• Complex offloading dynamics are rarely considered in offloading problems • The potential of collaborative systems is not fully leveraged by offloading methods • Suitable methods for solving offloading problems depend on system complexity • Offloading methods are mainly evaluated using study-specific simulation environments • Studies lack cross-validation across methods, networks, and configurations The rapid increase in the number of connected devices has created a vast computational infrastructure. While intelligent applications (e.g., VR/AR, autonomous driving, and AI assistants) demand substantial processing power, they are typically executed only in short bursts. As a result, devices remain idle for significant periods, presenting an opportunity to utilize their unused computational resources. Computation offloading has emerged as a viable solution, where resource-constrained devices leverage edge or cloud infrastructure for intensive tasks. More recently, horizontal offloading has gained attention as a complementary approach, enabling devices within the same layer to collaborate and utilize idle resources more efficiently. In this survey, we examine current research on computation offloading in collaborative computing systems that span from horizontal offloading among end devices to more complex systems involving horizontal as well as vertical offloading to edge/cloud systems. Our examination focuses on architectures, collaborative characteristics, and offloading dynamics, such as task dependency, mobility, and multi-hop offloading. We begin by introducing edge computing paradigms and key concepts in computation offloading and collaborative computing. The offloading approaches are then classified into classical, heuristic, metaheuristic, and machine learning methods, further divided into centralized, decentralized, and distributed categories. These approaches are analyzed and compared based on collaborative system, architecture, offloading dynamics, and evaluation methodology. Finally, we have identified open challenges and emphasized the need to unify research across edge computing paradigms, alongside a focus on more complex collaborative systems that resemble real-world systems, and the development of standardized evaluation frameworks as key directions for future work.

The integration of knowledge graphs (KGs) with large language models (LLMs) offers significant potential to enhance the retrieval stage in retrieval-augmented generation (RAG) systems.In this study, we propose KG-CQR 1 , a novel framework for Contextual Query Retrieval (CQR) that enhances the retrieval phase by enriching complex input queries with contextual representations derived from a corpuscentric KG.Unlike existing methods that primarily address corpus-level context loss, KG-CQR focuses on query enrichment through structured relation representations, extracting and completing relevant KG subgraphs to generate semantically rich query contexts.Comprising subgraph extraction, completion, and contextual generation modules, KG-CQR operates as a model-agnostic pipeline, ensuring scalability across LLMs of varying sizes without additional training.Experimental results on the RAGBench and MultiHop-RAG datasets demonstrate that KG-CQR outperforms strong baselines, achieving improvements of up to 4-6% in mAP and approximately 2-3% in [email protected], evaluations on challenging RAG tasks such as multi-hop question answering show that, by incorporating KG-CQR, the performance outperforms the existing baseline in terms of retrieval effectiveness.

ABSTRACT This paper proposes a two‐stage framework that combines uncertainty reduction and predictive modelling to enhance the accuracy of battery Remaining Useful Life (RUL) prediction. In the first stage, a simplified fuzzy optimization learning model is introduced to mitigate uncertainty caused by abnormal capacity fluctuations in battery data. The proposed fuzzy model reconstructs degradation data into a consistent downward trend based on mid‐ and short‐term tendencies of the battery, alleviating abnormal variability and improving suitability for predictive modelling. In the second stage, uncertainty arising during the recursive prediction process of a standalone Transformer model was mitigated through the integration of a particle filter. This approach dynamically manages prediction errors using particles, effectively controlling cumulative errors and enhancing the stability and reliability of long‐term predictions. This methodology can lead to extended battery life and increased operational reliability through accurate RUL prediction. The proposed methodology is validated through experiments using NASA and CALCE battery datasets, demonstrating superior prediction accuracy and stability compared to conventional approaches by systematically reducing uncertainties.

Abstract Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly.

Abstract Anomaly detection is critical in the internet of things (IoT) environment. To this issue, this study provides a novel approach for detecting anomalies in multivariate IoT time series. The proposed approach identified relationships between IoT time series to establish a dynamic graph and estimated the graph entropy to detect anomalies. The presented approach was applied to industrial IoT datasets. The results have shown that the presented method outperformed other models by 0.21 with respect to F1‐score. In addition, we used three distinct algorithms to detect the anomalies from the multivariate IoT time series. According to the results, the local outlier factor approach outperformed the others by 0.18 with respect to F1‐score.

