Foundation models pretrained on large-scale unlabeled data using self-supervised learning (SSL) have demonstrated remarkable capabilities in natural language processing (NLP) and computer vision (CV). Applying this paradigm to sensor-based Human Activity Recognition (HAR) holds promise for robust and generalizable models, but also poses unique challenges due to diverse sensor mounting positions and application domains. Despite this potential, unlike in NLP and CV, there has been a lack of systematic evaluation frameworks in HAR to rigorously assess the generalization and adaptation capabilities of foundation models across these critical dimensions. To address this gap, we introduce HARBench, a comprehensive benchmark designed to evaluate HAR foundation models along key axes, including Domain Robustness, Position Robustness, Few-shot Performance, and Zero-shot Performance. By leveraging a large collection of datasets from multiple domains, totaling over 2.4 million hours of sensor data, HARBench enables evaluation of HAR foundation models from multiple perspectives, providing a thorough and standardized assessment of their capabilities. We conduct large-scale evaluation and analysis of existing HAR approaches, ranging from conventional supervised methods to state-of-the-art SSL models. Our results quantitatively map the capabilities and limitations of current models, thereby providing guidance for future research and the development of next-generation foundation models for HAR. HARBench is publicly released to support the HAR research community, promoting reproducibility and accelerating the development of robust foundation models.

Socialmedia is an important information source for understanding community events, personal communications, and other social activities. Therefore, mining information from social media data has been widely studied. Recently, it has been shown that knowledge graphs constructed from social media data can be effectively mined, which usually results in embeddings that contain structural information about social media entities. However, it has been shown that embeddings learned from social media data can contain social bias. Bias in the embedding is usually considered as undesirable, but there is only limited number of works that can separate bias from the embedding. In this article, we propose a bias disentanglement model that separates embedding into two parts, one preserves general information but has bias removed, and the other contains distilled bias. The two sets of embeddings can be used for computational tasks with different information requirements. Our method, disentangling bias through multitask embedding learning (DisMEL), is based on knowledge graph embedding techniques and supervised disentangling models. In addition to qualitative analysis of the generated embeddings, we test our method in two downstream tasks, namely, user political bias prediction and tweet recommendation. We compare our method with several baseline methods in experimental evaluations, based on a large-scale Japanese political social media dataset we collected and prepared. We show that when the debiased embedding set is used, the tasks irrelevant to political bias can be improved. On the other hand, when the biased embedding set is used, the tasks relevant to political bias can be improved. While we deal with political bias in this article, our method can also be adopted for other kinds of bias, such as gender or ethnicity bias.

Spatio-temporal forecasting addresses the prediction of phenomena that evolve over space and time. A key application is headcount prediction, which enables socially beneficial functions, such as the intelligent control of HVAC systems, by estimating occupancy within defined areas. Conventional headcount-prediction methods rely on sensor-derived data that are often corrupted by noise and missing values. Therefore, existing studies require extensive preprocessing, including data cleaning, to mitigate these issues. To overcome this limitation, we propose a robust spatio-temporal graph convolutional network (RSTGCN) that integrates a reliability-weighting module into the feature-extraction process, thereby attenuating the influence of unreliable measurements. Experiments show that our proposed model, RSTGCN, achieves superior forecasting performance on unprocessed real-world datasets containing noise and missing values.

Optimizing building energy consumption is essential for advancing global sustainability goals. Data-driven occupancy estimation using non-intrusive sensors plays a vital role in this effort. However, large-scale deployment remains limited due to a fundamental challenge: existing models fail to generalize across diverse environments, performing poorly under domain shift caused by variations in physical dynamics and sensor behavior unique to each space. In this study, we present a novel learning strategy centered on a dynamic peak-aware loss (DPL) to address this issue within a strict zero-shot framework. Our method directly addresses the core physical problem of sensor lag, where variables, such as$C O_{2}$respond slowly to occupancy changes, by asymmetrically penalizing estimation errors. This process trains the model to compensate for delayed sensor readings, particularly for complex occupancy patterns with multiple peaks and valleys. Evaluated on a public real-world dataset, our learning-centric approach demonstrated model-agnostic effectiveness, consistently enhancing the performance of diverse backbone models and, notably, surpassing fully supervised baselines. Therefore, this study presents a robust and scalable solution that bridges the gap between data-driven models and real-world physical constraints, advancing the development of practical smart building energy management systems.

Nowadays, many online services use recommendation systems to provide personalized item recommendations to users. Collaborative filtering is the major paradigm in recommendation systems. Based on user-item interaction data, collaborative filtering recommends items to a user based on other similar users. The problem of interest disentanglement in recommendation now has attracted the attention of many researchers. Several works have proposed methods to disentangle conformity from user private interest, by assuming that conformity is correlated to item popularity. However, such modeling is simplistic and overlooks many possibilities between user public and private interest, and the item popularity. For example, a user can privately like a popular movie or buy a niche music album due to the stimulation of the social environment. In this paper, we propose a more comprehensive social behavior model that describes fine-grained relationships between user interest and item popularity. Our model does not use explicit user relationship data. Instead, we extract social behavior patterns directly from user-item interaction data. We also make our model into a recommendation framework called Disentangled Social Consumer Preference (DSCP), which can be integrated into existing recommendation models such as BPRMF. Our extensive experiments with four datasets from different services show that our model can outperform state-of-the-art baseline models. We achieve better recommendation accuracy in both the usual random test and the intervened test that shows debiasing effect.

Abstract How can we estimate the result size for a given query on complex spatial objects like polygons? Estimating a query’s result size, also known as the cardinality estimation, plays a significant role in query scheduling and optimization. Accurate and fast cardinality estimation substantially improves query efficiency. Existing compatible solutions, mainly histogram-based, deal with polygons as their minimal bounding rectangles for easier processing, which leads to inaccurate estimation. To address this issue, we present PolyCard, a learned cardinality estimator for intersection queries on spatial polygons. We successfully apply learning techniques to spatial polygons with variable sizes. PolyCard has the following properties. (i) Accurate: PolyCard improves 30% accuracy compared with existing solutions, (ii) Fast: PolyCard takes only 4 microseconds for an estimation, and (iii) Stable: PolyCard is robust against datasets and queries of different cardinalities. Our experiments on four real-world datasets of millions of polygons demonstrate the efficiency and effectiveness of PolyCard.

Abstract With the explosive growth of online information, users may also face information overload. To handle this problem, recommender systems have become an effective strategy, which can analyze the characters of users and items to provide valuable information. One of the important types of information is the item’s side information. For example, in Amazon dataset, side information mainly includes visual side information (e.g., image and video), textual side information (e.g., title and description), and auxiliary side information (e.g., brand and category). To analyze various types of side information, some research designed multiple modalities for different types of side information, which can improve the performance of the recommender system. To analyze the deeper relationships between users and items, recent works also use a graph structure to represent the interactions. Existing works on multi-modal recommender systems using graph neural networks largely depend on the interaction records, while little effort focuses on the relationships between interactions and various types of side information. In this paper, we propose a novel multi-task learning model. First, we construct the interaction records to graphs for each modality to gather the representations, and then we analyze the representations of each modality and the specific side information based on the similarities. We design a multi-task multi-modal graph neural network framework built upon message passing with the attention mechanism of graph neural networks, which can generate the representations of users and items from interaction records, and then analyze the relationships between the representations from GNNs and item’s side information. We conduct experiments on three public datasets, Amazon, Modcloth and MovieLens. The results of our model outperform the state-of-the-art methods.

In democratic countries, political interest is deeply involved in people’s daily lives. Research in political consumerism shows that product purchase decision is also influenced by the political orientation of the consumer. In traditional recommendation system design, user interest in an item is provided by a unified model. Recently, interest disentanglement methods have been introduced. It is shown that by disentangling interest factors such as conformity and private interest, recommendation performance can be significantly improved. However, few studies attempt to disentangle political interest in purchase behavior, which is bipolar. In this article, we propose a method to extract political interest model from e-commerce interaction data, which is supported by a novel word-level political bias assignment. For the bias assignment part, we improved a political bias distilling method. For the political interest model extraction part, we extend a one-side bias method to make it support bipolar bias. We compare our method with state-of-the-art baseline methods in several evaluation settings, and the experimental results show that our method can achieve superior performance. Further investigation shows that our method is consistent with theories of political consumerism.

Owing to the cost of collecting labeled sensor data, self-supervised learning (SSL) methods for human activity recognition (HAR) that effectively use unlabeled data for pretraining have attracted attention. However, applying prior SSL to COMPLEX activities in real industrial settings poses challenges. Despite the consistency of work procedures, varying circumstances, such as different sizes of packages and contents in a packing process, introduce significant variability within the same activity class. In this study, we focus on sensor data corresponding to characteristic and necessary actions (sensor data motifs) in a specific activity such as a stretching packing tape action in an assembling a box activity, and propose to train a neural network in self-supervised learning so that it identifies occurrences of the characteristic actions, i.e., Motif Identification Learning (MoIL). The feature extractor in the network is subsequently employed in the downstream activity recognition task, enabling accurate recognition of activities containing these characteristic actions, even with limited labeled training data. The MoIL approach was evaluated on real-world industrial activity data, encompassing the state-of-the-art SSL tasks with an improvement of up to 23.85% under limited training labels.

Political polarization is commonly observed in democratic countries. While it allows individual citizens to freely choose sides, it also causes the problem of separation and isolation. Especially in information-seeking behaviors, echo chambers and filter bubbles are observed. In this article, we present a political sentiment dictionary for analyzing political polarizatio and increasing information heterogeneity. It takes advantage of large-scale social media data and is thus superior in accuracy and coverage compared to manually crafted dictionaries. Generated from Japanese tweets, more than 50k words in this dictionary cover aspects ranging from political parties and public entities to foods and personal hobbies. We describe in detail the method to construct this dictionary, which can be replicated for other languages and countries. We demonstrate the use of this dictionary in the application of recommendation diversification. We show with real-world e-commerce data that the use of the dictionary can generally increase the diversity in product recommendations, effectively mitigating the filter bubbles.

Abstract Recommendation systems nowadays are deployed everywhere to solve the information overload problem. Thousands of news items that appear on online news platforms also need recommendations. However, news recommendation differs from product recommendation because of its focus on public interest. In this paper, we propose a multi-role social behavior model that includes a chaser model, which describes behavior patterns of chasing popular news, and a sider model, which describes behavior patterns of seeking news from a similar ideology standpoint. We propose a framework that integrates this model into a graph-based recommendation system. Unlike other intent disentangling techniques, our model explicit models social behavior patterns originated from public interest. We test our framework with two real-world news recommendation datasets. Compared to state-of-the-art baseline news recommendation models, our method achieves significantly higher recommendation accuracy. By analyzing the model outputs, we also gain a better understanding of news-seeking social behavior.

In this paper, to address the insufficient capturing of high-order correlations and the vulnerability to noise in conventional collaborative filtering models, we propose HyperGraph Contrastive Learning with graph structure learning for recommendation (HGCL). HGCL employs user and item hypergraphs as contrastive views in contrastive learning, where edges connect the item and user's k-order reachable neighbors. This approach allows HGCL to model relationships with distant nodes and explicitly capture high-order correlations, alleviating the issue of over-smoothing. Subsequently, HGCL performs contrastive learning between representations obtained from the user-item interaction graph and hypergraphs. This integrates the user-item relationship features from the interaction graph with the high-order correlations for each user and item from the hypergraphs, resulting in more effective representations. Furthermore, to address the weakness of hypergraphs against noise, we modify the hypergraph structure learning method for the recommendation task and incorporate it into HGCL. Based on the user and item representations, HGCL detects potential node relationships and noise in the hypergraph for the recommendation task. By adding or removing these nodes from the hypergraph, HGCL acquires a denoised hypergraph. By applying these processes to user and item hypergraphs, HGCL obtains improved hypergraphs with reduced noise effects and achieves more effective recommendations. Experimental results on real-world datasets demonstrate that HGCL outperforms baseline models, achieving up to 5.25% improvement in NDCG@20.

With the rapid development of deep learning algorithms, medical information understanding models have become an effective strategy for diagnosing clinical conditions. One of the most important factors in diagnosis is medical imaging (e.g., X-rays, CT scans). Current research on medical image analysis mainly relies on pretrained models with specific layers, which can improve the performance of medical image classification. However, existing methods rely heavily on large amounts of labeled data generated by medical experts. When labeled data is scarce, the performance of these models deteriorates, even with pretrained models. In this paper, we propose a novel Multi-modal Contrastive Learning (MMCLN) framework that can in-corporate less sensitive auxiliary information into the contrastive framework to improve the performance of the classification based on a limited amount of labeled images. We construct different pretrained with customized layers to generate embeddings of both image and auxiliary data, and then calculate the loss based on the contrastive learning method across various domains, which can analyze the implicit relationships between image and auxiliary data. We conducted experiments on a lung cancer CT dataset, and our proposed model outperformed state-of-the-art methods, demonstrating its effectiveness in scenarios with limited labeled data.

In recent years, many researchers focus on cross-domain recommendations (CDRs). CDRs leverage data across multiple services (domains) to enable the transfer of user preferences toward sparse domains. Although some approaches transfer knowledge through shared users across domains, there are privacy concerns. Thus, several CDRs use text features like product reviews, instead of shared users. However, real-world datasets often have noisy text features, complicating effective domain adaptation. In this paper, our objective is to enhance domain adaptation performance while ensuring effective recom-mendation by integrating visual features, alongside text features. First, to mitigate the effect by noisy item descriptions, we incorporate visual features that exclusively capture item-specific information. We obtain effective multimodal representations to improve domain adaptation by employing contrastive learning that aligns visual features with fixed text features. On the other hand, relying solely on this approach could potentially undermine the distinctiveness inherent in both text and images. As a second proposal to address this concern, we minimize mutual information between embeddings of text and image, which encourages the preservation of distinctive features within both modalities. These two proposals lead to a balanced approach to domain adaptation while keeping the effectiveness of recommendation systems. Experimental results show the domain adaptation by the proposed method is effective.

Unlike human daily activities, existing publicly available sensor datasets for work activity recognition in industrial domains are limited by difficulties in collecting realistic data as close collaboration with industrial sites is required. This also limits research on and development of methods for industrial applications. To address these challenges and contribute to research on machine recognition of work activities in industrial domains, in this study, we introduce a new large-scale dataset for packaging work recognition called OpenPack. OpenPack contains 53.8 hours of multimodal sensor data, including acceleration data, keypoints, depth images, and readings from IoT-enabled devices (e.g., handheld barcode scanners), collected from 16 distinct subjects with different levels of packaging work experience. We apply state-of-the-art human activity recognition techniques to the dataset and provide future directions of complex work activity recognition studies in the pervasive computing community based on the results. We believe that OpenPack will contribute to the sensor-based action/activity recognition community by providing challenging tasks. The OpenPack dataset is available at https://open-pack.github.io.

Building high-quality recommender systems is challenging for new services and small companies, because of their sparse interactions. Cross-domain recommendations (CDRs) alleviate this issue by transferring knowledge from data in external domains. However, most existing CDRs leverage data from only a single external domain and serve only two domains. CDRs serving multiple domains require domain-shared entities (i.e., users and items) to transfer knowledge, which significantly limits their applications due to the hardness and privacy concerns of finding such entities. We therefore focus on a more general scenario, non-overlapped multi-target CDRs (NO-MTCDRs), which require no domain-shared entities and serve multiple domains. Existing methods require domain-shared users to learn user preferences and cannot work on NO-MTCDRs. We hence propose MITrans, a novel mutual information-based (MI-based) preference disentangling and transferring framework to improve recommendations for all domains. MITrans effectively leverages knowledge from multiple domains as well as learning both domain-shared and domain-specific preferences without using domain-shared users. In MITrans, we devise two novel MI constraints to disentangle domain-shared and domain-specific preferences. Moreover, we introduce a module that fuses domain-shared preferences in different domains and combines them with domain-specific preferences to improve recommendations. Our experimental results on two real-world datasets demonstrate the superiority of MITrans in terms of recommendation quality and application range against state-of-the-art overlapped and non-overlapped CDRs.

