RELIABLE AND BALANCED DISTRIBUTION BASED CROWD SOURCING TASK ASSIGNMENT (RBCDR)

Internet-of-Things (IoT) is incorporated with enormous items that fixed with software, electronics and sensors. It has diverse application domains, namely smart homes, smart grids and smart cities. The sensor devices integrated with IoT, which operates as robot system to execute data collection work. The Internet-ofThings monitors, detects and collects reports on objects and devices. There are three key problems in the crowd sourcing network, namely coordinating report selectors, ensuring the Quality of Service (QoS) of time declining tasks and proving reliability. While the network has an effective report collection system, it still faces reliability challenges. Since, IT sensing devices have increased the relationship for performing the task is important. In this study, a novel framework named Reliable and Balanced Distribution based Crowd Sourcing Task Assignment (RBCDR) has been proposed. This proposed study checks whether the information provided by data collectors have reliability. This technique also reduces the cost of data selection and improves the system profit while compared with the previous methods. Experimental results define that compared with the existing works; likely Collaborative Multi task Data Collection Scheme (CMDCS), Task Unit Bid Spatial Coverage and Post Contribution Density (TUBSC_PCD) and Random Task selection with Contribution Density Reporter selection (RTCDR), the performance of the system is increased by 98.3%.