Stealthy False Data Injection Attack (FDIA) that intentionally modifies measurement data of smart grid meters to bypass the traditional bad data detection module is one of menacing cyber attacks in smart grid. Due to requiring no costly labeling abnormal measurement data, deep neural networks (DNNs) based unsupervised FDIA detection has attracted great attentions. However, the existing schemes have two weaknesses. First, most schemes didn’t take into account the inherent spatial relationships between measurements in the grid. Second, for practical usage, the robustness and generalization of the trained FDIA detection scheme will be influenced by potential noisy measurement data. To address the issues above, based on spatial Graph Neural Network (GNN) architecture, a novel FDIA detection and localization scheme is proposed, named as Recursive Variational Graph Autoencoder (ReVGAE). Specifically, our contributions are following. The VGAE module in our proposed ReVGAE innovatively plays dual roles: data and topology reconstructor, and denoising module. The first role aims to simultaneously reconstruct both nodes’ temporal measurements and topological relationship between nodes. In the second role, the outputs of VGAE as the reconstructor (i.e., the reconstructed temporal measurements) are intentionally used as the artificially noisy samples, and recursively fed into VGAE as input to improve the model’s robustness. Then the residual between the finally reconstructed and the observed measurement data on each node is viewed as anomaly score to judge whether FDIA temporally happens on each node. Thorough experiments on a real grid system demonstrate that the proposed ReVGAE outperforms other VAE and GNN based FDIA anomaly detection schemes.

Accurately forecasting wind speed is crucial for efficiently utilizing the renewable energy, stabilizing the energy system and advancing the progress of the decarbonization of our society. However, due to its inherently temporal volatility and intermittency, accurate wind speed forecasting in a wind farm is challenging. Recently, Large Language Models (LLMs) have demonstrated notable performance in abundant natural language processing and computer vision tasks. However, the conventional LLMs fail to learn the complex spatial and temporal correlations of the wind speed data at multiple turbines in a wind farm, which makes wind speed forecasting cant fully benefit from the significant breakthroughs of LLM. To fill in this gap, we propose a novel spatial-temporal cross-attention LLM framework for wind speed forecasting, namely STCA-LLM, composed of alignment phase, and fine-tuning phase. In detail, our contributions are given as follows. First, the alignment phase aligns the general-purpose LLM with task-specific data, i.e., training the LLM with wind speed data. Second, in the fine-tuning phase, two representation learning modules, i.e., convolution network and graph neural network (GNN) are respectively used to extract temporal features of intra time series in each turbine, and the correlation of inter time-series at multiple turbines in a wind farm. Moreover, the cross-attention module is innovatively proposed to establish the connections between spatial and temporal embeddings. Then, the spatial-temporal representation modules and the aligned LLM are fine-tuned in two-stage way. Finally, thorough experiments on real wind speed dataset demonstrate that our proposed STCA-LLM outperforms state-of-the-art time series forecasting models including Transformer-based models, spatial-temporal GNN-based models, and pertained LLM-based models. Our code is available at: https://github.com/Justinzzcj/STCA-LLM

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users’ efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computational and communication efficiency, and model accuracy, compared with other four state-of-the-art methods for next-generation networking security in modern distributed learning systems.

The accurate power load forecasting is beneficial in reasonably arranging the power supply load, and greatly promoting the safety and smooth operation of smart grid. However, on one hand, high volatility exists in electricity load, especially, in which a few spike loads suddenly occurred has significantly affected the accuracy of short term load forecasting. On the other hand, most existing forecasting schemes follows one-size-fits-all paradigm, i.e., train and utilize a unique model to forecast the whole loads, irrespective of the spike or normal loads. Based on the above observation, we propose a novel short-term load forecasting scheme assisted by cooopeative Multi-task learning (MTL)-based spike occurrence prediction, LFAS, which is composed of two stages. At the first stage, a MTL-based spike occurrence prediction is explicitly proposed to forecast whether the future loads would be spike or not. Then, instead of one-size-fits-all scheme, the second stage intentionally selects the suitable data pre-processing technologies and deep neural network (DNN) prediction models for respectively forecasting spike and normal loads. The experimental results on the Europe electricity load dataset from ENTSO-E Transparency Platform demonstrate that our proposed scheme increases the accuracy of spike occurrence prediction, significantly improves both the forecasting accuracy of spike and normal electricity loads, in comparison with the existing one-size-fits-all load forecasting schemes.

If drivers can be correctly recognized from their physiological signals, personalized services such as dynamical adjustments to the seat, backrest, and headrest can be provided during driving to ensure comfort and safety. However, such services require high accuracy identification of drivers, i.e., identification accuracy more than 99%. To determine whether such high accuracy can be achieved using drivers' physiological signals, we propose identifying only a limited group of drivers for a particular vehicle. Specifically, we built two deep learning models from three common physiological signals. To achieve high accuracy identification of drivers, we adjusted the size of the drivers to be identified (driver pool) to achieve a high identification accuracy of drivers. Our results show that when the maximum driver pool sizes are 5 and 2, the identification accuracies reached 95% and 99%, respectively.

With the flourish of digital technologies and rapid development of 5G and beyond networks, Metaverse has become an increasingly hotly discussed topic, which offers users with multiple roles for diversified experience interacting with virtual services. How to capture and model users’ multi-platform or cross-space data/behaviors become essential to enrich people with more realistic and immersed experience in Metaverse-enabled smart applications over 5G and beyond networks. In this study, we propose a Personalized Federated Learning with Model-Contrastive Learning (PFL-MCL) framework, which may efficiently enhance the communication and interaction in human-centric Metaverse environments by making use of the large-scale, heterogeneous, and multi-modal Metaverse data. Differing from the conventional Federated Learning (FL) architecture, a multi-center aggregation structure to learn multiple global models based on the changes of dynamically updated local model weights, is developed in global, while a hierarchical neural network structure which includes a personalized module and a federated module to tackle both issues on data heterogeneity and model heterogeneity, is designed in local, so as to enhance the performance of PFL with unique characteristics of Metaverse data. In particular, a two-stage iterative clustering algorithm with a more precise initialization is developed to facilitate the personalized global aggregation with dynamically updated multiple aggregation centers. A personalized multi-modal fusion network is constructed to greatly reduce the computational cost and feature dimensions from the high-dimensional heterogeneous inputs for more efficient cross-modal fusion, based on a hierarchical shift-window attention mechanism and a newly designed bridge attention mechanism. A MCL scheme is then incorporated to speed up the model convergence with less communication overload between the local federated module and global model, while an embedding layer which effectively enables the delivered global model to better adapt to the local personality in each client is further integrated. Compared with five baseline methods, experiment and evaluation results based on two different real-world datasets demonstrate the excellent performance of our proposed PFL-MCL model in a fine-grain personalized training strategy, toward more efficient communication and networking among human-centric Metaverse enabled smart applications.

With the explosion of information, personalized recommender plays a vital role in almost all economic platforms. Usually, the recommender exploits user–item (UI) interactive data to learn users’ latent interests, and then correspondingly conducts recommendations. To address the problem of sparse interactions, graph neural networks (GNNs) have been used to efficiently learn the latent representations of users and items, through structurally modeling the inter-relationships among users, items, and their attributes as graphs. However, most of the existing GNN-based methods ignore the issue of the irrelevant attributes, which means that some attributes of an item are irrelevant to a specific user’s preference. Incorporating them into a recommendation scheme may introduce noise and decrease recommendation accuracy. Thus, to address the issue above, this article proposes a novel personalized recommender based on learnable attribute sampling and heterogeneous graph neural network (LASGRec) to improve the recommender’s performance. The work’s contributions are mainly threefold. First, based on the user’s interactive history with items, the heterogeneous user–item–attribute (UIA) graph is constructed, and attributes of the items are sampled with a learnable neural network to alleviate the issue of irrelevant attributes. Second, using the pruned UIA, the heterogeneous GNN model is appropriately used to learn representations of users and items. Novelly, the learnt user’s embedding first aggregates the sampled attributes of items interactive with the user, and then aggregates these items. The learnt embedding of each item incorporates two relationships: the interacted users and its sampled attributes. Finally, comprehensive experiments on multiple real-world datasets demonstrate the superiority of the proposed LASGRec over the state-of-the-art deep neural network (DNN-) and GNN-based recommendation schemes.

