Secure algorithm based data aggregation for sensor networks

This paper attempts to solve the problem of aggregating data from a large number of sensors whereby an unknown number of sensors could potentially be reporting false data. These malicious sensors could either be operating independently or be working together to launch a collusive attack. This is in...

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Main Author: Chai, Jeremy Wen Zhang
Other Authors: Zhang Jie
Format: Final Year Project
Language:English
Published: 2016
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Online Access:http://hdl.handle.net/10356/66764
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-667642023-03-03T20:38:42Z Secure algorithm based data aggregation for sensor networks Chai, Jeremy Wen Zhang Zhang Jie School of Computer Engineering DRNTU::Engineering This paper attempts to solve the problem of aggregating data from a large number of sensors whereby an unknown number of sensors could potentially be reporting false data. These malicious sensors could either be operating independently or be working together to launch a collusive attack. This is in an attempt to force the algorithm to report an aggregated value that deviates from the true value. Three different algorithm will be analysed. These are Median, Robust Iterative Filtering and MaxTrust. Median is shown to be a poor performer because in the event of a collusive attack where the percentage of malicious sensors is less than 50, the median will always come from the tailing values of a non-malicious sensor. Robust Iterative Filtering is than shown to not work because of a wrong assumption that the sum of bias will be zero. MaxTrust is shown to be the best performer among these three algorithm. This paper than make three improvements to the MaxTrust algorithm in order to improve its performance. This improved algorithm is called Time-Sensitive MaxTrust. The first improvement introduces the idea of time period to the algorithm. A sensor that had previously reported a false reading is likely to report a false reading again in subsequent time periods. Therefore, the algorithm will aggregate the current time period data using an aggregated trust value comprising of 90% historical trust value and 10% current trust value. This resulted in an improvement to the RMSE by 13% to 23%. The second improvement introduce the idea of replacing all reported precision with an arbitrary precision. This is to prevent attackers from attacking the algorithm by reporting a false precision value and also to solve the problem of sensors not reporting their precision value. Ideally, this arbitrary precision will be near the sensor’s normal operating precision. If this is not possible because the value is unknown as in the case of crowd source data, the precisions can be replaced with a sufficiently high precision. The last improvement takes previous time period trust value as the initial value for the MaxTrust algorithm instead of a fixed pre-determined value. This is to reduce the number of iterations needed to reach optimal solution. Experimental result has shown that this resulted in an improvement in both the mean and standard deviation of iterations needed to reach optimal solution while maintaining the RMSE. Bachelor of Engineering (Computer Science) 2016-04-26T01:40:25Z 2016-04-26T01:40:25Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/66764 en Nanyang Technological University 62 application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Chai, Jeremy Wen Zhang
Secure algorithm based data aggregation for sensor networks
description This paper attempts to solve the problem of aggregating data from a large number of sensors whereby an unknown number of sensors could potentially be reporting false data. These malicious sensors could either be operating independently or be working together to launch a collusive attack. This is in an attempt to force the algorithm to report an aggregated value that deviates from the true value. Three different algorithm will be analysed. These are Median, Robust Iterative Filtering and MaxTrust. Median is shown to be a poor performer because in the event of a collusive attack where the percentage of malicious sensors is less than 50, the median will always come from the tailing values of a non-malicious sensor. Robust Iterative Filtering is than shown to not work because of a wrong assumption that the sum of bias will be zero. MaxTrust is shown to be the best performer among these three algorithm. This paper than make three improvements to the MaxTrust algorithm in order to improve its performance. This improved algorithm is called Time-Sensitive MaxTrust. The first improvement introduces the idea of time period to the algorithm. A sensor that had previously reported a false reading is likely to report a false reading again in subsequent time periods. Therefore, the algorithm will aggregate the current time period data using an aggregated trust value comprising of 90% historical trust value and 10% current trust value. This resulted in an improvement to the RMSE by 13% to 23%. The second improvement introduce the idea of replacing all reported precision with an arbitrary precision. This is to prevent attackers from attacking the algorithm by reporting a false precision value and also to solve the problem of sensors not reporting their precision value. Ideally, this arbitrary precision will be near the sensor’s normal operating precision. If this is not possible because the value is unknown as in the case of crowd source data, the precisions can be replaced with a sufficiently high precision. The last improvement takes previous time period trust value as the initial value for the MaxTrust algorithm instead of a fixed pre-determined value. This is to reduce the number of iterations needed to reach optimal solution. Experimental result has shown that this resulted in an improvement in both the mean and standard deviation of iterations needed to reach optimal solution while maintaining the RMSE.
author2 Zhang Jie
author_facet Zhang Jie
Chai, Jeremy Wen Zhang
format Final Year Project
author Chai, Jeremy Wen Zhang
author_sort Chai, Jeremy Wen Zhang
title Secure algorithm based data aggregation for sensor networks
title_short Secure algorithm based data aggregation for sensor networks
title_full Secure algorithm based data aggregation for sensor networks
title_fullStr Secure algorithm based data aggregation for sensor networks
title_full_unstemmed Secure algorithm based data aggregation for sensor networks
title_sort secure algorithm based data aggregation for sensor networks
publishDate 2016
url http://hdl.handle.net/10356/66764
_version_ 1759852917499625472