This paper presents the design and performance evaluation of an inexpensive testbed for network coding protocols composed of Raspberry Pis. First, we show the performance of random linear network coding primitives on the Raspberry Pi in terms of processing speed and energy consumption under a variety of configuration setups. Our measurements show that processing rates of up to 230 Mbps are possible with the Raspberry Pi. Also, the energy consumption per bit can be as small as 3 nJ/bit, which is several orders of magnitude smaller than the transmission/reception energy use. Surprisingly, overclocking the Raspberry Pi from 700 MHz to 1000 MHz not only produces an increase in processing speed of up to 68 % for large generation sizes, but also provides a reduction of 64 % in the processing energy per bit for most tested scenarios. Then, we show Raspberry Pi as an inexpensive, viable, and flexible platform to deploy large research networking testbeds for the evaluation of network coding protocols. We propose key parameters and representations to evaluate protocol performance in network nodes as well as validating the testbed's statistics using the case of a one-hop broadcast with random linear network coding, which is well understood in theory.
This paper presents a method for fitting a static regression model for the power consumption of a ground-sourced domestic heat pump, based on a low number of sample points extracted from a common measurement report developed in accordance to European Heat Pump Association (EHPA) regulation. Thereafter, we demonstrate how the coefficients can be updated with a Recursive Least Squares algorithm using only commonly accessible measurements. The regression model is designed to be used for control of a heat pump connected to an ON/OFF controlled floor heating system. The target of the method is especially systems where the flow in the floor heating circuits is unknown. The ability of the regression model to predict power consumption of the heat pump is evaluated using measurements obtained from a test-rig having the particular heat pump installed. The regression model is implemented as a module in a Mixed Integer Non-linear Model Predictive Control algorithm to illustrate the applicability of the model for control purposes. The promising results obtained from this investigation raise the question; should quality data be available in order to enable more advanced control of domestic heat pumps?
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