In recent years, the burgeoning urban population, coupled with expanding urban dimensions and various sociodemographic factors, has led to an alarming escalation in criminal activities within urban centers. This escalation has further exacerbated the existing strain on police resources, rendering them increasingly inadequate for effective law enforcement. Concurrently, police patrol operations have emerged as a pivotal instrument in the ongoing battle against violent criminal activities. The judicious planning of patrol routes has the potential to markedly enhance the efficiency of police patrolling endeavors, thereby bolstering the overall security infrastructure within the jurisdiction while simultaneously conserving invaluable police resources. The formulation of an efficient patrol strategy within the intricate and ever-evolving landscape of urban regions replete with crime hotspots represents an intellectually taxing challenge. To address this exigent problem, this paper proffers a novel real-time patrol route planning algorithm tailored to dynamic environments, employing the principles of deep reinforcement learning, specifically the Integrated Double Q-Network (IDQN) method. Subsequently, the efficacy of the proposed method is empirically substantiated through experimentation, attesting to its practical viability and utility in the field of computer science and urban security management.

This paper introduces a hand gesture classification implementation that combines FMCW radar and deep learning. Unlike previous work, we implement a Range-Aware methodology to automatically locate the hand range bin enabling more accurate classification. The inspiration behind our proposal is the observations made about the impact of the subject chest in the Range-Time (RT) information and the signature of the hand movement in the Range-Doppler (RD) space. Subsequently, we propose three distinct methods for hand range bin localization. Method I and II take advantage of the first observation by locating the chest and using it as a reference to select the hand bin. Method III, however, exploits the second observation about the Doppler signature of the hand movement to directly select the hand bin. The Doppler matrix resulting from each method is then fed to a CNN-based model for the hand gesture classification task. We perform subject-dependent and independent evaluations to classify six hand gestures and investigate the impact of several parameters including the type of input data, the use of an LSTM layer, and an increased range of up to 90 cm. The evaluation results show good performance even for subject-independent and from distance up to 90 cm achieving an average accuracy of 99.86% in subject-dependent evaluations and 94.23% in subject-independent scenarios.

Abstract As a distributed learning framework, Federated Learning (FL) allows different local learners/participants to collaboratively train a joint model without exposing their own local data, and offers a feasible solution to legally resolve data islands. However, among them, the data privacy and model security are two challenges. The former means that, if original data are used for trained FL models, various methods can be used to deduce the original data samples, thereby causing the leakage of data. The latter implies that unreliable/malicious participants may affect or destroy the joint FL model, through uploading wrong local model parameters. Therefore, this paper proposes a novel distributed FL training framework, namely LDP‐Fed+, which takes into account differential privacy protection and model security defense. Specifically, firstly, a local perturbation module is added at the local learner side, which perturbs the original data of local learners through feature extraction, binary encoding and decoding, and random response. Then, through using the perturbed data, local neural network model is trained to obtain the network parameters that meet local differential protection, to effectively deal with model inversion attacks. Secondly, a security defense module is added on the server side, which uses the auxiliary model and differential index mechanism to select an appropriate number of local disturbance parameters for aggregation to enhance model security defense and deal with membership inference attacks. The experimental results show that, compared with other federated learning models based on differential privacy, LDP‐Fed+ has stronger robustness for model security and higher accuracy for model training while ensuring strict privacy protection.

Summary The power of artificial intelligence of things (AIoT) stems from adapting machine learning (ML) and artificial intelligence (AI) models into abundant intelligent IoT fields, based on a large data stream with different formats, sizes, and timestamps generated by massive numbers of heterogeneous sensors. On the one hand, data acquisition is the fundamental basis for any AIoT systems, but data sensed by massive IoT devices may be noisy and even contain adversarial samples. On the other hand, ensuring the efficiency and robustness in data acquisition is vitally important for data‐driven ML and AI. Recently, besides perceiving ability, the literature has witnessed great development of empowering things with learning and reasoning ability through deep learning models, including recurrent neural networks (RNNs) and/or convolutional neural network (CNNs). However, the existing works have one significant weakness: fail to explicitly leverage the geospatial implications and latent connections among sensors for high‐quality data acquisition and quality control. Graphs are intrinsically suitable for representing the dependencies and inter‐relationships between AIoT data sensing devices. Due to the ability of capturing the complex interactive relationships between nodes and producing high‐level representations of the graph input, graph neural networks (GNNs) have exploded onto various ML and AI fields, to learn from graph‐structured data. Our review covers the latest progresses in GNN for the fundamental atomic task of data acquisition in AIoT. Instead of surveying the abundant GNN schemes in vertically various IoT sensing applications, this paper systematically reviews the horizontal infrastructure that all AIoT fields should have, that is, AIoT data acquisition, based on GNN and other related emerging AI factors. Our contributions include the following aspects: Provide the latest progresses in GNN for the horizontal task of data acquisition in AIoT, propose the unified GNN pipeline based on encoder–decoder paradigm, and systematically categorize and summarize the emerging technologies helpful to address the issues in AIoT data acquisition, especially the noisy and adversarial data, and point out some future directions about GNN‐based AIoT data acquisition.

Human presence detection and water-level detection are two essential functions of smart toilets, making smart toilets more intelligent and hygienic. Traditional solutions require different sensors for each of these functions. Furthermore, existing detection methods in smart toilets have some limitations, such as sensor size, the undesirable effect of environment on detection results, etc. Millimeter-wave (mmWave) radars offer better performance in terms of ranging accuracy and environmental stability. If the mmWave radar is used to achieve the above two functions, the radar system is required to radiate toward the Region of Interest (RoI). The RoI for presence detection is the front of the toilet, while RoI for water-level detection is the toilet bowl. Therefore, the radar needs to radiate signals forward and downward radiation simultaneously, which requires the radar to achieve an orthogonal bidirectional radiation. In this article, we innovatively propose a low-cost radar system with orthogonal bidirectional radiation with good antenna gain and isolation performance by using Vivaldi antennas and wideband high-efficiency electromagnetic structure (WHEMS) antennas. Additionally, appropriate antenna selection gives the antenna system the characteristics of wide bandwidth to match different toilet installation environments with higher reliability. The system can achieve presence detection and water-level detection well, and has the advantages of low cost, small size, and practical application value.

The Internet of Things (IoT) has become a focus of information infrastructure development in recent years. The smart blockchain can provide various solutions for trust, security, and privacy (TSP) challenges to protect IoT data, and smart contracts are the foundation of blockchain intelligence, and greatly enhance the ability of smart blockchain to solve TSP problems. So, the security of smart contracts must be addressed. We propose an efficient smart contract vulnerability detector to improve the safety of smart contracts. It comprises a graph extraction method and a complete vulnerability detection process. The graph extraction method consists of vulnerability pattern extraction and a graph generation process. The vulnerability detection process first uses the approximate graph matching algorithm to select representative SCGraphs from the data set to build vulnerability SCGraph libraries. Second, determine whether the contract contains vulnerabilities by calculating the similarity between the SCGraphs generated from the contracts to be detected and the SCGraphs in the vulnerability library. Experiments show that our approach achieves an inspiring high detection rate and is the fastest among existing vulnerability detection tools, which indicates that it can provide good vulnerability detection for smart contracts.

Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic, which is playing an increasingly indispensable role in modern intelligent applications. These applications are known for their real-time requirements under limited network and computing resources, thus it becomes a highly demanding task to transform and compute tremendous amount of raw data in a cloud center. The edge–cloud computing infrastructure allows a large amount of data to be processed on nearby edge nodes and then only the extracted and encrypted key features are transmitted to the data center. This offers the potential to achieve an end–edge–cloud-based big data intelligence for IoE in a typical two-stage data processing scheme, while satisfying a data security constraint. In this study, a deep-reinforcement-learning-enhanced two-stage scheduling (DRL-TSS) model is proposed to address the NP-hard problem in terms of operation complexity in end–edge–cloud Internet of Things systems, which is able to allocate computing resources within an edge-enabled infrastructure to ensure computing task to be completed with minimum cost. A presorting scheme based on Johnson’s rule is developed and applied to preprocess the two-stage tasks on multiple executors, and a DRL mechanism is developed to minimize the overall makespan based on a newly designed instant reward that takes into account the maximal utilization of each executor in edge-enabled two-stage scheduling. The performance of our method is evaluated and compared with three existing scheduling techniques, and experimental results demonstrate the ability of our proposed algorithm in achieving better learning efficiency and scheduling performance with a 1.1-approximation to the targeted optimal IoE applications.

Safety production surveillance is of great significance to industrial operation management. While augmented intelligence of things is demonstrating tremendous potential in industrial applications, the analyzed information offers lots of benefits to the higher level planning in the enterprise management systems, to further improve the operational efficiency. In this article, a video surveillance system with augmented intelligence of things is considered as a promising solution to enhance the operational efficiency of enterprises. However, the challenge is to process the surveillance video streams as soon as possible without ignoring any emergencies. This issue can be formulated as a two-stage scheduling problem, which is an NP-hard problem that can be integrated with higher level enterprise systems for operational efficiency improvement. An improved Deep Q-Network (DQN) model with a newly designed prioritized replay scheme, named Bi-Dueling DQN with Prioritized Replay, is proposed to solve this two-stage scheduling problem in a smart enterprise management system. A dense reward function based on a concrete state representation is designed to tackle the sparse reward challenge and to speed up the convergence in actual large-scale task scheduling process. A prioritized replay scheme is then developed to improve the sampling efficiency, so as to effectively reduce the training time in deep reinforcement learning for the optimal two-stage scheduling. The experiment results demonstrated that the proposed approach is able to provide an efficient scheduling policy to resolve the two-stage scheduling problem, while at the same time offering insight information to improve the performance of higher level smart enterprise management systems.

Physics theory integrated machine learning models enhance the interpretability and performance of artificial intelligence (AI) techniques to real-world industrial applications, such as the fault detection and diagnosis (FDD) of air handling units (AHU). Traditional machine learning-based automated FDD model demonstrates a high classification accuracy with sufficient training data samples, however, suffers from physical interpretation of the machine learning models. In this article, a physical model integrated Wasserstain generative adversarial network (WGAN) model is presented for AHU FDD with a scenario of insufficient training data samples. The proposed solution tackles the real-world problem of AHU FDD and enhances the model interpretability significantly. A transformer-WGAN model is designed to further improve the proposed FDD framework. Experimental results show that the proposed method outperforms existing AHU FDD methods with imbalanced real-world training data samples.

The impact of Internet of Things (IoT) has become increasingly significant in smart manufacturing, while deep generative model (DGM) is viewed as a promising learning technique to work with large amount of continuously generated industrial Big Data in facilitating modern industrial applications. However, it is still challenging to handle the imbalanced data when using conventional Generative Adversarial Network (GAN) based learning strategies. In this article, we propose a distribution bias aware collaborative GAN (DB-CGAN) model for imbalanced deep learning in industrial IoT, especially to solve limitations caused by distribution bias issue between the generated data and original data, via a more robust data augmentation. An integrated data augmentation framework is constructed by introducing a complementary classifier into the basic GAN model. Specifically, a conditional generator with random labels is designed and trained adversarially with the classifier to effectively enhance augmentation of the number of data samples in minority classes, while a weight sharing scheme is newly designed between two separated feature extractors, enabling the collaborative adversarial training among generator, discriminator, and classifier. An augmentation algorithm is then developed for intelligent anomaly detection in imbalanced learning, which can significantly improve the classification accuracy based on the correction of distribution bias using the rebalanced data. Compared with five baseline methods, experiment evaluations based on two real-world imbalanced datasets demonstrate the outstanding performance of our proposed model in tackling the distribution bias issue for multiclass classification in imbalanced learning for industrial IoT applications.

The smart grid is now increasingly dependent on smart devices to operate, which leaves space for cyber attacks. Especially, the intentionally designed false data injection attack (FDIA) can successfully bypass the traditional measurement residual-based bad data detection scheme. Considering that the smart grid data naturally contain linear and nonlinear components, inspired by parallel ensemble learning, especially by the stacking method, this article presents an effective two-level learner-based FDIA detection scheme using the Kalman filter and recurrent neural network (KFRNN). The first level includes two base learners, in which the Kalman filter is used for state prediction to fit linear data, and the recurrent neural network is used to fit the nonlinear data feature. The second-level learner uses the fully connected layer and backpropagation (BP) module to adaptively combine the results of two base learners. Then, through fitting Weibull distribution of the sum of square errors (SSEs) between the observed measurements and the predicted measurements, the dynamic threshold is obtained to judge whether FDIA occurs or not. Comprehensive simulation results show that our scheme has better performance than other neural network-based and ensemble learning-based FDIA detection schemes.

Modern Internet-of-Things (IoT) applications are heavily data driven and often require reliable data streams to achieve high-quality data mining. The concept of edge computing is introduced to reduce data latency and communication bandwidth between the cloud server and IoT edge devices. However, inefficient routing that may cause transmission failure or unnecessary data (re)transmission is still a key obstacle to obtain good and reliable data mining results. In this article, network coding combined with opportunistic routing is used to improve energy efficiency in wireless IoT infrastructure, considering the existence of link correlation. Studies have shown that packet receptions on wireless links are correlated, which is completely contrary to the assumption of link independence used in existing routing mechanisms. This assumption causes estimation errors in the calculation of expected number of transmissions for forwarders, which further affects the selection of forwarder set, and ultimately affects the performance of the protocol. We propose an intrasession network coding mechanism based on the mining of link correlation. A novel smart routing method is proposed to accurately estimate the number of transmissions required by forwarders, together with an algorithm for selecting a forwarder set with more optimal number of transmissions. Simulation results demonstrate that the proposed mechanism can achieve fewer transmissions and offer more energy-efficient communications for wireless edge IoT applications.

Various cyber attacks often occur in logistics network of the Industry 4.0, which poses a threat to Internet security. Intrusion detection can intelligently detect anomalous activities and secure the Internet with the help of anomaly detection algorithms. Different from static data, intrusion detection data are a dynamic data form and have the following characteristics. First, it is multiaspect. Second, it contains point anomalies and group anomalies. Third, there are correlations between different attributes. Nevertheless, these properties pose a challenge on existing anomaly detection approaches. Thus, a novel anomaly detection approach MDS_AD is proposed in this article to deal with the challenges. It combines locality-sensitive hashing (LSH), isolation forest, and PCA techniques. MDS_AD has the following properties. 1) The introduced LSH can operate on multiaspect data. 2) MDS_AD can effectively catch group anomalies from the experimental results. 3) The PCA is utilized to reduce dimensionality for correlations between different attributes. 4) MDS_AD is a streaming approach, which can perform model update and process data in constant memory and time. To confirm the performance of MDS_AD, multiple experiments are designed and implemented on UNSW-NB15 dataset. Experimental results show that MDS_AD outperforms state-of-the-art baselines.

Recently, on one hand, human activity recognition (HAR) has witnessed great application on portable smart devices (e.g., smart phones and wearables, etc.) as they are widely used around the world. On the other hand, HAR methods based on deep learning have attracted much attention, for they possess excellent performance due to their strength on extracting virtual features automatically and hierarchically. However, to establish a personalized deep learning based HAR scheme based on smart devices, insufficient records from target users and heavy computation cost on training from scratch are two challenges. Considering that, in transfer learning, the knowledge learnt in the source domain could be appropriately transferred to help accomplish tasks in the target domain, this paper proposes a personalized HAR scheme through exploiting virtual feature adaptation based on transfer learning (i.e., VFAT) to achieve high recognition accuracy with low computation time. VFAT is composed of pre-training phase on sufficient labeled records in source-domain, and adaption phase on target-domain that uses the few labeled records available. Specifically, VFAT scheme pre-trains the LSTM-based feature extraction component in the pre-training phase and then introduces domain loss in the adaptation phase to minimize the similarity between target-domain virtual features and source-domain activity patterns (i.e., virtual features averaged by activity labels). The HAR scheme applied to the MotionSense dataset and results demonstrate the effectiveness of our proposed VFAT scheme. Moreover, we also investigate the impact of domain division on the performance of transfer learning based HAR.

