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A Java-based tool has been designed and implemented to simulate distributed estimation on a wireless sensor network (WSN) platform. The tool allows users to vary several system parameters of the WSN such as the parameter to be estimated; the amount of and type of noise in the system; types of channel impairments; algorithms for data processing at the sensors; and types of estimators. The simulator has been developed by fulfilling the requirements of usability principles in visual software design, and it produces outputs that have relevance to various sensor network applications such as localization, synchronization, and fault detection. Educational opportunities introduced by the tool are two-fold. It can be used in senior-level undergraduate digital signal processing, communications, and digital controls classes to introduce students basic statistical signal processing concepts such as noise and sample estimation in noise. The current version of the tool has been evaluated via student feedback in a workshop. Preliminary results are promising, and will allow us to make modifications to the simulation tool as necessary.

The modern era of computing involves increasing the core count of the processor, which in turn increases the energy usage of the processor. How to identify the most energy-efficient way of running a multiple-program workload on a manycore processor while still maintaining a satisfactory performance level is always a challenge. Automatic tuning on the voltage and frequency level of a many-core processor is an effective method to aid solving this dilemma. The metrics we focus on optimizing are energy usage and energy-delay product (EDP). To this end, we propose SVM-JADE, a machine learning enhanced version of an adaptive differential evolution algorithm (JADE). We monitor the energy and EDP values of different voltage and frequency combinations of the cores, or power islands, as the algorithm evolves through generations. By adding a well-tuned support vector machine (SVM) to JADE, creating SVM-JADE, we are able to achieve energy-aware computing on many-core platform when running multiple-program workloads. Our experimental results show that our algorithm can further improve the energy by 8.3% and further improve EDP by 7.7% than JADE. Besides, in both EDP-based and energy-based fitness SVM-JADE converges faster than JADE.