This paper provides a comprehensive review of the emerging research area of mobile molecular communication. In mobile molecular communication, sender and receiver bionanomachines as well as associated nodes in the environment exhibit dynamic behavior in the sense that they are mobile and communicate while they move. This paper presents a model of mobile bionanomachines and uses the model to discuss how groups of such bionanomachines working in unison can provide useful functionalities. This paper illustrates several functionalities by applying mobile molecular communication to the concept of cooperative drug delivery. Unsolved research challenges in this area are outlined and discussed.

Many applications use recommender systems to predict user preferences, improve user experience, and increase the amount of sales. However, because of the cold-start problem, it is not easy to recommend items to new users accurately. Recommendation performance degrades in the case of users with little interaction, in particular latent users who have never used the service. To alleviate the cold-start problem, we develop a framework that combines an online shopping domain with information from an Ads platform. Our framework employs deep learning to build a cross-domain recommender system based on shared users in these two domains. We apply Word2Vec to turn textual information on users and items into latent vectors as their representations. The experimental results show the effectiveness of deep neural approaches with knowledge transferred from another domain for the cold-start problem. Textual information may contain useless information, and Word2Vec cannot capture some structural and semantic correlations between different users. Therefore, we propose R-metapath2Vec to enhance user modeling and use the Stacking model to integrate these two kinds of user representations. The experimental results demonstrate the effectiveness of our integration model: our framework can recommend products to users of another domain through Ads distribution in a more accurate level.

Recently, there has been increasing interest in Mobile Wireless Sensor Networks (MWSNs) that are constructed by mobile sensor nodes held by ordinary people, and it has led to a new concept called urban sensing. In such MWSNs, mobile sensor nodes densely exist, and thus, there are basically many sensor nodes that can sense a geographical point in the entire sensing area. To reduce the communication cost for gathering sensor data, it is desirable to gather the sensor data from the minimum number of mobile sensor nodes which are necessary to guarantee the sensing coverage or the quality of services. In this paper, to achieve this, we propose a data gathering method using mobile agents in dense MWSNs. The proposed method guarantees the sensing coverage of the entire area using mobile agents that autonomously perform sensing operations, transmit sensor data, and move between sensor nodes. By gathering only sensor data generated by sensor nodes where mobile agents are running, our proposed method can achieve efficient gathering of sensor data.

Huge amounts of spatio-textual objects, such as geo-tagged tweets, are being generated at an unprecedented scale, leading to a variety of applications such as location-based recommendation and sponsored search. Many of these applications need to support moving top-k spatio-textual subscriptions. For example, while walking, a tourist issues a moving subscription and looks for top-k advertisements published by nearby shops. Unfortunately, existing methods that monitor the results of spatio-textual subscriptions support only static top-k subscriptions or moving boolean subscriptions. In this article, we propose a novel system, called Lamps (Location-Aware Moving Top-k Pub/Sub), which continuously monitors the top-k most relevant spatio-textual objects for a large number of moving top-k spatio-textual subscriptions simultaneously. To the best of our knowledge, this is the first study of a location-aware moving top-k pub/sub system. As with existing works on continuous moving top-k subscription processing, Lamps employs the concept of a safe region to monitor top-k results. However, unlike with existing works that assume static objects, top-k result updates may be triggered by newly generated objects. To continuously monitor the top-k results for massive moving subscriptions efficiently, we propose SQ-tree, a novel index based on safe regions, to filter subscriptions whose top-k results do not change. Moreover, to reduce the expensive cost of safe region re-evaluation, we develop a novel approximation technique for safe region construction. Our experimental results on real datasets show that Lamps achieves higher performance than baseline approaches.

In ad hoc networks, to acquire only necessary data items, it is effective that each mobile node retrieves data items using a top-k query, in which data items are ordered by the score of a particular attribute, and the query-issuing mobile node acquires data items with the k highest scores. In this paper, we propose a message processing method for a top-k query for reducing traffic and keeping the accuracy of the query result, i.e., guaranteeing that data items with the k highest scores in the entire network are acquired. When a mobile node transmits query and reply messages, our method can be used to reduce candidates of data items included in the top-k result to prevent unnecessary data transmissions. Moreover, if a mobile node detects a disconnection of a radio link, it searches for an alternative path to transmit the reply message to the query-issuing node. We present simulation results to evaluate the performance of our proposed method.