Oregon State University

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Event Details

PhD Final Oral Examination – Rahul Khanna

Friday, March 11, 2016 3:00 PM - 5:00 PM

Evolutionary Approach to Efficient Provisioning and Self-organization in Wireless Sensor Networks (WSN)
Advances in low-power digital integration and micro-electro-mechanical systems (MEMS) have paved the way for micro-sensors. However, these sensors may be constrained in energy, bandwidth, storage, and processing capabilities. Large number of such sensors along with these constraints creates a sensor-management problem. At the network layer it amounts to setting up the secure and efficient route that transmits the non-redundant data from source to the sink in order to maximize one or more sensor objectives (e.g. battery (and sensor's) life, Sensor-Data yield). We propose a reduced-complexity genetic algorithm (GA) for optimization of multi-hop battery-constrained sensor networks. It results in minimization of the power consumption of the sensor system while maximizing the sensor objectives (coverage and exposure) with optimal security attributes (Like authentication and encryption). Optionally we can augment this approach to support intrusion detection by selecting trusted proxy agent capable of securely monitoring its neighbors.

Application of this approach into Information Technology (and Industrial) Systems and devices facilitate the use of sensor networks to deliver non-intrusive and effective telemetry for business intelligent systems. These systems (Like Data Centers or Shipment tracking) face major challenges in seamless integration of telemetry and control data that is essential to various autonomic management functions related to power, thermal, reliability, predictability, survivability, locality and adaptability. Such systems that are supported by a dense network of sense-points operating in noisy environment (Metals, Cables) are required to deliver reliable trends, measurements and analysis in a timely fashion. We apply the proposed GA approach for this unique environment that replaces static wired sensors with dynamically reconfigurable battery-powered wireless sensors. The proposed technique employs machine learning approach to optimize sensor node function assignment, clustering decisions, and route establishment for improved throughput that results in effective controls.

Major Advisor: Huaping Liu
Committee: Ben Lee
Committee: Thinh Nguyen
Committee: Patrick Chiang
GCR: Abi Farsoni

Kelley Engineering Center (campus map)
Nicole Thompson
1 541 737 3617
Nicole.Thompson at oregonstate.edu
Sch Elect Engr/Comp Sci
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