This paper proposes a concept of linked participatory sensing, called LiPS, that divide a complex sensing task into small tasks and link each other to optimize social resource allocation. Recently participatory sensing have been spreading, but its sensing tasks are still very simple and easy for participants to deal with (e.g. Please input the number of people standing in a queue. etc.). To adapt to high-level tasks which require specific skills such as those in engineering, the medical profession or authority such as the organizer of the event, we need to optimize social resource allocation because the number of such professionals are limited. To achieve the complex sensing tasks efficiently, LiPS enables to divide a complex sensing task into small tasks and link each other by assigning proper sensors. LiPS can treat physical sensors and human as hybrid multi-level sensors, and task provider can arrange social resource allocation for the goal of each divided sensing task. In this paper, we describe the design and development of the LiPS system. We also implemented an in-lab experiment as the first prototype of hybrid sensing system and discussed the model of further system through users' feedback.