Modular methods, which decompose complex joint policies into function-specific sub-policies, have been widely adopted to enhance asymptotic performance in single-task cooperative multi-agent reinforcement learning (MARL). However, modular policies trained on source tasks often struggle to generalize to unseen scenarios due to variations across tasks, such as mismatched action spaces and divergent state dynamics. To address this challenge, we propose Mutual Information-Guided Subtask Selection(MIGSS), a novel framework that enhances zero-shot generalization in MARL through two key innovations: a Discriminative Group Trajectory Encoder and Global Attention-Driven Coordination. Specifically, the Discriminative Group Trajectory Encoder remaps agent trajectories by maximizing mutual information between agent trajectories and dynamically assigned groups. This optimizes cross-task consistent group trajectory with broader embedding distributions. This encourages agents in distinct states to select specialized subtasks, effectively promoting functional modularity. Meanwhile, the Global Attention-Driven Coordination employs a global attention mechanism to integrate state information, coordinating group trajectories for expressive credit assignment. Extensive experiments in StarCraft II cooperative scenarios demonstrate that MIGSS significantly outperforms superior zero-shot generalization baselines in both single-task and multi-task settings.Visualization analyses confirm that the learned group trajectories successfully disperse agent trajectories into a consistent and broader embedding space, thereby enhancing subtask modularization.

Unmanned Aerial Vehicles (UAVs) are emerging as a pivotal component in the field of intelligent transportation systems. Leveraging virtual-physical interactions, digital twin technology significantly enhances the adaptability of UAVs in complex traffic environments. However, current approaches still pose three major challenges: contextual adaptability, timely responsiveness, and effective multi-UAV coordination. In this paper, we introduce EnFlexiTwin, a digital twin enhancement assistance platform seamlessly integrated with AdaSor, a lightweight adaptive data selector. EnFlexiTwin automates the construction of incremental learning datasets, enabling real-time enhancements that allow digital twins to adapt to new time series data while preserving historical knowledge. We test EnFlexiTwin on a real-world dataset from low-altitude small-parcel delivery. The results show improved performance and adaptability of digital twins. Furthermore, time-varying simulations on real-world dataset and experiments on a practical air-ground cooperative UAV swarm application highlight that EnFlexiTwin achieves superior enhancements under varying real-time requirements and swarm scale compared to baseline approaches.

This study introduces a novel approach for preventing overexertion in home fitness through fine-grained detection of respiratory parameters. To overcome the robustness limitation associated with using a composite signal for wireless sensing, we introduce an optimization-based signal separation model. This model effectively disentangles composite signals into static and dynamic components, while preserving the intricate details of target movements or activities. Specifically, by constructing a reference signal derived from the dominant static component, we eliminate time-varying phase shifts and leverage the invariant property of the dynamic component’s amplitude for precise separation. A system calledFinerSenseis developed, which is able to accurately and robustly detect fine-grained respiratory parameters such as respiration rate, depth, and inhalation-to-exhalation ratio with accuracy rates exceeding 97%, 95%, and 91%, respectively. Extensive experiments show that the developed system outperforms state-of-the-art baselines significantly, empowering users to optimize exercise intensity and duration while mitigating the risk of overexertion. We believe that this work is able to facilitate the seamless transition of wireless sensing systems from laboratory prototypes to practical and user-friendly applications.

A neural processing unit (NPU) is a microprocessor which is specially designed for various types of neural network applications. Because of its high acceleration efficiency and lower power consumption, the airborne embedded system has widely deployed NPU to replace GPU as the new accelerator. Unfortunately, the inherent scheduler of NPU does not consider real-time scheduling. Therefore, it cannot meet real-time requirements of airborne embedded systems. At present, there is less research on the multi-task real-time scheduling of the NPU device. In this article, we first design an NPU resource management framework based on Kubernetes. Then, we propose WAMSPRES, a workload-aware NPU performance model based soft preemptive real-time scheduling method. The proposed workload-aware NPU performance model can accurately predict the remaining execution time of the task when it runs with other tasks concurrently. The soft preemptive real-time scheduling algorithm can provide approximate preemption capability by dynamically adjusting the NPU computing resources of tasks. Finally, we implement a prototype NPU scheduler of the airborne embedded system for the fixed-wing UAV. The proposed models and algorithms are validated on both the simulated and realistic task sets. Experimental results illustrate that WAMSPRES can achieve low prediction error and high scheduling success rate.

During the last decade, there have been lots of efforts on wireless sensing using Wi-Fi signals, which can be divided into two categories, i.e., the pattern-based approach and the model-based approach. Recently, more and more attention has been paid on the model-based approach, mainly due to its superiority of no need for collecting a large dataset or retraining the model for new environments. However, existing models are mainly designed for Line-of-Sight (LoS) scenarios, which are not applicable to Non-Line-of-Sight (NLoS) scenarios, such as through-wall sensing. To bridge this gap, we put forward a through-wall wireless sensing model to reveal the sensing mechanism of Wi-Fi signals in NLoS scenarios. In particular, arefraction-awareFresnel zone model is developed by taking into account both the reflection propagation and the refraction propagation of Wi-Fi signals. For the first time, we discover that the geometric distribution of Fresnel zones becomes uneven, due to the difference in dielectric constants between the air and the wall. Specifically, some areas become denser and other areas become sparser, leading to thesqueeze effectandstretch effectof Fresnel zones. Inspired by the insight, we further put forward a new metric namedcompression-ratioto quantify the through-wall sensing capability of Wi-Fi signals. Meanwhile, a set of algorithms are developed to guide the deployment of Wi-Fi sensing systems. To validate the proposed model, we implement a through-wall respiration sensing prototype system. Experiments show that the respiration detection performance varies significantly when the user locates in different areas. Specifically, for two sensing locations (one in the compression area and the other in the expansion area) symmetrically distributed on both sides of the transceivers’ connection line, the difference in mean absolute errors (MAE) can exceed 3 times.

