Distributed Processing Allocation of Machine Learning in Wireless Sensor Networks
Jun Motoyama, Rina Ooka, Takumi Miyoshi, Taku Yamazaki, Takuya Asaka · 2020
Distributed processing technology in wireless sensor network (WSN) has attracted attention because of the performance improvement of sensor nodes. Although the conventional methods divide and allocate computational processing of machine learning to sensor nodes, appropriate allocation has not been realized from the viewpoint of the whole network. In this paper, assuming that multiple machine learning processes occur simultaneously, we propose a processing division and allocation method to equalize processing load on sensor nodes. The evaluation results show that the method can almost fairly distribute the load to sensor nodes.