Summary Managers make decisions on team tactics, formations, and player selection based on their own experiences. The managers have limitations in understanding the team's situation and sometimes they can think wrong. The purpose of this study is to make decisions on player selection and tactical formation according to the level of the opponent based on the data, not on the intuition of the manager. In our previous study, the Boruta algorithm was used to extract important features from 69 features in soccer player data by position. The detailed roles of each position were defined by using K‐means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep‐lying playmaker, and so on. That is, forward positions are classified as Target Man (TM) and Shadow Striker (SS). TM is a high‐goal, high‐competitive forward, and SS is a high‐dribble, high‐pass forward. In this study, we analyze a clustering dataset and the game appearance dataset. The game appearance dataset are divided into CL (Champions league Level), EL (Europa league Level), ML (Middle Level), and RL (Relegation Level). Association rule mining algorithm analyzes the synergy between positions, and selects a position with high synergy. Weighted association rule mining algorithm establishes player selection and tactical formation with the weight, which is the player's rating data. Finally, using the obtained results, we visualize the synergy between positions, tactical formation, and player characteristics depending on the level of the opponent.

Summary Collaborative filtering recommendation systems, which analyze sets of user ratings, have been applied to various domains and have resulted in considerable improvements in the traditional recommendation system. However, they still have problems with data sparsity and cold‐start of the user ratings. To solve these problems, we present a hybrid recommendation approach by combining collaborative filtering methods and word embedding‐based content analysis. This study focuses on the movie domain, and therefore, the contents of the items are represented as a set of features such as titles, genres, directors, actors, and plots. The main aim of this paper is to understand the content of the movie plot using a word embedding to improve the measurement of similarity of each plot content to other plot content (called plot embedding). To enhance the accuracy in measuring the similarity between movies, we also consider other features such as titles, genres, directors, and actors extracted from movies. In the experiments, the movie dataset was collected by our crowdsourcing platform, which is the OMS platform. The experimental findings indicate that the proposed approach can enhance the efficiency of applied collaborative filtering recommendation systems.

Images have been repeatedly used as the perfect environment to hide information through the use of steganography techniques. Whether messages, documents or even other images, the bitmap of an digital picture provides a place where hidden data can be embedded without human notice. So far, a plethora of steganography methods can be found in the state-of-the-art literature, together with steganalysis techniques, devoted to detect the presence of hidden information in files. Recent steganography techniques rely on Convolutional Neural Networks, trying to embed as information as possible while minimising visual changes in the image. Following this trend, this article tries to demonstrate that a Generative Adversarial Network (GAN) can be used to improve the ability of a spatial domain steganalysis method and to insert secret information with minimal image alteration. Through a training process, the GAN learns how to adapt an image to later introduce a message using the Least Significant Bit steganography algorithm. The results evidence that the approach is successful at avoiding detection by a state-of-the-art Deep Learning steganalysis architecture.

In this chapter, the authors propose to use contextual Word2Vec model for understanding OOV (out of vocabulary). The OOV is extracted by using left-right entropy and point information entropy. They choose to use Word2Vec to construct the word vector space and CBOW (continuous bag of words) to obtain the contextual information of the words. If there is a word that has similar contextual information to the OOV, the word can be used to understand the OOV. They chose the Weibo corpus as the dataset for the experiments. The results show that the proposed model achieves 97.10% accuracy, which is better than Skip-Gram by 8.53%.

Summary This study aims to recommend metadata for building a high ranking in Search Engine Result Page (SERP) by considering Search Engine Optimizations (SEO). For online marketing, it is important to place their websites on the top rank in a result of search engines. However, on‐page techniques of traditional SEO do not have logical foundation to select metadata. Metadata is an important element to prioritize of websites when search engine indexing for user queries. Thereby, for online marketing, this study proposes a method for recommending metadata, which consists of two steps: i) combining keywords and metadata from high‐ranked websites, and ii) evaluating the importance of terms based on semantic relevance. First, terms are selected with influential keywords and metadata by using their frequency and weight. Second, prioritize the terms according to semantic relevance based on a competitive learning model. We evaluated the validity of the proposed method by using three queries in Google. Experimental results demonstrate that it increases traffic of a website, by using terms, which are high‐ranked websites and semantic relevance.

Summary Transfer markets in football have attracted the interest of researchers in economy and management. In this paper, we propose a high level analysis approach for classifying player valuation based on their performance during recent seasons. In particular, several data analysis techniques such as regression analysis, feature selection, and cluster analysis are presented for classifying players in term of performances and transfer fee. Specifically, by collecting and analyzing data from Wholescored, the largest detailed football statistics website, we have defined players into four groups, which include (1) Low performance and low transfer fee (LPLF), (2) Low performance and high transfer fee (LPHF), (3) high performance and high transfer fee (HPHF), and (4) high performance and low transfer fee (HPLF). The results in the implementation section show that, with the differences positions, there are different required skills that affect to the performance of players. We expect that this study can contribute to the management of Football Teams in terms of integrating these analyses into their management strategy.

