DTN Routing Loop Prevention via Predictability Database
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Solution Overview
Problem
The PROPHET routing method in DTN networks suffers from routing loops when messages conveying delivery predictability are lost, leading to coherence loss in high/low relationships between adjacent nodes.
Innovation Solution
A network communication system that includes a delivery predictability database, calculation means, exchange means, reception decision means, and path calculation means to determine and manage delivery predictability for destination nodes, preventing registration of next hop nodes that match local nodes to avoid routing loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If PROPHET routing method is used to determine paths based on delivery predictability, then routing decisions can be made using local and adjacent node information, but routing loops occur when messages conveying delivery predictability are lost
Solution Approach 1:
The invention performs preliminary actions by having nodes periodically update and broadcast their delivery predictability values before routing decisions are made. Nodes maintain these predictability values in advance and propagate them proactively to adjacent nodes, ensuring that routing information is available even if some messages are lost during transmission.
Solution Approach 2:
The invention implements feedback mechanisms where nodes continuously monitor and update their delivery predictability values based on actual transmission success rates. This feedback loop allows nodes to adapt their routing decisions dynamically, and the system can detect when predictability values need updating due to changed network conditions or message losses.
2Measurement precision
If delivery predictability values are periodically updated and exchanged between adjacent nodes, then accurate path determination is achieved, but message loss causes coherence loss in high/low relationships
Solution Approach 1:
The invention applies beforehand cushioning by implementing redundancy in the delivery predictability update mechanism. Nodes broadcast their predictability values periodically to multiple adjacent nodes, and the system maintains historical records of these values. This cushioning effect ensures that even if some messages are lost, the network can reconstruct accurate delivery predictability relationships through alternative message paths or historical data.
Solution Approach 2:
The invention uses intermediate nodes as mediators in the delivery of predictability information. When a node needs to update its delivery predictability, it broadcasts this information to all adjacent nodes, which then propagate it further. This intermediary mechanism ensures robust information dissemination even in the presence of message losses, as multiple paths can carry the same information.
3Productivity
If nodes transfer bundles to adjacent nodes with higher delivery predictability, then transport efficiency improves, but routing loops can occur when predictability information is lost
Solution Approach 1:
The invention performs preliminary actions by having nodes continuously maintain and broadcast their delivery predictability values before bundle transfer decisions are made. This ensures that when bundle transfer decisions are needed, accurate and up-to-date predictability information is already available at all relevant nodes, enabling efficient routing without relying on real-time message exchange during the decision moment.
Solution Approach 2:
The invention implements feedback mechanisms where nodes continuously monitor transmission success rates and update their delivery predictability values accordingly. This feedback allows the system to adapt to changing network conditions dynamically, ensuring that bundle transfer decisions are always based on the most current and accurate predictability information, thereby preventing routing loops while maintaining high transport efficiency.
Data Source
AI summary
Each of nodes A through Z calculates delivery predictability for a destination node and exchanges the delivery predictability as an SV message with an adjacent node. At this time, the delivery predictability calculated in each of the nodes A through Z is transmitted together with a next hop node for the destination node. Upon receiving the delivery predictability for the destination node and the next hop node for the destination node, each of the nodes A through Z checks whether or not the next hop node matches the local node itself, wherein whey they match with each other, the delivery predictability is not registered with a delivery predictability database. Since the node transmitting the delivery predictability is not selected as the next hop node for the destination node, it is possible to prevent the occurrence of a routing loop between nodes.