Information technology systems face considerable challenges in seamless integration of telemetry and control information. These are essential to various autonomic management functions related to power, thermal, reliability, predictability, survivability, and adaptability. Sensors and control agents supporting this telemetry are a part of large multiprocessor environments that are scattered across the platform. The conventional approaches to support distributed observability and control using wired solutions are static, expensive, and non-scalable. We present an alternative approach for this unique environment that replaces static wired sensors with dynamically reconfigurable wireless sensors. It employs a genetic algorithm based approach to optimize sensor node function assignment, clustering decisions, resource distribution, and route establishment for improved control quality. Based on this new, wireless sensor network approach, we evaluate the average data-flow delay characteristics between sensor and control endpoints. We also investigate the "quality of control" by measuring the conformity of the controlled objective to platform policy specifications (like power limits).