This study aims to represent stories in narrative works (i.e., creative works that contain stories) with a fixed-length vector. We apply subgraph-based graph embedding models to dynamic social networks of characters that appeared in stories (character networks). We suppose that interactions between characters reflect the content of stories. We discretize the interactions by discovering the subgraphs and learn representations of stories by predicting occurrences of the subgraphs in corresponding character networks. We find subgraphs rooted in each character on each scene in multiple scales, using the WL (Weisfeiler-Lehman) relabeling process. To predict occurrences of subgraphs, we apply two approaches: (i) considering changes in subgraphs according to scenes and (ii) focusing on subgraphs on the last scene. We evaluated the proposed models by measuring the similarity between real movies with vector representations that were generated by the models.

Recently, the advance of the Internet of Things (IoT) and wireless communication technology, specifically Vehicles-to-Everything (V2X), makes a huge contribution to road transportation. The fully connected and autonomous system of road transportation can be basically made in practice by integrating V2X with a current autonomous vehicle. In this paper, we focus on dynamic traffic routing for IoT-based connected vehicles. First, we define the problem of identifying the best paths for all vehicles with different sources and different destinations, or multi-source multi-destination (MSMD) traffic flows. Then, Ant Colony Optimization (ACO)-based approach with coloring ants concept is proposed to solve the problem in a decentralized and self decision-making manner. The simulation is carried out on the NetLogo platform with a multi-intersection scenario. The simulation results show that the ACO-based routing approach outperforms the non-ACO-based approach in terms of average traveling time and the number of vehicles passing metrics.

Summary In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi‐supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi‐supervised learning on EEG data analytics and discussing the importance of semi‐supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.

Abstract In this study, we propose a computational intelligence model for the Internet of Things applications by applying the concept of swarm intelligence (SI) into connected devices. Particularly, decentralized management of smart home energy management system (HEMS) is taken into account in which connected appliances, by sharing information with each other, make the individual decisions for optimizing electricity prices of smart HEMS. Specifically, the study includes two main issues: (a) We propose a framework for decentralized management in smart HEMS; and (b) artificial bee colony (ABC) algorithm, a typical algorithm of SI techniques, has been applied for connected appliances in terms of communication and collaboration with each other to optimize the performance of the energy management system. Moreover, regarding the implementation, we develop and simulate a connected environment of smart home systems to evaluate the proposed approach. The simulation indicates the promising results in terms of optimizing the load balancing problem comparing with the conventional approach of the decentralized management system in smart home applications.

Everyone, knowingly or not, is in a position to generate data at all times in our lives. Mainly in the professional life, where the production of data is addressed and aimed at achieving a series of objectives, and in social and personal life where the production of data, direct and indirect, is equally important although not always clearly identifiable. Always and in any case, the representation of our actions in digital contents or the analysis of our behaviour as consumers, just to give two examples, are a common denominator of our relationship with the digital framework which has a direct impact at the level of relationship with companies or with public administrations. In the business, the most appropriate example today is that of Industry 4.0 which with the virtualization of the processes and products themselves is allowing companies to explore novel paradigms of innovation that were once unthinkable.

In this paper, we proposed an approach to clustering based on bio-inspired behaviour and distributed energy efficient model. The motivation to propose this clustering approach is due to the challenge of performance in terms of finding an efficient way to send data packets to base stations and to maintain the lifetime performance of wireless sensor networks. The bioinspired approach adopted the behaviour of a bird called Kestrel. This behaviour is expressed using mathematical formulation and then translated into an algorithm. The bio-inspired algorithm is combined with the distributed energy efficient model for clustering to ensure efficient energy optimization. The proposed clustering approach, referred to as DEEC-KSA, is evaluated through simulation and compared with benchmarked clustering algorithms. The result of simulation showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime and network throughput. Additionally, the proposed DEEC-KSA has the optimal time (in seconds) to send packets to base station successfully.

Recently, explainable recommender systems to improve their persuasiveness have attracted attentions. In this regard, some approaches extract information from posts or comments on items and apply them to simple and effective template. These information (e.g., topics and interests), however, are indirectly reflected to the existing recommendation algorithms or models therefore do not directly improve the recommendation accuracy. Moreover, extra resources in deriving information are required. Thereby, we propose a collaborative filtering approach using a tensor which is modeled considering 5Ws aspects and generate explanations by combining factorization results with templates. Quality and explanation of recommendations were evaluated on quantitative/qualitative analyses.

With the rapid development of the internet of things (IoT), connected vehicles are set to become a huge industry over the next few years. In this study, we take an investigation of the distributed intelligent traffic system by pushing intelligence into connected vehicles in terms of dynamic decision making for traversing a certain area (e.g., roundabout and intersection). In particular, we propose a model for the next generation of intelligent transportation system, which focuses on dynamic decision making of connected vehicles based on Ant Colony Optimization, a typical Swarm Intelligence (SI)-based algorithm. Specifically, we first present a communication framework among connected vehicles for sharing information of traffic flow. Then, by applying the concept of SI, connected vehicles are regarded as artificial ants which are able to self-calculate to make an adaptive decision following the dynamics of traffic flow. Furthermore, for evaluating the effectiveness of the proposed approach, we have constructed a framework to model and simulate the traffic system in IoT environment. Simulations with different scenarios of transportation systems indicate promising results comparing with previous works.

