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Dew computing, an evolution of Fog computing, aims at fulfilling computing needs, such as deep learning applied to object classification, close to where data is originated and using computing resources that include consumer electronic devices such as smartphones. Simulation tools like DewSim aid the study of resource allocation mechanisms for exploiting clusters of smartphones, however, there is a gap w.r.t software tools that allow to perform similar studies over real Dew computing testbeds. We have developed LiveDewStream, an open source project to model executable tasks derived from data streams to be run on real smartphone clusters. The project offers a key functionality missing in other tools: reproducibility of battery-driven Dew experiments. Our major contribution is to provide the community a common in vivo platform to study best-performing allocation mechanisms under different stream processing scenarios and/or deep learning inference models.

Motrol is a simple device that satisfies functional requirements necessary for the automatic execution of battery-driven tests and profiling of connected mobile devices. It is specifically a hardware/software platform that allows Dew Computing researchers and developers to automate performance tests on Android-based smartphones. The hardware is based on a NodeMCU Esp8266 microcontroller that runs a firmware for managing the outputs. This software allows enabling/disabling the relays that connect the sockets that power the chargers of up to 4 mobile devices minimizing the need for human intervention. The firmware runs a web server that serves Rest requests from a Rest client with the commands to drive the digital outputs. These digital outputs activate or deactivate the relays to allow current to pass or not to the sockets. Such capability is essential to automate the study of battery behavior on battery-driven devices such as smartphones. Motrol is easy to assemble, knowledge in electronics or programming languages is not necessary, it is constructed with open hardware, and it is cheap, being its total cost ∼USD 30.