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🔥 Content Introduction
The method for improving wireless sensor network (WSN) routing and packet delivery based on the Mamdani fuzzy inference system aims to solve issues such as energy constraints, uneven node distribution, and packet loss in WSNs. This method integrates fuzzy logic to comprehensively evaluate parameters such as node energy, distance, and channel quality, dynamically adjusting routing strategies to enhance packet delivery success rates and extend network lifespan.
Principle of Mamdani Fuzzy Inference System
The Mamdani fuzzy inference system is a reasoning system based on fuzzy logic, which includes the following key components:
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Fuzzification: The process of mapping input data from precise values to fuzzy sets, typically implemented using membership functions.
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Rule Base: Contains a set of fuzzy rules, each consisting of premises and conclusions, where premises are fuzzy sets, and conclusions are either fuzzy sets or precise values.
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Fuzzy Inference: Based on the rules in the rule base and fuzzified input data, reasoning is performed using fuzzy logic to produce fuzzy outputs.
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Defuzzification: The process of converting fuzzy outputs back to precise values, commonly using methods such as the centroid method or the maximum membership degree method.
Principle of Improved Routing and Packet Delivery
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Node Energy Assessment: Using fuzzy logic to evaluate the health status of nodes based on their remaining energy and decide whether to select the node as part of the routing path.
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Distance Assessment: Using fuzzy logic to evaluate the energy consumption of data transmission based on the distance between nodes and select the path with the least energy consumption.
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Channel Quality Assessment: Using fuzzy logic to evaluate the reliability of data transmission based on channel quality (such as signal-to-noise ratio, bit error rate, etc.) and select the path with the best channel quality.
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Routing Strategy Adjustment: Dynamically adjusting the routing strategy by integrating the assessment results of node energy, distance, and channel quality to choose the optimal routing path.
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Packet Delivery: Transmitting packets from the source node to the destination node based on the adjusted routing strategy while monitoring the packet delivery process. If packet loss occurs, the routing path is re-selected.
Advantages
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Dynamic Adaptability: Capable of dynamically adjusting routing strategies based on network status, adapting to network changes.
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Energy Efficiency: Extends network lifespan by selecting the path with the least energy consumption.
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Reliability: Increases the success rate of packet delivery by selecting the path with the best channel quality.
Considerations
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Rule Base Design: Appropriate fuzzy rules need to be designed based on the actual network environment and application scenarios.
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Membership Function Selection: The choice of membership functions will affect the results of fuzzy inference and needs to be selected based on actual conditions.
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Defuzzification Methods: Different defuzzification methods will affect the final routing decisions and need to be selected based on actual requirements.
Conclusion
The method for improving wireless sensor network routing and packet delivery based on the Mamdani fuzzy inference system effectively enhances packet delivery success rates and extends network lifespan by comprehensively evaluating parameters such as node energy, distance, and channel quality using fuzzy logic and dynamically adjusting routing strategies. In practical applications, model design and parameter adjustments should be based on specific network environments and application scenarios.
⛳️ Run Results
🔗 References
[1] Feng Ranjian, Cheng Jian, Xu Xiaofeng, et al. A Trustworthy Cluster Head Election Algorithm for Wireless Sensor Networks Based on Mamdani Fuzzy Inference [J]. High Technology Communications, 2010(12):7. DOI:10.3772/j.issn.1002-0470.2010.12.008.
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