Hierarchical Cluster-Based Trust Propagation for Scalable and Responsive Mobile Sensor Networks

Due to the increasing demand for mobile sensor networks (MSNs) used in applications such as environmental monitoring, smart cities, and defense, the need for scalable and efficient trust management mechanisms is critical. Conventional trust-propagation schemes in wireless sensor networks (WSNs) often rely on fully distributed or centralized architectures, leading to high overheads, delays, and poor responsiveness in dynamic scenarios. This paper addresses these challenges by proposing a hierarchical, cluster-based trust propagation model (HCTPM) tailored for resource-constrained and mobile environments. In the proposed model, mobile sensor nodes are organized into dynamic clusters using an adaptive k-hop clustering algorithm. Each cluster elects a Cluster Head (CH) based on trust, residual energy, and mobility. Trust evaluations are conducted locally within clusters by CHs, which aggregate and forward summarized trust metrics to higher-level nodes or base stations, thereby reducing communication overhead and latency. We implement HCTPM in simulation environments with varying node densities, mobilities, and percentages of malicious nodes. Performance benchmarks include network scalability (up to 500 nodes), trust propagation delay, communication overhead, detection accuracy, and energy consumption. Comparative analysis is conducted against state-of-the-art schemes such as TrustSense, LTS, and ETRES. Results show that HCTPM achieves a reduction of up to 40% in trust propagation delay and 30% lower communication overhead, while maintaining high detection accuracy (>95%) in dynamic topologies. The hierarchical model significantly enhances responsiveness in real-time trust evaluation, making it suitable for time-critical mobile MSN applications.