Abstract Organized event is an important form of human activity. Nowadays, many digital platforms offer organized events on the Internet, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate prediction model. In this paper, we propose to utilize social media retweeting activity to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilize retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models in both warm and cold tests.

Clustering multi-dimensional points is a fundamental task in many fields, and density-based clustering supports many applications because it can discover clusters of arbitrary shapes. This article addresses the problem of Density-Peaks Clustering (DPC) in Euclidean space. DPC already has many applications, but its straightforward implementation incurs O ( n 2 ) time, where n is the number of points, thereby does not scale to large datasets. To enable DPC on large datasets, we first propose empirically efficient exact DPC algorithm, Ex-DPC. Although this algorithm is much faster than the straightforward implementation, it still suffers from O ( n 2 ) time theoretically. We hence propose a new exact algorithm, Ex-DPC++, that runs in o ( n 2 ) time. We accelerate their efficiencies by leveraging multi-threading. Moreover, real-world datasets may have arbitrary updates (point insertions and deletions). It is hence important to support efficient cluster updates. To this end, we propose D-DPC for fully dynamic DPC. We conduct extensive experiments using real datasets, and our experimental results demonstrate that our algorithms are efficient and scalable.

In recent years, Point Of Interest (POI) recommendations have been actively studied because of the widespread use of location-based social network services. Out-Of-Town POI recommendation methods recommend POIs outside a user’s residence, such as travel and business trip destinations. Most existing methods require a certain number of past visit sequences at destination regions or can be used only between two specific unidirectional regions. In this study, we propose an Unvisited Out-Of-Town POI Recommendation framework (UOPR), which can recommend POIs for Out-Of-Town regions that have not yet been visited by users. In addition, UOPR can learn users’ visit patterns for multiple regions simultaneously. UOPR takes into account the fact that user’s Out-Of-Town visiting tendencies vary depending on the geographical distance from their residences. It therefore adopts an approach that calculates two scores, one determined by user’s Home-Town visiting tendencies and the other by their Out-Of-Town visiting tendencies, and uses them differently. Furthermore, we propose a mask-input learning and user-embedding creation method that enables learning with enhanced interaction between POI embeddings in an Out-Of-Town POI recommendation environment, where the data are often sparse. UOPR achieves higher recommendation accuracy than existing methods on a dataset collected on a real-world service.

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. 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.

Graph-based recommendation models, which utilize a user-item interaction graph whose edges represent users' preferences, are well known because of their effectiveness. However, many existing models are intrinsically transductive, meaning they can make recommendations only for users and items that exist in the training data. To solve this challenge, some researchers focus on recommendations in the inductive scenario where new users and new items emerge in the inference phase. In this paper, we explore the potential of contrastive learning for the inductive scenario to further improve recommendation accuracy. The situation where new users and new items emerge can be interpreted as a change in the interaction graph. Therefore, it is crucial to capture the change in the interaction graph for inductive recommendations. Based on the above perspective, we propose the contrastive learning framework, INductive Contrastive Learning (INCL). INCL creates an augmented graph by adding or removing edges of the original interaction graph and simulates changes in the interaction graph. By performing contrastive learning, INCL fosters each representation generated from the original interaction graph and the augmented graph to be similar. As a result, INCL is trained to generate robust representations that can adapt to changes in the interaction graph. Experimental results on real-world datasets demonstrate that INCL outperforms existing models in the inductive scenario, achieving up to 2.13% improvement in Recall@20.

With the rapid growth of online information, recommender systems have become an effective strategy for handling information overload. One of the most important factors affecting the user's purchase activities is the side information of the item. Recently, some research has shown that analyzing the side information (e.g., visual and textual information) in multiple modalities can improve the performance of the recommender system. Existing works on multimedia recommendation using graph neural networks largely depend on the interaction records, while less effort focuses on the deeper relationships between interactions and different types of side information. In this paper, we propose a novel graph structure by combining the edges of user-item interactions and relational edges of side information in different modalities. Each modality contains a specific graph structure to learn the representations of users and items to make the recommendation. We design a Multi-modal Relational Side Information Graph Attention Networks (MRSI-GAT) framework built upon the message-passing with attention mechanism of graph attention networks. We conduct experiments on two public datasets, Amazon and MovieLens, and the results of the model outperform the state-of-the-art methods.

Nowadays social media has become a major information source for understanding various aspects of social activities, such as political polarization and shopping preferences. To use social media data with machine learning algorithms, the data can be transformed into graphs, from which graph embeddings can be learned. However, it has been shown that embeddings usually inherit the social bias prevalent in the data. Sometimes such bias is necessary, and sometimes they are undesirable. Previous work that deals with embedding bias usually tries to neutralize embeddings with regard to a bias measure. In this paper, we propose an embedding bias model and a control method, which can reduce or enlarge the bias by changing the value of one parameter. In addition to explicit bias scores, we also test our method in two downstream tasks, namely, party support prediction and tweet recommendation. Through experimental evaluations, we show that when we reduce the embedding bias, the task irrelevant to the sensitive attribute can be improved. On the other hand, when we enlarge the bias, the task relevant to the sensitive attribute can be improved.

Location-based services are becoming more involved with our daily lives, so many works have considered efficiently retrieving useful objects from spatial-keyword databases. These works are promising on the user sides, but none of them considers the service provider sides. To gain profits and enrich recommendation lists, service providers conduct market analyses and want to know potential users who may be interested in their services. In this paper, to satisfy this requirement, we propose a new query, approximate reverse top-k spatial-keyword (ART) query. Given a set O of spatial-keyword objects, a set S of users (their locations and preferable keywords), a query object q, k, and an approximation ratio ϵ, an ART query retrieves such users that q is included in their approximate top-k results among O and q. A straightforward approach to processing this query is to run a top-k spatial-keyword search for each user in S. This is clearly expensive, as the number of users is generally large. We therefore propose PART, an efficient algorithm for ART query processing. In addition, we propose B-PART, which enables the processing of multiple ART queries in a batch. We conduct extensive experiments using real datasets, and the results demonstrate the efficiencies of our algorithms.

Recommender systems nowadays are commonly used in various platforms to provide information based on user preferences. In POI recommendation, systems can generally learn the users' short-term and long-term preferences, which are based on sessions and global information. Existing systems, however, usually overlook the mid-term information, which may contain important indications of user preferences. In this work, we propose a session-based POI recommender system based on Graph Neural Network (GNN). In contrast to existing work, our model can learn short-term, long-term, and mid-term preferences at the same time. In order to learn the mid-term item representation, we construct a week graph and process it by a GAT-based graph model. We further use a gate fusion to integrate three temporal dimensions to obtain the hybrid item representation. We conduct experiments with a real-world POI visiting dataset, and the evaluation results show that our model outperforms compared state-of-art models. By adding mid-term information, the prediction accuracy can be improved by 5% compared to the best baseline.

In this study, we proposed a new method for pedestrian safety named ObsSense that estimates the distance and relative velocity of both static and mobile roadside obstacles using probing signals emitted by an off-the-shelf smartphone carried by a pedestrian. Sound-based estimation of the movement properties of an obstacle has been actively studied in pervasive computing, and generally provides the distance from a smartphone user to an obstacle and the relative velocity of the obstacle. However, most prior studies have focused on estimating the properties of either static or mobile obstacles. Furthermore, they estimated either the distance to obstacles or their relative velocity. Nonetheless, both the distance and relative velocity of the oncoming obstacles are necessary to derive the timing of a possible collision. To address this issue, we proposed a new probing signal composed of sine waves and sine sweeps, designed to acquire information about both the distance and relative velocity of static and mobile obstacles. In addition, we proposed a neural network model that can jointly predict distance and relative velocity from reflected sounds of the probing signal captured by a smartphone. The proposed neural network was designed to consider the mobility status of obstacles, i.e., whether they are mobile or static, in order to select the most appropriate method to estimate their distance and relative velocity. In addition, our method uses the relationship between distance and velocity to increase the precision of its estimations. For example, the distance can be estimated by integrating the velocity-time curve. The performance of ObsSense was evaluated using real-world data, and the experimental results demonstrated the effectiveness of our proposed probing signal and neural network architecture.

The problem of inner product search (IPS) is important in many fields. Although maximum inner product search (MIPS) is often considered, its result is usually skewed and static. Users are hence hard to obtain diverse and/or new items by using the MIPS problem. Motivated by this, we formulate a new problem, namely the fair and independent IPS problem. Given a query, a threshold, and an output size k, this problem randomly samples k items from a set of items such that the inner product of the query and item is not less than the threshold. For each item that satisfies the threshold, this problem is fair, because the probability that such an item is outputted is equal to that for each other item. This fairness can yield diversity and novelty, but this problem faces a computational challenge. Some existing (M)IPS techniques can be employed in this problem, but they require O(n) or o(n) time, where n is the dataset size. To scale well to large datasets, we propose a simple yet efficient algorithm that runs in O(log n + k) expected time. We conduct experiments using real datasets, and the results demonstrate that our algorithm is up to 330 times faster than baselines.

Order Dependencies (ODs) have many applications, such as query optimization, data integration, and data cleaning. Although many works addressed the problem of discovering OD (and its variants), they do not consider datasets with missing values, a standard observation in real-world datasets. This paper introduces the novel notion of Embedded ODs to deal with missing values, and we propose an efficient algorithm for validating embedded ODs. We conduct experiments on real-world datasets, and the results confirm the efficiency of our algorithm.

Social media as a background information source has been utilized in many practical computational tasks, such as stock price prediction, epidemic tracking, and product recommendation. However, proper representation of an evolving social media background is still in an early research stage. In this article, we propose a representation method that considers temporal novelties as well as the fine details of word inter-dependencies. Our method is based on the tf-idf and graph embedding techniques. The proposed method has superiority over other representation methods because it takes the advantage of both the temporal aspect of tf-idf and the semantic aspect of graph embeddings. We compare our method with a variety of baselines in two practical application scenarios using real-world data. In tweet popularity prediction, our representation achieves 5.7% less error and 12.8% higher correlation compared to the best baseline. In e-commerce product recommendation, our representation achieves 17% higher hit-rate and 20% higher NDCG compared to the best baseline.

Recommender systems nowadays are commonly deployed in e-commerce platforms to help customers making purchase decisions. Dynamic recommender considers not only static user-item interaction data, but the temporal information at the time of recommendation. Previous researches have suggested to incorporate social media as the temporal information in dynamic neural recommenders after transforming them into embeddings. While such an approach can potentially improve recommendation performance, the effectiveness is difficult to explain. In this article, we propose an explainable method to integrate social media in a dynamic neural recommender. Our method applies association rule mining, which can generate human-understandable behavior patterns from social media and e-commerce platforms. With real-world social media and e-commerce data, we show that the integration can improve accuracy by up to 14% while using the same data. Moreover, we can explain the positive cases by examining relevant association rules.

The maximum inner product search (MIPS), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. Usually e-commerce companies face situations where they want to promote and sell new or discounted items. In these situations, we have to consider the following questions: Who is interested in the items, and how do we find them? This article answers this question by addressing a new problem called reverse maximum inner product search (reverse MIPS). Given a query vector and two sets of vectors (user vectors and item vectors), the problem of reverse MIPS finds a set of user vectors whose inner product with the query vector is the maximum among the query and item vectors. Although the importance of this problem is clear, its straightforward implementation incurs a computationally expensive cost. We therefore propose Simpfer, a simple, fast, and exact algorithm for reverse MIPS. In an offline phase, Simpfer builds a simple index that maintains a lower bound of the maximum inner product. By exploiting this index, Simpfer judges whether the query vector can have the maximum inner product or not, for a given user vector, in a constant time. Our index enables filtering user vectors, which cannot have the maximum inner product with the query vector, in a batch. We theoretically demonstrate that Simpfer outperforms baselines employing state-of-the-art MIPS techniques. In addition, we answer two new research questions. Can approximation algorithms further improve reverse MIPS processing? Is there an exact algorithm that is faster than Simpfer? For the former, we show that approximation with quality guarantee provides a little speed-up. For the latter, we propose Simpfer++, a theoretically and practically faster algorithm than Simpfer. Our extensive experiments on real datasets show that Simpfer is at least two orders of magnitude faster than the baselines, and Simpfer++ further improves the online processing time.

Deep learning-based recommender systems may lead to over-fitting when lacking training interaction data. This over-fitting significantly degrades recommendation performances. To address this data sparsity problem, cross-domain recommender systems (CDRSs) exploit the data from an auxiliary source domain to facilitate the recommendation on the sparse target domain. Most existing CDRSs rely on overlapping users or items to connect domains and transfer knowledge. However, matching users is an arduous task and may involve privacy issues when data comes from different companies, resulting in a limited application for the above CDRSs. Some studies develop CDRSs that require no overlapping users and items by transferring learned user interaction patterns. However, they ignore the bias in user interaction patterns between domains and hence suffer from an inferior performance compared with single-domain recommender systems. In this paper, based on the above findings, we propose a novel CDRS, namely semantic clustering enhanced debiasing graph neural recommender system (SCDGN), that requires no overlapping users and items and can handle the domain bias. More precisely, SCDGN semantically clusters items from both domains and constructs a cross-domain bipartite graph generated from item clusters and users. Then, the knowledge is transferred via this cross-domain user-cluster graph from source to the target. Furthermore, we design a debiasing graph convolutional layer for SCDGN to extract unbiased structural knowledge from the cross-domain user-cluster graph. Our Experimental results on three public datasets and a pair of proprietary datasets verify the effectiveness of SCDGN over stateof-the-art models in terms of cross-domain recommendations.

Spatial data analysis is a classic yet important topic because of its wide range of applications. Recently, as a spatial data analysis approach, a neighbor graph of a set P of spatial points has often been employed. This paper also considers a spatial neighbor graph and addresses a new problem, namely top-N weighted spatial k-clique retrieval. This problem searches for the N minimum weighted cliques consisting of k points in P, and it has important applications, such as community detection and co-location pattern mining. Recent spatial datasets have many points, and efficiently dealing with such big datasets is one of the main requirements of applications. A straightforward approach to solving our problem is to try to enumerate all k-cliques, which incurs O(nkk2) time. Since k ≥ 3, this approach cannot achieve the main requirement, so computing the result without enumerating unnecessary k-cliques is required. This paper achieves this challenging task and proposes a simple practically-efficient algorithm that returns the exact answer. We conduct experiments using two real spatial datasets consisting of million points, and the results show the efficiency of our algorithm, e.g., it can return the exact top-N result within 1 second when N ≤ 1000 and k ≤ 7.

Big data mining is well known to be an important task for data science, because it can provide useful observations and new knowledge hidden in given large datasets. Proximity-based data analysis is particularly utilized in many real-life applications. In such analysis, the distances to k nearest neighbors are usually employed, thus its main bottleneck is derived from data retrieval. Much efforts have been made to improve the efficiency of these analyses. However, they still incur large costs, because they essentially need many data accesses. To avoid this issue, we propose a machine-learning technique that quickly and accurately estimates the k-NN distances (i.e., distances to the k nearest neighbors) of a given query. We train a fully connected neural network model and utilize pivots to achieve accurate estimation. Our model is designed to have useful advantages: it infers distances to the k-NNs at a time, its inference time is O(1) (no data accesses are incurred), but it keeps high accuracy. Our experimental results and case studies on real datasets demonstrate the efficiency and effectiveness of our solution.

Maximum inner product search (or k-MIPS) is a fundamental operation in recommender systems that infer preferable items for users. To support large-scale recommender systems, existing studies designed scalable k-MIPS algorithms. However, these studies do not consider diversity, although recommending diverse items is important to improve user satisfaction. We therefore formulate a new problem, namely diversity-aware k-MIPS. In this problem, users can control the degree of diversity in their recommendation lists through a parameter. However, exactly solving this problem is unfortunately NP-hard, so it is challenging to devise an efficient, effective, and practical algorithm for the diversity-aware k-MIPS problem. This paper overcomes this challenge and proposes IP-Greedy, which incorporates new early termination and skipping techniques into a greedy algorithm. We conduct extensive experiments on real datasets, and the results demonstrate the efficiency and effectiveness of our algorithm. Also, we conduct a case study of the diversity-aware k-MIPS problem on a real dataset. We confirm that this problem can make recommendation lists diverse while preserving high inner products of user and item vectors in the lists.

Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.

Recommender systems (RSs) have been extensively studied in academia and industry, while few works focus on flash sale recommendations. In flash sale scenarios, period-specific high discounts are applied on ordinal sales during each flash sale period to attract users. According to periodic sales strategies, available and discounted items change significantly across periods. Users are attracted by the high discounts and show a period-specific preference. Besides, the frequently changed available items provoke a cold-start problem, i.e., an impaired recommendation caused by lacking interactions. However, most existing RSs either cannot handle users’ period-specific preferences or suffer from the cold-start problem. Therefore, this work proposes a novel meta-learning-based RS to mitigate the cold-start problem and simultaneously handle users’ period-specific preferences. Moreover, we introduce a novel hierarchical meta-training algorithm to guide the learning of our recommendation model via period-and user-specific gradients. In this way, the learned model contains user- and period-shared knowledge and can fast adapt to the recommendations for new flash sale periods and users. To evaluate the effectiveness of our system in flash sale recommendations and non-flash sale recommendations, we conduct experiments on a real-world flash sale e-commerce dataset and a widely used recommendation dataset, considering both warm and cold scenarios. The experimental results show that our proposed model is remarkably improved over the current state-of-the-art methods in flash sale recommendations and most of the non-flash sale cold-start recommendations.

One of the research issues for stream data is fast notification of continuous query results. Recent researches assume that query processing servers receive data from remote data sources continuously. Therefore, when a large number of processing servers are distributed in networks, large amounts of communication traffic for those are produced. This is the main cause of delaying query result notifications. To tackle this fundamental problem, we focus on the conjunction of operations and conditions in continuous queries for distributed processing systems. When queries consist of some series of operations and conditions, processing servers can stop query executions if the remaining operations or condition checking are dispensable. Thus, communications less arise and communication traffic is further reduced. Our proposed system represents continuous queries by conjunctive normal forms (CNF queries). The proposed system constructs trigger trees for evaluating the sum terms of CNF queries and determines the timing for their evaluations, in order to reduce communication traffic. We confirmed that one of the proposed methods can reduce the average amount of communication traffic by 23 percent compared with a sensor data serial aggregation method in an evaluation situation.

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.

Abstract In many fields, e.g., data mining and machine learning, distance-based outlier detection (DOD) is widely employed to remove noises and find abnormal phenomena, because DOD is unsupervised, can be employed in any metric spaces, and does not have any assumptions of data distributions. Nowadays, data mining and machine learning applications face the challenge of dealing with large datasets, which requires efficient DOD algorithms. We address the DOD problem with two different definitions. Our new idea, which solves the problems, is to exploit an in-memory proximity graph. For each problem, we propose a new algorithm that exploits a proximity graph and analyze an appropriate type of proximity graph for the algorithm. Our empirical study using real datasets confirms that our DOD algorithms are significantly faster than state-of-the-art ones.

This paper addresses the problem of top-k inner product join, which, given two sets of high-dimensional vectors and a result size k, outputs k pairs of vectors that have the largest inner product. This problem has important applications, such as recommendation, information extraction, and finding outlier correlation. Unfortunately, computing the exact answer incurs an expensive cost for large high-dimensional datasets. We therefore consider an approximate solution framework that efficiently retrieves k pairs of vectors with large inner products. To exploit this framework and obtain an accurate answer, we extend a state-of-the-art proximity graph for inner product search. We conduct experiments on real datasets, and the results show that our solution is faster and more accurate than baselines with state-of-the-art techniques.

Due to the proliferation of GPS-enabled mobile devices and IoT environments, location-based services are generating a large number of objects that contain both spatial and keyword information, and spatial-keyword databases are receiving much attention. This paper addresses the problem of top-k spatial-keyword similarity join, which outputs k object pairs with the highest similarity. This query is a primitive operator for important applications, including duplicate detection, recommendation, and clustering.

Although deep learning-based activity recognition using wearable sensors has been actively studied to implement smart applications such as supporting elderly care, healthcare, and home automation, techniques for understanding the inside of activity recognition networks have not yet been investigated thoroughly. In the computer vision research field, activation maximization (AM) was proposed to visualize the internal functions of networks. However, when conventional AM techniques, which are tailored to image-based recognition, are directly applied to acceleration-based activity recognition networks, meaningless and noisy signals are generated because of the difficulties in regularizing AM by directly using the values of the acceleration signals that are generated. This study proposes novel regularization techniques for AM using activity recognition networks that leverage activation values used in AM to indirectly control the acceleration signals that are generated. We evaluated the proposed method quantitatively using publicly available datasets and confirmed the effectiveness of the proposed techniques.

In recommender systems, a cold-start problem occurs when there is no past interaction record associated with the user or item. Typical solutions to the cold-start problem make use of contextual information, such as user demographic attributes or product descriptions. A group of works have shown that social media background can help predicting temporal phenomenons such as product sales and stock price movements. In this work, our goal is to investigate whether social media background can be used as extra contextual information to improve recommendation models. Based on an existing deep neural network model, we proposed a method to represent temporal social media background as embeddings and fuse them as an extra component in the model. We conduct experimental evaluations on a real-world e-commerce dataset and a Twitter dataset. The results show that our method of fusing social media background with the existing model does generally improve recommendation performance. In some cases the recommendation accuracy measured by hit-rate@K doubles after fusing with social media background. Our findings can be beneficial for future recommender system designs that consider complex temporal information representing social interests.

The MIPS (maximum inner product search), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. It is usual that e-commerce companies face situations where they want to promote and sell new or discounted items. In these situations, we have to consider a question: who are interested in the items and how to find them? This paper answers this question by addressing a new problem called reverse maximum inner product search (reverse MIPS). Given a query vector and two sets of vectors (user vectors and item vectors), the problem of reverse MIPS finds a set of user vectors whose inner product with the query vector is the maximum among the query and item vectors. Although the importance of this problem is clear, its straightforward implementation incurs a computationally expensive cost.

Clustering multi-dimensional points is a fundamental task in many fields, and density-based clustering supports many applications as it can discover clusters of arbitrary shapes. This paper addresses the problem of Density-Peaks Clustering (DPC), a recently proposed density-based clustering framework. Although DPC already has many applications, its straightforward implementation incurs a quadratic time computation to the number of points in a given dataset, thereby does not scale to large datasets.

Distance-based outlier detection is widely adopted in many fields, e.g., data mining and machine learning, because it is unsupervised, can be employed in a generic metric space, and does not have any assumptions of data distributions. Data mining and machine learning applications face a challenge of dealing with large datasets, which requires efficient distance-based outlier detection algorithms. Due to the popularization of computational environments with large memory, it is possible to build a main-memory index and detect outliers based on it, which is a promising solution for fast distance-based outlier detection.

Summary As a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real‐world conditions, hence human perception is the key indicator to consider the correctness of the data. One of the data that is provided by crowdsourcing platform is public trajectory. Public trajectory data contains details of historical trips obtained from contributors' GPS logger devices that are embedded in mobile devices, wearable devices, satnavs, or vehicle GPS trackers to record the user's trajectory path. While public trajectory data can be used as an alternate data source for human movement analysis, this crowdsourced dataset is also prone to noise and inaccuracy which makes the preprocessing step an important phase prior of any processing step. In this article, we discuss the characteristics and the most common noise from crowdsourcing GPS trajectories and utilize a non‐map‐matching approach convex hull‐based reduction method to minimize spike noise, followed by granularity reduction to reduce the number of trajectory points while maintaining the nature of the trajectories.

Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social networking services, a large number of objects that contain both spatial and keyword information have been generated continuously, and the number of subscribers also continues to increase. This poses a challenge to Pub/Sub systems: they need to continuously extract useful information from massive objects for each subscriber in real time.

A bio-nanomachine network is useful in diverse applications of molecular communication. In this paper, we develop a network formation model of bio-nanomachines based on how vascular endothelial cells form a blood vessel-like structure. In our model, bio-nanomachines migrate toward each other using attractant molecules. When two bio-nanomachines move close to each other, they adhere to form a physical connection or a link between them. In this way, a group of bio-nanomachines forms a network collectively. We perform computer simulations using the model to identify critical parameter values at which a large-scale bio-nanomachine network emerges. The model developed in this paper helps us design synthetic communication networks of bio-nanomachines.

A scenario-based serious game for teaching about healthcare solutions was developed and tested in multiple iterations. The serious game captures the complexity of a real-world healthcare scenario, projecting it into a “scenario system” that can be accessed by the game participants to explore and experience the playthrough of a critical healthcare situation. The aim is to teach about existing health technology, and its uses and interactions between healthcare professionals, technologies, and patients. The platform for the serious game is mobile, such that different locations can be chosen for different scenarios. Actions of the participants trigger further actions, tasks, and storyline progress in the scenario so that a self-paced experience with defined dynamics can be created for the participants. The scenario-based serious game was tested with high school students and senior citizens in multiple iterations.

Nowadays, advertising (ad) deliveries are conducted in a targeted manner to improve their effectiveness and efficiency. However, human behavior data in ad-platforms such as Web browsing history is complex and contains a lot of “noise”. On the other hand, information in the advertiser's domain (e.g. e-commerce sites) seems to contain less noise (e.g. product browsing history) with respect to ad-targeting. We introduce a new denoising method for behavioral ad-targeting based on the idea of feature distribution alignment induced by the advertiser's domain. This denoised distribution induction can be achieved by employing domain adversarial training with stabilization techniques. We evaluate our model on real world data originating from an e-commerce site and an ad-platform. The results of an ablation study have demonstrated the advantage of utilizing an advertiser's domain for denoising human behavior data of an ad-platform domain.

In this paper, we propose a location anonymization method which can control the degree of preserving user preferences on visiting locations, where the user's preferences are defined as a series of categories of the visiting locations. Based on the user's requirement, this method mixes dummies that move by following the user's preferences and that do not. The extensive simulation results show that our method enables controlling the degree of preserving user preferences as well as protects the location privacy.

Inertial sensor data collected from wearable smart devices such as smartwatches are expected to be used in various smart applications such as video game controllers, hand drawing, hand writing, gestural input devices, human activity recognition, and remote communication using sign language. However, since the maximum sampling rate of inertial sensors in commercial smartwatches is restricted, capturing fine-grained body movements using the low-sampled signals is difficult for these sensors. Therefore, this study proposes a new method for generating high sampling rate signals from the low-sampled signals by upsampling the low-sampled signals using interpolation with an artificial neural network. Because it is impossible to obtain "non-existent" data from low-sampled signals according to the information theory, we estimate these data from experience, i.e., using high-sampled signals prepared in advance for training. This is possible because trajectories of a sensor are restricted by the skeletal structure of the body part to which the sensor is attached.

This paper investigates a new query, called an MIO query, that retrieves the Most Interactive Object in a given spatial dataset. Consider that an object consists of many spatial points. Given a distance threshold, we say that two objects interact with each other if they have a pair of points whose distance is within the threshold. An MIO query outputs the object that interacts with other objects the most, and it is useful for analytical applications e.g., neuroscience and trajectory databases. The MIO query processing problem is challenging: a nested loop algorithm is computationally inefficient and a theoretical algorithm is computationally efficient but incurs a quadratic space cost. Our solution efficiently processes MIO queries with a novel index, BIGrid (a hybrid index of compressed Bitset, Inverted list, and spatial Grid structures), with a practical memory cost. Furthermore, our solution is designed so that previous query results and multi-core environments can be exploited to accelerate query processing efficiency. Our experiments on synthetic and real datasets demonstrate the efficiency of our solution.

In many applications, data objects can be represented as sets. For example, in video on-demand and social network services, the user data consists of a set of movies that have been watched and a set of users (friends), respectively, and they can be used for recommendation and information extraction. The problem of set similarity self-join hence has been studied extensively. Existing studies assume that sets are static, but in the above applications, sets are dynamically updated, and this requires continuous updating the join result. In this paper, we study a novel problem, dynamic set kNN self-join, i.e., for each set, we continuously compute its k nearest neighbor sets. Our problem poses a challenge for the efficiency of computation, because just an element insertion (deletion) into (from) a set may affect the kNN results of many sets. To address this challenge, we first investigate the property of the dynamic set kNN self-join problem to observe the search space derived from a set update. Then, based on this observation, we propose an efficient algorithm. This algorithm employs an indexing technique that enables incremental similarity computation and prunes unnecessary similarity computation. Our empirical studies using real datasets show the efficiency and scalability of our algorithm.

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.

Throughout the world, people can post information about their local area in their own languages using social networking services. Multilingual short text clustering is an important task to organize such information, and it can be applied to various applications, such as event detection and summarization. However, measuring the relatedness between short texts written in various languages is a challenging problem. In addition to handling multiple languages, the semantic gaps among all languages must be considered. In this article, we propose two Wikipedia-based semantic relatedness measurement methods for multilingual short text clustering. The proposed methods solve the semantic gap problem by incorporating the inter-language links of Wikipedia into Extended Naive Bayes (ENB), a probabilistic method that can be applied to measure semantic relatedness among monolingual short texts. The proposed methods represent a multilingual short text as a vector of the English version of Wikipedia articles (entities). By transferring texts to a unified vector space, the relatedness between texts in different languages with similar meanings can be increased. We also propose an approach that can improve clustering performance and reduce the processing time by eliminating language-specific entities in the unified vector space. Experimental results on multilingual Twitter message clustering revealed that the proposed methods outperformed cross-lingual explicit semantic analysis, a previously proposed method to measure relatedness between texts in different languages. Moreover, the proposed methods were comparable to ENB applied to texts translated into English using a proprietary translation service. The proposed methods enabled relatedness measurements for multilingual short text clustering without requiring machine translation processes.

Behavioral targeting (BT) is an important function for a company to reach a wide range of potential users. Trans-domain BT which targets potential users of one (source) domain (e.g. E-Commerce) who lie in another (target) domain (e.g. Ad Network) is a promising method to expand the range. However, it is difficult for trans-domain BT to keep its targeting quality high in case when ID linkage across domains is limited. To realize high quality trans-domain BT with limited ID linkage, we propose a method to cross-connect private touchpoints to users in each domain, which we call Virtual Touch-Point (VTP). Here, we utilize transfer learning to acquire knowledge to tie two domains. We made a VTP prototype by implementing typical transfer learning algorithms and evaluated its effectiveness using real-world data of two domains: (source) E-Commerce → (target) Ad Network.

We can get real-world information (social sensor data) by analyzing posts on social networking services (SNSs), which have become popular in recent years. We are studying and developing a system to generate and share social sensor data and social sensors (programs to analyze posts on SNSs) among users. Descriptions of previously created social sensors such as analysis programs are helpful for creations of new social sensors. When users search previously created social sensors in conventional S^3 systems, search results are shown in list form without indicating relevances between social sensors. Therefore, users need to check descriptions of many social sensors until finding helpful descriptions. This annoys users to create new social sensors. By reducing the number of social sensors to be checked, users can find helpful descriptions easier. In this research to achieve fast search, we designed and implemented a system to visualize relevances between social sensors. The proposed system regards that previously referenced social sensors are relevant and also that social sensors with high similarity in descriptions are relevant. The proposed visualization system shows relevances between social sensors by representing social sensors as nodes connected to their referenced social sensors by edges.

Epidemic information dissemination is a promising technique for delivering messages in sparse mobile networks. This paper proposes an epidemic information dissemination method for mobile molecular communication systems by leveraging the mobility of bio- nanomachines. This paper first develops a mathematical model of epidemic information dissemination in mobile molecular communication systems, considering the detailed dynamics of information molecules, such as their spatial diffusion in the environment, their binding to bio-nanomachines, and their unbinding from bio-nanomachines. Using the mathematical model, we then conduct simulation experiments and show that the proposed method outperforms the existing methods where statically placed bio- nanomachines are used to relay information. Simulation results also show that the optimal moving speed of bio-nanomachines exists to minimize the propagation delay in the proposed method.