A large amount of time-series data has been frequently used to extract the useful patterns and trends and to visualize them for better understanding. This work is focusing on visualizing personal lifelogging data for tracking back to personal histories. Thereby, we present several similarity measures between multidimensional data at two different time points. For human evaluation, the method has been applied to MyMovieHistory (which a social recommendation system by storing personal movie logs) and tested with many users. Experimental results shown that the proposed visualization method and interfaces can help to understand user history.

Summary Recently, the growth of Internet of Things (IoT) and its remarkable impacts on human well‐being life style are deniable. On the connectivity side, IoT is highly related to Wireless Sensor Network (WSN) concept. The key elements include the data, which is machine‐produced, specifically by sensors, and the data communication through connectivity technologies. On the security side, primary user emulation attack (PUE) is one of the well‐defined attacks in cognitive radio (CR)–based WSN. Here, we investigate a smart primary user emulation attacker that has the most destructive effect on the spectrum sensing unit of cognitive radio users. To deal with this attack, a soft cooperative spectrum sensing using an energy detector is proposed. In the proposed method, the values of sensing information of each secondary user are sent to a fusion center. Once the values are received, they will be combined with some appropriate coefficients in order to minimize spectrum sensing probability of error for a given probability of false alarm. The coefficients are the variables of a constrained optimization problem. Based on simulation results, our method has a lower error probability in spectrum sensing in comparison to hard combination schemes (eg, OR rule) and soft combination schemes (eg, CSINR method).

Summary The rapid growth of distributed computing systems that heavily communicate and interact with each other has raised the importance of confrontation against cyber intruders, attackers, and subversives. With respect to the emergence of cloud computing and its deployment all over the world, and because of its distributed and decentralized nature, a special security requirement is needed to protect this paradigm. Intrusion detection systems could differentiate usual and unusual behaviors by means of supervising, verifying, and controlling the configurations, log files, network traffic, user activities, and even the actions of different processes by which they could add new security dimensions to the cloud computing systems. The position of the intrusion detection mechanisms in cloud computing systems as well as the applied algorithms in those mechanisms are the 2 main factors in which many researches have focused on. The goal of those researches is to uncover intrusions as much as possible and to increase the rate and accuracy of detections while reducing the false warnings. Those solutions, however, mainly have high computational loads, low accuracy, and high implementation costs. In this paper, we present a comprehensive and accurate solution to detect and prevent intrusions in cloud computing systems by using a hybrid method, called HIDCC. The implementation results of the proposed method show that the intrusion coverage, intrusion detection accuracy, reliability, and availability in cloud computing systems are considerably increased, and false warnings are significantly reduced.

