Trust-ACO Routing Protocols in Wireless Sensor Networks

Seminar presentation slide

1. Trust-ACO Routing Protocols in Wireless Sensor Networks CSE 4120 Technical Writing & Seminar Khulna University Of Engineering & Technology Presented by : Md Mosabbir Hossen Sabbir Roll : 2007032 Dept. of CSE, KUET A Comparative Analysis of

2. Source & Publication Details Serial Title DOI Published Year 01 Trust Based Secure and Energy Efficient Routing Protocol for Wireless Sensor Networks 10.1109/ACCESS.2021.3075959 2021 02 A Secure Trust Aware ACO-Based WSN Routing Protocol for IoT   10.25046/aj070311 2022 03 Ant Colony Optimization ACO Based Autonomous Secure Routing Protocol for Mobile Surveillance Systems   10.3390/drones6110351   2022

3. Outline PROBLEM 1 POBLEM 2 Problem Statement Background Motivation Introduction Methodology Overview Comparative Analysis Conclusion References

4. Problem Statement • Primary Problem: Developing routing mechanisms that simultaneously ensure security while minimizing energy consumption • Specific Challenges: Security Vulnerabilities: Various routing attacks targeting network integrity Energy Constraints: Limited battery power requiring conservation Dynamic Topologies: Frequent network changes in mobile applications Scalability Issues: Maintaining performance as networks grow • Traditional Approaches Limitations: – Cryptographic methods: computationally expensive – Authentication mechanisms: fail against internal attacks – Energy-efficient protocols: lack robust security

5. Background • Evolution of Routing Protocols: 1. Early Protocols (e.g., LEACH): – Focused on energy conservation – Cluster-based organization – Limited security mechanisms 2. Cryptographic Approaches (e.g., SPINS): – Added encryption and authentication – Computationally expensive – Failed against internal attacks 3. Trust-Based Approaches (e.g., RFSN): – Behavior-based trust evaluation – Monitoring node actions – More effective against internal threats 4. Bio-Inspired Approaches (e.g., ACO): – Minimal computational overhead – Collective intelligence of swarm systems – Adaptive to network changes

6. WSN & IoT networks face critical challenges in security and energy efficiency due to resource-limited sensor nodes Trust-based routing helps detect and avoid malicious nodes for secure communication ACO-based optimization improves path selection and reliability, motivating a review of recent Trust + ACO routing approaches. Motivation

7. This review focuses on secure routing protocols that integrate trust management and Ant Colony Optimization (ACO) for improved performance in WSN, IoT , and mobile surveillance systems . The three selected papers propose different Trust + ACO routing approaches to enhance security , energy efficiency , and reliability under various network conditions . By comparing their techniques and results, this review identifies common contributions, differences, and opportunities for future improvements in secure routing research . INTRODUCTION

8. Methodology Overview 1.TBSEER: Trust-Based Secure and Energy Efficient Routing – Multi-dimensional trust evaluation – Adaptive penalty mechanisms – Cluster-based architecture 2. Trust-Aware ACO-Based Protocol: – Integration of trust with Ant Colony Optimization – Enhanced state transition formula – Beta reputation system 3. P-BIOSARP: Power-Aware Autonomous ACO Routing – Energy-centric probability function – Mobile surveillance focus – Lightweight encryption • Analysis Approach: Performance evaluation, security assessment, energy efficiency comparison

9. METHODOLOGY Methodology Overview 1. Direct Trust: – Adaptive penalty coefficient – Volatilization factor – Rapid malicious node identification 2. Indirect Trust: – Computed by Sink node – Reduces communication overhead – Centralized computation 3. Energy Trust: – Based on remaining node energy – Ensures energy-aware routing • Key Innovation: Adaptive mechanisms that accelerate malicious node identification • Architecture: Cluster-based with high-trust nodes as cluster heads • Multi-Dimensional Trust Evaluation System: TBSEER: Architecture and Mechanisms

