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FIND: Faulty Node Detection for Wireless Sensor Networks

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FIND: Faulty Node Detection for Wireless Sensor Networks

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  1. FIND: Faulty Node Detection for Wireless Sensor Networks SenSys 2009 ShuoGuo, ZiguoZhong, TianHe University of Minnesota, Twin Cities Jeffrey

  2. Outline • Abstract • Introduction • Related Work • Model and Assumptions • Main Design • Practical Issues • System Evaluation • Large Scale Simulation Study • Conclusions

  3. Outline • Abstract • Introduction • Related Work • Model and Assumptions • Main Design • Practical Issues • System Evaluation • Large Scale Simulation Study • Conclusions

  4. Abstract • Wireless sensor networks (WSN) promise researchers a powerful instrument • For observing sizable phenomena with fine granularity over long periods • Since the accuracy of data is important to the whole system’s performance • Detecting nodes with faulty readings is an essential issue in network management

  5. Abstract • As a complementary solution to detecting nodes with functional faults • This paper proposes FIND, a novel method to detect nodes with data faults • Neither assumes a particular sensing model • Nor requires costly event injections • After the nodes in a network detect a natural event • FIND ranks the nodes based on their sensing readings as well as their physical distances from the event

  6. Abstract • FIND works for systems where the measured signal attenuates with distance • A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank • Theoretically, we show that average ranking difference is a provable indicator of possible data faults

  7. Abstract • FIND is extensively evaluated in • Simulations • Two test bed experiments with up to 25 micaz nodes • Evaluation shows that • FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments

  8. Outline • Abstract • Introduction • Related Work • Model and Assumptions • Main Design • Practical Issues • System Evaluation • Large Scale Simulation Study • Conclusions

  9. Introduction • Wireless Sensor Networks (WSNs) have been used in many application domains • Habitat monitoring • Infrastructure protection • Scientific exploration • The accuracy of individual nodes’ readings is crucial in these applications • In a surveillance network, the readings of sensor nodes must be accurate • To avoid false alarms and missed detections

  10. Introduction • Although some applications are designed to be fault tolerant to some extent • It can still significantly improve the whole system’s performance • Removing nodes with faulty readings from a system with some redundancy • Or replacing them with good ones • At the same time prolong the lifetime of the network

  11. Introduction • To conduct such after-deployment maintenance (e.g., remove and replace) • It is essential to investigate methods for detecting faulty nodes

  12. Introduction • In general, wireless sensor nodes may experience two types of faults that would lead to the degradation of performance • Function fault • Data fault

  13. Function Fault • Typically results in • Crash of individual nodes • Packet loss • Routing failure • Network partition • This type of problem has been extensively studied and addressed by • Either distributed approaches through neighbor coordination • Or centralized approaches through status updates

  14. Data Fault • A node behaves normally in all aspects except for its sensing results • Leading to either significant biased or random errors • Several types of data faults exist in wireless sensor networks

  15. Data Fault • Although constant biased errors can be compensated for by after-deployment calibration methods • Random and irregular biased errors can not be rectified by a simple calibration function

  16. Outlier Detection? • One could argue that random and biased sensing errors can be addressed with outlier detection • A conventional technique for identifying readings that are statistically distant from the rest of the readings • However, the correctness of most outlier detection relies on the premise that data follow the same distribution

  17. Outlier Detection? • This holds true for readings such as temperatures • Considered normally uniform over space • However, many other sensing readings (e.g., acoustic volume and thermal radiation) in sensor networks attenuate over distance • A property that invalidates the basis of existing outlier-based detection methods

  18. FIND • This paper proposes FIND • a novel sequence-based detection approach for discovering data faults in sensor networks • assuming no knowledge about the distribution of readings • In particular, we are interested in Byzantine data faults with either biased or random errors • since simpler fail-stop data faults have been addressed sufficiently by existing approaches, such as Sympathy

  19. Ranking Violations In Node Sequences • Without employing the assumptions of event or sensing models • Detection is accomplished by identifying ranking violations in node sequences • A sequence obtained by ordering ids of nodes according to their readings of a particular event.

