A sensor node, a sensor network and a method for autonomous decision-making in sensor networks

Simple SummaryContent extracted from patent full text and abstract with AI.

This patent describes a method, sensor node, and sensor network that enable autonomous decision-making in resource-constrained sensor networks using a lightweight machine learning technique — specifically, linear discriminant analysis (LDA). By splitting feature vectors into smaller parts, it reduces computational demands, allowing sensor nodes with limited energy and processing power to classify data, detect anomalies, and make decisions locally, without depending on centralized computing resources.

Use CasesContent extracted from patent full text and abstract with AI.

• Anomaly and intrusion detection in wireless sensor networks (e.g., to identify malfunctioning or compromised nodes)
• Proactive fault detection and self-healing in industrial Internet of Things (IoT) deployments
• Environmental monitoring (e.g., detecting abnormal events such as forest fires, gas leaks, or unusual seismic activity)
• Smart grid monitoring to autonomously detect and respond to irregular power usage or equipment faults
• Health monitoring systems that autonomously flag abnormal patient readings from distributed biosensors
• Military or surveillance sensor networks operating in communication-challenged environments

BenefitsContent extracted from patent full text and abstract with AI.

• Enables autonomous decision-making without the need for central servers, increasing network resilience and responsiveness
• Adapts advanced machine learning for low-power, resource-constrained devices
• Reduces communication overhead and energy consumption by enabling decisions at the network edge
• Parallelizes and distributes computation across multiple nodes to further save energy and speed up processing
• Supports detection of complex anomalies by combining data from multiple sensor nodes and network layers
• Improves robustness of sensor networks in disconnected or harsh environments

Technical Classifications (CPCs)

Inventors & Applicants

Patent Abstract

For providing an improved approach to employ machine learning techniques in a network of embedded sensor nodes for making decisions autonomously, such as detecting anomalous behavior in a sensor network, the invention proposes a method for classifying data generated by at least one sensor node (100, 211-218) of a sensor network (200) by statistical processing of said data based on linear discriminant analysis, wherein said data is represented by an n-dimensional test feature vector, comprising a learning phase (310-360) with the steps of defining at least two classes into which an n-dimensional feature vector may be classified, providing a set of labeled n-dimensional learning feature vectors, each of which is associated with one of said classes, and pre-processing of said set of labeled learning feature vectors, and an inference phase (410, 420) with the steps of calculating (410) for each class a linear discriminant function of the n-dimensional test feature vector representing the data to be classified, based on the results of said pre-processing, and assigning (420) the data to the class for which the calculated linear discriminant function yields the highest result. In particular, the method is capable of correlating different kinds of data in order to make decisions. The invention further proposes a sensor node (100) and a sensor network (200) adapted for performing the method.