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%.

与软件，电子设备和传感器固定在一起的物联网（IoT）关联的项目很多。它具有广泛的应用，即智能家居，智能电网和智能城市。传感器设备与物联网（IoT）相结合，可作为机器人系统执行数据收集任务。物联网控制对象，传感设备并收集数据。在众包网络中，存在两个主要问题，即保证任务的服务质量（QoS）和降低数据收集成本。就利润而言，任务循环器和数据报告器之间还会出现一些问题。由于IoT传感设备已大量增加，因此完成任务的关系非常重要。在本文中，已经提出了一种新颖的框架，称为任务单元基于位的空间覆盖和输入后密度（TUBSC_PID）。应用输入密度来估计单个数据收集器对特定传感任务的贡献。提出了一种基于任务单元投标的任务选择策略，以选择为系统提供更大贡献密度和更高利润的任务。还应用了一种新颖的空间覆盖技术来覆盖从数据收集器获得的所有信息。当前和输入后的密度用于估计单个数据收集器对特定传感任务以及未来传感任务的贡献。这种方法降低了数据选择的成本，并使系统收益最大化。实验结果表明，与传统技术相比，