Designing a model for improvement of network efficiency in mobile crowd-sensing

Abstract

Mobile crowd sensing is human centric computing technology with sensing and computing devices in the mobile users. The tendency to the recent technology, the smartphone user rapidly increases from day to day. This provides the good opportunity to sense with smartphone about the environment and urban events without deploying special sensor devices. There are numbers of application and data uploading models that motivates users to contribute data by using their smartphones. Recently, mobile crowd sensing (MCS) architecture have two components in sensing and uploading architecture, those are, sensing devices that receive information form environment and back-end server for analysis of sensor data. However, each data contributing user collect and send the redundant sensed data to the cloud server. There are no common devices or mechanism that faces the common challenges such as resource allocation, data collection and data processing tasks. In this paper, we designed a model for improvement of network efficiency in mobile crowd-sensing by providing device to device communication with their group leader and data fusion before uploading to the backend server. We conducted simulation with NS2 network simulator for experimentation and to evaluate the performance of our model versus raw data uploading model with different evaluation metrics. The result show that our scenario model improves the performance and reduce the data redundancy in mobile crowd sensing network.