This paper proposes the leader-follower-amplifier-based model of mobile molecular communication systems for cooperative drug delivery. In the proposed model, system functionalities are divided and implemented onto three types of bio-nanomachine: leader, follower and amplifier bio-nanomachines. Leader bio-nanomachines are responsible for detecting targets, releasing attractant molecules and creating attractant gradients around the targets. Follower bio-nanomachines are responsible for carrying drug molecules and transporting through directed motion to the target locations. Amplifier bio-nanomachines are responsible for amplifying attractant molecules and enhancing attractant gradients created by leader bio-nanomachines. This paper first describes a design of the leader-follower-amplifier-based model. It then describes computer simulation models and numerical results to examine the performance of the proposed model.

This paper presents a robust machine learning-based method for detecting malicious nodes in mobile ad hoc networks (MANETs). Since general machine learning methods rely on training data, trained detectors do not work well in test environments that are different from training environments. This is an inherent problem in malicious detection in dynamic MANETs environments, where network parameters, such as the average node speed and density of nodes, differ in each environment. In this study, we propose a method for environment-adaptive malicious node detection based on ensemble learning. We first prepare weak malicious node detectors trained in diverse environments, and then construct a strong ensemble malicious node detector, which is tailored to a given test environment, by fusing weak detectors whose performances are estimated to be high in the test environment. We investigate the performance of our method and confirm that our method significantly outperforms the state-of-the-art methods in terms of detection accuracy and false detection rate.

We study the novel problem of continuous mining of top-k closed co-occurrence patterns across multiple streams. We employ sliding window setting in this problem, and each pattern is ranked based on count, which is the number of streams that have generated the pattern. Since objects are consecutively generated and deleted, the count of a given pattern is dynamic, which may change the rank of the pattern. This renders a challenge to monitoring the top-k answer in real-time. We propose an index-based algorithm that addresses the challenge and provides the exact answer. Specifically, we propose the CP-Graph, a hybrid index of graph and inverted file structures. The CP-Graph can efficiently compute the count of a given pattern and update the answer while pruning unnecessary patterns. Our experimental study on real datasets demonstrates the efficiency and scalability of our solution.

In this paper, we propose a method that fully combines top-k query processing with replication strategy in mobile ad hoc networks (MANETs). The goal is to acquire perfect accuracy of query results with a minimal overhead and delay. Currently, no replication strategy achieves efficient allocation of replicas for top-k queries, and no top-k query processing guarantees perfect accuracy of query results in MANETs. We propose a new replication strategy FReT (topology-Free Replication for Top-k query) and new top-k query processing methods. FReT advantages efficient top-k query processing from limited search area even if mobile nodes move. In our top-k query processing method, the search area gradually increases until receiving an exact answer. We demonstrate, through extensive simulations, that our approaches function well in terms of small delay and overhead.

Unmanned aerial vehicles (UAVs) can provide important communication advantages to ground-based wireless mesh networks with multiple connected routers. Their usage in civilian areas has become commonplace, especially more promising recently as emergency communication networks for rescuing victims, dispatching resources and recovering communications to bridge the gap between the post-disaster areas and outsides. First, usually a flash crowd of victims will attempt to make a video or VoIP connection with outsides. Furthermore, it results in a large volume of traffic shift among mesh routers due to high migration of people such as leaving and joining in the refuges. Thus, in this paper we consider the optimization of the location and movement of a single UAV to improve the network throughput by means of high mobility of the UAV to adapt to heavy fluctuation of network traffic. Though there are several methodologies to the UAV movement, it is still indistinct which one is better, especially while employing different routing protocols. A detailed comparison between two typical schemes based on the mean value and the collision domain is made for UAV-assisted wireless mesh networks with multimedia burst. Specifically, we designed two schemes accordingly to select the UAV position and the mesh router as the gateway to connect the UAV. A numerical experiments with different routing protocols are evaluated and compared in a realistic simulation environment of NS-3 and show that they both have advantages and disadvantages with respect to QoS when employing different routing protocols in the considered scenario.

In dense mobile wireless sensor networks (MWSNs), a number of major applications require monitoring of the geographical boundaries of sensor readings among physical phenomena with as low traffic as possible. In this paper, we propose a periodical boundary detection method reducing traffic by exchanging sensor data only among nodes near the boundaries. Our proposed method achieves this by incorporating recording the time when a node has crossed the boundary and overhearing sensor data. We confirmed the effectiveness of our proposed method through simulation experiments.

A top-k dominating query returns k data objects that dominate the highest number of data objects in a given dataset. This query provides us with a set of intuitively preferred data, thus can support a wide variety of multi-criteria decision-making applications, e.g., e-commerce and web search. Due to the growth of data centers and cloud computing infrastructures, the above applications are increasingly being operated in distributed environments. These motivate us to address the problem of distributed top-k dominating query processing. We propose an efficient decentralized algorithm that exploits virtual points and returns the exact answer. The virtual points are utilized to focus on the data space to be preferentially searched and also to limit the search space to prune unnecessary computation and data forwarding. We also propose two other algorithms, which return an approximate answer set while further reducing query processing time. Extensive experiments on both real and synthetic data demonstrate the efficiency and scalability of our algorithms.

Due to the spread of smartphones and tablet devices, many photographs are uploaded and shared on the Internet. Cameras often have sensors such as GPS and digital compass. These sensor data are attached to photograph data as metadata. Metadata is helpful to retrieve photographs that show the designated points. For this request, existing systems search the photographs taken in constant distance from the designated points and show them to the users. However, some of them do not cover the designated point when their photographing orientations are largely different from the direction for the point or obstacles hide the point. So, the users often check many photographs to find their desirable photographs showing the designated points. In this paper, we propose a method to calculate the probability that photographs show the designated points. By showing the photographs that have high scores with priority, the users can find their desirable photographs with less checking. Also, we develop a system to retrieve photographs using our proposed scoring method.

Due to recent developments in sensor technologies, mobile sensor device use has become widespread, and many researchers have been attempting to leverage data collected by these devices. We call such data `mobile sensor data'; and environments where mobile sensor data arrive continuously, `mobile sensor stream' environments. Mobile sensor data are geo-referenced data with environmental attribute values; and they enable us to determine the geographical distribution of hot spots by retrieving data with comparatively extreme environmental attribute values (such as higher air-pollution index values). Top-k search result diversification in geographical space is valid for applications of this sort. By monitoring a diversified set over mobile sensor streams, we can trace changes in the distribution of hot spots. However, the computation costs for maintaining such diversified sets are high when we have to monitor a large amount of mobile sensor data. Thus, in this paper, we propose an efficient diversified set monitoring method for mobile sensor stream environments. Our proposed method can reduce the amount of examined data by exploiting our proposed regular grid-based data structure, and the diversified set can thereby be maintained much more efficiently. Our experimental results confirm that the proposed method involves much shorter computation time in comparison with the baseline method.

We propose a language-independent datadriven method to exhaustively extract bursty phrases of arbitrary forms (e.g., phrases other than simple noun phrases) from microblogs.The burst (i.e., the rapid increase of the occurrence) of a phrase causes the burst of overlapping Ngrams including incomplete ones.In other words, bursty incomplete N-grams inevitably overlap bursty phrases.Thus, the proposed method performs the extraction of bursty phrases as the set cover problem in which all bursty N-grams are covered by a minimum set of bursty phrases.Experimental results using Japanese Twitter data showed that the proposed method outperformed word-based, noun phrase-based, and segmentation-based methods both in terms of accuracy and coverage.

In a remote building management system (BMS) that manages facilities of buildings, there is an application called a crawler that measures statuses of facilities via a network. By running a crawler on a Virtual Machine (VM) for each building owner, the number of Physical Machines (PMs) in the remote BMS can be reduced. However, running multiple crawlers on the same PM causes measurement time fluctuation, which may results in inaccurate monitoring and control of facilities. We propose an estimation method for measurement time accuracy of crawlers according to simulation with a model of crawlers, VMs, and PMs. The relationship between the number of crawlers running on the same PM and their measurement time accuracy can be determined by the proposed method. We verify the estimation accuracy of the proposed method by comparing with measurement time accuracy of real machines.

Wireless sensor networks have significant limitationsin available bandwidth and energy. The limited bandwidthin sensor networks can cause higher message delivery latencyin applications such as monitoring poisonous gas leak. In suchapplications, there are multi-modal sensors whose values such astemperature, gas concentration, location and CO2 level need tobe transmitted together for faster detection and timely assessmentof gas leak. In this paper, we propose novel Z-order based datacompression schemes (Z-compression) to reduce energy and savebandwidth without increasing the message delivery latency. Insteadof using the popular Huffman tree style based encoding, Zcompressionuses Z-order encoding to map the multidimensionalsensing data into one-dimensional binary stream transmittedusing a single packet. Our experimental evaluations using realworlddata sets show that Z-compression has a much bettercompression ratio, energy saving, streaming rate than knownschemes like LEC (and adaptive LEC), FELACS and TinyPackfor multi-modal sensor data.

Wireless sensor networks (WSNs) have significant limitations in available bandwidth and energy. The limited bandwidth in sensor networks can cause higher message delivery latency. In this demo, we demonstrate working of novel Z-order based data compression schemes (Z-compression) to reduce energy and save bandwidth without increasing the message delivery latency using a TelosB motes based sensor network testbed. Instead of using the popular Huffman tree style based encoding, Z-compression uses Z-order encoding to map the multidimensional sensing data into one-dimensional binary stream transmitted using a single packet. We have also designed and developed a data concatenating algorithm which can concatenate small packets into large packets, thus increases the throughput of the WSNs.

In this paper, we present results of investigation on the dynamics of group decision making - how people discuss and make a decision-with collaborative web search. Prior works proposed systems that support group decision making with web search but have not examined the influence of discussion behaviors especially on the satisfaction levels with the final conclusion. In this study, we conducted a set of experiments to observe discussion behaviors and the consequent satisfaction with the conclusion using our experimental system and a set of questionnaires. The task for each participant was to make a decision on a restaurant. Our primary results revealed (1) the similar activities across all groups at the beginning and the end of the group discussion, (2) a lack of correspondence between the satisfaction with the conclusion and the time spent to reach the conclusion, and (3) the presumption that a member who actively engaged in the activities that were visible for the other members was likely to be voted as a leader in the group discussion beyond the discussion. Finally, we discussed how to implement intelligent systems that aid group decision making.

This paper proposes a leader-follower-based model of mobile molecular communication networks for target detection applications. The proposed model divides the application functionalities of molecular communication networks into two types of mobile bio-nanomachine: leader and follower bio-nanomachines. Leader bio-nanomachines distribute in the environment to detect a target and create an attractant gradient around the target. Follower bio-nanomachines move according to the attractant gradient established by leader bio-nanomachines; they approach the target and perform necessary functionalities, such as releasing drug molecules. This paper develops mathematical expressions for the proposed model, describes wet laboratory experiments designed to estimate model parameters, and performs biologically realistic computer simulation experiments to evaluate the performance of the proposed model. The main contributions of this paper are to demonstrate the functional division of molecular communication networks, which will facilitate the design and development of molecular communication networks. Furthermore, insight into the application-level performance of molecular communication networks will be provided based on the proposed model.

The recent Bigdata and IoTera has presented a number of applications that generate objects in a streaming fashion. It is well-known that real-time mining of important patterns from data streams support many domains. In retail markets and social network services, for example, such patterns are itemsets and words that frequently appear in many user-accounts, i.e., co-occurrence patterns. To efficiently monitor co-occurrence patterns, we address the novel problem of mining top-k closed co-occurrence patterns across multiple streams. We employ sliding window setting in this problem, and each pattern is ranked based on count, which is the number of streams that have generated the pattern. Since objects are consecutively generated and deleted, the count of a given pattern is dynamic, which may change the rank of the pattern. This renders a challenge to monitoring the top-k answer in real-time. We propose an index-based algorithm that addresses the challenge and provides the exact answer. Specifically, we propose the CP-Graph, a hybrid index of graph and inverted file structures. The CP-Graph can efficiently compute the count of a given pattern and update the answer while pruning unnecessary patterns. Our experimental study on real datasets demonstrates the efficiency and scalability of our solution.

Inverse Document Frequency (IDF) is widely accepted term weighting scheme whose robustness is supported by many theoretical justifications. However, applying IDF to word N-grams (or simply N-grams) of any length without relying on heuristics has remained a challenging issue. This article describes a theoretical extension of IDF to handle N-grams. First, we elucidate the theoretical relationship between IDF and information distance, a universal metric defined by the Kolmogorov complexity. Based on our understanding of this relationship, we propose N-gram IDF, a new IDF family that gives fair weights to words and phrases of any length. Based only on the magnitude relation of N-gram IDF weights, dominant N-grams among overlapping N-grams can be determined. We also propose an efficient method to compute the N-gram IDF weights of all N-grams by leveraging the enhanced suffix array and wavelet tree. Because the exact computation of N-gram IDF provably requires significant computational cost, we modify it to a fast approximation method that can estimate weight errors analytically and maintain application-level performance. Empirical evaluations with unsupervised/supervised key term extraction and web search query segmentation with various experimental settings demonstrate the robustness and language-independent nature of the proposed N-gram IDF.

Recently, IoT (Internet of Things) environments have begun to attract a great deal of attention. In IoT environments, various “things”, such as sensors, are connected to the Internet and act as distributed data sources to continuously generate big data (stream data). One of the main research issues for such stream data is fast execution of continuous queries. Most conventional schemes assume that processing servers receive data streams from remote data sources. Therefore, when large numbers of data sources are distributed in IoT environments, large amounts of communications traffic from those data sources are produced. This is the main cause of long stream processing times. To tackle this key problem, we take this feature of IoT environments into account and try to reduce the stream processing time by reducing the stream processing load. Our proposed scheme extracts conditional expressions, and determines the order of evaluation of these expressions, in order to reduce processing and communication loads. Our evaluation shows that the proposed scheme can reduce the maximum number of communication hops and the average amount of communication traffic in IoT environments.

Due to the increase of GPS enabled devices and a lot of locationbased services, spatial objects are continuously generated. This paper addresses a problem of monitoring MaxRS (Maximizing Range Sum) in spatial data streams. Given a set of weighted spatial (2dimensional) objects, this problem is to monitor a location of a given user-specified sized rectangle where the sum of the weights of the objects covered by the rectangle is maximized. Many real life applications obtain a benefit from monitoring MaxRS, e.g., traffic analysis and event detection in urban sensing, but this problem has not yet been addressed so far. Although some algorithms for static objects have been proposed, executing such an algorithm whenever new objects are generated is computationally expensive. These motivate us to develop an efficient algorithm that can monitor MaxRS efficiently. In this paper, we first design a basic algorithm that is based on an index framework and incrementally updates the result. We then enhance the algorithm and show that the enhanced algorithm can deal with error-guaranteed approximation and monitoring top-k MaxRS. Our experimental results confirm the efficiency of our approach.

In a remote building management system (BMS) that manages facilities of buildings or factories, there is an application called a crawler that collects statuses of facilities via a network. By running a crawler on a Virtual Machine (VM) for each building owner, operators of the remote BMS can limit influence of a failure of one crawler and can reduce the number of physical machines in the remote BMS. However, performance of the crawler running on a VM could be degraded owing to overhead of virtualization, inappropriate resource allocation, and interference among VMs coexisting on the same physical machine. In addition, the crawler needs to meet its performance requirements with high probability, 99.999%, and therefore it is important to clarify the characteristics of the performance degradation. In this paper, we evaluate performance of the crawler run on a VM with various resource allocation patterns and multiple VMs. From this evaluation, we find out that 1) CPU usage measurement with millisecond granularity is important for appropriate CPU allocation and 2) CPU contention among VMs is one of the main factors of the performance degradation.

