1. LDTS: A Lightweight and Dependable Trust System for Clustered Wireless Sensor Networks 1 Presented by: Ting Hua Authors: Xiaoyong Li, Feng Zhou, and Junping Du

2. Outline 2 Motivation Clustered WSN Model Lightweight Scheme for Trust Decision-Making Theoretical analysis and evaluation Simulation-based analysis and evaluation Conclusion

3. Motivation 3 Limited work focus on – Resource efficiency of clustered WSNs fail to consider the problem of resource constraints of nodes used complex algorithms to calculate nodes’ trustworthiness – Dependability of the trust system itself Current: collect remote feedback and then aggregate s such feedback to yield the global reputation for the nodes Problem: How about open or hostile WSN environment contains a large number of undependable (or malicious) nodes?

4. Outline 4 Motivation Clustered WSN Model Lightweight Scheme for Trust Decision-Making Theoretical analysis and evaluation Simulation-based analysis and evaluation Conclusion

5. Clustered WSN Model 5 Nodes – CH: cluster head – CM: cluster member – BS: base station Communications – Inter-cluster: A CM can communicate with their CH directly. – Intra-cluster: A CH can forward the aggregated data to the central BS through other CHs.

6. Outline 6 Motivation Clustered WSN Model Lightweight Scheme for Trust Decision-Making Theoretical analysis and evaluation Simulation-based analysis and evaluation Conclusion

7. Trust Decision-Making at CM Level 7 Decision making: past interaction records? – Yes: CM-to-CM Direct trust degree (DTD) # of successful and unsuccessful interactions Interaction: cooperation of two CMs, e.g., node x sends a message to CH i via node y – Successful: node y forwarded such message to CH – Unsuccessful: » No retransmission of the packet within a threshold time » Overheard packet is illegally fabricated – No: CH-to-CM Indirect trust degree (ITD) send a feedback request to CH

8. CM-to-CM Direct Trust Calculation 8 a window of time # of successful interactions of node x with y # of unsuccessful interactions of node x with y strict punishment for unsuccessful interactions

9. CH-to-CM Feedback Trust Calculation 9 # of positive feedback # of negative feedback Assumption: CH is trustworthy within its cluster!

10. Trust Decision-Making at CH Level 10 Decision making: calculate for direct trust and feedback trust simultaneously CH-to-CH direct trust – # of successful and unsuccessful interactions BS-to-CH feedback trust – BS periodically asks all CHs for their trust ratings on their neighbors. – CH send a feedback request to BS

11. CH-to-CH Direct Trust Calculation 11 # of unsuccessful interactions of CH i with CH j strict punishment for unsuccessful interactions a window of time# of successful interactions of CH i with CH j

12. BS-to-CH Feedback Trust Calculation 12 feedback of CH k toward CH j # of positive feedback # of negative feedback quality of feedback

13. Self-Adaptive Global Trust Aggregation at CHs 13 # of successful interactions BS-to-CH feedback trust CH-to-CH Direct Trust # of positive feedbacks increasing α, Φ(x) quickly approaches 1

14. Outline 14 Motivation Clustered WSN Model Lightweight Scheme for Trust Decision-Making Theoretical analysis and evaluation Simulation-based analysis and evaluation Conclusion

15. Dependability Analysis Against Malicious Attacks

25. Communication Overhead Analysis and Comparison Assume: Network consists of m clusters (including the BS) average size of clusters is n (including the CH of the cluster) # of CM send n requests and receive n responses communicati on overhead of one node

26. Storage Overhead Analysis and Comparison

27. Outline 27 Motivation Clustered WSN Model Lightweight Scheme for Trust Decision-Making Theoretical analysis and evaluation Simulation-based analysis and evaluation Conclusion

28. LDTS Simulator and Environment

29. Overhead Evaluation and Comparison

31. Dependability Evaluation and Comparison

33. Outline 33 Motivation Clustered WSN Model Lightweight Scheme for Trust Decision-Making Theoretical analysis and evaluation Simulation-based analysis and evaluation Conclusion

34. Lightweight trust evaluating scheme – cooperations between CMs – cooperations between CHs Dependability-enhanced trust evaluating approach – cooperations between CHs Self-adaptive weighting method – CH’s trust aggregation