  20. Objective of FIND • The objective of FIND is to provide a blacklist containing all possible faulty nodes (with either biased or random error), in order of likelihood. • With such a list, further recovery processes become possible, including • (i) correcting faulty readings, • (ii) replacing malfunctioning sensors with good ones, • or (iii) simply removing faulty nodes from a network that has sufficient redundancy • As a result, the performance of the whole system is improved

  21. Main Contribution 1 • This is the first faulty node detection method • that assumes no a priori knowledge about the underlying distribution of sensed events/phenomena • The faulty nodes are detected based on their violation of the distance monotonicity property in sensing • which is quantified by the metric of ranking differences

  22. Main Contribution 2 • FIND imposes no extra cost in a network where readings are gathered as the output of the routine tasks of a network • The design can be generically used in applications with any format of physical sensing modality • Heat/RF radiation • Acoustic/seismic wave • As long as the magnitude of their readings roughly monotonically changes over the distance a signal travels

  23. Main Contribution 3 • We theoretically demonstrate that • The ranking difference of a node is a provable indicator of data faults • If the ranking difference of a node exceeds a specified bound, it is a faulty node

  24. Main Contribution 4 • We extend the basic design with three practical considerations • First, we propose a robust method to accommodate noisy environments • Where distance monotonicity properties do not hold well • Second, we propose a data pre-processing technique • To eliminate measurements from simultaneous multiple events • Third, we reduce the computation complexity of the main design • Using node subsequence.

  25. Outline • Abstract • Introduction • Related Work • Model and Assumptions • Main Design • Practical Issues • System Evaluation • Large Scale Simulation Study • Conclusions

  26. Related Work • In general, faults in a sensor network can be classified into two types • Function fault • Abnormal behaviors lead to the breakdown of a node or even a network as a whole • Data fault • A node behaves as a normal node in the network but generates erroneous sensing readings • difficult to identify by previous methods because all its behaviors are normal except the sensor readings it produces

  27. How to Resolve Data Fault • One way to solve this problem is after-deployment calibration • a mapping function (mostly linear) is developed to map faulty readings into correct ones • Performance of existing calibration methods • Depends on the correctness of the proposed model • Exhibits significant degradation in a real-world system • too-specific additional assumptions no long hold

  28. Outlier Detection • Outlier detection is a conventional method for identifying readings that depart from the norm • Correctness is based on the assumption that neighboring nodes have similar readings

  29. Outline • Abstract • Introduction • Related Work • Model and Assumptions • Main Design • Practical Issues • System Evaluation • Large Scale Simulation Study • Conclusions

  30. Model and Assumptions • Assumption on Monotonicity • RSS of Radio Signals • Propagation Time of Acoustic Signals

  31. RSS of Radio Signals

  32. Propagation Time of Acoustic Signal

  33. Outline • Abstract • Introduction • Related Work • Model and Assumptions • Main Design • Practical Issues • System Evaluation • Large Scale Simulation Study • Conclusions

  34. Main Design

  35. Examples of Map Division

  36. Main Design

  37. Detection Sequence Mapping

  38. Detection Sequence Mapping

  39. Detection Sequence Mapping

  40. Main Design

  41. A Simple Example of Ranking Difference

  42. Ranking Differences Affected By Faulty Nodes

  43. Outline • Abstract • Introduction • Related Work • Model and Assumptions • Main Design • Practical Issues • System Evaluation • Large Scale Simulation Study • Conclusions

  44. Practical Issues • Detection in Noisy Environments • Simultaneous Events Elimination • Subsequence Estimation

  45. Outline • Abstract • Introduction • Related Work • Model and Assumptions • Main Design • Practical Issues • System Evaluation • Large Scale Simulation Study • Conclusions

  46. System Evaluation • Evaluate the performance of FIND in two scenarios • In the first scenario, an accurate is assumed so that • The firstN nodes with the largest ranking differences are selected as faulty nodes • without using the detection algorithm in Algorithm 1

  47. System Evaluation • In the second scenario, accurate a is unavailable and Algorithm 1 is used for selecting faulty nodes • whose ranking differences are greater than B • is still used for computing Pr( ¯s|s) but it can be less accurate).

  48. On Radio Signal • Deploy 25 MicaZ nodes in grids (5×5) on a parking lot • The distance between each row and column is 5m

  49. On Radio Signal