Due to recent developments in sensor technologies, mobile sensor device use has become widespread, and many researchers have been attempting to leverage data collected by these devices. We call such data 'mobile sensor data'. Mobile sensor data are geo-referenced data with environmental attribute values, and they enable us to determine the geographical distribution of hot spots by retrieving data (such as higher air-pollution index values) with comparatively extreme environmental attribute values. Top-k search result diversification in geographical space is valid for applications of this sort. However, the preference scores for data items are different from each user's interest, and must be calculated for each query from scratch. In this case, the computational cost of a naive method is excessively high when the amount of mobile sensor data is very large. Thus, in this paper, we propose an efficient top-k search result diversification method for mobile sensor data. In a naive method, it is necessary to scan all data existing in a given query range when seeking the best data. Our proposed method, however, can reduce the amount of scanned data by exploiting cluster information, and the query result can thereby be returned much more rapidly. Moreover, a number of optimization problems can be solved using only one cluster file set. Experimental results show that our proposed method involves short computation time and reduces the disk IO cost in comparison with a naive method.

This paper describes non-diffusion-based molecular communication and its application to an inbody mobile bionanosensor network. In the inbody mobile bionanosensor network, bio-nanomachines migrate in the environment while they release adhesive molecules that bind to a surface in the environment. The concentration gradient of adhesive molecules is thus formed over the surface, and bio-nanomachines migrate based on the gradient to coordinate their behavior. In the non-diffusion-based molecular communication, the formation of concentration gradient of adhesive molecules relies on the mobility of bio-nanomachines and thus the effective communication range may be limited. However, the non-diffusion-based molecular communication can maintain a high and stable concentration of molecules in the environment, allowing bio-nanomachines to detect the concentration to coordinate their behavior. Through mathematical modeling and performance evaluation, this paper demonstrates that the non-diffusion-based molecular communication may apply to induce coordinated behavior among mobile bio-nanomachines in the inbody environment considered in this paper.

Location-based service (LBS) is a typical application for mobile ad hoc networks (MANETs). In an LBS, it is effective for nodes to acquire data using convex hull queries, which retrieve the information necessary for calculating the convex hull of all nodes composing a given network. In a naive approach, the query-issuing node can determine the convex hull of the network by flooding a query throughout the entire network and receiving location information on all the nodes, but this involves excessive traffic. In addition, since the network topology changes dynamically, due to the movement of nodes, the set of vertices of the convex hull changes. Therefore, the query-issuing node must determine the convex hull in a short period of time. In this paper, we propose convex hull query processing methods for reducing traffic and response time while maintaining high accuracy of the query result in MANETs. In these methods, nodes reply with information on nodes which are convex hull vertices. In the Local Convex Hull (LCH) method, nodes reply with information on nodes which are vertices of the local convex hull, which is the convex hull composed of nodes whose information has already been received. In the Local Wrapping (LW) method, the query is transmitted only to nodes on the outer boundary. Experimental results show that our proposed methods reduce traffic and achieve short response time and high accuracy of the query result, in comparison with a naive method.

In this paper, we consider a problem of range-based continuous query processing in mobile ad hoc networks (MANETs). Due to the advantage that no infrastructure is needed, MANETs are useful in various situations such as disaster sites. In such applications, information monitoring techniques are required, for example, to obtain the information on victims. In MANETs, the bandwidth and batteries of nodes are limited and nodes move freely, thus conventional techniques for fixed and wired networks are hard to apply to MANETs. Moreover, periodic data retrieval approach is inefficient because it incurs large traffic to obtain data items continuously. We therefore propose a communication-efficient method for continuously monitoring data existing around a query originating node. Our proposed method does not rely on periodic approach, but rather extends query transmission ranges dynamically and continuously to deal with nodes' movement. Query receiving nodes estimate the current position of the query originating node and forward data items toward the position. This approach also enables efficient data forwarding when data updates occur. Simulation experiments demonstrate the efficiency and scalability of our method.

In this paper, we propose a signature-based top-k query processing method against data replacement attacks in mobile ad hoc networks (MANETs). In order to rapidly identify a greater number of malicious nodes, nodes share information about identified malicious nodes with other nodes. If nodes share only this information, however, malicious nodes may successfully transmit false information identifying normal nodes as malicious. Therefore, in the proposed method, when nodes send reply messages during query processing, they attach encrypted information about the sent data items (i.e., digital signatures), providing the query-issuing node with critical information about the data items sent by nodes in the network, and thereby enabling it to identify malicious nodes, using the received signatures. After identifying the malicious nodes, it floods the network with a notification message including the signatures in which the identified malicious nodes have replaced higher-score data items to their own lower-score items.

As we are in the big data era, techniques for retrieving only user-desirable data objects from massive and diverse datasets is being required. Ranking queries, e.g., top-k queries, which rank data objects based on a user-specified scoring function, enable to find such interesting data for users, and have received significant attention due to its wide range of applications. While many techniques for both centralized and distributed top-k query processing have been developed, they do not consider query keywords, i.e., simply retrieving k data with the best score. Utilizing keywords, on the other hand, is a common approach in data (and information) retrieval. Despite of this fact, there is no study on retrieving top-k data containing all query keywords. We define, in this paper, a new query which enriches the conventional top-k queries, and propose some algorithms to solve the novel problem of how to efficiently retrieve k data objects with the best score and all query from distributed databases. Extensive experiments on both real and synthetic data have demonstrated the efficiency and scalability of our algorithms in terms of communication cost and running time.

This paper first reveals the relationship between Inverse Document Frequency (IDF), a global term weighting scheme, and information distance, a universal metric defined by Kolmogorov complexity. We concretely give a theoretical explanation that the IDF of a term is equal to the distance between the term and the empty string in the space of information distance in which the Kolmogorov complexity is approximated using Web documents and the Shannon-Fano coding. Based on our findings, we propose N-gram IDF, a theoretical extension of IDF for handling words and phrases of any length. By comparing weights among N-grams of any N, N-gram IDF enables us to determine dominant N-grams among overlapping ones and extract key terms of any length from texts without using any NLP techniques. To efficiently compute the weight for all possible N-grams, we adopt two string processing techniques, i.e., maximal substring extraction using enhanced suffix array and document listing using wavelet tree. We conducted experiments on key term extraction and Web search query segmentation, and found that N-gram IDF was competitive with state-of-the-art methods that were designed for each application using additional resources and efforts. The results exemplified the potential of N-gram IDF.

This paper considers mobile bionanosensor networks designed for target tracking in molecular environments. The mobile bionanosensor network considered in this paper consists of nano-to-microscale bio-nanomachines that coordinate their activity by propagating the two types of signaling molecules: attractants for a group of bio-nanomachines to move toward targets, and repellents to spread over the environment. A mathematical model for target tracking is developed and the performance of mobile bionanosensor networks is defined based on distributions of targets and bio-nanomachines. Numerical results are then shown to facilitate discussion of the impact of attractants and repellents on target tracking performance.

In dense mobile wireless sensor networks, it is desirable to gather sensor data with as low traffic as possible. When an application requires geographical boundaries of sensor readings, it is able to satisfy the requirement while reducing traffic by gathering sensor data only from nodes located close to the boundaries. In order to do so, it is necessary to detect nodes located close to the boundaries (boundary detection). Until now, many boundary detection methods have been proposed, assuming networks constructed by fixed nodes. In these methods, each node preliminarily recognizes locations of itself and all its neighboring nodes. When applying this approach to a dense and mobile environment, it takes a large amount of traffic for exchanging information on locations of nodes. In this paper, we propose an efficient boundary detection method in dense mobile wireless sensor networks, which makes use of overhearing sensor data. In our proposed method, each node determines whether it exists close to a boundary or not based on sensor data overheard from its neighboring nodes. When a node determines that it dose not exist close to the boundary, it stops transmitting its own sensor data. By doing so, traffic for boundary detection can be reduced. We confirmed the effectiveness of our proposed method through simulation experiments.

Location-based services (LBSs) are important applications for mobile users. We consider an application in which the source node periodically disseminates location-based information in mobile ad hoc networks (MANETs). We define beneficial information as that which may be of benefit to mobile users planning to pass near the location related to the information. However, typically, mobile users change their route or travel direction when the mobile node receives beneficial information such as traffic jam and accident information (we call these information the negative location-based information). Thus, a significant problem lies in determining the proper target area for the dissemination of such information in dynamic networks because the source node is difficult to effectively know where to disseminate it. In this paper, we propose a novel location-based information dissemination method in MANETs by use of some fixed nodes. Our proposed method requires neither a positioning system, nor previous knowledge, to efficiently disseminate the information. However, the fixed nodes autonomously expand the relevant dissemination area. Through simulation experiments, we confirm that the proposed method suppresses ineffectual overhead, and that mobile users can efficiently and early receive the beneficial information.

In mobile ad hoc networks (MANETs), it is effective for mobile nodes to retrieve data items using top-k queries, in which data items are ordered according to a particular attribute score, and the query-issuing node acquires the data items with the k highest scores. However, accurate results may not be acquired in environments where malicious nodes are present. In top-k queries, it is important to neutralize attacks in which malicious nodes attempt to replace necessary data items with unnecessary ones (we call these, data replacement attacks). In this paper, we propose methods for top-k query processing and malicious node identification against data replacement attack in MANETs. In the top-k query processing method, in order to maintain accuracy of the query result, nodes reply with data items with the k highest scores, along multiple routes. Moreover, to enable detection of data replacement attacks, reply messages include information on the route along which reply messages are forwarded, and thus the query-issuing node can know the data items that properly belong to the message. In the malicious node identification method, the query-issuing node first narrows down the malicious node candidates, using the received message information, and then requests information on the data items sent by these candidates. In this way, the query-issuing node can identify the malicious node. Finally, we verify, through simulation experiments, that the proposed top-k query processing method achieves high accuracy of the query result, and that the malicious node identification method effectively identifies a malicious node.

Top-k queries, which retrieve the k data items with the highest scores, have been receiving considerable attention because of their utility for many real life applications. In this paper, we propose an efficient query routing scheme enabling top-k data retrieval by only necessary nodes, named CTR. CTR employs a new clustering framework for top-k query processing, and in this framework, nodes holding high rank data items become ClusterHeads (CHs), and top-k queries are transmitted between CHs via gateway nodes which belong to multiple clusters. Each CH maintains a record of hop counts between itself and high rank data items, enabling it to judge whether or not to transmit a query on the fly. As a result, CTR reduces overhead while keeping high accuracy of the query result. Extensive experiments have demonstrated that CTR functions well in terms of accuracy of the query result, overhead, and delay.

In recent years, video-on-demand services such as YouTube or Hulu have become popular. These VoD services often adopt P2P (Peer-to-Peer) streaming techniques to manage many users' requests. Some services provide different quality services for the users. For example, the users who pay money for the service provider can watch high-quality videos, and others can only watch low-quality videos. In such a multiple bit rate video delivery, since high-quality videos have high bit rate, their interruption time is very long compared with that for low-quality videos when many peers watch high-quality videos. In this paper, we propose a method to reduce interruption time for users watching high-quality videos in multiple bit rate video delivery. Our evaluation results show that our proposed method can reduce their interruption times.

In mobile ad hoc networks (MANETs), locationbased service (LBS) is a typical application. In a LBS, it is valuable for each node to grasp the convex hull of nodes which compose a network by using a convex hull query. However, if the query-issuing node acquires the information on all the nodes, the information on nodes which are not the vertexes of the convex hull is replied, which is unnecessary for a convex hull detection. In this paper, we propose a convex hull query processing method, which reduces traffic and also maintains high accuracy of the query result in MANETs. An experimental result shows that our proposed method reduces traffic and achieves high accuracy of the query result compared with a naive method.

In dense mobile wireless sensor networks, it is desirable to reduce traffic for data gathering as much as possible while satisfying an application requirement. In this paper, focusing on an application which monitors the time-variant shape of events, we discuss an approach to achieve an efficient monitoring with small traffic in dense mobile wireless sensor networks.

With the increase of data generation in distributed fashions such as peer-to-peer systems and sensor networks, top-k query processing which returns only a small set of data that satisfies many users' preferences, becomes a substantial issue. When data are periodically updated in each epoch e.g., weather information, without any techniques, a naive solution is to aggregate all data and their updates to ensure the correctness of final answers, however, it is too costly in terms of data transfer especially for data aggregator nodes. In this paper, we propose a top-k monitoring query processing method in 2-tier distributed systems based on a publish-subscribe scheme. A set of top-k subscriptions specifying summary scope of users' interests is informed to aggregators to limit the number of transferred data records for each epoch. In addition, instead of issuing subscriptions of all queries, our method identifies a small set of minimal subscriptions resulting in lower communication overhead. Our experiments show that our technique is efficient and outperforms other comparative reactive methods.

In this demonstration, we introduce MLJ (MultiLingual Journalism, http://mljournalism.com), a first Web-based system that enables users to search any topic of latest tweets posted by media outlets and journalists beyond languages. Handling multilingual tweets in real time involves many technical challenges: language barrier, sparsity of words, and real-time data stream. To overcome the language barrier and the sparsity of words, MLJ harnesses CL-ESA, a Wikipedia-based language-independent method to generate a vector of Wikipedia pages (entities) from an input text. To continuously deal with tweet stream, we propose one-pass DP-means, an online clustering method based on DP-means. Given a new tweet as an input, MLJ generates a vector using CL-ESA and classifies it into one of clusters using one-pass DP-means. By interpreting a search query as a vector, users can instantly search clusters containing latest related tweets from the query without being aware of language differences. MLJ as of March 2014 supports nine languages including English, Japanese, Korean, Spanish, Portuguese, German, French, Italian, and Arabic covering 24 countries.

Abstract Traditional security schemes developed for sensor networks are not suitable for cluster‐based wireless sensor networks (WSNs) because of their susceptibility to Denial of Service (DoS) attacks. In this paper, we provide a security scheme against DoS attacks (SSAD) in cluster‐based WSNs. The scheme establishes trust management with energy character, which leads nodes to elect trusted cluster heads. Furthermore, a new type of vice cluster head node is proposed to detect betrayed cluster heads. Theoretical analyses and simulation results show that SSAD can prevent and detect malicious nodes with high probability of success. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we propose two beacon-less kNN query processing methods for reducing traffic and maintaining high accuracy of the query result in mobile ad hoc networks (MANETs). In these methods, the query-issuing node first forwards a kNN query using geo-routing to the nearest node from the point specified by the query (query point). Then, the nearest node from the query point forwards the query to other nodes close to the query point, and each node receiving the query replies with the information on itself. In this process, we adopt two different approaches: the Explosion (EXP) method and the Spiral (SPI) method. In the EXP method, the nearest node from the query point floods the query to nodes within a specific circular region, and each node receiving the query replies with information on itself. In the SPI method, the nearest node from the query point forwards the query to other nodes in a spiral manner, and the node that collects a satisfactory kNN result transmits the result to the query-issuing node. Experimental results show that our proposed methods reduce traffic and achieve high accuracy of the query result, in comparison with existing methods.

ABSTRACT Wireless sensor networks (WSNs) which is proposed in the late 1990s have received unprecedented attention, because of their exciting potential applications in military, industrial, and civilian areas (e.g., environmental and habitat monitoring). Although WSNs have become more and more prospective in human life with the development of hardware and communication technologies, there are some natural limitations of WSNs (e.g., network connectivity, network lifetime) due to the static network style in WSNs. Moreover, more and more application scenarios require the sensors in WSNs to be mobile rather than static so as to make traditional applications in WSNs become smarter and enable some new applications. All this induce the mobile wireless sensor networks (MWSNs) which can greatly promote the development and application of WSNs. However, to the best of our knowledge, there is not a comprehensive survey about the communication and data management issues in MWSNs. In this paper,focusing on researching the communication issues and data management issues in MWSNs, we discuss different research methods regarding communication and data management in MWSNs and propose some further open research areas in MWSNs.Copyright © 2011 John Wiley & Sons, Ltd.

