A Collaboration Platform for Effective Task and Data Reporter Selection in Crowdsourcing Network

Due to the huge number of objects/things connected to the Internet of Things (IoT) which are embedded with electronics, software, and sensors, the IoT creates many exciting applications such as smart grids, smart homes, and smart cities. In the IoT, the sensing and control of objects/things can be abstracted as a task, in which many sensing devices sense and collect data. However, the substantial case studies show that by simply connecting them without further collaboration among the objects/things will lead to the bad performance of the system. With the number of sensing devices connected to the IoT increases, the collaboration for completing the task is becoming more and more urgent. In this paper, a Collaborative Multi-Tasks Data Collection Scheme (CMDCS) is proposed to solve the problem by constructing a collaborative platform for task publisher and data reporter. The main contribution of CMDCS includes the following two aspects: (1) a Task Unit Bid-based task selection strategy is proposed to select the task which can bring higher profits to the system, in which the Task Unit Bid is the ratio of task bid to the amount of data which are needed to collect sensing tasks; and (2) a greedy contributed density-based data collector set selection method is proposed to reduce the cost of data collection so as to maximize system profit, in which the contribution density is used to measure the contribution of a single data collector to a specific sensing task. A large number of experiments have been carried out to verify the effectiveness of our proposed strategy. The experiments show that compared to the traditional data collection strategy Random Task selection with Coverage First Reporter selection, in which the Task Unit Bid and Contribution Density are not used, the profit of the system is improved by 92.08%.

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