Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of distributed detection in a large wireless sensor network. An adaptive data fusion scheme, group-ordered sequential probability ratio test (GO-SPRT), is proposed. This scheme groups sensors according to the informativeness of their data. Fusion center collects sensor data sequentially, starting from the most informative data and terminates the process when the target performance is reached. To analyze the average sample number,  establish the asymptotic equivalence beten GO-SPRT, a multinomial experiment, and a normal experiment. Closed-form approximates are obtained. Our analysis and simulations show that, compared with fixed sample size test and traditional sequential probability ratio test (SPRT), the proposed scheme achieves significant savings in the cost of data fusion.
['Yingwei Yao']
I am writing a research paper on a idea Group-ordered SPRT for distributed detection
[{"from": "human", "value": "I am writing a research paper on a idea Group-ordered SPRT for distributed detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of distributed detection in a large wireless sensor network. An adaptive data fusion scheme, group-ordered sequential probability ratio test (GO-SPRT), is proposed. This scheme groups sensors according to the informativeness of their data. Fusion center collects sensor data sequentially, starting from the most informative data and terminates the process when the target performance is reached. To analyze the average sample number,  establish the asymptotic equivalence beten GO-SPRT, a multinomial experiment, and a normal experiment. Closed-form approximates are obtained. Our analysis and simulations show that, compared with fixed sample size test and traditional sequential probability ratio test (SPRT), the proposed scheme achieves significant savings in the cost of data fusion."}]