In mobile ad hoc networks (MANETs), 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 k highest scores. In our previous works, we proposed top-k query processing methods for reducing traffic and also keeping high accuracy of query result. Those methods focus on the traffic reduction by not sending back unnecessary data items. However, queries are forwarded to all of nodes in the entire area by flooding, and thus, many nodes which do not contribute to the query result have to send redundant top-k queries and replies, which increases unnecessary traffic. In this paper, we propose a routing method for top-k query processing in MANETs. In this method, when each mobile node sends a top-k query, it refers to its own routing table consisting of the ranking of scores of data items in the network and identifiers of nodes to which queries are forwarded to obtain the necessary data items. Moreover, each node updates its own routing table when the link disconnection occurs and scores of data items are updated.

In dense mobile sensor networks, to reduce traffic for gathering sensor data, it is desirable to efficiently gather sensor data from the minimum number of mobile sensor nodes necessary to guarantee the sensing granularity, which the application requires. For this aim, in our previous work, we have proposed a data gathering method using mobile agents (DGUMA). However, DGUMA does not consider geographical distribution of data values, and all agents have to send their own readings to the sink. Thus, multiple readings with the same value are sent to the sink. In this paper, we extend DGUMA in order to further reduce the traffic considering geographical distribution of data values. In the extended method, mobile agents aggregate multiple readings with the same value into one reading and forward the aggregated data to reduce the traffic. Moreover, our extended method dynamically controls communication routes from each agent to the sink so that the agents can effectively aggregate the readings. We confirmed the effectiveness of our proposed method through some simulation experiments.

This paper describes a novel probabilistic method of measuring semantic similarity for real-world noisy short texts like microblog posts. Our method adds related Wikipedia entities to a short text as its semantic representation and uses the vector of entities for computing semantic similarity. Adding related entities to texts is generally a compound problem that involves the extraction of key terms, finding related entities for each key term, and the aggregation of related entities. Explicit Semantic Analysis (ESA), a popular Wikipedia-based method, solves these problems by summing the weighted vectors of related entities. However, this heuristic weighting highly depends on the rule of majority decision and is not suited to short texts that contain few key terms but many noisy terms. The proposed probabilistic method synthesizes these procedures by extending naive Bayes and achieves robust estimates of related Wikipedia entities for short texts. Experimental results on short text clustering using Twitter data indicated that our method outperformed ESA for short texts containing noisy terms.

In dense mobile wireless sensor networks, it is desirable to gather sensor data from the minimum number of nodes necessary to guarantee the sensing granularity. For this aim, in our previous work, we have proposed a data gathering method using mobile agents. However, this method cannot gather all the sensor data in the situation where some mobile agents cannot be deployed due to low node density. In this paper, we extend our previous method in order to gather all the sensor data even when some mobile agents disappear. First, when a mobile agent disappears, the extended method redeploys the disappeared agent. In addition, when the redeployment is failed, the extended method regathers sensor data which is missed by the disappearance of the agent. We confirmed the effectiveness of the extended method through some simulation experiments.

In mobile ad hoc networks (MANETs), to acquire only necessary data items, it is effective for each mobile node to retrieve data items using a top-k query. In our previous work, we proposed a routing method for top-k query processing to reduce traffic while keeping highly accurate query result by using a routing table. This method performs query transmission by unicast to each node which contributes to collect the data items with k-highest scores. However, in highly dynamic networks, the accuracy of query result decreases because its query transmission approach makes only single route to each data item. To solve this problem, we extend the previous method in order to achieve robust routing. In our proposed method of this paper, each node does not unicast a query message, but multicasts the query message to the nodes in its routing table. This method makes multipath transmission of query messages, so that prevents from decreasing of the accuracy of the query result in highly dynamic networks. The simulation results show that our proposed method outperforms our previous method as to accuracy of the query result.

Vehicular ad hoc networks (VANETs) have been attracting increasing research interests for the past decade. To address the routing problem, many protocols have been proposed in the past several years. Routing protocols for VANETs, mostly based on the ideas of “Geographical Routing” (or geo-routing for short), typically have nodes periodically broadcast one-hop beacon messages to reveal their positions to neighbors. Nevertheless, packet loss and thus deterioration of routing performance in these protocols are anticipated in urban areas due to high density of vehicles in the network. In this paper, we propose two new VANET routing protocols, namely, Routing Protocol with Beacon Control (RPBC) and Routing Protocol with BeaconLess (RPBL), to alleviate packet losses. In RPBC, each vehicle determines whether to transmit a beacon message based on a new beacon control scheme proposed in this paper, which by minimizing redundant beacon messages reduces transmission overhead significantly. On the other hand, RPBL is a beaconless protocol where a node broadcasts a packet to its neighboring nodes and transmits packet via multiple paths to achieve high delivery ratio. Moreover, as packets in geo-routing protocols include the location of the sender, it can be used for routing without heavily relying on beacons. Accordingly, we propose the idea of virtual beacons and use it to further improve our proposed protocols. We conduct comprehensive experiments by simulation to validate our ideas and evaluate the proposed protocols. The simulation results show that our proposals can achieve high delivery ratios, short delays, and small overhead.

Context aware systems on smart-phones aim to provide useful information by analysing and recognizing users' situations from built-in sensors logs. Especially, predicting user actions is one of the important functions for the context aware systems on smart-phones because this function enables context aware systems to provide proactive and responsive services. Therefore the next location prediction is also an important function for context aware systems. This paper introduces a next location prediction method based on context recognition. In this method, we define a context as combinations of features which are extracted from a set of relational data generated from a phone's sensor logs. We applied Mixed Membership Stochastic Blockmodels (MMSB) to context extraction. We then collected sensor logs of a single user over a period of three months and conducted an evaluation using this collected dataset. An an evaluation using the dataset was conducted and the result shows that 60% of the test dataset ranked in the top 30% of all candidates of the next locations.

ABSTRACT In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system‐equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

Mobile devices have become popular to access the information on-the-move. However, the physical restrictions of mobile devices such as display size and input capabilities and the changes of users' contexts may affect the users' information needs and search behaviors. In order to design a new search service which takes mobile users' situation into consideration, it is essential to understand search behaviors of mobile users in detail. In this work, we conducted a diary study using 26 participants in Japan, focusing on how mobile context affects users' information needs and search behaviors. In particular, we focus on detailed behaviors when users failed to find the information they needed. Our main findings include that user's search behaviors were affected not only by the information needs but also the place where the searches were conducted, which might be an important cue to design future mobile search services and applications.

A variety of services utilizing users' positions have become available because of rapid advances in Global Positioning System (GPS) technologies. Since location information may reveal private information, preserving location privacy has become a significant issue. We proposed a dummy-based method of anonymizing location to protect this privacy in our previous work that generated dummies based on various restrictions in a real environment. However, the previous work assumed a simplified mobility model in which users kept moving and did not stop. If we assume a more realistic mobility model in which users often pause to visit various attractions, it becomes increasingly more difficult to generate dummies that will move naturally. In this paper, we assumed that the users' movements are known in advance and propose a dummy-based anonymization method based on user movements, where dummies move naturally while stopping at several locations. We simulated user movements on real map information and verified the method we propose was more effective than the previous one.

Geographic routing is assumed to be the most potential routing scheme in wireless sensor networks (WSNs) due to its scalability and efficiency. Recently more and more research work about geographic routing pay attention to its application scenarios in duty-cycled WSNs because of the natural advantage of saving energy consumption with duty-cycling. However, it may cause significant latency issue when applying geographic routing in duty-cycled WSNs and almost all current researches try to handle the latency problem from the point of changing the geographic forwarding mechanism, apart from the connected-k neighborhood (CKN) algorithm which focuses on sleep scheduling. In this paper, we discuss and analyze the first transmission path's performance of the two-phase geographic forwarding (TPGF) in a CKN based WSN and further propose a geographic routing oriented sleep scheduling (GSS) algorithm to shorten the first transmission path of TPGF in duty-cycled WSNs. Further theoretical and simulation results show that GSS can achieve a good tradeoff between the length of the first transmission path explored by TPGF and the total energy consumption to transmit data with the explored first transmission path, compared with the CKN sleep scheduling algorithm.

Caching is a common technique to improve efficiency of data access in MANETs (Mobile Ad hoc Networks), where users communicate using small portable devices connected by resource constraint wireless networks. In some MANET applications, controlling/reducing the cache locations are desirable due to security issues, restricted shared memory and maintenance cost. However, reducing the number of caches should be done by finding optimized cache locations (at highly connected and centrally located nodes) so that it does not affect the performance efficacy of data access in terms of response time. Existing cooperative caching approaches are deficient in finding such optimized cache locations as they do not focus on reducing the number of copies by finding their optimized locations to be shared among nodes. In this paper, we design and evaluate such a caching scheme using a single broker based MANET architecture to improve data access latency. Our scheme reduces the number of caches by efficiently placing them at locations which brings distant data closer to the source. The performance comparison of our scheme with one such recent caching scheme showcases improvement in data access efficiency by 30% along with reduction in number of cache locations by 72%. We evaluated data access efficiency using average hops and average roundtrip delay.

In a kNN query processing method, it is important to appropriately estimate the range that includes kNNs. While the range could be estimated based on the node density in the entire network, it is not always appropriate because the density of nodes in the network is not uniform. In this paper, we propose two kNN query processing methods in MANETs where the density of nodes is ununiform; the One-Hop (OH) method and the Query Log (QL) method. In the OH method, the nearest node from the point specified by the query acquires its neighbors' location and then determines the size of a circle region (the estimated kNN circle) which includes kNNs with high probability. In the QL method, a node which relays a reply of a kNN query stores the information on the query result for future queries.

People carry around mobile devices all the time in their daily life and get various information from the Internet in various situations. When searching for information (content) by using mobile devices, users' activities (e.g., walking and standing) and their situations (e.g., commuting in the morning and going out downtown in the evening) often change and this change may affect their degree of concentration on the display of mobile devices and their information needs. Therefore, search systems should provide users with an amount of information suitable for their activities and with a type of information suitable for their situations. In this paper, we present the design and implementation of a content search system considering mobile users' activities and situations, which aims to reduce users' load of operations in content searching. Our system recognizes user's activities and switches between two kinds of content search systems according to the user's activity: the location-based content search system runs when the user is standing, while the menu-based content search system runs when the user is walking. Both systems present information based on the user's situation. We also introduce a user's activity recognition method for mobile devices. This method classifies user's activity into standing, walking, and running using the sensors equipped in mobile devices.

Smart-phone users can search for information about surrounding facilities or a route to their destination. However, it is difficult to get or search for information while walking because of low legibility. To address this problem, users have to stop walking or enlarge the screen. Our previously proposed system for smart-phone switches the information presentation policies in response to the user's context. In this paper we describe our context recognition mechanism for this system. This mechanism estimates user context from sensors embedded in a smart-phone. We use a Support Vector Machine for the context classification and compare four types of feature values consisting of FFT and 3 types of Wavelet Transforms. Experimental results show that recognition rates are 87.2 % with FFT, 90.9 % with Gabor Wavelet, 91.8 % with Haar Wavelet, and 92.1 % with MexicanHat Wavelet.

In this paper, we propose a dynamic route construction method based on measured characteristics of radio propagation in a real environment. Our method first measures characteristics of radio propagation for each link, and determines a communication route from every node to the sink node and its transmission power based on the measured characteristics. While operating the system, our method dynamically reconstructs communication routes according to the change in the characteristics of radio propagation. We conduct an experiment to verify that our method can construct efficient communication routes in terms of energy-efficiency and quality of communication even when the characteristics of radio propagation dynamically change.

Map-based application is one of the most popular applications using a built-in GPS receiver. Users often use applications based on geographic information in situations where they share the same information needs with friends or families and conduct some tasks together. However, it is difficult to communicate geographic information with each other because existing map-based applications are basically designed for solitary work. In this paper, to support collaborative work using map-based applications, we explore important factors for collaborative use of map interface. We conducted a user experiment using a map-based application implemented with some functions for supporting collaborative work. The result shows that it is important to i) enable users to share information among multiple devices and view it individually on their own displays when they use a map together, and ii) synchronize users' displays and map operations when they communicate geographic information with each other.

Location information of nodes is the basis for many applications in wireless sensor networks (WSNs). However, most previous localization methods make the unrealistic assumptions: (i) all nodes in WSN are always awake and (ii) the radio range of nodes is an ideal circle. This overlooks the common scenario that sensor nodes are duty-cycled in order to save energy and the radio range of nodes is irregular. In this paper we revisit the Distance-Vector-based (DV-based) positioning algorithms, particularly, Hop-Count-Ratio based Localization (HCRL) algorithm and investigate the following problems: (i) how is the relationship between the number of sleeping neighbor sensor nodes and the localization accuracy and (ii) how is the relationship between the degree of irregularity (DOI, which is a parameter of radio range irregularity) and the localization accuracy. We conduct a large number of experiments in WSNs' simulator NetTopo, and find that the parameters: the number of waking nodes, DOI, anchor node density and localization error, are interactional, i.e., for a given deployed static WSN, there is an optimal number of waking nodes and an optimal anchor node density, which can minimize network energy consumption without losing much of the localization accuracy. Furthermore, waking up more sensor nodes cannot always help to increase the localization accuracy, which actually is different from our intuitive thinking: more waking nodes can help to increase the localization accuracy of DV-based localization algorithms at all time.

Network lifetime is one of the most critical issues for wireless sensor networks (WSNs) since most sensors are equipped with non-rechargeable batteries with limited energy. To prolong the lifetime of a WSN, one common approach is to dynamically schedule sensors' active/sleep cycles (i.e., duty cycles) with sleep scheduling algorithm. In this paper, we propose a new sleep scheduling algorithm, named EC-CKN (Energy Consumed uniformly-Connected K-Neighborhood) algorithm, to prolong the network lifetime. The algorithm EC-CKN, which takes the nodes' residual energy information as the parameter to decide whether a node to be active or sleep, not only can achieve the k-connected neighborhoods problem, but also can assure the k awake neighbor nodes have more residual energy than other neighbor nodes at the current epoch. Based on the algorithm EC-CKN, we can obtain the state transition probability at the n'th epoch, and upper bound and lower bound of the network lifetime by Markov chain and Markov decision chain.

Top-k query is a very useful and important query in wireless sensor networks (WSNs), aiming to find the k nodes with highest readings among the sensor nodes. In WSNs, there are generally two kinds of networks: always-on WSNs (AO-WSNs) in which sensors always keep awake and duty-cycled WSNs (DC-WSNs) where sensors dynamically sleep and wake. To the best of our knowledge, there are a lot of work about top-k query in AO-WSNs but little research has been done regarding top-k query in DC-WSNs. However, DC-WSN is a very practical network model in which energy consumption can be greatly saved. In this paper, we analyze the research issues when implementing top-k query in DC-WSNs and propose the DCDC-WSNs (DC-WSNs with data replication (DR) and connected k-neighborhood (CKN)) to implement top-k query. Further theoretical analysis and simulation results show that implementing top-k query in DCDC-WSNs can achieve the best tradeoff with respect to query data accessibility and query cost (total energy consumption, query response time), compared with AO-WSNs and DCC-WSNs (DC-WSNs with only CKN).

In mobile ad hoc networks (MANETs), location based service (LBS) is a typical application. In a LBS, it is effective for each node to acquire data using a k Nearest Neighbor (kNN) query, which retrieves the information on the nearest k nodes from the location specified by the query. However, existing methods for kNN query processing in wired networks and wireless sensor networks cannot be applied in MANETs dueto the movement of mobile nodes. In this paper, we propose the Explosion (EXP) method, which is a kNN query processing method for reducing traffic and also keeping high accuracy of the query result in MANETs. An experimental result shows that our proposed method reduces traffic and achieves high accuracy of the query result compared with a naive method.

