A number of mobile hosts might be densely staying in an area caused by traffic congestions. The greater part of the mobile hosts will require commonly useful data, such as traffic information, parking information and other driving related information in such environment. Simultaneous data transmission broadcasts using a common link are regarded as a suitable means to distribute this location-dependent information. However, there is no guarantee that mobile hosts can finish receiving the information completely within a limited time. In this paper, we propose a data retransmission method for communications between a base station and mobile hosts and a data recovery processing method for use between base stations. The data retransmission method called "TOA" (The Order of Arrival) schedules retransmission data specified in the first NACK request received after retransmission processing. We have proposed "Advanced" Join system in which a base station makes consolidated join requests to a multicast group on behalf of mobile hosts. Applying the TOA method to resending in the Advanced Join system, data-receiving efficiency is higher than with the simple Advanced Join system and the absolute number of completed mobile host data reception is higher. Using the TOA method, even with the base station disposition rate of 50% the number of completed reception is higher than with the Advanced Join system at 80%. The proposed reliable multicasting system to the DSRC-based ITS network can realize an efficient base station arrangement in the ITS network infrastructure and contribute to the deployment of a superior ITS.

In recent years, with the aging of society, many kinds of research have been actively conducted to recognize human activity in a home to watch over the elderly. Multiple sensors for activity recognition are used. However, we need to consider privacy when using these sensors. One of the candidates of the sensors that keep privacy is a sound sensor. MFCC (Mel-Frequency Cepstral Coefficient) is widely used as a feature extraction algorithm for voice recognition. However, it is not suitable to apply conventional MFCC to activity recognition by sounds of daily life. We denote “sounds of daily life” as “life sounds” simply in this paper. The reason is that conventional MFCC does not extract well several features of life sounds that appear at high frequencies. This paper proposes the improved MFCC and reports the evaluation results of activity recognition by machine learning SVM (Support Vector Machine) using features extracted by improved MFCC.