小室 信喜

Indoor position fingerprint-based location estimation methods have been widely used by applications on smartphones. In these localization estimation methods, it is very popular to use the RSSI (Received Signal Strength Indication) of signals to represent the position fingerprint. This paper proposes the design of a particle filter for reducing the estimation error of the machine learning-based indoor BLE location fingerprinting method. Unlike the general particle filter, taking into account the distance, the proposed system designs improved likelihood functions, considering the coordinates based on fingerprint points using mean and variance of RSSI values, combining the particle filter with the k-NN (k-Nearest Neighbor) algorithm to realize the reduction in indoor positioning error. The initial position is estimated by the position fingerprinting method based on the machine learning method. By comparing the fingerprint method based on k-NN with general particle filter processing, and the fingerprint estimation method based on only k-NN or SVM (Support Vector Machine), experiment results showed that the proposed method has a smaller minimum error and a better average error than the conventional method.

This paper proposes a Duty-Cycle (DC) control method in order to improve the Packet Delivery Ratio (PDR) for IEEE 802.15.4-compliant heterogeneous Wireless Sensor Networks (WSNs). The proposed method controls the DC so that the buffer occupancy of sensor nodes is less than 1 and assigns DC to each sub-network (sub-network means a network consisting of a router node and its subordinate nodes). In order to use the appropriate DC of each sub-network to obtain the high PDR, this paper gives analytical expressions of the buffer occupancy. The simulation results show that the proposed method achieves a reasonable delay and energy consumption while maintaining high PDR.