Marine Sensor Network Data Reconstruction Under Energy Constraints
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Solution Overview
Problem
Existing methods for reconstructing lost data in marine wireless sensor networks (MWSNs) fail to accurately address the dynamic nature of marine environments, unstable wireless channels, and limited energy resources, making it difficult to achieve energy-efficient and high-precision data reconstruction.
Innovation Solution
Implementing an improved hierarchical energy balance multipath (IHEBM) routing protocol for node clustering, using an improved radial basis function neural network (RBFNN) for data prediction, and applying centralized principal component analysis (PCA) for data compression and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data from all nodes is directly transmitted to the convergence terminal without compression, then data completeness is maintained, but energy consumption increases and network life cycle is greatly affected
Solution Approach 1:
The patent segments the network into clusters with cluster heads that perform local data processing and compression before transmission to the convergence terminal. This segmentation allows data to be compressed at intermediate nodes rather than transmitting all raw data, reducing energy consumption while maintaining data completeness through structured aggregation.
Solution Approach 2:
The patent applies preliminary data compression and processing at cluster heads before data is transmitted to the convergence terminal. This preliminary action reduces the data volume early in the transmission path, decreasing overall energy consumption while preserving data integrity through controlled compression techniques.
2Duration of action of stationary object
If clustering of nodes is implemented to save energy and improve scalability, then network life cycle is prolonged, but data reconstruction accuracy decreases due to dynamic topology and unstable wireless channels
Solution Approach 1:
The patent implements dynamic cluster head selection and topology adaptation mechanisms that respond to changing marine environmental conditions. The clustering structure is not static but dynamically adjusts to maintain optimal performance under varying wireless channel conditions and node mobility, thereby preserving both energy savings and data reconstruction accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms where cluster heads and nodes continuously monitor data quality and transmission success rates. This feedback information is used to adjust clustering parameters, select optimal cluster heads, and refine data reconstruction models, ensuring high accuracy despite dynamic topology changes.
3Measurement precision
If advanced data reconstruction methods are applied to improve precision, then data completeness is enhanced, but computational complexity and energy consumption increase
Solution Approach 1:
The patent applies different levels of computational complexity at different locations in the network. Simple compression and reconstruction operations are performed at cluster heads, while more complex processing is done at the convergence terminal. This local quality differentiation ensures high data reconstruction accuracy where needed while minimizing overall computational burden and energy consumption.
Data Source
AI summary
Provided are a method and a device for reconstructing lost data of marine wireless sensor networks (MWSNs). The data reconstruction method includes following steps: establishing an initial topological structure of the MWSNs; using an improved hierarchical energy balance multipath (IHEBM) routing protocol to cluster network nodes; using an improved radial basis function neural network (RBFNN) to predict lost data of nodes in the cluster based on a clustering of the nodes; and using a centralized principal component analysis (PCA) method to compress and reconstruct data of a cluster head node in a process of data transmission from a cluster head to a ship base station.