## 3 Proposed Architecture
### Target Tracking Using Multiple State-Space Models
The PF was used to estimate the position of the target, and we parallelized the calculation process of each particle in the GPU kernel. Also, we accelerated TDOA calculation through a parallel FFT process using GPU. Additionally, we extended the PF processing algorithm using a GPU-based multiple state-space model to estimate sudden movement change of the target. The proposed algorithm was initially simulated using MATLAB, and then, the proposed GPU-based algorithm was verified on target GPU. As a result, the execution time of the proposed algorithm using GPU was reduced by about 55% as compared with the CPU-based algorithm in target embedded board, NVIDIA Jetson TX1. The multiple state-space model based PF with parallel processing has a similar execution time in target GPU NVIDIA Tesla K40c with a difference of less than 0.16 ms when estimating the sudden change of movement of the target. Based on this result, the proposed architecture is more effective in terms of high performance and detecting sudden movement changes of the target with lots of sensor data in large-scaled sensing applications. Therefore, the proposed architecture can be effectively applied in these IoT applications due to scalability and parallelism. In a future study, we plan to extend our

Figure 10: Execution time of algorithm according to the number of particles and state-space models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Model** & **Block** & **Thread** & **Time for PF Process (ms)** & **Time for Copy Particles (ms)** \\ \hline
1 & 1 & 1024 & 0.66 & 0.59 \\
10 & 10 & 10,240 & 0.66 & 1.83 \\
20 & 20 & 20,480 & 0.65 & 3.24 \\
30 & 30



study on the GPU-based high speed processing algorithm considering the perspective of real-time processing in the big data sensing applications. This study was supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A6A3A04059410) and Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (2017M3C4A7064179). [PERSON] designed the entire core architecture and performed the hardware/software implementation and experiments; [PERSON] performed the validation using real embedded board; [PERSON] is a principle investigator for this project, who has responsibility as a corresponding author. The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. The following abbreviations are used in this manuscript:

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