How to use artificial intelligence technology to mine human abnormal behavior from considerable video data generated by the Internet-of-Things system has been intensively studied for a long time. Existing deep learning anomaly detection algorithms deployed in the cloud typically perform supervised learning based on constant kinds of abnormal behavior data. However, this supervised learning model with preset abnormal behavior categories ignores the diversity and unpredictability of abnormal occurrences in open scenarios. Thus, we propose an abnormal behavior detection algorithm as an edge network service by combining the advantages of cloud computing and the efficiency of edge networks. This method combines the double verification of global behavior detection and local fine-grained action cycle alignment to detect whether a behavior is abnormal. Moreover, to enable abnormal behavior detection models to predict test samples whose categories do not appear during the training stage, we propose an active label learning algorithm based on cycle clustering, which not only improves the efficiency of data transmission between the edge and the cloud but also makes model updates in the cloud more efficient. Extensive and quantitative experimental results show that our method can not only accurately detect abnormal human behavior at the edge of limited resources but also has strong robustness under the interference of test samples of unknown categories.

Social Internet of Things comes as a new paradigm of Internet of Things to solve the problems of network discovery, navigability, and service composition. It aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT scenarios, a device can offer multiple services and different devices can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service filtering and customization, this problem is known as services explosion. The selection of a suitable service that fits the requirements of the applications and devices is a challenging task. Several works have addressed service discovery, composition, and selection in IoT. However, these works did not emphasize on the fact that incorporating the users’ social features can increase the efficiency of the recommended services and help us to offer context-aware services. In this article, we present a service recommendation system that takes advantage of the social relationships between devices’ owners, where the recommendation is based on the different relationships between the service requester and service provider. Experimental results show, in the context of IoT, that incorporating the users’ social relationships in service recommendation increases the accuracy and diversity of the offered services.

Cyber-physical-social big data generated from ubiquitous devices and diverse spaces generally are multi-source, heterogeneous, and deeply intertwined. To efficiently analyze and handle the ubiquitous cyber-physical-social big data, tensor is considered as an effective tool, but the curse of dimensionality is still the main bottleneck of tensor-based big data analysis. Tensor networks can considerably alleviate or overcome it through the tensor approximate theory. Therefore, this paper focuses on developing an efficient big data processing framework based on tensor networks and providing an incremental tensor train decomposition approach for the streaming big data. Concretely, this paper first presents a hierarchical cyber-physical-social big data processing framework composed of three planes, namely, data representation and decomposition, data storage and processing, and data analysis and service, in which tensor train (TT) and quantized TT decompositions are particularly introduced to remarkably overcome the curse of dimensionality. Besides, to efficiently handle the continuous streaming big data and avoid the repeated decomposition for the history data, an incremental tensor train decomposition (ITTD) approach is proposed and the complexities are further analyzed in detail. Experimental results demonstrate that ITTD demonstrably outperforms the nonincremental TT decomposition in execution time on the precise of guaranteeing the nearly equal approximation error.

A recommendation system is an integral part of any modern online shopping or social network platform. The product recommendation system as a typical example of the legacy recommendation systems suffers from two major drawbacks: recommendation redundancy and unpredictability concerning new items (cold start). These limitations take place because the legacy recommendation systems rely only on the user's previous buying behavior to recommend new items. Incorporating the user's social features, such as personality traits and topical interest, might help alleviate the cold start and remove recommendation redundancy. Therefore, in this article, we propose Meta-Interest, a personality-aware product recommendation system based on user interest mining and metapath discovery. Meta-Interest predicts the user's interest and the items associated with these interests, even if the user's history does not contain these items or similar ones. This is done by analyzing the user's topical interests and, eventually, recommending the items associated with the user's interest. The proposed system is personality-aware from two aspects; it incorporates the user's personality traits to predict his/her topics of interest and to match the user's personality facets with the associated items. The proposed system was compared against recent recommendation methods, such as deep-learning-based recommendation system and session-based recommendation systems. Experimental results show that the proposed method can increase the precision and recall of the recommendation system, especially in cold-start settings.

With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly discussed topic in digital and intelligent industry field. The security problem existing in the signal processing on large scale of data stream is still a challenge issue in industrial internet of things, especially when dealing with the high-dimensional anomaly detection for intelligent industrial application. In this article, to mitigate the inconsistency between dimensionality reduction and feature retention in imbalanced IBD, we propose a variational long short-term memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. An encoder-decoder neural network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data. Three loss functions are defined and quantified to constrain the reconstructed hidden variable into a more explicit and meaningful form. A lightweight estimation network is then fed with the refined feature representation to identify anomalies in IBD. Experiments using a public IBD dataset named UNSW-NB15 demonstrate that the proposed VLSTM model can efficiently cope with imbalance and high-dimensional issues, and significantly improve the accuracy and reduce the false rate in anomaly detection for IBD according to F1, area under curve (AUC), and false alarm rate (FAR).

With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial cyber-physical systems (CPS). Intelligent anomaly detection for identifying cyber-physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this article, we propose a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve false alarm rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.

The crowdsourcing system is a distributed problem-solving platform, in which tasks are delivered to the crowd (i.e., crowdworkers) in the form of an open call. Usually, large-scale crowdsourcing systems contain abundant microtasks, and the overhead of a crowdworker spending on searching the appropriate task may be comparable to the cost of completing the task. Therefore, task recommendation is necessary. However, existing work ignores the dynamics in crowdsourcing system, i.e., new tasks continually arrive, which leads to the issues of task cold-start. To overcome the challenge of the new coming task recommendation, this article proposes a feature-based Bayesian task recommendation (BTR) scheme. The key idea to deal with the dynamics of the crowdsourcing system lies in that the BTR learns the latent factor of the task through the task features instead of task ID and then learns the user's preference according to their historical behaviors. Specifically, based on task features and the user's historical behavior records, BTR can not only timely provide crowdworkers with personalized task recommendations but also solve the task cold-start problem. The simulations based on the real crowdsourced data set demonstrate that BTR performs better than other typical schemes that target at recommending the newly arrived tasks to crowdworkers.

Deep neural network (DNN) learning has witnessed significant applications in various fields, especially for prediction and classification. Frequently, the data used for training are provided by crowdsourcing workers, and the training process may violate their privacy. A qualified prediction model should protect the data privacy in training and classification/prediction phases. To address this issue, we develop a differential privacy (DP)-enabled DNN learning framework, DNN-DP, that intentionally injects noise to the affine transformation of the input data features and provides DP protection for the crowdsourced sensitive training data. Specifically, we correspondingly estimate the importance of each feature related to target categories and follow the principle that less noise is injected into the more important feature to ensure the data utility of the model. Moreover, we design an adaptive coefficient for the added noise to accommodate the heterogeneous feature value ranges. Theoretical analysis proves that DNN-DP preserves ε-differentially private in the computation. Moreover, the simulation based on the US Census data set demonstrates the superiority of our method in predictive accuracy compared with other existing privacy-aware machine learning methods.