Recently, there has been increasing interest in Mobile Wireless Sensor Networks (MWSNs) that are constructed by mobile sensor nodes held by ordinary people, which 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 for each geographical point that can sense the point in the entire sensing area. To reduce the communication cost for sensing operations, it is desirable to gather the sensor data from the minimum number of the 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 readings, and move between sensor nodes. By gathering only sensor readings generated by sensor nodes where agents are running, our proposed method can achieve efficient gathering of sensor readings.

In Wireless Sensor Networks (WSNs) a multitude of location-dependent applications have been proposed recently, which is very intriguing for researchers to discover and design more accurate and cost-effective localization algorithms. In an isotropic networks, the Euclidean distance between a pair of nodes may not correlate closely with the hop count between them because the corresponding shortest path may have to curve around intermediate holes, resulting in poor distance estimation. And without the help of a large number of uniformly deployed seed nodes, those schemes fail in an isotropic WSNs. To address this issue and improve the accuracy of localization, we propose the Removing Heavily Curved Path (RHCP) scheme in this paper. RHCP takes advantage of selecting the paths which are not heavily affected by the holes to recalculate the location of each unknown node. Through simulation, the results reveal that RHCP performs better than original DV-Hop in an isotropic networks with different shape of holes. In addition, through iterations of RHCP, the results get improved for different anchor node densities.

Focusing on achieving better geographic routing performance of the two-phase geographic greedy forwarding (TPGF) in duty-cycled wireless sensor networks (WSNs) when there is a mobile sink, this paper proposes a geographic distance based connected-k neighborhood (GCKN) algorithm. The algorithm analysis and simulation results show that GCKN can obtain shorter length of the transmission paths explored by TPGF in duty-cycled mobile sink WSNs, compared with the original connected-k neighborhood (CKN).

In mobile wireless sensor networks where sensor nodes are general mobile terminals equipped by vehicles and pedestrians, it is difficult for applications to estimate coverages of sensor nodes due to their mobility. In addition, when a network is sparse, it is also difficult to estimate delivery delays of sensor readings to the sink node(s) because delays depend on both the distribution of nodes and the change of the network topology. These two factors, sensing coverages and data delivery delays, are very significant when designing applications and underlying protocols. In this paper, we aim to quantify sensing coverages and data delivery delays in mobile sensor networks. We propose several general metrics to quantify these two factors for a given mobility pattern. We also report results of some simulation experiments that measure the proposed metrics assuming several typical mobility models.

In this paper, we propose a method which acquires related words (entities) from multiple words by naturally disambiguating their meaning and considering their contexts. In addition, we introduce a bootstrapping method for improving the coverage of association relations. Experimental result shows that our method can acquire related words depending on the contexts of multiple words compared to the ESA-based method.

In a mobile ad hoc network (MANET), since mobility of mobile hosts causes frequent network partitioning, consistency management of data operations on replicas becomes a crucial issue. In our previous work, we have defined several consistency levels for MANET applications and designed protocols to achieve these consistency levels. These protocols are mainly based on a dynamic quorum system to cope with network partitioning and node and network failures. In this paper, we further investigate the impact of quorum construction on the system performance through simulation studies. Specifically, we change the number of mobile hosts that replicate data items and which hosts replicate each data item in the simulations and examine the impact on the system performance in terms of data availability and traffic.

Recent mobile devices are mostly equipped with a GPS receiver. This trend has arisen a variety of location based services (LBSs) which enable users to search local information of their current locations. To use LBSs, a user needs to send his/her location information to a service provider. Users' location information is inherently private, since it can reveal the critical information. In this paper, we propose a method to protect the user's location privacy by sending the user's location with dummy locations, which are determined based on the user's current location. We conduct an experiment to evaluate the effectiveness of our proposed method using real users' trajectories on a real map.

Two Phase geographic Greedy Forwarding (TPGF) is a pure on-demand geographic greedy forwarding protocol for wireless multimedia sensor networks (WMSNs). Unlike position-based routing protocols, TPGF has explicit route discovery, i.e., a node greedily forwards a routing packet to the neighbor that is the closest one to the destination to build a route. Thus, TPGF is vulnerable to some greedy forwarding attacks, e.g., spoofing or modifying control packets. In this paper, we identify such vulnerabilities and propose corresponding countermeasures for TPGF, e.g., secure neighbor discovery, route discovery.

This paper focuses on studying the impacts of a duty-cycle based CKN sleep scheduling algorithm for our previous designed TPGF geographical multipath routing algorithm in wireless sensor networks (WSNs). It reveals the fact that waking up more sensor nodes cannot always help to improve the exploration results of TPGF in a duty-cycle based WSN. Furthermore, this study provides the meaningful direction for improving the application-requirement based QoS of stream data transmission in duty-cycle based wireless multimedia sensor networks.

In a mobile ad hoc network (MANET), mobile hosts basically run with battery and their battery capacity is limited. Data transmissions are one of the main factors of the power consumption of mobile hosts. Therefore energy efficient data access is one of the most important research issues in MANETs. In this paper, we introduce our recent work addressing issues of energy efficient data access in MANETs. We first present data access and replication methods that take into account the remaining amount of the battery of each host. We then present a message processing method for top-k queries in MANETs, which is effective to reduce the amount of data volume transmitted.

Photo sharing is one of the most popular Web services. Photo sharing sites provide functions to add tags and geo-tags to photos to make photo organization easy. Considering that people take photos to record something that attracts them, geo-tagged photos are a rich data source that reflects people's memorable events associated with locations. In this paper, we focus on geo-tagged photos and propose a method to detect people's frequent trip patterns, i.e., typical sequences of visited cities and durations of stay as well as descriptive tags that characterize the trip patterns. Our method first segments photo collections into trips and categorizes them based on their trip themes, such as visiting landmarks or communing with nature. Our method mines frequent trip patterns for each trip theme category. We crawled 5.7 million geo-tagged photos and performed photo trip pattern mining. The experimental result shows that our method outperforms other baseline methods and can correctly segment photo collections into photo trips with an accuracy of 78%. For trip categorization, our method can categorize about 80% of trips using tags and titles of photos and visited cities as features. Finally, we illustrate interesting examples of trip patterns detected from our dataset and show an application with which users can search frequent trip patterns by querying a destination, visit duration, and trip theme on the trip.

In mobile ad hoc networks, there are many applications in which mobile users share information, e.g., collaborative rescue operations at a disaster site and exchange of word-of-mouth information in a shopping mall. For such applications, improving data availability is a significant issue and various studies have been conducted with this aim. However, each of these conventional works assumed a particular mobility model and did not fully investigate the influence of the mobility on the proposed approach. In this paper, we aim to quantify the influences of mobility on data availability from various perspectives. We assume neither specific applications nor specific protocols but we propose and quantify several metrics that affect data availability. We also report results of some experiments that measure the proposed metrics assuming several typical mobility models.

There are many constraints on electrical power and communication in wireless sensor networks since sensor nodes can only be equipped with poor devices. Therefore, it is important to construct an effective communication route in terms of energy-efficiency and quality of communication. Many studies have been conducted to address this issue. In particular, controlling the transmission power of each individual node has been one of the hottest issues in recent years. Most conventional studies have assumed an environment in which radio signals transmitted by nodes propagate equally in all directions. However, this model cannot be applied to real environments due to several effects such as the presence of obstacles and antenna characteristics. In this paper, we propose a routing method based on the measured characteristics of radio propagation in a real environment. Our method first involves measuring the characteristics of radio propagation for all radio links. Second, the transmission power of each individual node and a communication route from the node to the sink node are determined based on the measured characteristics.

In a mobile ad hoc network, consistency management of data operations on replicas is a crucial issue for system performance.In our previous work, we classified several primitive consistency levels considering requirements from applications and provided protocols to realize them.In this paper, we assume special types of applications in which the instances of each data item can be partitioned and propose two consistency management protocols which are combinations of an escrow method and our previously proposed protocols.We also report simulation results to investigate the characteristics of these protocols.

In this paper, we propose a novel energy-efficient data transmission reduction method for periodical data gathering in wireless sensor networks (WSNs). Our method exploits overhearing, which is a typical characteristic of wireless communications. In our method, each node in a WSN autonomously determines whether its own reading is redundant or not by using the overheard packets transmitted by its neighbors. The redundancy of a reading is evaluated by a simple spatial interpolation using the readings of its neighbors. If the reading is determined as redundant, the node stops transmitting it. Furthermore, we present an evaluation based on simulation experiments with various parameters and show that our method suppresses data transmissions and reduces total energy consumption.

This paper introduces a new sensor network testbed, X-sensor, that integrates multiple sensor networks deployed at different sites. X-sensor provides three functionalities: (a) a sensor network search which enables users to find a sensor networks appropriate for experiment and data acquisition, (b) a sensor data archive which provides users with various sensor data acquired by sensor nodes, and (c) an experimental testbed which enables remote users to evaluate their network and data management protocols.

Recent spread of various data broadcasting services leads to provide enormous and various data. Since, data that a client needs are a part of them, there has been an increasing interest in information filtering techniques where a client automatically chooses and stores the necessary data. Generally, when a client performs filtering, it applies some filters sequentially and the time required for filtering changes according to the order of filters. On the other hand,in recent years, there have been many studies about attractor selection which is an autonomous parameter control technique based on the knowledge from living organisms.In this paper, in order to reduce the load for filtering, we propose a novel method which adaptively changes the order of filters according to the change in broadcast contents.This method adaptively decides the control parameters for filtering by using attractor selection.

In recent years, there has been an increasing interest in a broadcast database system where the server periodically broadcasts contents of a database to mobile clients such as PDAs and smart phones. There are three query processing methods in the broadcast database system. Generally, mobile clients have limits in energy consumption, i.e., battery,and each of the three methods consumes different amount of energy for query processing. In this paper, we propose a new method which dynamically chooses one of the three query processing methods considering energy consumption.

Cellular phones are widely used to access the Web. However, because of their small screens and poor interfaces, it is inconvenient to browse Web pages designed for desktop PCs. In this paper, we propose two novel functions that virtually extend a cellular phone's screen so that users can acquire more information from the small screens than the traditional Web browsing using cellular phones. The first function presents previews of adjacent areas of the currently displayed area of a Web page so that users can understand what information is in the neighboring areas. The other presents previews of Web pages linked from the current page to help users to select an appropriate link connecting to a page containing the information of interest. As a platform for the implementation of these functions, we use cellular phones equipped with sensors to provide a user-friendly interface.

Advances in wireless communication technology have led to an increasing interest in mobile sensor networks.In our previous work, we proposed a method that controls the movement of sensor nodes by using push-based broadcast and communication among nodes.In this method, nodes autonomously move based on the broadcast information on where they can communicate with the sink,and they construct a temporal network to deliver their sensor readings to the sink.However, this method has two problems.First, it cannot sufficiently reduce the moving distances of nodes.Seconds, this method assumes an ideal environment in which node failures do not occur, whereas node failures do occur in real environments.In this paper, we extend our previous method to solve these problems. We also show the results of simulation experiments regarding a performance evaluation of the extended method.

In this paper, we propose an efficient node deployment strategy in sparse sensor networks. Our strategy, named DATFM/DF (Data Acquisition and Transmission with Fixed and Mobile node with deliberate Deployment of Fixed nodes), deploys sensor nodes based on the analysis of our previous mobile sensor control method, DATFM. As a result, DATFM/DF improves the efficiencies of sensing and data gathering. Furthermore, we conduct simulation experiments to evaluate the performance of our strategy.

Cellular phones are widely used to access the Web, however, it is inconvenient to browse large-sized pages designed for desktop PCs. Since cellular phones display only small part of a Web page, users easily get lost within the page while scrolling around. Even though pages are well structured by nature, users cannot recognize it because visible portion is strictly limited. To smartly browse pages using cellular phones, navigation is essential. In this paper, we propose a novel Web browser that models browsing as motoring cities, by setting routes on Web pages and presenting traffic signs to navigate users. Users can follow routes by auto-scrolling without burdensome operations.

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.

In this paper, we present the results of performance evaluation of our energy-efficient data transmission reduction method in wireless sensor networks (WSNs). In our method, each node in a WSN autonomously determines whether its own reading is redundant or not by using the overheard packets transmitted by its neighbors. The redundancy of a reading is evaluated by a simple spatial interpolation using the readings of its neighbors. If the reading is determined as redundant, the node stops transmitting it. Since our method may be affected by packet losses, we conduct simulation experiments with a practical model of lossy links. The results show that our method suppresses data transmissions and reduces total energy consumption even in a lossy environment.

This paper introduces a sensor network testbed, X-sensor, that integrates multiple sensor networks deployed at different sites. X-sensor provides three functionalities: (a) a sensor network search which enables users to find a sensor networks appropriate for experiment and data acquisition, (b) a sensor data archive which provides users with various sensor data acquired by sensor nodes, and (c) an experimental testbed which enables remote users to evaluate their proposed methodologies.

In recent years, there has been an increasing interest in sensor networks, in which sensor nodes such as temperature or humidity sensors communicate with each other for environmental monitoring and so on. In sensor networks, it is important to reduce the energy consumption, since sensor nodes have limits in battery. Most researches achieve this by reducing sensor amount of transmitted data. However, there is a problem that the amount of transmitted data become large and their energy consumption becomes high when there are a lot of nodes in the sensing area. To solve the problem, we previously proposed a data gathering method. In this method, the data sink estimates the data value of nodes, broadcasts the estimated values, and then, sensor nodes whose observed values are largely different from the estimated values send the data. However, this method has a problem that it cannot sufficiently reduce the energy consumption. In this paper, we propose a new broadcast-based data gathering method. In this method, the data sink reduces the nodes which send their observed values when the reliability of gathered values by the data sink is high.

In mobile ad hoc networks, when a mobile host receives a data request from another host, the host usually transmits the requested data item by unicast. However, if mobile hosts hold data items that are frequently requested by others, they have to transmit the data items many times and consume a large amount of power. In this paper, we propose a data transmission method for not only maintaining data availability but also reducing traffic for data access. In our proposed method, each mobile host sends a data request attached with the deadline to receive the requested data item by the determined time. Moreover, each mobile host collects multiple requests for data items and transmits the requested data items by multicast, and thus, reduces data traffic. We also show the results of simulation experiments regarding the performance of our proposed method.

Geographical context is very important for images. Millions of images on the Web have been already assigned latitude and longitude information. Due to the rapid proliferation of such images with geographical context, it is still difficult to effectively search and browse them, since we do not have ways to decide their relevance. In this paper, we focus on the geographical relevance of images, which is defined as to what extent the main objects in an image match landmarks at the location where the image was taken. Recently, researchers have proposed to use game based approaches to label large scale data such as Web images. However, previous works have not shown the quality of collected game logs in detail and how the logs can improve existing applications. To answer these questions, we design and implement a Web-based and multi-player game to collect human knowledge while people are enjoying the game. Then we thoroughly analyze the game logs obtained during a three week study with 147 participants and propose methods to determine the image geographical relevance. In addition, we conduct an experiment to compare our methods with a commercial search engine. Experimental results show that our methods dramatically improve image search relevance. Furthermore, we show that we can derive geographically relevant objects and their salient portion in images, which is valuable for a number of applications such as image location recognition.

In a mobile ad hoc network, data replication drastically improves data availability. However, since mobile hosts' mobility causes frequent network partitioning, consistency management of data operations on replicas becomes a crucial issue. In such an environment, the global consistency of data operations on replicas is not desirable by many applications. Thus, new consistency maintenance based on local conditions such as location and time need to be investigated. This paper attempts to classify different consistency levels according to requirements from applications and provides protocols to realize them. We report simulation results to investigate the characteristics of these consistency protocols in a mobile ad hoc network.

Research on the automatic construction of bilingual dictionaries has achieved impressive results. Bilingual dictionaries are usually constructed from parallel corpora, but since these corpora are available only for selected text domains and language pairs, the potential of other resources is being explored as well. In this article, we want to further pursue the idea of using Wikipedia as a corpus for bilingual terminology extraction. We propose a method that extracts term-translation pairs from different types of Wikipedia link information. After that, an SVM classifier trained on the features of manually labeled training data determines the correctness of unseen term-translation pairs.

