Aggregation Node Selection Using Energy-Trust Integration
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Solution Overview
Problem
Traditional wireless sensor networks are vulnerable to internal attacks due to the capture of legitimate nodes by malicious attackers, which can compromise the security of the network and lead to data breaches, as existing security mechanisms are ineffective in identifying and excluding compromised nodes in a timely manner.
Innovation Solution
A method and device for selecting an aggregation node based on an energy-trust integrated value, calculated using trust values and remaining energy ratios, which involves acquiring trust values through direct and indirect monitoring, normalizing energy ratios, and integrating them with trust values to identify nodes with high security and energy efficiency for aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cryptographic mechanisms are used for security, then data confidentiality and authentication are ensured, but the network becomes vulnerable to internal attacks when nodes are captured
Solution Approach 1:
The patent implements preliminary trust evaluation and monitoring before nodes are compromised. Trust values are continuously calculated and updated based on node behaviors, energy consumption patterns, and communication activities. This preliminary assessment allows the system to identify and isolate captured nodes before they can cause significant damage to the network
Solution Approach 2:
The patent establishes a feedback mechanism where trust values are dynamically adjusted based on ongoing monitoring of node behaviors. When anomalies are detected (such as unexpected energy consumption or communication patterns), the trust value decreases, triggering re-evaluation and potential isolation of the node. This continuous feedback loop enables real-time detection and response to internal threats
2Reliability
If all nodes are monitored for trust evaluation, then security against internal attacks improves, but energy consumption and system complexity increase
Solution Approach 1:
The patent implements local quality by differentiating monitoring intensity based on node characteristics and risk levels. High-trust nodes with stable behaviors undergo less frequent evaluation, while nodes with suspicious patterns or critical roles receive intensified monitoring. This localized approach concentrates energy expenditure on high-risk areas rather than uniformly monitoring all nodes
Solution Approach 2:
The patent dynamically adjusts monitoring parameters such as evaluation frequency, trust calculation depth, and data collection intensity based on current network conditions and node trust levels. When overall network trust is high, monitoring intensity is reduced to conserve energy. When threats are detected, parameters are adjusted to increase surveillance on specific nodes or regions
3Measurement precision
If trust values are calculated based on direct and indirect monitoring, then accuracy of node evaluation improves, but calculation complexity and time increase
Solution Approach 1:
The patent segments the trust evaluation process into distinct components: direct trust calculation from immediate neighbors, indirect trust inference from multi-hop observations, and behavioral analysis separate from structural analysis. Each component can be calculated independently and then integrated, allowing for modular optimization and reducing overall computational complexity while maintaining comprehensive evaluation accuracy
Data Source
AI summary
A method and a device for selecting an aggregation node are provided. The method includes: acquiring a trust value list for each of nodes in a cluster, the trust value list for each of the nodes including trust values for each of the nodes, acquired by remaining nodes in the cluster; acquiring an actual trust value for each of the nodes according to the trust value list for each of the nodes; calculating an actual remaining energy ratio of each node according to a self-calculated remaining energy ratio of the node calculated by itself and other-calculated remaining energy ratios for the node calculated by the remaining nodes in the cluster; calculating an energy-trust integrated value for each of the nodes according to the actual trust value and the actual remaining energy ratio; and selecting an aggregation node according to the energy-trust integrated values for the nodes.