Task unit bid- spatial coverage and post input density (TUBSC_PID) based crowd sourcing network

Abstract

A huge number of items are associated with the Internet of Things (IoT) which is fixed with software, electronics and sensors. It has a wide variety of applications, namely smart homes, smart grids and smart cities. The sensor devices combine with Internet of Things (IoT) operates as robot system to execute data collection task. The IoT control objects, sense devices and gathers data. In crowd sourcing network there are two main issues, namely to guarantee the Quality of Service (QoS) of tasks and to reduce the data collection cost. There is also some problems arise between the task circulator and the data reporter in terms of profit. Since, IoT sensing devices have increased a lot, the relationship for finishing the task is very much important. In this paper, a novel framework called Task Unit Bit-based Spatial Coverage and Post Input density (TUBSC_PID) has been proposed. The input density is applied to estimate the contribution of a single data collector to a particular sensing task. A Task Unit Bid-based task selection strategy is proposed to choose the task which provides more contribution density and higher profit to the system. A novel spatial coverage technique is also applied to cover all the information obtained from the data collector. The present and post input density is applied to estimate the contribution of a single data collector to a particular sensing task as well as future sensing tasks. This method reduces the cost of data selection and maximizes the system profit. Experimental results predict that compared to the traditional techniques, namely Random Task selection with Input Density Reporter selection (RTCDR) and Collaborative Multi-Tasks Data Collection Scheme (CMDCS), the profit of the system is improved by 96.1%.