Graph-Based Representation Learning for Sensor Networks with Dynamic Topology

Abstract

This paper introduces a novel graph embedding approach specifically designed for sensor networks exhibiting dynamic topologies. Traditional graph embedding techniques often rely on static graph structures, rendering them ineffective in environments where network connections are constantly changing due to node failures, mobility, or environmental factors. Our method, termed Dynamic Graph Embedding (DGE), addresses this limitation by continuously learning node representations, taking into account evolving network connectivity and temporal dependencies. The core idea is to construct a graph representation that reflects the current state of the sensor network, incorporating node state information and leveraging temporal information to refine node embeddings over time. The proposed DGE framework employs a recurrent neural network (RNN) architecture to capture these temporal dependencies and adapt node representations accordingly. We formulate the learning process as an optimization problem, minimizing a loss function that combines node similarity constraints and temporal consistency measures. This approach enables robust and adaptable graph-based representation learning, crucial for applications such as anomaly detection, localization, and data aggregation in dynamic sensor networks. The presented method offers improved performance compared to static graph embedding techniques, particularly in scenarios with significant topological changes.