Executive Summary
This thesis explores distributed algorithms for beamforming in wireless sensor networks (WSNs) to enable communication with UAVs despite random sensor deployment and position errors. It proposes two new distributed algorithms based on least squares approximation to share processing load among nodes, reducing individual power consumption. The research demonstrates that these distributed approaches can effectively manage processing and communication costs, enhancing network longevity and resilience.
Why It Matters
This document is crucial for defense analysts as it details methods to enhance military wireless sensor network capabilities, particularly for UAV communication and battlefield surveillance, by improving power efficiency and resilience in distributed systems.
Key Takeaways
- Two novel distributed algorithms for beamforming in WSNs are proposed, based on least squares approximation, to spread processing load.
- The distributed algorithms reduce average power consumption per sensor node compared to centralized approaches, improving network longevity.
- Position errors in randomly deployed sensor arrays significantly impact beamforming performance, but LS beamforming can mitigate this degradation.
Strategic Relevance
The development of efficient distributed beamforming algorithms for WSNs directly impacts military intelligence, surveillance, and reconnaissance (ISR) capabilities. By enabling robust communication between dispersed sensors and UAVs, it enhances situational awareness, reduces the risk of human loss in hazardous environments, and improves the resilience of sensor networks against single points of failure. This contributes to more effective and sustainable battlefield monitoring and data collection.