10. Methodology Overview Figure: TBSEER : Architecture and Mechanisms

11. Methodology Overview • Integration of Trust with Ant Colony Optimization : • Enhanced State Transition Formula : – Incorporates trust metrics – Considers residual energy levels – Accounts for node mobility – Balances multiple factors • Trust Evaluation System: – Beta reputation system (Bayesian formulation) – Direct observations and second-hand information – Fuzzy classification system: • Distrust • Uncertain • Completely trust Trust-Aware ACO-Based Protocol: Architecture

12. METHODOLOGY METHODOLOGY Methodology Overview Figure: Trust-Aware ACO-Based Protocol: Architecture

13. METHODOLOGY Methodology Overview P-BIOSARP: Architecture and Mechanisms • Power-Aware Autonomous ACO for Mobile Surveillance : • Key Innovation: Modified probability function – Maximum weight to remaining energy parameters – Minimal weights to link quality and delay – Energy-centric approach • Mobile Surveillance Features: – Specialized mobile node handling – Moderate speed assumptions – Gateway relocation mechanisms – Trusted sink node • Security Mechanisms: – Lightweight encryption – Packet header protection – Resource-constrained operation

14. Methodology Overview Figure: P-BIOSARP : Architecture and Mechanisms

15. Comparative Analysis Trust Computation • Trust Computation Mechanisms Comparison: Protocol Trust Approach Key Features Complexity TBSEER Multi-dimensional Direct, indirect, energy trust; adaptive penalty High Trust-Aware ACO Beta reputation Bayesian formulation; fuzzy classification Medium P-BIOSARP Simplified Energy-based; trusted gateway assumption Low

16. Comparative Analysis • Design Philosophies: – TBSEER: Comprehensive trust evaluation – Trust-Aware ACO: Integrated trust-optimization – P-BIOSARP : Streamlined for energy efficiency • Trade-offs: – Complexity vs. efficiency – Security thoroughness vs. computational overhead – Adaptability vs. resource consumption

17. Comparative Analysis Energy Efficiency Approaches • TBSEER : – Centralized indirect trust computation – Reduced communication overhead – Cluster-based energy conservation • Trust-Aware ACO : – ~50% energy reduction vs. conventional ACO – Integrated optimization approach – Avoids separate security mechanisms • P-BIOSARP: – Maximum energy prioritization – Modified probability function – Specialized for mobile surveillance • Security-Energy Trade-offs: – TBSEER: Balanced approach – Trust-Aware ACO: Integrated optimization – P-BIOSARP: Energy-first philosophy

18. CONCLUSION Key Findings: No One-Size-Fits-All Solution: Optimal approach depends on specific application requirements Integration is Key: Combining multiple techniques offers the most promising path forward Mobility is Critical: Increasing importance of dynamic topology support Energy-Security Balance: Fundamental trade-off requires careful consideration

19. CONCLUSION • Protocol Strengths: – TBSEER: Comprehensive trust evaluation for static environments – Trust-Aware ACO: Balanced optimization for semi-dynamic networks – P-BIOSARP: Energy efficiency for mobile surveillance • Remaining Challenges: – Truly scalable trust management systems – Machine learning integration for enhanced security – Self-adaptive protocols for varying conditions

20. Refrences REFERENCES : [ 1] H. Hu, Y. Han, M. Yao, and X. Song, “Trust based secure and energy efficient routing protocol for wireless sensor networks,” IEEE Access , vol. 9, pp. 10585–10600, 2021 . [2] A. Sharmin , F. Anwar, S. M. A. Motakabber , and A. H. A. Hashim , “A secure trust aware ACO-based WSN routing protocol for IoT ,” Advances in Science, Technology and Engineering Systems Journal , vol. 7, no. 3, pp. 95–105, 2022 . [3] K. Saleem and I. Ahmad, “Ant colony optimization ACO based autonomous secure routing protocol for mobile surveillance systems,” Drones , vol. 6, no. 11, p. 351, 2022.

21. THANK YOU