Recently, in this article, due to pervasive usability, smartphone-based human activity recognition (HAR) has witnessed significant development in smart health. Meanwhile, deep recurrent neural network (DRNN) shows a strong ability to automatically extract features from the time-series data, and therefore, DRNN-based HAR schemes have achieved more effective recognition (i.e., recognition accuracy) than those adopting the traditional machine learning. However, the efficiency in training and recognition (in terms of running time) has not fully taken into account, especially for resource-constraint smartphones. To solve the above issue, we propose the PSDRNN and tri-PSDRNN schemes that employ the explicit feature extraction before DRNN. Specifically, considering that the power spectral density (PSD) feature can capture the frequency characteristics and meanwhile retain the successive time characteristics of data gathered from smartphone accelerometer, PSD feature vectors are, respectively, extracted from linear accelerations and triaxle accelerations and explicitly used as the input to the following DRNN classification model. Thorough experiments based on a real dataset demonstrate that the PSDRNN can achieve the comparable effectiveness as the xyz-DRNN (the most accurate DRNN-based HAR scheme only using acceleration data), and the average recognition and training time were reduced by 56% and 80%, respectively. Moreover, tri-PSDRNN advantages over the xyz-DRNN in terms of recognition accuracy, and the running time is still lower than the xyz-DRNN. Besides, our proposed PSDRNN scheme achieved superiority in the recognition of complex transition activities.

Crowdsourcing has proved to be a splendid tool to aggregate the knowledge from a pool of individuals in order to perform abundant microtasks efficiently. Recently, with the explosive growth of online social network, Word of Mouth (WoM)-based crowdsourcing systems have emerged, in which besides conducting the tasks by themselves, participants simultaneously recruit other individuals through exploiting their social networks to help solve crowdsourced tasks. This crowdsourcing paradigm can greatly facilitate to grow the pool of crowdworkers. However, there exist two conflicting challenges in designing an effective WoM-based incentive mechanism: 1) sybil attack and 2) heterogeneous effect of participants. That is, intuitively, incentivizing (usually compensating for) common-ability individuals will inevitably stimulate the behavior of sybil attack (i.e., some individuals create multiple sybils, and split the total efforts into those sybils to expect more compensation). This paper proposes a novel biased contest-based crowdsourcing incentive mechanism through exploiting social networks (BciNet), aiming to balance those two conflicting objectives. BciNet is composed of two phases. First, based on spreading activation model, an enhanced geometric virtual point dissemination mechanism is able to provide sybil-proof property and accommodate the realistic social network structure. Second, based on participants' virtual points, a biased contest gives more reward to less able participants. Through carefully calibrating the bias factor, simulation results based on the real dataset show that BciNet can greatly improve the amount of participants' effort levels, and actually be robust against the sybil attack. In brief, for a practical incentive mechanism, the methodology to address conflicting goals is to put rational individuals into dilemma: to be sybil or not to be, it is the problem, i.e., the potential gain from the sybils in the second phase may be offset by the loss in the first phase.

With the alarming increase in breast cancer cases, researchers have considered it a challenging research problem to propose dependable solutions. It is quite essential for early detection, prevention, and control against breast cancer. Existing schemes still does not utilize recent information technology support, and hence preventive measures and factors are also not appropriate. This paper adopts cloud computing to present association rule-based breast cancer prevention and control system. We have categorized our work into two phases. In phase 1 titled prevention and control, we propose item association rule (IAR) algorithm and N-IAR algorithm for n-item associations. It can be used to discover risk factors for breast cancer. Our algorithm discovers more risk factors than the traditional logistics method. Some factors which can be modified are used for breast cancer prevention and control. In addition, existing risk assessment models are not applicable to Chinese women as well. In phase 2, we manage this by introducing a new model based on machine learning. It utilizes real data from Chinese women and more risk factors for breast cancer. Moreover, we have identified and evaluated a number of new common risk factors. Results prove that our system achieves higher assessment values as compared to preliminaries.

With the rapid development of Internet of Things (IOT) technologies, modern power systems have become complex cyber-physical systems. A large number of smart devices have promoted efficient generation, transmission and distribution in the smart grid. State estimation (SE) is one of fundamental components in smart grid that evaluates the operation state of a grid by using a set of sensor measurements and grid topologies. A major issue is the authenticity of the measurements collected by the sensors. Specifically, the false data injection attack (FDIA) aims to temper the information that reflect the grid operation state. In this paper, we propose an efficient prediction-based FDIA detection and location scheme, PDL, in which the state vector of smart grid can be represented as multivariate time series, and can be predicted by vector autoregressive processes (VAR) through intentionally exploiting the temporal and spatial correlations of states. Different from most previous works which assumed the state transfer matrix constant and diagonal, a time-varying and non-diagonal matrix is adopted in this scheme. Then, the consistency between the predicted measurements and the observed measurements is utilized to detect and locate abnormal data. Besides, the detected abnormal data can be replaced with the predicted data, which simplifies the calibration process. Extensive simulation results verify the performance of the proposed scheme.

Summary Crowdsourcing is the generalized act of outsourcing tasks, traditionally performed by an employee or contractor, to a large group of Internet population through an open call. With the great development of smartphones with rich built‐in sensors and multiple ratio interfaces, mixing smartphone‐based mobile technologies and crowdsourcing offers significant flexibilities and leads to a new paradigm called mobile crowdsourcing (MCS), which can be fully explored for real‐time and location‐sensitive crowdsourced tasks. In this paper, we present a taxonomy for the MCS applications, which are explicitly divided as using human as sensors, and exploiting the wisdom of crowd (i.e., human intelligence). Moreover, two paradigms for mobilizing users in MCS are outlined: direct mode and word of mouth mode. A comprehensive MCS framework and typical workflow of MCS applications are proposed, which consist of nine functional modules, pertaining to three stakeholders in MCS: crowdsourcer, crowdworkers, and crowdsourcing platform. Then, we elaborate the MCS challenges including task management, incentives, security and privacy, and quality control, and summarize the corresponding solutions. Especially, from the viewpoints of various stakeholders, we propose the desired properties that an ideal MCS system should satisfy. The primary goal of this paper is to comprehensively classify and provide a summary on MCS framework, challenges, and possible solutions to highlight the MCS related research topics and facilitate to develop and deploy interesting MCS applications. Copyright © 2016 John Wiley & Sons, Ltd.

With the progressive development of information and communication technologies, we are now forming a new world called hyperworld that is composed by the cyber world and the physical world with various digital explosions including data, connectivity, service and intelligence. Therefore, Cyber-I has been proposed, which is a real individual's counterpart in cyberspace, and is to create a unique, digital, comprehensive description for every individual person. As similar to our human, a Cyber-I once born should be able to grow. Therefore, a Cyber-I's model must be a dynamic one, and can be built successively by utilizing an increasing amount of personal data with adaptive methods. Namely, a growable Cyber-I model is necessary to achieve the adaptation for successive approximations to its corresponding real individual (Real-I). This paper presents our research and development of an adaptable system, called Cyber-I growth modeling system (CGMS). This research is mainly to (1) schedule a Cyber-I's growth according to data and time; (2) manage the quantity of raw data that is involved in a specific growth process; (3) generate the Cyber-I model data with appropriate growth forms, and (4) keep the update records of a Cyber-I model's growth process into a log file in personal database.

Transport infrastructure generates a huge amount of city transportation data due to the significant increasing of advanced devices, such as sensing devices, mobile devices and real-time monitors. However, transportation big data cannot be fully analyzed and utilized by urban traffic data services currently. This paper proposes a novel City Traffic Data-as-a-Service (CTDaaS), which fuses data from distributed providers. Initially, we build an Internet of Traffic Data Service (IoTDS) model to identify associations and relationships among data resources. Then a CTDaaS agent is developed under Transparent Computing paradigm and service oriented architecture. It receives user requests, fuses knowledge from a variety of data sources according to different computing models, and responses differentiated Quality of Data (QoD). Finally, an application scenario, named Park and Ride (P+R), is implemented and evaluated to demonstrate how the service works using existing dynamic city traffic data.

Cooperative data delivery among mobile nodes can improve the performance of data delivery in mobile social networks. However, data routing in the presence of socially selfish (SS) nodes is challenging, where they mitigate the degree of their cooperation level based on their social features and ties to achieve their social objectives. This issue becomes more challenging when they prevent revealing their reactions about incoming messages, which leads data forwarding under uncertain behavior. In this paper, we propose a signaling game approach, namely, Sig4UDD, to study the impact of uncertain cooperation among well-behaved and SS nodes on the performance of data forwarding. In Sig4UDD, we employ Bayesian Nash equilibrium to analyze one-stage interactions among nodes. Then, perfect Bayesian equilibrium is applied to analyze their multistage interactions. In this stage, we establish a belief system to help SS nodes predict the type of their opponents and take appropriate actions to maximize their utilities. To update the beliefs of SS nodes, we devised the weighted social distance metric to measure the global social distance among nodes. Finally, we compare the performance of Sig4UDD to some benchmark cooperative and noncooperative data forwarding protocols using Reality Mining and Social Evolution data sets.

