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Fault-Tolerant Computing in Sensor Networks: Addressing Byzantine Faults and Agreement Problems

This document explores fault-tolerant computing within sensor networks, focusing on Byzantine faults and the agreement problem. Sensor fusion is essential in environments where multiple sensors operate, yet some may be faulty. The paper discusses precision and accuracy requirements in decision-making processes, emphasizing the necessity for non-faulty nodes to achieve consensus. It further outlines methods to ensure exact agreement among nodes despite inconsistencies, thus enhancing the reliability of sensor networks for effective data representation and decision-making under fault conditions.

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Fault-Tolerant Computing in Sensor Networks: Addressing Byzantine Faults and Agreement Problems

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  1. ECE 753: FAULT-TOLERANT COMPUTING Kewal K.Saluja Department of Electrical and Computer Engineering Byzantine faults and Agreement Problem (Sensor Networks)

  2. Overview • Sensor fusion problem • Agreement in the presence of faults • Precision and accuracy issue • Byzantine faults ECE 753 Fault Tolerant Computing

  3. Introduction • What is sensor network • Sensor network • Its relation to agreement problem • Motivation • Multiple sensors sensing same environment in different manner, but need to arrive at a common and same decision – some of the sensors may be faulty ECE 753 Fault Tolerant Computing

  4. Sensor Fusion Problem v5 1 v3 1 v1 1 v4 v6 1 1 v2 v7 1 1 v10 1 v9 1 v8 1 v13 1 v12 v11 1 1 ECE 753 Fault Tolerant Computing

  5. Fault Tolerant Fusion • Fusion as described earlier but some of the nodes/sensors may be faulty • Agreement requirement: • Precision requirement: all non faulty nodes in region make same decision. • Accuracy requirement: the decision is representative of the environment. For example: decision is “detect” if there is an object in the region. ECE 753 Fault Tolerant Computing

  6. Agreement Accuracy Precision ECE 753 Fault Tolerant Computing

  7. Agreement (cont.) A A 0 0 0 1 B C B C 1 1 B can not differentiate between 2 scenarios. Agreement requires 3m+1 nodes to tolerate m Byzantine faults ECE 753 Fault Tolerant Computing

  8. Fault Tolerant Fusion • Precision: Exact agreement solves inconsistency problem • All non faulty nodes obtain the same set of values 1 S 0 S ? ? 0 S ECE 753 Fault Tolerant Computing

  9. Fault Tolerant Fusion • Precision: Exact agreement solves inconsistency problem • All non faulty nodes obtain the same set of values {1} 1 1 1 1 1 1 0 1 ? ? 1 1 0 {1} 1 1 1 0 1 {1} ECE 753 Fault Tolerant Computing

  10. Fault Tolerant Fusion • Precision: Exact agreement solves inconsistency problem • All non faulty nodes obtain the same set of values {1,0} 1 0 1 0 0 1 0 1 0 ? 0 ? ? 0 {1,0} 1 0 0 0 {1,0} ECE 753 Fault Tolerant Computing

  11. Fault Tolerant Fusion • Precision: Exact agreement solves inconsistency problem • All non faulty nodes obtain the same set of values {1,0,0} 0 1 0 1 0 0 0 0 ? 0 0 ? ? {1,0,0} 0 0 1 0 0 {1,0,0} ECE 753 Fault Tolerant Computing

  12. Fault Tolerant Fusion • Precision: Exact agreement solves inconsistency problem • All non faulty nodes obtain the same set of values {1,0,0,0} 1 0 0 0 1 0 0 0 0 0 ? ? ? {1,0,0,0} 1 0 0 0 0 {1,0,0,0} ECE 753 Fault Tolerant Computing

  13. Fault Tolerant Fusion (cont.) • Accuracy: consistent outliers remain in the set of values • Dropping highest and lowest values m S N-2m used for decision m ECE 753 Fault Tolerant Computing

  14. Value fusion Decision fusion Some research issues:Two approaches for detection v1 v2 ? v4 v1 v2 ? v4 fusion decision S S ? S 1 1 ? 0 decision fusion 1 1 ? 1 1 1 ? 1 ECE 753 Fault Tolerant Computing

  15. Value fusion 1. Perform exact agreement on values 2. Drop highest m and lowest m values 3. Compute average of remaining values 4. Compare to threshold Decision fusion 1. Compare to threshold 2. Perform exact agreement on decision 3. Drop highest m and lowest m decisions 4. Compute average of remaining decision and compare to threshold Two approaches for detection ECE 753 Fault Tolerant Computing

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