This special issue collates a selection of representative research articles that were primarily presented at the 2nd International Workshop on Data Mining on Internet of Things (IoT) Systems. This annual workshop brings together researchers and practitioners from both academia and industry who are working on data mining approaches on the IoT with applications in the Smart City framework and in the Cultural Heritage research domain in order to promote an exchange of ideas, discuss future collaborations, and develop new research directions. The Internet of Things envisages a plethora of heterogeneous objects interacting with the physical environments. It can be foreseen that IoT applications will raise the scale of data to an unprecedented level. Collecting, analyzing, and correlating data from different resources is a key role to drive smart interactions between actors of IoT environments. In this scenario, the Internet of Data (IoD) represents a concept of network composed by data entities coming from the Interne of Things (IoT). The IoD can be considered an extension of the IoT into the digital world, since the amount of data being collected is staggering. The opportunities created by IoD have the potential to be infinite. The IoD presents an ambitious purpose, ie, organizing the data to be interconnected as a network in order to infer useful information for data analysis and creates useful, customized, and location-based services. The scope of this special issue is broad and is representative of the multidisciplinary nature of the Internet of Data research field. Zheng et al1 propose a term representation framework for four different features, ie, temporal feature, geographical feature, term co-occurrence, and hashtag. The framework deals with a series of attribution definition to detect topics on a Twitter dataset and confirm the effectiveness of LTDMF. The authors have also deeply analyze the importance of different features in topic detection. Kumar et al2 present an efficient and secure time-limited hierarchical key assignment scheme key management suitable for data outsourcing scenario. They compare the proposed scheme with other recent similar schemes with respect to the cost of static and dynamic operations. The obtained results show that with similar unit private storage cost; the proposed scheme manages to reduce the key generation cost at the data owner and key derivation cost by each user. Wi and Tsai3 propose a system to analyze at-home behavior and physiological indices of the elderly people. The design concepts for software and hardware equipment emphasize the following features: (1) low-cost sensor devices, (2) user-friendly interface for the wearable device, (3) easy installation for the equipment, and (4) low-power consumption for the wearable device. Experiment results show that the system simulation proves the algorithm to be feasible. Malik et al4 discuss a data transformation methodology for geo-related semantic annotation and the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data. Research on currently available tools and methodologies along with their frameworks can help for bringing state-of-the-art mechanisms for data fusion and transformation. Xue et al5 present a new type of application based on the structure of the social network graph, ie, top-k followee recommendation, called FRFB. FRFB gives the ranking score by exploring the topological-effect of the users' following behaviors. Specifically, we recognize the inherent characteristics and the dynamic propagation of the following behaviors from a topological perspective. The effectiveness of the proposed algorithm is verified by extensive experiments, which show that FRFB has remarkable advantage compared with some well-known state-of-the-art work w.r.t. the topology-based followee recommendation methods. Park et al6 propose a method for query expansion with contextual information called “Query Contextualization.” This method can remove the ambiguity of the queries by sensing and rebuilding the users' contextual information. As a result of experiments with actual SNS data, the authors confirmed that the concept changes based on spatio-temporal information. Giannino et al7 present and discuss an IoT system based on a DSS integrating a predictive mathematical model, specifically designed to infer information from data collected directly from biotechnological cultivations. The modular IoT-based system could be efficiently used to support the manager in his everyday decisions regarding the optimal balance of light and temperature needed to maintain algae production at the highest possible level. Emami et al8 propose an interesting soft cooperative spectrum sensing by means of an energy detector. The core of the proposed approach include the produced data and the data communication through connectivity technologies. The experimental tests, in terms of error probability, have shown promising results in comparison to hard combination schemes and soft combination schemes. Cuomo et al9 discuss models and approaches used to analyze the social network realm. In particular the authors focus on data preparation and privacy concerns. The main objective is to find a set of individual to be targeted with the aim to drive social contagion and generate a diffusion cascade. The proposed analysis aims at modeling the diffusion processes governing social interactions within the networks. We thank all the international reviewers for their professional services. We deeply thank Professor Geoffrey C. Fox, the Editor-in-Chief, for providing the opportunity to publish this special issue. With his continuous support, encouragement, and guidance throughout this publishing project, this special issue has been very successful.

Small cells are low-powered cellular radio access nodes which make best use of available spectrum by reusing the same frequencies many times within a geographical area. However, the deployment of small cells (e.g., femtocells) may introduce extra interferences such as cross-tier(macrocell-femtocell) and co-tier (femtocell-femtocell) interferences. In this regard, an effective interference management mechanism is required for improving the performance of the network. In this study, a distributed resource allocation is introduced to deal with the interference problem in two-tier network. Specifically, we first apply FFR scheme to mitigate interference for cross-tier problem. To reduce co-tier interference, a cooperative game model is proposed where each femtocells are regard as players of the game. Simulation results reveal that the proposed approach significantly improves capacity of the networks.

A large amount of time-series data has been frequently used to extract the useful patterns and trends and to visualize them for better understanding. This work is focusing on visualizing personal lifelogging data for tracking back to personal histories. Thereby, we present several similarity measures between multi-dimensional data at two different time points. For human evaluation, the method has been applied to MyMovieHistory (which a social recommendation system by storing personal movie logs) and tested with a number of users.

Recently, most of context-aware services are trying to exploit the emotional contexts of the target users. The aim of this conceptual paper is to discuss affective lifelogging framework which can recognize the emotions by integrating multimodal information from multiple sources. Moreover, we will mention the open problems on affective lifelogging.

Our trust always changes due to various conditions. We call it dynamics of trust. Researching about the change of trust is a necessary task because it could be applied into diverse applications such as trust-based recommendation systems. In this paper, our target is to i) propose a general definition of trust, ii) define temporal and spatial patterns for representing how trust changes. They can be used as the output of trust-based recommendation systems, and iii) proving the effectiveness of our proposition on a case study between a Twitter user and a restaurant. Moreover, this research could be applied to various areas (e.g., e-leaning environment and search system).

Existing group recommender systems generate a consensus function to aggregate individual preference into group preference. However, the systems encounter difficulty in gathering rating-scores and validating their reliability, since the aggregation strategy requires user rating-scores. To solve these problems, we propose Group Recommendation based on Social Affinity and Trustworthiness (GRSAT) based on social affinity and trustworthiness, which is obtained from the user’s watching-history and content features, without rating-score. Our experiment proves that GRSAT has outstanding performance for group recommendation compared with the other consensus functions, in terms of the number of the movies and users, on both biased and unbiased groups.