In our previous work, we proposed few updated data dissemination methods to refresh old replicas efficiently in mobile ad hoc networks. These methods disseminate updated data items every time when owners of original data items update the items or every time two mobile hosts are newly connected with each other and this causes heavy traffic in the entire network. In this paper, we assume applications that periodically execute read operations with strict deadlines to data items and propose few alternative updated data dissemination methods. These methods reduces the traffic for data dissemination while keeping a high success ratio for read operations.

Wikipedia, a huge scale Web based encyclopedia, attracts great attention as an invaluable corpus for knowledge extraction because it has various impressive characteristics such as a huge number of articles, live updates, a dense link structure, brief anchor texts and URL identification for concepts. We have already proved that we can use Wikipedia to construct a huge scale accurate association thesaurus. The association thesaurus we constructed covers almost 1.3 million concepts and its accuracy is proved in detailed experiments. However, we still need scalable methods to analyze the huge number of Web pages and hyperlinks among articles in the Web based encyclopedia.

Web-based dictionaries, such as Wikipedia, have become dramatically popular among the Internet users in past several years. The important characteristic of Web-based dictionary is not only the huge amount of articles, but also hyperlinks. Hyperlinks have various information more than just providing transfer function between pages. In this paper, we propose an efficient method to analyze the link structure of Web-based dictionaries to construct an association thesaurus. We have already applied it to Wikipedia, a huge scale Web-based dictionary which has a dense link structure, as a corpus. We developed a search engine for evaluation, then conducted a number of experiments to compare our method with other traditional methods such as cooccurrence analysis.

In this paper, we investigate adaptive control for broadcast scheduling and base station caching in the hybrid wireless broadcast (HWB) environment. The proposed adaptive method can adapt well to different access conditions and bandwidth capabilities and adequately exploit the three data delivery modes: push-based broadcast, pull- based broadcast, and pull-based point-to-point communication. Simulation studies demonstrated that our proposed method achieves significant performance improvement in average waiting time and success rate.

In mobile ad hoc networks, a mobile host holding data items frequently accessed by other hosts needs to transmit them many times and consumes more power than other hosts. In this paper, we propose replica allocation methods for not only improving data availability but also balancing the power consumption among mobile hosts. In these methods, each mobile host replicates data items taking into account their access frequencies, the numbers of their replicas, and the host's remaining battery power. We present simulation results to evaluate the performance of our proposed methods.

Cellular phones are widely used to access the WWW. However, most available Web pages are designed for desktop PCs. Cellular phones only have small screens and poor interfaces, and thus, it is inconvenient to browse such large sized pages. In addition, cellular phone users browse Web pages in various situations, so that appropriate presentation styles for Web pages depend on users' situations. In this paper, we propose a novel Web browsing system for cellular phones that allocates various functions for Web browsing on each numerical key of a cellular phone. Users can browse Web pages comfortably, selecting appropriate functions according to their situations by pushing a single button.

The twenty-one papers in this special issue are devoted to peer-to-peer communications and their applications. Covers such topics as: overlay networks, searching, video streaming, files and servers, and theories and applications.

Dynamic data replication improves data accessibility in mobile ad hoc networks. In our previous works, we proposed few methods to manage locations of replicas and efficiently forward access requests to the locations. In this paper, we extend these methods to adapt to an environment where data items are updated. The extended methods predict the locations of data items by storing and using the information on replica allocation at every relocation time and the logs of past data accesses. In addition, these methods give the priority to the stored information and access logs according to update and access times of the replicas

In this paper, we propose a new method that can inspect visual and non-visual features of objects simultaneously by using image and sound signal processing techniques. A method for discriminating a property of an object with the use of generated sound when striking it with a hammer is called a hammering test. This method can investigate non-visual features of objects such as inner structure of objects, e.g., the existence of defects and cracks inside objects. However, this method depends on human experience and skills. In addition, if we perform this test over a wide area of objects, it is required to manually record hammering positions one by one. To solve these problems, this paper proposes a hammering test system consisting of two video cameras that can acquire image and sound signals of a hammering scene. The shape of the object (visual feature) is measured by the image signal processing from the result of 3-D measurement of each hammering position, and the thickness or material (non-visual feature) is estimated by the sound signal processing in time and frequency domains. The validity of proposed method is shown through experiments

In this paper, based on the Hybrid Wireless Broadcast (HWB) model, we investigate a cooperative data management strategy to provide more efficient data delivery to the mobile clients. This strategy integrates broadcast scheduling with cache management of the base station by making effective use of the HWB data dissemination, namely push- and pull- based broadcast and pull-based point-topoint wireless communication. Simulation study demonstrates that the proposed cooperation strategy can improve the system performance.

In mobile ad hoc networks where data items are updated, each mobile host cannot verify whether its own replica is the same version as the original when it does not connect to the mobile host holding the original (original holder). In this paper, we propose two update log dissemination methods that efficiently verify the validity of tentative accesses to replicas. In the first method, the original holder efficiently disseminates the update logs to other hosts. In the other method, mobile hosts which are not the original holder also manage the update logs and two newly connected mobile hosts re-disseminate the logs. We also present simulation results to evaluate the performance of our methods.

In this paper, we propose a TDMA slot assignment protocol to realize the high channel utilization in an ad hoc sensor network where nodes have different communication ranges. Our protocol prevents inconsistency of the slot assignment information by considering the directions of wireless links with the neighbors when each node assigns a slot to itself. Furthermore, we verify the effectiveness of our protocol by simulation experiments. The results show that our protocol utilizes the channel bandwidth effectively even when nodes have different communication ranges.

In recent years, there has been an increasing interest in broadcast database systems, where the server periodically broadcasts contents of a database to mobile clients such as PDAs. There are three query processing methods in the broadcast database system: (i) the server processes a query and then broadcasts the query result to the client; (ii) the client stores all data that are necessary in processing the query and then processes it locally; and (iii) the server and the client collaborate in processing the query. Since the performance of each method changes according to the system situation, such as query frequency, it is difficult to decide the optimal method among them statically. In this paper, we propose a new query processing method which dynamically changes the query processing method based on query frequency. This method improves not only the response time but also the success rate of query processing compared with the traditional methods.

In this paper, we introduce two our approaches to browse large Web pages designed for desktop PCs using mobile devices with small screens and poor input interfaces. In the first approach, multiple mobile users collaboratively browse large Web pages. We call this approach collaborative browsing. In the second approach, automatic scrolling in a large Web page is performed, where contents in the page are traversed. This enables users to rapidly view the page with the users’ minimum operations.

GeoNote.net is a social network system which allows users to share geographic information among their friends network. The system has various notable features and uses several key technologies such as social network construction methods, security for personal information, geographic information management on mobile devices including cellular phones, retrieval of geographic information according to the friends network, and so on. In this paper, we describe the system under design aspects and implementation aspects.

Data replication is effective for improving data availability in ad hoc networks. In an environment where data updates occur, replicas of a data item may be inconsistent. To solve this problem, quorum based consistency management is a promising approach. To reduce communication overhead, it is better to make the number of mobile hosts in each quorum small. In this paper, we propose a consistency management method that constructs quorums with fewer mobile hosts.

Recently, there has been an increasing interest in a social network. In a social network, nodes and links represent participants and their friendships, respectively. We have designed and implemented a query propagation mechanism and its applications to realize a social network composed by cellular phone users. In these applications, users can retrieve information from their friends or their friends’ friends by propagating the query in the network. To propagate a query in a wide range and improve the query success ratio, most users who receive the query must relay it to all their friends. However, this increases communication packets. In this paper, we propose a query routing method to decrease the number of communication packets by using user profiles.

In mobile computing environments, handheld devices with low functionality restrict the services provided for mobile users. We propose a new concept of collaborative browsing, where mobile users collaboratively browse Web pages designed for desktop PC. In collaborative browsing, a Web page is divided into multiple components, and each is distributed to a different device. In mobile computing environments, the number of handheld devices, their capabilities, and other conditions can vary widely amongst mobile users who want to browse content. Therefore, we developed a page partitioning method for collaborative browsing, which divides a Web page into multiple components. Moreover, we designed and implemented a collaborative Web browsing system in which users can search and browse their target information by discussing and watching partial pages displayed on multiple devices

It is difficult for users of mobile devices such as cellular phones equipped with a small screen and a poor input interface to browse Web pages designed for desktop PCs with large displays. Many studies and commercial products have tried to solve this problem. Web pages include images that have various roles such as site menus, line headers for itemization, and page titles. However, most studies of mobile Web browsing haven't paid much attention to the roles of Web images. In this paper, we define eleven Web image categories according to their roles and use these categories for proper Web image handling. We manually categorized 3,901 Web images collected from forty Web sites and extracted image features of each category according to the classification. By making use of the extracted features, we devised an automatic Web image classification method. Furthermore, we evaluated the automatic classification of real Web pages and achieved up to 83.1% classification accuracy. We also implemented an automatic Web page scrolling system as an application of our automatic image classification method.

In ad hoc networks, due to frequent network partition, data accessibility is lower than that in conventional fixed networks. In this paper, we solve this problem by replicating data items on mobile hosts. First, we propose three replica allocation methods assuming that each data item is not updated. In these three methods, we take into account the access frequency from mobile hosts to each data item and the status of the network connection. Then, we extend the proposed methods by considering aperiodic updates and integrating user profiles consisting of mobile users' schedules, access behavior, and read/write patterns. We also show the results of simulation experiments regarding the performance evaluation of our proposed methods

Summary form only given. Recently, there has been increasing interest in research of data sharing in peer-to-peer networks. In this paper, we propose a new update propagation strategy that achieves not only load balancing but also delay reduction, where the update information is immediately notified to all peers holding the replicas when the update occurs. The proposed strategy creates an n-ary tree whose root is the owner of the original data and the other nodes are peers holding its replicas, and propagates the update information according to the trees. Moreover, we verify the effectiveness of the proposed strategy by simulation experiments.

Recent advances in wireless communication along with peer-to-peer (P2P) paradigm have led to increasing interest in P2P mobile ad hoc networks. In this paper, we assume an environment where each mobile peer accesses data items held by other peers which are connected by a mobile ad hoc network. Since peers' mobility causes frequent network partitions, replicas of a data item may be inconsistent due to write operations performed by mobile peers. In such an environment, the global consistency of data items is not desirable by many applications. Thus, new consistency maintenance based on local conditions such as location and time need to be investigated. This paper attempts to classify different consistency levels according to requirements from applications and provides protocols to realize them. We report simulation results to investigate the characteristics of these consistency protocols in a P2P wireless ad hoc network environment and their relationship with the quorum sizes.

In this paper we propose a TDMA slot assignment protocol to improve the channel utilization, which controls the excessive increase of unassigned slots by changing the frame length dynamically. Our proposed protocol assigns one of the unassigned slots to a node which joins the network. If there are no unassigned slots available, our proposed protocol generates unassigned slots by depriving one of the multiple slots assigned to a node, or enlarging frame length of nodes which can cause collision with each other. Moreover, by setting frame length as a power of 2 slots, our proposed protocol provides the collision-free packet transmission among nodes with different frame length. The simulation results show that our proposed protocol improves the channel utilization dramatically as compared with the conventional protocols.

Recently, there has been an increasing interest in the research of push-based information systems that deliver data using broadcast in both wired and wireless environments. In push-based systems, since a server can meet all requests for broadcast data pending in a system with one broadcast, a larger throughput can be expected when there exists a large number of clients. In this paper to reduce the response time of data access, we propose a new scheduling strategy of broadcast program considering an environment where clients issue requests for multiple data items consecutively with think-time. The proposed strategy reduces the average response time by broadcasting the optimal data item at present according to clients' access characteristics such as access frequencies, correlations among data items, and think-time.

Recent advances in computer and wireless communication technologies have increased interest in push-based information systems in which a server repeatedly broadcasts data to clients through a broadband channel. In this paper, assuming an environment where clients in push-based information systems construct ad hoc networks, we propose three caching strategies in which clients cooperatively cache broadcast data items. These strategies shorten the average response time for data access by replacing cached items based on their access frequencies, the network topology, and the time remaining until each item is broadcast next. We also show the results of simulation experiments conducted to evaluate the performance of our proposed strategies.

Recent advances in computer and wireless communication technologies have led to an increasing interest in ad hoc networks which are temporarily constructed by only mobile hosts. In ad hoc networks, since mobile hosts move freely, disconnections occur frequently, and this causes frequent network division. Consequently, data accessibility in ad hoc networks is lower than that in the conventional fixed networks. Assuming an environment where each data item is periodically updated, we propose three replica allocation methods to improve data accessibility by replicating data items on mobile hosts. In these three methods, we take into account the access frequency from mobile hosts to each data item, the status of the network connection, and the time remaining until each item is updated next. We also show the results of simulation experiments regarding the performance evaluation of our proposed methods.

Cellular phones have become popular and have been recognized as light-weight portable computers because of their various advanced functions such as E-mail, WWW browsing, and schedule management. However, since they are too small to display a large amount of information, users cannot make use of them as conveniently as normal computers. To solve this problem, we propose a remote display environment. In a remote display environment, there are large displays every- where in the real world, which we call remote displays. A user makes use of a cellular phone as a remote controller to display information on a remote display. We also describe our implementation of a WWW browsing system as an application in a remote display environment. Moreover, we describe our design and implementation of an access control mechanism by assuming applications in which multiple users simultaneously access one remote display.

The advances in computer and wireless communication technologies have led to an increasing interest in ad hoc networks which are temporarily constructed by only mobile hosts. In ad hoc networks, since mobile hosts move freely, disconnections occur frequently, and this causes frequent network division. Consequently, data accessibility in ad hoc networks is lower than that in the conventional fixed networks. We propose three replica allocation methods to improve data accessibility by replicating data items on mobile hosts. In these three methods, we take into account the access frequency from mobile hosts to each data item and the status of the network connection. We also show the results of simulation experiments regarding the performance evaluation of our proposed methods.

Dynamic relocation of databases through networks, or database migration, will soon become a powerful standard database operation effectively using recent advances in broadband networks. In our previous work, a transaction processing method has been proposed in distributed database environments, based on database migration operations. Generally, database replication is an effective technique to improve transaction processing throughput in conventional systems. We propose a replica management method for use with database migration. Database migration is used at the beginning of a transaction to dynamically relocate database replicas. The performance of the proposed method is evaluated using simulation results.

Because of the recent development of network technologies such as ATM (Asynchronous Transfer Mode), broader channel bandwidth is becoming available everywhere in the world wide networks. As one of the new technologies to make good use of such broadband channel, dynamic relocation of databases through networks, which we call database migration, will soon become a powerful and basic database operation of practical use. We discuss our proposal of a distributed database system, DB-MAN (distributed database system based on DataBase Migration in ATM Networks), which takes advantage of database migration in virtual LANs (local area networks) of ATM networks. DB-MAN has two notable mechanisms: a mechanism for selecting the transaction processing method and a mechanism for concurrency control with database migration. The former, is a mechanism which chooses the more efficient method between two transaction processing methods: the conventional method based on the two phase commit protocol and our method employing database migration. The latter is a mechanism to prevent the transaction processing throughput from deteriorating in environments where data contention is a significant factor. Then we show simulation results regarding performance comparison between our proposed system and the conventional distributed database system based on the two phase commit protocol. The obtained results demonstrate that effective use of database migration gives higher performance than that of the conventional system.

Due to recent developments in network technologies, broader channel bandwidth is becoming prevalent in worldwide networks. As one of the new technologies making good use of such broadband channels, dynamic relocation of databases through networks, database migration, will soon be used in practice as a powerful and basic database operation. We propose two transaction processing methods to take advantage of database migration in broadband networks. These methods choose the most efficient transaction processing method between the conventional method, based on the two-phase commit protocol, and our method, using database migration. We also propose a concurrency control mechanism and a recovery mechanism for our proposed methods. Simulation results are presented comparing the performance of our proposed methods and the conventional transaction processing method based on the two-phase commit protocol. The results demonstrate that the effective use of database migration produces better performance than the conventional method.