Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration
Abstract
:1. Introduction
2. Literature Survey
2.1. Review Based on Internet of Things
2.2. Review Based on Embedded Systems
2.3. Review Based on Fuzzy Rule Algorithms
2.4. Reviews Based on Trust Planning Mechanism
3. Problem Statement
4. Research Methodology
4.1. Fuzzy Rule Algorithm (FRA)
Algorithm 1: Trust node prediction |
Configuration -one Node for each query FC: Collection of fuzzy scores = First trust score = Last trust score = Prior determined cutoff score = Trusted node Outcome: Trusted Node Process Start then If Else if End if End if → Collection If then Solutions End if End |
Algorithm 2: Malignant nodes detection |
Configuration = Collection of fuzzy scores = Prior determined cutoff score = Novel trust score for malignant node detection U: Trust value up/down (0.05) per request Outcome: Malignant node Procedure: Initialize then then then Else End |
4.2. Trust Planning Mechanism (TPM)
5. Result and Discussion
5.1. Trust Prediction Accuracy (%)
5.2. Throughput (Mbps)
5.3. Latency (ms)
5.4. Energy Consumption (%)
5.5. Malignant Node Detection (%)
5.6. Computation Time (s)
6. Discussion
7. Discussion of Analysis of the Presented Fuzzy Method with the Exact Method
8. Conclusions
- The integration of the FRA and trust planning mechanism offers enhanced trust management capabilities in IoT-embedded systems. By incorporating fuzzy logic, the approach effectively handles uncertainty, imprecision, and complex relationships among entities, enabling more accurate and adaptive trust assessment. This can provide decision makers with valuable insights into the trustworthiness of IoT devices and facilitate informed decision making processes.
- The proposed approach’s ability to adapt to changing conditions and handle uncertainties contributes to the robustness of trust management in dynamic IoT environments. The fuzzy-logic-based reasoning allows for flexible adjustments to evolving trust relationships and varying levels of trustworthiness, ensuring the system’s resilience against environmental changes and potential threats.
- It is important to consider the computational complexity associated with the integration of FRA and trust planning mechanism. The process of fuzzy inference, rule evaluation, and defuzzification can introduce computational overhead, especially for large-scale IoT deployments or real-time applications. Further research and optimization techniques may be needed to mitigate potential performance issues.
- The proposed approach’s applicability may vary depending on the specific characteristics and requirements of the IoT-embedded systems integration. The effectiveness of the FRA and trust planning mechanism may depend on the availability and quality of input data, the complexity of trust relationships, and the nature of the IoT environment. Thorough evaluation and validation of the approach in various scenarios would help assess its suitability for different contexts.
- The integration of the hybrid fuzzy rule algorithm and trust planning mechanism offers valuable insights into trust management in IoT-embedded systems. The approach enhances trust assessment, provides robustness in dynamic environments, and enables decision makers to make informed choices. However, the computational complexity and context-specific limitations should be considered when applying the proposed approach, highlighting the need for further research and validation to fully realize its potential in practical IoT deployments.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Methods | Trust Prediction Accuracy (%) |
---|---|
Trust2Vec [24] | 56 |
Trust-FTSR [25] | 81 |
RFA [26] | 62 |
EMBTR [27] | 71 |
FRA + TPM [Proposed] | 99 |
Number of Nodes | Throughput (Mbps) | ||||
---|---|---|---|---|---|
Trust2Vec [24] | Trust-FTSR [25] | RFA [26] | EMBTR [27] | FRA + TPM [Proposed] | |
10 | 3.2 | 6.2 | 9.2 | 5.9 | 10 |
20 | 3.1 | 6.9 | 8.1 | 4.5 | 9.7 |
30 | 2.6 | 6.2 | 8 | 4.3 | 8.2 |
40 | 1.5 | 6 | 7.3 | 4.1 | 9.9 |
50 | 1 | 5.3 | 7.5 | 4 | 9 |
Number of Nodes | Latency (ms) | ||||
---|---|---|---|---|---|
Trust2Vec [24] | Trust-FTSR [25] | RFA [26] | EMBTR [27] | FRA + TPM [Proposed] | |
10 | 7 | 9.1 | 6.3 | 3.3 | 2.3 |
20 | 7.1 | 6.4 | 6.7 | 4.1 | 2 |
30 | 8.3 | 5.3 | 6.1 | 4 | 1.5 |
40 | 9.3 | 7.9 | 6 | 5 | 1.1 |
50 | 6.3 | 8.9 | 5 | 5.2 | 1.7 |
Methods | Energy Consumption (%) |
---|---|
Trust2Vec [24] | 73 |
Trust-FTSR [25] | 81 |
RFA [26] | 62 |
EMBTR [27] | 91 |
FRA + TPM [Proposed] | 53 |
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Reddy, N.V.R.; Padmaja, P.; Mahdal, M.; Seerangan, S.; Vimal, V.; Talasila, V.; Cepova, L. Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration. Mathematics 2023, 11, 2546. https://doi.org/10.3390/math11112546
Reddy NVR, Padmaja P, Mahdal M, Seerangan S, Vimal V, Talasila V, Cepova L. Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration. Mathematics. 2023; 11(11):2546. https://doi.org/10.3390/math11112546
Chicago/Turabian StyleReddy, Nagireddy Venkata Rajasekhar, Pydimarri Padmaja, Miroslav Mahdal, Selvaraj Seerangan, Vrince Vimal, Vamsidhar Talasila, and Lenka Cepova. 2023. "Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration" Mathematics 11, no. 11: 2546. https://doi.org/10.3390/math11112546