Energy Characterization for Data Collection and Transmission in Participatory Mobile Crowdsensing Environment

Abstract

Energy consumption is a major issue for data collection in the Mobile crowdsensing(MCS) environment as it incurs a significant amount of power/energy consumption for data sensing, data collection, and further data transmission to the cloud platform using various data transmission modes such as Bluetooth, Wi-Fi, and cellular network. Due to this, user participation in the crowdsensing process has been affected. Therefore, it is highly essential to estimate and minimize power/energy consumption made for data collection and transmission from the user’s side to encourage them for participation in the crowdsensing process. In the proposed work, an energy efficient dynamic data transmission model has been proposed using Bluetooth, Wi-Fi, and Long-Term Evolution(LTE) network for minimization of user’s side power/energy consumption made for data transmission to cloud platform in the participatory MCS environment. It has been implemented using the CrowdSenSim simulator, and its performance is compared with the energy consumption for different transmission modes. The number of data contributors plays a vital role in the statistical analysis for energy characterization in various data transmission modes. It impacts the size of data transmitted and also the equivalent energy consumed. It has been observed that energy consumed using Bluetooth is minimum in comparison to Wi-Fi and LTE. It has been observed that energy consumption mostly depends on the transmission mode apart from the size of the data transmitted. It also depends on the size of data transmitted in every data transmission mode