning

confidence: 99%

Big Data Quality Modeling And Validation

Desai

View full text Add to dashboard Cite

BIG DATA QUALITY MODELING AND VALIDATION by Khushali Desai The chief purpose of this study is to characterize various big data quality models and to validate each with an example. As the volume of data is increasing at an exponential speed in the era of broadband Internet, the success of a product or decision largely depends upon selecting the highest quality raw materials, or data, to be used in production. However, working with data in high volumes, fast velocities, and various formats can be fraught with problems. Therefore, software industries need a quality check, especially for data being generated by either software or a sensor. This study explores various big data quality parameters and their definitions, and proposes a quality model for each parameter. By using data from the Water Quality U. S. Geological Survey (USGS), San Francisco Bay, an example for each of the proposed big data quality models is given. To calculate composite data quality, prevalent methods such as Monte Carlo and neural networks were used. This thesis proposes eight big data quality parameters in total. Six out of eight of those models were coded and made into a final year project by a group of Master's degree students at SJSU. A case study is carried out using linear regression analysis, and all the big data quality parameters are validated with positive results. v ACKNOWLEDGMENTS I would like to thank my thesis advisor, Dr. Jerry Gao, for his tremendous support, always helping and guiding me whenever I was stuck. I would also like to thank San Jose State University for giving me the opportunity to do a thesis as part of my master's work. I am grateful for my father, mother and sister for proofreading my work and giving me input, a valuable perspective from people who do not belong to this field. Without them, my work would not be as fruitful. And special thanks to my thesis committee members, who always offered moral support, advice and technical reviews of the material for both this thesis and the original work. I would also like to mention Ariel Andrew and Jenn Hambly from the English writing center of SJSU for giving me essential input on my grammar mistakes. My friends were also my strength; I am indebted to Jayapriya, Chaitra, Sumana, Pranathi, and Nithya for being there with me during my research endeavor, and giving me moral support all the time. Finally, a huge thank you to David McCormick for copy editing the thesis to help reach the final version. vi

show abstract

Transmission Reliability Evaluation for Wireless Sensor Networks

Cited by 27 publications

References 26 publications

Multi-Level Multi-Constraint Multi-Priority Fuzzy Sensor Routing (M3L-C-PFSR)

Multi-Level Multi-Constraint Multi-Priority Fuzzy Sensor Routing (M3L-C-PFSR)

A comprehensive wireless sensor network reliability metric for critical Internet of Things applications

Big Data Quality Modeling And Validation

Contact Info

Product

Resources

About