Description

The GreenHub initiative is a collaborative approach to Android energy consumption analysis. Its most important component is a dataset. The entries in the GreenHub dataset include multiple pieces of information, e.g., active sensors, memory usage, battery voltage, and temperature, running applications, model and manufacturer, and network details. This raw data was obtained by continuous crowdsourcing through a mobile application called BatteryHub. It is worth noting that all such data is publicly available, while maintaining the anonymity and privacy of all its users. Indeed, it is impossible to associate any data with the user who originated it. The dataset is sizable and thus far it comprises of 23+ million unique samples, including more than 700+ million data points pertaining to processes running on these devices. The dataset is also diverse. It includes data stemming from 1.6k+ different brands, 11.8k+ smartphone models, from over 50 Android versions, across 160 countries. Overall, the dataset comprises more than 120GB of uncompressed data.

For this assignment, you will have to examine a small part of this dataset, the dataset-samples part (hereafter just the “dataset-samples” dataset). It consists of multiple files, each one grouping approximately 300,000 observations collected from a number of devices. Each of these observations includes data such as the temperature of the device when the observation was collected, whether wifi was on or off, amount of free memory, cpu usage, current battery level, among others. The files are organized so that each row corresponds to one observation. The values in each observation are separated by semicolon (‘;’) and in this manner organized as columns. The first row of each file contains the labels for the columns. The following is the list of columns of each of these files:

[‘id’, ‘device_id’, ‘timestamp’, ‘battery_state’, ‘battery_level’, ‘timezone’, ‘country_code’, ‘memory_active’, ‘memory_inactive’, ‘memory_free’, ‘memory_user’, ‘charger’, ‘health’, ‘voltage’, ‘temperature’, ‘usage’, ‘up_time’, ‘sleep_time’, ‘network_status’, ‘network_type’, ‘mobile_network_type’, ‘mobile_data_status’, ‘mobile_data_activity’, ‘wifi_status’, ‘wifi_signal_strength’, ‘wifi_link_speed’, ‘screen_on’, ‘screen_brightness’, ‘roaming_enabled’, ‘bluetooth_enabled’, ‘location_enabled’, ‘power_saver_enabled’, ‘nfc_enabled’, ‘developer_mode’, ‘free’, ‘total’, ‘free_system’, ‘perform the following tasks:

0. Obtain the dataset and familiarize yourself with the data contained in the .csv files.

1.Build a function named getColumnValues() that takes as argument a list of column names and creates a NumPy array containing the values in the corresponding columns of the dataset-samples dataset, for all the files in the dataset. This function should be parameterized by the number of files to be read! The number of columns of the matrix should be the same as the list of column names received as argument. The function should return that NumPy array, filled in with the values read from the observations, for the corresponding columns.

2.Write a function named minMaxTemperatures() that receives the matrix produced by getColumnValues() as argument to determine the minimum and maximum temperatures among all the observations in the dataset. What are the ids of the devices with these values?

3.Write a function named percentageLocationEnabled() to determine what is the percentage of observations where location is enabled. Again, its argument should be a matrix produced by getColumnValues().

4.Implement a function named averageBatteryLevel() to calculate the average battery level when power saver mode is enabled or disabled. The function should take as argument the matrix produced by getColumnValues() and a flag named powerSaver whose value should be True to select observations where power saver mode is enabled and False otherwise.

5.Write a function to calculate the percentage of the observations where different kinds of network connection are off. This function should receive the matrix with the observations as argument, as well as a string whose values can be ‘network’, ‘mobile’, or ‘wifi’. These values determine whether the function will calculate the percentage of observations pertaining to the cell network, mobile data, or wi-fi.

Deliverables

This assignment has two deliverables:

◦A general explanation of the solution idea for each task

◦A summary of the obtained results

◦Main obstacles faced during the execution of the project and how the team circumvented them

◦A quick summary of the responsibilities of each team member

General Requirements