Deterministic Cluster Routing for Wireless Sensor Networks
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Solution Overview
Problem
Existing stochastic cluster-based routing methods in wireless sensor networks lack a deterministic approach to optimize energy efficiency, particularly in determining the number of clusters and their formation.
Innovation Solution
A method is developed to determine the number of clusters and their formation by deriving energy consumption functions for head and member nodes, calculating the optimal number of clusters, and arranging cluster regions to minimize energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If stochastic cluster-based routing methods are used, then the routing process is simpler to implement, but energy efficiency cannot be optimized deterministically
Solution Approach 1:
The patent changes the fundamental parameter of cluster selection from stochastic (random) to deterministic based on node location. By using the node's coordinates and a deterministic hash function, the system deterministically determines which cluster a node belongs to, enabling precise control over cluster formation and energy optimization without complicating the implementation process.
2Use of energy by moving object
If the number of clusters is increased, then energy consumption per node decreases, but the complexity of determining optimal cluster number increases
Solution Approach 1:
The patent performs preliminary determination of the optimal number of clusters K before the actual clustering process. By calculating K in advance based on network parameters and energy models, the system avoids complex real-time decisions during operation, simplifying the overall process while achieving optimal energy consumption.
3Use of energy by moving object
If deterministic cluster method is used, then energy efficiency is optimized, but research and analysis for determining variables is lacking
Solution Approach 1:
The patent incorporates feedback mechanisms where nodes report their energy levels, location information, and cluster membership to the base station. The base station uses this feedback to dynamically adjust cluster assignments and the number of clusters, enabling continuous optimization of energy efficiency while simplifying the analysis of system variables through centralized monitoring.
Data Source
AI summary
The disclosed embodiment provides a technology for determining optimized variables in routing based on deterministic cluster. Existing technologies only optimize the number of clusters in stochastic-based routing, so there is a limitation in optimizing variables in routing based on deterministic cluster. In the disclosed embodiment, the amount of energy consumed by the head node and the member nodes may be calculated using a mathematical equation that uses the number of clusters as a variable to derive the optimum number of clusters that minimizes the sum thereof. In addition, in the disclosed embodiment, the most efficient formation of clusters may be determined based on the maximum density packing method. Thus, the disclosed embodiment may maximize the lifespan of a wireless network by maximizing energy efficiency through optimization of variables. Therefore, the present invention has superior competitiveness compared to existing technologies.