Abstract
This work presents a strategy to implement a distributed form of genetic algorithm (GA) on low power, low cost, and small-sized memory aiming for increased performance and reduction of energy consumption when compared to standalone GAs. This strategy focuses on making a distributed version of GA feasible to run as a low cost and a low power consumption embedded system utilizing devices such as 8-bit microcontrollers (µCs) and Serial Peripheral Interface (SPI) for data transmission between those devices. Details about how the distributed GA was designed from a previous standalone implementation made by the authors and how the project is structured are presented. Furthermore, this work investigates the implementation limitations and shows results about its proper operation, most of them collected with the Hardware-In-Loop (HIL) technique, and resource consumption such as memory and processing time. Finally, some scenarios are analyzed to identify where this distributed version can be utilized and how it is compared to the single-node standalone implementation in terms of performance and energy consumption.
Highlights
Distributed systems are present in our lives every day
The distributed embedded system targeted Atmel microcontrollers, the same microcontroller ATmega328P that runs on Arduino Uno and was used in the previous work
The reason to choose an 8-bit microcontroller is that it is one of the simplest and limited devices available with lots of restrictions, if the implementation works for it, it will work for more robust devices
Summary
Distributed systems are present in our lives every day. They can be simple or complex such as the ones found in the World Wide Web, social networks, e-commerce, and others. Another advantage of using numerous devices is that it is possible to reduce the clock of all of them and have reasonable performance, but reducing power consumption [14] By achieving these goals, this implementation can be used in emerging areas such as the Internet of Things (IoT), Smart Grid, and Machine to Machine (M2M), where those devices are commonly used exactly for having a low cost, reduced size and restricted consumption of power. This implementation can be used in emerging areas such as the Internet of Things (IoT), Smart Grid, and Machine to Machine (M2M), where those devices are commonly used exactly for having a low cost, reduced size and restricted consumption of power This implementation can be applied in situations where genetic algorithms or other optimization metaheuristics are necessary to solve non-linear optimization problems but with limitations on power consumption.
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