A Cloud Computing Approach for Minimal Exposure Routing in Resource-Constrained Wireless Sensor Networks

Wireless sensor networks (WSNs) are vulnerable to security attacks due to the constraint capabilities of sensor nodes. Determining routing paths in WSNs with minimal exposure to adversaries is critical for preserving privacy and combating threats. However, the complexity of computing optimal minimal exposure paths (MEP) exceeds the limited processing capacity of embedded sensor devices. This research proposes a cloud computing-based methodology to address this challenge by leveraging the vast storage and computational resources of the cloud. A coordinated cloud control scheme is devised for global optimization of concealable routing configurations in WSNs. The methodology models the dynamic WSN topology and monitors exposure levels of links in a cloud database. Leveraging parallel processing capabilities, the cloud platform efficiently executes an exposure-aware routing algorithm to find the current MEP between sensors and propagates the route to edge nodes. This adaptive technique refines the path in real-time based on updated exposure data analytics from the field for contextual security maximization. Comprehensive experiments validate the efficiency, scalability and robustness of the proposed technique over conventional localized approaches. This research comprehensively investigates a cloud-assisted exposure-aware routing paradigm to enhance security, privacy and resilience in resource-constrained WSNs. The key focus areas of exposure routing, cloud leverage, resource constraints, and security maximization are highlighted while articulating the core research problem, method, innovations, and significance in the compact abstract.