In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a content-oriented networking model. The impact of ICN in reducing the location dependency of data and its high compatibility with ad hoc networks has led to research on realizing Information-Centric ad hoc Networks (ICANET). There has also been extensive research into caching content in the network, which is one of the features of ICN. In static networks, methods have been proposed to cache highly popular content in nodes that are more likely to be used for shortest paths. However, in dynamic networks, content with high popularity should be cached on nodes that are more likely to reach all nodes, as missing nodes need to be taken into account. In this study, we propose a cache control scheme for content caching in ICANET that utilizes both content popularity and the closeness centrality of nodes within the ad hoc network as indicators. To realise the proposed method, a new packet flow based on the Pending Interest Table (PIT) and Content Store (CS) was implemented in the forwarding strategy of ICN. The experiments used ndnSIM, a protocol implementation of NDN based on Network Simulator3, which is widely used in wireless network research. The experimental results showed that the cache hit rate could be increased by up to 4.5% in situations with low content bias. In the same situation, the response delay was also reduced by up to 28.3%.

This paper reports the results of applying our approach discovering safe evacuation routes to practical situations. Our approach is based on the ant colony optimization (ACO) and it is practical in the light of a real case with a tsunami. ACO have been often employed for finding evacuation routes in traditional approaches, which only take advantage of ants behavior more frequently following traces of other ants’ through pheromone communications. We assume that there are a lot of danger zones in the damaged area. For example Rikuzentakata is a city that extensively damaged in the 2011 Great East Japan Earthquake. In such a case, the traditional approaches may present some unsafe routes through the danger zones. We have proposed an ACO based approach that calculates evacuation routes avoiding danger zones. In our approach, evacuees can deposit deodorant pheromone around danger zones, which makes normal pheromone ineffective, so that our approach gives routes not passing through the danger zones. We have implemented our approach as a simulator, conducting experiments in the same situation as the Rikuzentakata case. Through the results of the experiments, we show that our approach decreases the number of people suffering from collapsed and burning buildings.

In this paper, we propose an evacuation support system that provides evacuation routes in case of disasters, and verify the usefulness of the system. Current popular wireless communication infrastructure is supported by a series of base stations and one base station handles a lot of communication. Therefore, when communication base stations break down due to disasters such as an earthquake, it may become difficult for people to use their smartphones based on the Internet. When the communication infrastructure is paralyzed, people will have great difficulty collecting information about the conditions of transportation systems and about the safety of family and friends using smartphones. Our proposed system addresses this problem by using multiple kinds of mobile agents in addition to static agents on smartphones that use a mobile ad hoc network (MANET). The proposed system collects information with mobile agents and diffuses information via mobile agents so that the system is able to provide an optimized evacuation route for each user in a dynamically changing disaster environment. In this paper, we extend our previously proposed evacuation support system to consider elevation information when constructing evacuation routes. When a tsunami or a flood tide occurs, low elevations may be under water. Therefore, this revised evacuation support system helps people to move to safer places by selecting higher elevation routes when warranted by the situation.

Model soups synthesize multiple models after fine-tuning them with different hyperparameters based on the accuracy of the validation data. They train different models on the same training and validation data sets. In this study, we maximized the model fine-tuning accuracy using the inference time and memory cost of a single model. We extended the model soups to create subsets of k training and validation data using a method similar to k-fold cross-validation and trained models on these subsets. First, we showed the correlation between the validation and test data when the models are synthesized, such that their training data contain validation data. Thereafter, we showed that synthesizing k of these models, after synthesizing models based on subsets of the same training and validation data, provides a single model with high test accuracy. This study provides a method for learning models with both high accuracy and reliability for small datasets such as medical images.