This special issue collates a selection of representative research articles that were primarily presented at the 1st International Workshop on Data Mining on Internet of Things (IoT) Systems. This annual workshop brings together researchers and practitioners from both academia and industry who are working on data mining approaches on the IoT with applications in the Smart City framework and in the Cultural Heritage research domain, in order to promote an exchange of ideas, discuss future collaborations, and develop new research directions. The IoT envisages a plethora of heterogeneous objects, interacting with the physical environments and producing a huge amount of data. It can be foreseen that IoT applications and services will raise the scale of data to an unprecedented level. Collecting, analysing, and correlating data from different resources is a key role to drive smart interactions between actors of an IoT environment. The success of an IoT system depends on the efficient integration of its devices, sensors, and data management techniques. The scope of this special issue is broad and is representative of the multidisciplinary nature of the IoT framework. Khac-Hoai Nam Bui et al1 propose an approach for improving traffic flow at intersections, which based on the new era technologies of IoT. The approach collects streaming data from connected vehicles, which are seen as smart objects in IoV paradigm, whenever they arrive to the intersection. HyeonCheol Zin et al2 propose a simple and straightforward, exclusive channel–allocation algorithm in IoT networks. In particular, the authors describe 5 rules to support the proposed algorithm. Varun Ramesh et al3 propose a solution for the max-flow min-cut problem on large random lognormal graphs and real-world IoT datasets using the distributed Edmonds-Karp algorithm. To assess the feasibility of the proposed extension, authors tested the model with large road network datasets having more than 2.7 million edges. Jae Kwoin Kim et al4 describe a rare class prediction model for minority class prediction. The proposed method applies data preprocessing for the imbalanced classes and includes data cleaning, feature scaling, feature selection, and oversampling. Analysing data can be considered a crucial task within an IoT framework. Salvatore Cuomo et al5 propose a computational scheme in which the clustering methodology is used to classify information that are adopted as observations of an evolutionary method. The aim is to track and forecast the users' dynamics and behaviours starting from real data. Gribaudo et al6 propose a technique for modeling the performances of the IoT-based monitoring systems that support the planning of incident management in a protected site by exploiting multiple, sparse, heterogeneous, partially controlled sensors to monitor the behaviour of the crowd. This approach allows both for the modeling the possible scenarios and the design of the main parameters of the needed computing infrastructure. Francesco Piccialli et al7 present an innovative system relying on location-based IoT and multimedia services. The assessment of the proposed system has been performed investigating the user satisfaction dimension and the analysis of the users' behaviour using the system. Silvia Rossi et al8 starting from 2 state-of-the-art approaches, propose 2 different variants and compare them with respect to a baseline approach with the use of a dataset in the movie domain. Results show that the elicitation processes permit to obtain preference profiles in a time substantially less than the baseline method, while the differences in terms of prediction accuracy are minimal. We thank all the international reviewers for their professional services. We deeply thank Professor Geoffrey C. Fox, the Editor-in-Chief, for providing the opportunity to publish this special issue. With his continuous support, encouragement, and guidance throughout this publishing project, this special issue has been very successful.

Summary The max‐flow min‐cut problem is one of the most explored and studied problems in the area of combinatorial algorithms and optimization. In this paper, we solve the max‐flow min‐cut problem on large random lognormal graphs and real‐world datasets using the distributed Edmonds‐Karp algorithm. The algorithm is deployed on a stand‐alone cluster using Spark. In our experiments, we compare the runtime between a single‐machine implementation and cluster implementation. We analyze the impact of communication cost on runtime for large lognormal graphs. Further, we record the runtime of the algorithm for various real‐world datasets having similar average outdegree and number of edges but different diameters. The observations indicate that for such a set of similar graphs, runtime increases slowly with an increase in diameter. Additionally, we apply this model on large urban road networks to evaluate the minimum number of sensors required for surveillance of the entire network. To validate the feasibility of this extension, we tested the model with large road network datasets having more than 2.7 million edges. Thus, we believe that our model can enhance the safety of today's dynamic large urban road networks.

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today’s metropolitan life cannot be overemphasized. The vehicular ad hoc network (VANET), as an integral component of intelligent transportation systems (ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems (TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses: (1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and (2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service (QoS). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is used. The V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network QoS criteria.

Social network information has recently been used for the improvement of the performances of recommender systems with regard to both individual users and groups. During the selection of the items for a group, the role of the corresponding relationships (e.g., position, dependency, and the strength of the social ties) is often more important than the individual preferences; however, the existing works do not sufficiently consider this important factor for group recommendations. We therefore propose a novel recommendation method that is based on a social affinity between the common histories of users. The proposed method consists of an intermovie similarity calculation that is based on weighted features for the generation of an initial social-affinity graph, and the subsequent computation of a user’s affinity to a group that is based on the graph. To apply the method for a service, we developed a “MyMovieHistory” application for the Facebook social media platform, and the synthetic dataset results of the experiment show that our proposed method can discover social affinities in an efficient manner.

