Link Selection in Lossy Smart Grid Networks
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Solution Overview
Problem
Current routing protocols in smart grid communication networks fail to effectively select reliable paths in networks with time-varying links and multiple link technologies, leading to suboptimal performance due to lack of consideration for link transmission rates and variability.
Innovation Solution
A method for selecting communication links between nodes using a Finite State Markov Channel model with two states, updating link metrics based on state transition probabilities and packet transmission success probabilities, and selecting the link based on a threshold comparison to ensure efficient packet transmission in lossy and time-variant environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple averaging filters are used to update link metrics, then the routing process is simple and fast, but it fails to account for link transmission rate variation and does not provide sufficient routing performance in time-varying networks
Solution Approach 1:
The patent applies dynamics by transitioning from static simple averaging filters to dynamic Markov models that adapt to time-varying link conditions. The system continuously updates transition probabilities based on observed packet delivery ratios, allowing the routing metric to dynamically respond to changing network conditions while maintaining mathematical tractability through the Markov property.
Solution Approach 2:
The patent changes the fundamental parameters used for link metric calculation from fixed averaging windows to time-varying transition probabilities derived from Markov chain analysis. By modeling link behavior with state transition probabilities that capture temporal dependencies, the system adapts its routing decisions based on the statistical characteristics of time-varying links rather than assuming stationary conditions.
2Adaptability or versatility
If stochastic learning algorithms with minimal probability parameters are used, then the algorithm is simple, but it lacks sufficient variability for state of the art smart grid systems
Solution Approach 1:
The patent extends the stochastic learning approach by adding temporal dimension through Markov modeling. Instead of using simple probability parameters, the system incorporates state transition probabilities that capture the temporal evolution of link quality. This dimensional extension allows the algorithm to distinguish between transient and persistent link conditions, providing greater variability and adaptability for smart grid applications.
3Adaptability or versatility
If traditional routing protocols are used, then the implementation is straightforward, but they do not take into account multiple link layer technologies and time-varying link characteristics
Solution Approach 1:
The patent achieves universality by developing a unified Markov-based routing framework that can handle multiple link layer technologies (wireless, wired, power line communication) through a common mathematical model. The approach abstracts away technology-specific details while capturing essential time-varying characteristics through transition probabilities, allowing the same routing logic to adaptively select across heterogeneous link types based on their current state.
Data Source
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AI summary
The present invention is concerned with establishing and maintaining a routing protocol based on a measured link metric p, in particular for a smart grid communication system. A link 1 between a first node such as a router A and a neighbouring second node B of a communication path from a source to a destination in a packet oriented communication network is selected wherein the two nodes are connected via a first communication link 1 and a second communication link 2. The link 1, 2 may have stochastic or time variable properties due to the lossy underlying communication technology. These stochastic and time variable properties are modelled as a Finite State Markov Channel FSMC with two states 5, 6. An updated link metric p(t+1) at a point in time t+1 of the first communication link is determined. The metric p is based on state transition probabilities λ1 9 and λ2 10 of the first and second state of the FSMC respectively. The first or the second communication link 1, 2 is selected for transmitting a further packet from node A to node B by comparing the updated link metric p(t+1) to a threshold pthr.