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Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processing

Raju, Madhanmohan
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1393237072

Abstract Details

2013, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
We propose a group based real-time fault-tolerant physical intrusion detection system in an indoor scenario using Received Signal Strength Indicator (RSSI), to enhance security in wireless sensor networks considering its importance. Since there are a lot of techniques available to solve this problem in an outdoor scenario, we focus our research for the indoor environment. We provide a unique and novel approach, by applying a set of Fuzzy Logic (FL) rules on our distributed protocol before merging the beliefs of the fuzzy membership classes using Transferable Belief Model (TBM). Even though other techniques that have been designed earlier provide a solution to this problem, almost all of the techniques depend on incorporating additional sensor hardware. In some cases, sensor technology is even combined with other technologies such as cameras, motion sensors, video camera, etc. This makes the solutions complex, expensive, and difficult to deploy. However, there are published works that address this problem by measuring the drop in the RSSI. At the same time, many of the published works show that RSSI is an unreliable and unstable metric. Hence, we carry out an exhaustive experimentation to identify the behavior of RSSI both indoors and outdoors. The unstable characteristic of RSSI is clearly evident from these results. But, we embrace the unreliability of RSSI by using an additional metric, Link Quality Indicator (LQI) as a filter to localize the node in a network. Our approach helps in obtaining a tighter bound on the number of possible distances that any given two nodes are away from or to one another. Again, through experimental results, we observe a drastic reduction in the number of possible distances and show how RSSI and LQI can be used in combination for node localization. While, this reduced the number of possible distances, there were still numerous distances. Therefore, we propose a distributed protocol which employed Fuzzy Logic (FL) and Transferable Belief Model (TBM). FL aided in translating the distances in linguistic terms and in calculating the beliefs of each of the membership classes. On the other hand, TBM enabled us to merge these beliefs to arrive at an improved decision. This combination helped us to handle uncertainty with ease. Finally, we carry out a similar approach to the physical intrusion detection problem. So, we model a real-time fault-tolerant intrusion detection system. Our system solution relies only on RSSI and in a distributed manner. We do not depend on any special hardware. In fact, we try to remove the requirement of all but bare minimum hardware. The combination of FL and TBM provided immense advantages. Thus, we propose a readily applicable solution for physical intrusion detection in wireless sensor networks.
Dharma Agrawal, D.Sc. (Committee Chair)
Prabir Bhattacharya, Ph.D. (Committee Member)
Anca Ralescu, Ph.D. (Committee Member)
112 p.
Computer Science
RSSI; LQI; Transferable Belief Model; Fuzzy Logic; Security in wireless sensor networks; Physical intrusion Detection

Recommended Citations

Citations

  • Raju, M. (2013). Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processing [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1393237072

    APA Style (7th edition)

  • Raju, Madhanmohan. Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processing. 2013. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1393237072.

    MLA Style (8th edition)

  • Raju, Madhanmohan. "Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processing." Master's thesis, University of Cincinnati, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1393237072

    Chicago Manual of Style (17th edition)

ucin1393237072
388
© 2013, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.