Summary Nowadays, it has become common for users to geotag resources on many online social networking services. However, a large amount of data exists on social network services without annotations of their geographical location. Thus, it would be useful to tag these resources with geotags. This paper proposes a method to predict the location of unlabeled resources on social networking services. We use the Naive Bayes and support vector machine methods to classify the resources that are collected by using the term frequency of the tags in each class. In addition, we improve the calculation for these methods by using the values of the term frequency, and we invert the class frequency to optimize the input data. These results can be applied to tag unlabeled resources on social networking services. Copyright © 2015 John Wiley & Sons, Ltd.

With various emerging Social Networking Services (SNS), it is possible for users to join multiple SNS for social relationships with other users and to collect a large amount of information (e.g., statuses on Facebook and tweets on Twitter). However, these users have been facing difficulties in managing all the data collected from the multiple SNS. It is important to match social identities from the multiple SNS. In this study, we propose a privacy-aware framework for a social identity matching (SIM) method across these multiple SNS. It means that the proposed approach can protect user privacy, because only the public information (e.g., username and the social relationships of the users) is employed to find the best matches between social identities. As a result, we have shown by evaluation that the F-measure of the proposed SIM method is about 60%.

Recommendation systems are based on a fast and effective personalized mechanism to provide items relevant to users. In this article, an expert-based approach for recommendation is proposed. We extend the spamming-resistant expertise analysis and ranking (SPEAR) algorithm to determine a set of experts from a set of attributes and values, calling the modification the -SPEAR algorithm. This system can recommend a set of items to users using expert opinions. In this approach, we use ontology to build profiles of users. The experimental results are implemented in the movie domain as a case study. Our data set was collected from IMDB and MovieLens data sets.

Recommendation systems have extracted items that users may be interested in. However, most of the recommendation applications have restricted on recommending only items in a specific domain (e.g., movies, books, and musics). In this paper, we propose a novel approach to enable the existing recommendation systems to extract addition items in various other domains. Thereby, this work is focusing on integrating all available Linked Open Data (LOD) for selecting more relevant attributes which can improve the performance of recommendation processes.

Since the number of enterprises is getting larger, business collaborations become more complicated. Particularly, in a dynamic environment (e.g., supply chains), it has been difficult for the traditional enterprises to find and select relevant partners by using their rigid business relationship. In this paper, we propose a semantic service framework to conduct a supply chain formation, and more importantly, given a certain event, to choose the best business paths between two arbitrary enterprises. The participant enterprises are required to provide their own ontologies so as to to obtain semantic matches between the services and justify their semantic interoperability. As a result, in terms of two indicators (i.e., precision and agility), we have shown that the proposed framework outperforms traditional enterprise collaboration schemes.

Many organizations have been employing Knowledge Management Systems (KMS) to improve task performance. In this paper, we propose a novel KMS by using semantic wiki framework based on a centralized Global Wiki Ontology (GWO). The main aim of this system is (i) to collect as many organizational resources as possible, and (ii) to maintain semantic consistency of the system. When enriching the KMS in a particular domain, not only linguistic resources but also conceptual structures can be efficiently captured from multiple users, and more importantly, the resources can be automatically integrated with the GWO of the KMS in real time. Once users add new organization resources, the proposed KMS can formalize and contextualize them into a set of triplets by referring to a predefined pattern-triplet mapping table and the GWO. Especially, since the ontology mapper is interleaved, the KMS can determine whether the new resources are semantically conflicted with the GWO. To evaluate the proposed methodology, we have implemented the semantic wiki-based KMS. As a case study, two user groups were invited to collect the organization resources. We found that the group with the proposed KMS has shown better performance than the other group with traditional wiki-based KMS.

Semantic social networks are social networks using ontologies for characterising resources shared within the network. It has been postulated that, in such networks, it is possible to discover social affinities between network members through measuring the similarity between the ontologies or part of ontologies they use. Using similar ontologies should reflect the cognitive disposition of the subjects. The main concern of this paper is the methodological aspect of experimenting in order to validate or invalidate such an hypothesis. Indeed, given the current lack of broad semantic social networks, it is difficult to rely on available data and experiments have to be designed from scratch. For that purpose, we first consider experimental settings that could be used and raise practical and methodological issues faced with analysing their results. We then describe a full experiments carried out according to some identified modalities and report the obtained results. The results obtained seem to invalidate the proposed hypothesis. We discuss why this may be so.