The design of cyber-physical-social systems (CPSS) is a novel and challenging research field due that it emphasizes the deep fusion of cyberspace, physical space, and social space. In this article, we extend our previously proposed system-level design framework [Zeng et al. 2015] to tailor it to the needs of social scenario of multiple users. A hierarchical Petri net-based model and social flow are presented to extend the control flow and formally describe the social interactions of multiple users, respectively. By using the extended model, the system-level optimization for CPSS can be achieved by the improved design flow. Specifically, object emplacement and user satisfaction are further extended into the social environment. Also maximal power estimation algorithm is improved, leveraging the extended intermediate representation model. Finally, we use a smart office case to demonstrate the feasibility and effectiveness of our improved design approach for multiple users.

Mobile message push has become a ubiquitous technology in various applications such as online resource sharing, traffic surveillance, mobile health care and environmental monitoring. In mobile terminals, energy efficiency optimization is one of the most important issues due to battery power limitations, resource constraints and quality-of-service (QoS) requirements. Considering the timely delivery, network load and terminal diversity, this paper proposes an adaptive message push strategy (AMPS) for energy efficiency optimization in mobile terminals. In AMPS, running parameters including energy parameter, operating system (OS) version and connection/polling cost in mobile terminal are first acquired and sent to the server together with the requisition data, and then the dispatching module will automatically choose a message pushing mode between polling-based and connection-based ones. The AMPS was tested in real environments using mobile phones with different OSs. Experiment results show that AMPS can efficiently optimize energy exploitation with dynamic tradeoff between terminal using time and QoS performance in comparison with polling-based and connection-based message push strategies.

Summary In peer‐to‐peer (P2P) networks, contributions are made by peers voluntarily for the autonomous character of peers. However, selfish peers may refuse to be cooperative when considering their limited transmission resources. Incentive mechanisms are always used to guarantee successful cooperations among peers. Although the inventive mechanisms have been widely investigated on the basis of game theory, most researches assume that peers are well mixed in the network, regardless of the influence of peers' transaction relationships. In this paper, a novel analysis framework based on spatial evolutionary game theory is proposed to verify the effectiveness of incentive mechanisms. In the framework, a transaction overlay network is used to model the transaction relationships of peers. The transactions between clients and servers are modeled as the donor‐recipient game to satisfy their asymmetric characters. Influences of the learning noise and some common behaviors of peers on incentive mechanisms are also considered. Moreover, in order to demonstrate the utility of the framework, a reciprocation‐based incentive mechanism, which considers the requestors' behaviors of providing and consuming services, is thoroughly investigated under the framework in scenarios with homogeneous and heterogeneous benefits of services. By using the framework, besides the effectiveness of incentive mechanisms, the detailed spatiotemporal evolutions of peers' strategies driven by incentive mechanisms can also be obtained. Copyright © 2014 John Wiley & Sons, Ltd.

This paper focuses on incentivizing cooperative behavior in community-based autonomous networking environments (like mobile social networks, etc.), in which through dynamically forming virtual and/or physical communities, users voluntarily participate in and contribute resources (or provide services) to the community while consuming. Specifically, we proposed a simple but effective EGT (Evolutionary Game Theory)-based mechanism, VPEF (Voluntary Principle and round-based Entry Fee), to drive the networking environment into cooperative. VPEF builds incentive mechanism as two simple system rules: The first is VP meaning that all behaviors are voluntarily conducted by users: Users voluntarily participate (after paying round-based entry fee), voluntarily contribute resource, and voluntarily punish other defectors (incurring extra cost to those so-called punishers); The second is EF meaning that an arbitrarily small round-based entry fee is set for each user who wants to participate in the community. We presented a generic analytical framework of evolutionary dynamics to model VPEF scheme, and theoretically proved that VPEF scheme's efficiency loss defined as the ratio of system time, in which no users will provide resource, is $4/(8+M)$. $M$ is the number of users in community-based collaborative system. Finally, the simulated results using content availability as an example verified our theoretical analysis.

Unlike traditional epidemic virus spreading, behavior diffusion in social networks is conducted by rational users, who would make strategic choices instead of being randomly infected with some probability. Specifically, individuals always try to maximize their utilities through rationally selecting specific behaviors (adopting a new product, or spreading a rumor, etc.). However, utility obtained by an individual, naturally contains uncertainty, and it may stem from two sources: users' imperfect and incomplete knowledge about others, and the inherently stochastic property in human behavior. Thus, it is imperative to model and analyze the diffusion pattern under the resulting uncertainty in social networks, which, however, has not yet been deeply examined by the existing works. This paper deeply explores the pattern of gossip diffusion in social networks when uncertainty exists in users' decision making. In detail, the innovative results provided in this paper are: first, inspired by random utility theory, we formulate the diffusion model based on mixed logit model that allows for user's uncertainty in determining whether to adopt a specific strategy; second, the formal analysis framework characterizing the diffusion process is derived through the approximation method of mean field theory; finally, we explore the extensive applicability of our proposed analysis framework through modeling rumor diffusion in social networks as a coordination game. Our findings are, for various structural characteristics, small uncertainty can significantly speed up the diffusion of gossip; furthermore, social networks with scale-free property can facilitate the gossip diffusion in the easiest way, but, the range of uncertainty factor that can maximize gossip diffusion is the smallest. The obtained results perfectly comply with the philosophical saying about rumor diffusion in real social life: easy come, easy go.

Nowadays, most popular social networking services adopt centralized architecture, in which continual Internet connectivity is prerequisite for each user to exploit those services, and centralized servers are used for storage and processing of all application/context data, even though mobile users are within proximity area (like campus, event spot, and community), and can directly exchange media through various wireless technologies (e.g., Bluetooth, Wi-Fi Direct, etc.). On one hand, the omniscient centralized server may cause serious privacy concern, due to the fact that it collects and stores all users' data (messages, profiles, location, relations, etc.), On the other hand, transmitting a large amount of media generated by users in proximity through Internet, would not only bring a lot of pressure to the network infrastructure and service provider, but incur heavy data traffic cost to users. In this paper, our contributions are twofold. First, we propose a Wi-Fi Direct based P2P social networking framework, which enables direct data exchange among users without using infrastructure network when users are located in proximity, and provide solutions to two core problems in this framework, i.e., discoverability and privacy. Second, the prototype of this framework is preliminarily implemented in Android, which is composed of the following functions: localization based on Google Geocoding, Chat, and file-sharing component supporting intermittent transmission.

Social media facilitates the process of information sharing, and meanwhile, prompts the generation of a considerable amount of data. Although the data, or user-generated contents, enrich the results for the process of information seeking, it causes the complexity to identify the value of data. Thus, an approach that achieves efficient management of user-generated data was proposed. It especially concentrates on the correlations among data and interactions with users. A state machine is designed to identify the user-generated data, and corresponding usage scenarios. The performance and feasibility can be revealed by the experiments sourced by the data collected from open social networks.

This paper addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm: socially aware recommendation of venues and environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real-world dataset. In our experiments, we compare SARVE with two related state-of-the-art methods, namely context-aware mobile recommendation services and conference navigator (recommender) model. Our experimental results show that in terms of the utilized evaluation metrics, i.e., precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.

Replication is an important mechanism in modern wireless networks and has attracted significant efforts to improve its performance with different metrics including read cost, consistency and relocation cost. Traditionally, different ideal approaches are widely used to facilitate data availability. However, the quality of wireless links would be affected by many factors like mobility and overhead. The accessibility and reliability of Ad-hoc Social Network (ASNET) services can be assured by replication approaches. It is used to increase data availability by replicating data items locally or nearby. In ASNETs, replication helps to avoid data losses in case of an unpredictable group mobility that causes community partition and also aids in reducing the number of hops when a data is transmitted from source to destination. A new data replication method called ComPAS (community-partition aware replica allocation method) is proposed in this paper. This method can significantly improve ASNETs performance by exploiting social relationship while replicating in the community to achieve better efficiency and consistency while keeping the replica relocation cost as low as possible. This type of replica allocation method will increase the availability of different data items in a partitioned social community. Evaluation results verify the effectiveness of the method.

