Compressive Data Gathering in Wireless Sensor Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large-scale wireless sensor networks face challenges in reducing global communication cost and energy consumption due to the high volume of sensor data being transmitted, with existing compression methods introducing significant computation and control overheads, and energy imbalance among sensor nodes.
Innovation Solution
The proposed compressive data gathering scheme involves sensor nodes transmitting weighted sums of their readings, using pseudo-random numbers, which reduces the total number of transmissions and balances energy consumption by dispersing communication costs across all nodes, allowing for efficient data reconstruction and handling of abnormal readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-hop routing is used to transmit sensor data from individual nodes to the data sink, then data gathering is achieved in large-scale networks, but energy consumption becomes unbalanced with nodes closer to the data sink consuming more energy
Solution Approach 1:
Multiple sensor readings from different nodes are merged into a single aggregate value through random projection. Each node contributes its reading multiplied by a random coefficient, and these contributions are summed along the transmission path, resulting in one value being transmitted per node per time instant instead of multiple readings
Solution Approach 2:
The patent transforms the data transmission parameter from sending raw sensor readings to sending random projections of these readings. By changing what is transmitted (from original data to transformed data), the system achieves both compression and load balancing
2Loss of energy
If in-network data compression is applied to reduce global traffic, then communication cost is reduced, but computation and control overheads increase significantly
Solution Approach 1:
The patent replaces complex mechanical/computational compression systems with a simpler mathematical transformation. Instead of using entropy coding or transform coding that require complex algorithms, the system uses random projection which involves only multiplication by random coefficients and addition, significantly reducing computational overhead
Solution Approach 2:
The patent changes the approach to data compression from traditional compression algorithms to random projection. This parameter change in the compression method achieves traffic reduction while maintaining low computational requirements at each node
3Loss of information
If all sensor readings are transmitted to the data sink, then complete data is available for processing, but the total number of transmissions increases tremendously
Solution Approach 1:
Multiple sensor readings are merged into a single aggregate value through random projection. Each node's reading is multiplied by a random coefficient and added to the sum from other nodes, resulting in one transmitted value per node that captures information from all readings in the network
Solution Approach 2:
The patent creates a compressed representation (copy) of the original data that preserves essential information. The random projection creates a transformed copy of the sensor readings that can be reconstructed to recover the original data, reducing transmission requirements while maintaining data utility
Data Source
AI summary
Techniques for data gathering in large-scale wireless sensor networks are described. A data collection device receives aggregate data from at least one sensor node of a group of N sensor nodes. The aggregate data includes M weighted sums. Each of the M weighted sums includes a respective sum of N products each of which being a product of a respective coefficient and a sensor reading from a respective one of the N sensor nodes. M and N are positive integers and M is less than N. Computation is performed on the aggregate data to recover sensor readings from the N sensor nodes.