Many existing studies have investigated to discover a variety of co-occurrence patterns between entities (e.g. users, tags and resources) from a folksonomy system. The common purposes among them are (i) to understand collective behaviors between online users and (ii) to provide online services (e.g. tag recommendation and information searching) to users. However, most of the existing studies assume that all tags in the folksonomy should be written in an identical language. In this paper, we focus on analyzing a multilingual folksonomy generated by various lingual practices of online users, and discovering meaningful relationships between multilingual tags (e.g. between ‘Seoul’ in English and ‘Corée’ in French) co-occurred in the folksonomy. Thereby, we propose novel methods for (i) identifying lingual practices from user tagging patterns to build a community of lingual practice and (ii) exploiting the tag matchings to extend simple term-based queries. Thus, additional resources tagged by other languages can be retrieved. To evaluate the proposed multilingual tag matching method, we have collected real tagging datasets from several well-known social tagging websites (e.g. Del.icio.us), and applied to translating queries to other languages without any external dictionaries.

Efficient development of knowledge and semantic systems is regarded as an important challenge in many research communities. The aim of this special issue is to bring together researchers and practitioners in areas of knowledge and intelligence, semantics, agents and grid computing to share their visions, research achievements and solutions, to resolve the challenge issues and to establish worldwide cooperative research and development.

Given a certain question, named entity recognition (NER) methods can be an efficient strategy to extract relevant answers. The goal of this work is to extend NER methods for analyzing a set of micro texts, which are short text on online social media. To do so, we propose two contextual closure properties to discover contextual clusters of micro texts, which can be expected to improve the performance of NER tasks. Experimental results demonstrate the feasibility of the proposed method for extracting relevant information in online social network applications.

In recommendation systems, rating is an important user activity reflecting their opinions. Once the users return their rates about the items suggested from the systems, the user rates can be used to adjust recommendation process. However, users can make some mistakes (e.g., nature noises) during rating the items. As the recommendation systems receive more incorrect rates, the performance of such systems might be decreased. To solve the problem, in this paper, we focus on an interactive recommendation system which can help users to correct their own rates.

This paper presents a cloud computing architecture for discrete event system modeling and simulation. Simulators with models used in the cloud computing are considered as web services which are accessed via web browser. This environment supports the simulation of homogeneous or heterogeneous models without much knowledge of discrete event modeling and simulation.

It is important to make spam mail filters more intelligent.This paper proposes a collective intelligence-based approach to collaboratively build an unified knowledge by a cooperative multi-agent system (MAS), which is composed of a facilitating agent and a number of personal agents. The aim of this work is to support each personal agent to automatically filter spam mails from its mail box as referring to the centralized knowledge. The whole process is composed of two steps; i) personal agents can learn userpsilas actions by automatic feature extraction from the spams, and then, ii) they can communicate with the other agents to exchange the features. Due to domain-specific properties of the spam mail filtering, we have tried to formalize the features extracted from e-mails by an agent, so that it can be highly understandable and efficiently sharable with other agents. In particular, we have defined two types of features from the spam mails; i) field features, and ii) concept features based on keyphrases. Moreover, facilitator organizes hierarchical cluster structure to manage knowledge from these agents. Finally, we show the filtering performance of collaborative learning by comparing with personal agent.

Social communication technologies (SCT) might be alternative sources of knowledge diffusion and enable employee to achieve their goals. We have investigated that the effects of SCT in the relationship with tasks and social aspects toward task performance. This study can contribute the understanding of contemporary social media usage and its performance in organizations. We collected survey data of 280 employees in companies and analyze it with multivariate regression analysis. In sum, our findings are as followings: characteristics of tasks (analyzability, urgency, complex) influence the use of SCT and social aspects (social influence, social affinity) moderate the relationship between the tasks and the use of SCT.

To timely support collaborations between people (agents), an ontology-based platform is proposed to find out the most relevant users, according to their contexts. We have modeled two kinds of contexts (i.e., personal and group contexts) with semantic information derived from ontologies, and, more importantly, formulated measurement criteria to compare them. Consequently, groups can be dynamically organized with respect to the similarities among the personal contexts. Individual users can engage in complex collaborations related to multiple semantics. In this paper, we want to discuss the experimental results collected from a collaborative information searching system based on the proposed context synchronization.

It is difficult to be aware of the personal context for providing a mobile recommendation, because each person's activities and preferences are ambiguous and depend upon numerous unknown factors. In order to solve this problem, we have focused on a reality mining to discover social relationships (e.g., family, friends, etc.) between people in the real world. We have assumed that the personal context for any given person is interrelated with those of other people, and we have investigated how to take into account a person's neighbor's contexts, which possibly have an important influence on his or her personal context. This requires that given a dataset, we have to discover the hidden social networks which express the contextual dependencies between people. In this paper, we propose a semiautomatic approach to build meaningful social networks by repeating interactions with human experts. In this research project, we have applied the proposed system to discover the social networks among mobile subscribers. We have collected and analyzed a dataset of approximately two million people.