In peer-to-peer (P2P) network, incentive mechanism is crucial to encourage cooperation among peers. Hence, how to construct a framework to analyze and evaluate the effectiveness of incentive mechanism is a very significant problem. Considering the peers' interactions are influenced by the network structure in real network, we propose a novel framework based on spatial evolutionary game theory. Different from most of other researches based on classical and evolutionary game theory, square lattice network is adopted as the network structure in this paper, without the assumption that peers are well-mixed in P2P network. The square lattice network structure can be easily extended to other realistic complex networks, such as small-world network and scale-free network. The reciprocative incentive mechanism is analyzed and evaluated under the framework with different service benefit. Through the simulation, the range of the parameter Q (cost/benefit) that makes the incentive mechanism work effectively under the framework is got, and the reason is analyzed. In addition, the influences of zero-cost identity and strategy mutation of peers on the incentive mechanism are evaluated. The framework is general to analyze and evaluate the effectiveness of other incentive mechanisms.

With the increasing complexity of computing tasks, the resource capability of a single cloud is generally limited, some applications often require various cloud source over internet to deliver services together. Resource collaborative scheduling becomes a critical problem in cloud computing. This paper propose a resources collaboration scheduling model to improve the efficiency of the virtual resources collaboration scheduling, the model bases on virtual organization and makes use of the trust mechanism to estimate the credibility of the virtual organization and improves it. The trust mechanism represents and calculates the credibility of the resources from three dimensions of system trust, user trust and collaboration trust by taking advantage of 2-Tuple fuzzy linguistic representation. The simulation results show that the model can analyze the trust and reputation of resources and improve the credibility of virtual organization. At the same time, it can slash the impact of the malicious evaluation and improve the efficiency of resource scheduling.

Cooperation within selfish individuals can be promoted by natural selection only in the presence of an additional mechanism. In this paper, we focus on an indirect reciprocity mechanism in dynamical structured populations. In social networks rational individuals update their strategies and adjust their social relationships. We propose a three-strategy prisoner's dilemma game model to investigate the evolution of cooperation on dynamical networks. In the coevolution of state and structure process, reciprocators adapt their behaviors and switch their partners based on reputation. Simulation results show that the dynamics of strategies and links can promote cooperation provided the partners switch proceeds much faster than the strategy updating.

This paper thoroughly investigated the Evolutionary Game Theory (EGT) based modeling and analysis of reciprocation-based incentive mechanisms. Unlike existing work which adopts replicator equation to analyze the stability of incentive mechanisms (actually, replicator equation is only applicable to describe deterministic selection in infinitely large and well-mixed population), we paid special attentions to the intrinsic heterogeneity in real autonomous networks: finite users, mutation probability and structured network graph, and proposed the unified framework to characterize the evolutionary dynamics. Specifically, through modeling and analyzing Prisoner's Dilemma (PD)-like game based and Public-goods game based incentive mechanisms, we show that although it is impossible for incentive mechanisms to get the whole network into static "absolute full cooperation (or reciprocation)" state, they can still drive the whole system into "almost reciprocation" state, that is, most of the system time would be occupied by the cooperation (or reciprocation) state.

This paper proposes an agent based approach to deal with the world wide concerned eco problems, i.e., saving energy consumption and reducing CO2emission. This paper demonstrates a simulated home with the facilities of calculation the average energy consumption and CO2emmision of possible devices or appliances. Various agents for the support of reducing energy consumption and CO2emission at home are designed and deployed in a multi-agent framework. They are working in a collaboration way in the terms of sharing their knowledge resources and working together toward a same goal. It is challenge to make influence on people towards energy saving and CO2reducing life habit and style. This issue is to be discussed in this paper as well.

Smart spaces not only are surrounded by networked computers, mobile devices, and ubiquitous sensors, but also encapsulate a large amount of social and communication information. Humans, the center ...

Smart spaces are not only surrounded by networked computers, mobile devices, and ubiquitous sensors, they encapsulate a large amount of social and communication information. Humans, the center of p...

Cyber-Individual, with a short term 'Cyber-I', is a real individual's counterpart in cyberspace. It is closely related to human-centric computing ideology which focuses on placing human in the center of computing. The study on Cyber-I tries to re-examine and analyze human essence in the digital era. Cyber-I's vision is to create a unique, digital, comprehensive description for every real person being in the cyberspace. Human's social context, mood, temper, physical status and so on also need to be considered for such a full description. Further research on social computing, antropology, human behaviour study, psycology and other fields/disciplines are required to enrich Cyber-I concept, meanwhile, Cyber-I will also raise new problems to these fields/disciplines. The IT technologies on the whole including ubiquitous computing, pervasive sensors, wired/wireless networks and clouds will bring Cyber-I vision into practice. In this paper, we first present the Cyber-I concept, its important characteristics and basic architecture. Then, we discuss the special features of Cyber-I as compared with other related concepts and studies including AR (augmented reality), HCI (human computer interaction), AI, artificial life, etc. The Cyber-I layered architecture and basic elements are described in detail, and the associations between Cyber-I and the corresponding real individual are explained as well. Finally, fundamental problems and challenging issues brought by Cyber-I are addressed in terms of necessary technique, security, privacy, ethic, philosophy, etc.

Grid computing has made rapid strides from their first serving the scientific computing domain to having great impact on the life science area and their use in the daily activities of users from the resource constrained ubiquitous devices such as PDA and mobile phone. To allow ubiquitous devices to use grid services, there is a necessity to having a platform or middleware, a bridge linking the devices to grid services. This paper presents such bridge named BtoG (bridge to grid). The design idea and system architecture are described, a sample application of skin checking, accessing to a skin-expert service from a mobile phone via the proposed bridge, is explained, and evaluation and comparisons with other related platforms are given in the paper.

As a fundamental component in wireless networks, location management consists of two operations: location update and paging. These two supplementary operations enable the mobile user ubiquitous mobility. However, in case of failed location update, a significant consequence is the obsolete location identity in the network databases and thereafter the incapability in establishing the valid route for the potential call connection, which will seriously degrade the network quality-of-service (QoS). This issue is not theoretically studied in the literature. In this paper, we perform a quantitative analysis of the location management effect on QoS in the wireless networks. The metrics call blocking probability and the average number of blocked calls are introduced to reflect the QoS. For the sake of general applicability, the performance metrics are formulated with the relaxed tele-traffic parameters. Namely, the call inter-arrival time, cell residence time, location area residence time and location update inter-arrival time follow a general probability density function. The formulae are additionally specified in the static and several dynamic location management mechanisms. Numerical examples are presented to show the interaction between the performance metrics and location management schemes. The discussions on the sensitivity of tele-parameters are also given. Index Terms -Location management, QoS, location update, wireless networks, call blocking probability, dynamic location management

Sim proposed a market-driven negotiation agent model that makes adjustable amounts of concession by reacting to different market situations and trading constraints, and it was improved with an enhanced market-driven strategy by taking opponent eagerness into consideration. In both Sim’s original model and improved model, however, it was implied that a negotiation agent has same behaviors and actions to all trading partners referring to a same trading issue. It is not quite true in a real world trading negotiation. Based on both models, this paper proposes a real trading model based price negotiation agents that take each trading partner as an individual with different strategies and actions. Moreover, negotiation actions between a negotiation agent and a trading partner are kept in secret and unknown to others.

This system focuses on a location-aware computing application and environment which provide preemptive information on dangerous locations in the kid’s surroundings; i.e., the system alerts a kid on the move of the possible dangers whenever he/she is near accident prone areas. Using satellite-based GPS position sensing, this system can gradually learn and remember about the different locations that a kid routinely visits and/or passes by in his daily life, based on space-oriented contexts. The full set of system functionalities, including the map display, requires a graphical user interface. This Location Based Services (LBSs) can compute the serious situations and alert kids to avoid dangerous situations. Our research can simulate the virtual environment as hyperspaces for kids safety care.

A smart space is an electronics-enhanced physical environment that can sense the existence of users and other entities, and provide them the right services, in the right way, at the right time. Such senses and services are based on collaborative work of many interconnected devices and computers, which may be small enough to be embedded to everyday objects in the space. So far, many creative researches have been conducted to develop and experiment a variety of smart spaces. The next research issue to emerge, we believe, is to interconnect and integrate these isolated smart spaces together into a higher level space known as a hyperspace. In this paper, after briefly reviewing the smart space concepts and related research, we describe what a smart hyperspace is all about and what related challenging issues exist. We then explain our vision and motivations towards building a specific smart hyperspace, called Ubikids, to assist parents and provide services to make them more convenient, prompt, reliable, precise, and can remotely take care of their kids. Finally, we show the fundamental approaches, conceptual designs, crucial problems and so on, regarding the project Ubikids.

Peer-to-peer (P2P) computing offers many attractive features, such as collaboration, self-organization, load balancing, availability, fault tolerance and anonymity. However, it also faces many serious challenges. In our previous work, we implemented a synchronous P2P collaboration platform called TOMSCOP. Based on the elementary peer group services offered by the JXTA general framework, TOMSCOP provides the extra four types of services: synchronous message transportation, peer room administration, peer communication support and application space management. By using the four services, different kinds of shared applications for various specific purposes can be relatively easily developed and associated collaborative cyber spaces or communities can be quickly built across the JXTA virtual network overlaid on top of the existing physical networks. However, the TOMSCOP was implemented only in Windows XP OS. In this paper, we extend our previous work and present the implementation of a Multi-Platform P2P System (MPPS). The proposed system operates very smoothly in UNIX Solaris 9 OS, LINUX Suse 9.1 OS, Mac OSX, and Windows XP. In the future study, we would like to evaluate the implemented P2P platform and compare its performance with other P2P systems.

Lots of kids accidents are caused by various dangerous factors frequently existing in some special spaces. In this paper, we show our design of a system for ubiquitous outdoor kid's safety care using space-oriented concepts and contexts. This system can acquire the kid's location information and a kid's profile. Then, it summarizes a kids situation using acquisition knowledge base of accidents or parents' stored information, and detects a possible danger near the kid based on semantics of real spaces. All spaces and related information are defined by specialized schemas, and are described as space-oriented contexts using XML format. The kid's safety care is accomplished cooperatively by both the agents and cognitive patterns-based diagnostic mechanism in the system. Advisory agents can give advices to kids and parents after comparative computation between the real and expected situations using hypothesis based on cognitive patterns, and they can predict dangers. From our survey, this system seems to be the first of its kind, i.e., a system designed for outdoor kid's safety care.

This paper presents a multiagent system to assist a teacher managing his/her course contents placed on Web servers. In this system there is a set of agents and every agent may work independently from or collaboratively with others. Once generated, an agent can reside in a teacher's daily working computer (called administration host) or a proxy host, and can move between the two hosts. Each agent is devoted to one piece of job and all of them, as a whole, coordinately conduct a sequence of management work during the entire process of teaching a course. A teacher may administrate agents via a specific system shell on his/her administration host, or a usual Web browser on another computer/PDA/mobile phone. The system has been carefully modularized, and thus a new type of agent, if necessary, can be relatively easily developed and quickly incorporated into the system to further enhance or extend its management capability.

This paper focuses on general design and prototype implementation of a peer-to-peer (P2P) and a proactive mechanism based version management system called GRAM (group revision assistance management). It provides four special features in comparison with other version management systems: higher system reliability and robustness, effective revision collision prevention using proactive agents, context-aware environment for team software revision, a unified XML format for configuration and history files as well as system and agent exchange messages. Every peer holds a shared space synchronized with other peers' ones, and a workspace for a user's ordinary editing. GRAM is implemented using the JXTA technology that consists of the virtual JXTA network and basic peer group services. The system GUI and basic functions in the current prototype are also presented to show its basic usages. By using GRAM, version management of various software development projects can be comfortably conducted.

The most shared applications use the client/server model in which, however a server is usually very complex and heavy since all of group managements are done by the server and sometimes becomes a communication bottleneck as all of date exchange among group members are mediated via it. To solve the above problems, our shared browser adopted a pure peer-to-peer (P2P) architecture without using any server A group member or a device called a peer dynamically finds other peers via distributed searching, and directly exchange data with other peers. It supports not only sharing a Web document in a peer group but also synchronously viewing the document and manipulating the browser with further support of some group users' awareness information like a user's moving a cursor, entering a new URL and clicking a hyperlink. It is implemented using JXTA protocols and Java programming language. To make the system applicable over the Internet across firewalls and NATs, the HTTP protocol can be used to transfer data via a pipe, a communication mechanism in JXTA.

Network based groupware systems are for supporting collaborations among a group of people who are engaged in a common task or goal using computers connected by a variety of networks including the Internet. A synchronous groupware system supports group members' collaborative activities at the same time. Due to the almost all of current synchronous collaborative systems have been implemented either using a centralized topology or a hybrid topology, the research presented in this paper has been devoted to investigation, design and implementation of a peer-to-peer (P2P) groupware system, called DSC, using a decentralized topology. As it does not use any server at all, peers in a group need to coordinately manage their group by themselves, and each peer has to fully handle the correct message passing by itself. The DSC system is implemented using the JXTA technology and platform that enable peers to find each other, form groups and exchange messages across firewalls and NATs. It currently offers three shared objects of Web browser, file viewer and drawing pad as well as a text chat tool. The synchronous controls of a shared space, the objects, telepointer and so on are also provided. Its evaluations with a practical test environment are given in detail.

Objects in this article refer to sharable applications, such as a whiteboard and a video player, used by multi-users who are in different sites and have computers connected to networks. The objects are important elements in our Internet-based desktop collaborative system, called virtual collaboration room (VCR). We argue that a natural collaborative environment should be developed in a framework of using both a room metaphor and an object metaphor, i.e., emulating the fundamental characteristics of real rooms and real objects, respectively. This article gives the first systematic specifications of object physical and social characteristics, and discusses how to exploit and implement the object characteristics in VCR. A preliminary prototype of platform independent real-time audio/video communications among multiple users is also described. It can be used together with VCR.

Distance learning is one of the interesting research topics of distributed computing. This paper discusses a joint research project between the University of Aizu in Japan and Tamkang University in Taiwan. A software system supporting virtual university operations is proposed. The software architecture is designed based on three criteria of virtual university operations: administration, awareness and assessment. Specifications of each tools in the system are proposed, with some implementation details and solutions being discussed. We also point out some interesting research directions in the realization of a virtual university.

Web based distance learning is a trend of instruction delivery. One of the most difficult challenges of such a learning mechanism is the assessment of students' learning criteria. It is hard to judge the behavior of a student since the instructor is separated spatially and temporally from the students. However, it is possible to rely on some Web based tools to keep track of a student's course attendance, as well as the navigation behavior of that student. In addition, the navigation behavior of an individual can be compared to those of others. Analysis can be conducted, and an interactive tutorial can be generated to assist the student with a poor score. The paper proposes such a mechanism, as well as its supporting system run on Windows browsers.

The general purpose virtual collaboration room (VCR) is an Internet based desktop groupware system that enables a group of remote individuals to flexibly and naturally conduct their collaborative teaching/learning/working without constraints on collaboration types, working styles, group scales, and system platforms. To cope with the complexity, a room metaphor, i.e., emulating a physical room and objects in it, is used as a framework of the system. System implementation becomes no more complex than the case of using one object by identifying associated objects in communication messages between a room server and clients. The VCR provides rich and effective support of awareness of the user, object, space, and their mutual relations. With the use of Java applets for system implementations, users can enter and use a VCR from any standard Java enabled Web browser.