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs.

The Ubiquitous Operating System(UOS) is a new type of operating system in response to the new patterns and scenarios of future human-cyber-physical ternary ubiquitous computing. Compared with traditional operating systems, one of the fundamental requirements of UOS is to adaptively manage numerous heterogeneous sensors according to dynamic environments and diverse tasks. However, traditional management focuses on the sensors’ parameters and interfaces without highlighting the perception effect that is users’ concern and dynamic changing. It also lacks a unified management approach for heterogeneous sensors. To overcome the limitations, we propose a novel heterogeneous sensors dynamic management model UbiCap, i.e., Ubiquitous Capability, which is based on the capability abstraction and adaptive run-time capability management mechanism. The capability provides a unified abstract for heterogeneous sensors. The adaptive run-time capability management mechanism transfers the management object from low-level hardware sensors to high-level sensing capability. The capability required and the available capability are matched to support run-time adaptive sensors selection. We implement a software prototype iS2ROS(intelligent Sensor Selection Robot Operating System) based on the UbiCap model. We then simulate a forest fire spot monitoring scenario where iS2ROS selects the optimal image sensor during the identification task execution while light or weather condition changes. Experiment results show that the iS2ROS achieves comparative sensing effectiveness through UbiCap with 50% power consumption lower compared to the traditional both-sensors approach.

It is commonly agreed that a recommender system should use not only explicit information (i.e., historical user-item interactions) but also implicit information (i.e., incidental information) to deal with the problem of data sparsity and cold start. The knowledge graph (KG), due to its expressive structural and semantic representation capabilities, has been increasingly used for capturing auxiliary information for recommender systems, such as the recent development of graph neural network (GNN) based models for KG-aware recommendation. Nevertheless, these models have the shortcoming of insufficient node interactions or improper node weights during information propagation, which limits the performance of recommender systems. To address this issue, we propose a Multi-dimension Interaction based attentional Knowledge Graph Neural Network (MI-KGNN) for enhanced KG-aware recommendation. MI-KGNN characterizes similarities between users and items through information propagation and aggregation in knowledge graphs. As such, it can optimize the updating direction of node representation by fully exploring multi-dimension interactions among nodes during information propagation. In addition, MI-KGNN introduces a dual attention mechanism, which allows users and items to jointly determine the weight of neighbor nodes. As a result, MI-KGNN can effectively capture and represent both structural (i.e., the topology of interactions) and semantic information (i.e., the weight of interactions) in the knowledge graph. Experimental results show that the proposed model significantly outperforms baseline methods for top-K recommendation. Specifically, the recall rate is increased by 5.78%, 6.66%, and 3.22% on three public datasets, compared with the best performance of existing methods.

To further improve the capacity of airborne embedded system for dealing with deep learning (DL) applications and reduce overall power consumption, it is necessary to equip Neural Processing Units (NPUs). Comparing with the cloud system, the airborne embedded system usually has a fixed application set, but strict real-time constraints. Unfortunately, the inherent NPU scheduler does not consider the application priority, which cannot provide the sufficient real-time capability for the airborne embedded system. At present, there are few researches on multi-task real-time scheduling for NPUs. Therefore, we propose WMDRS, a workload-aware performance model multi-task dynamic-quota real-time scheduling for Neural Processing Units. The NPU performance model based on workload-awareness can accurately predict the remaining execution time of a task, which is running concurrently with other tasks on NPU. The multi-task dynamic-quota real-time scheduling algorithm can provide the approximate preemption by dynamically adjusting NPU computing resources for active applications. In addition, we implement a prototype NPU scheduler without any hardware extension. Furthermore, the proposed NPU performance model and real-time scheduling algorithm are evaluated in realistic application sets. Experimental results demonstrate that WMDRS can achieve low prediction error and high scheduling success ratio.

Considering the increasing complexity of application scenarios, the evaluation of the intelligence levels for unmanned swarms has attracted scholarly attention. However, the existing evaluation studies cannot suitably reflect the intelligence of unmanned swarms, and they rarely provide a specific evaluation process. By introducing the collective intelligent behavior model for unmanned swarms based on the collective Observe–Orient–Decide–Act (OODA) loop, this study constructs a comprehensive evaluation index system that can systematically reflect the overall intelligence of unmanned swarms in complex scenarios. Considering the fuzziness and randomness in the processes of weight calculation and level evaluation, this study proposes a comprehensive evaluation method of the intelligence levels for unmanned swarms based on a group extension cloud model. This method calculates the weights of various evaluation indexes at different layers by adopting the group extension analytic hierarchy process. Moreover, it obtains the comprehensive evaluation conclusion of the intelligence levels for unmanned swarms by adopting the cloud model. Applying the proposed method, this study evaluates two specific types of unmanned aerial vehicle swarms. The results show that the proposed method can more flexibly and accurately evaluate the intelligence levels of unmanned swarms than the previous fully qualitative methods.

Mobile crowd sensing and computing (MCSC) has become a hot research area in recent years. This article presents our vision of the next generation of MCSC, crowd intelligence with the deep fusion of human, machine, and Internet of Things (IoT), namely, CrowdHMT. It aims to build a self-organizing, self-learning, self-adaptive, and continuous-evolving smart space with the deep fusion of Crowdsourced human, machine, and IoT intelligence. This article first characterizes the concept of CrowdHMT. We further investigate its challenges and techniques, and present its main application areas. Finally, we make discussions about the open issues and future research directions of CrowdHMT.

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose anautomaticQTSanomalydetectionframework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to achieve a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3 percent accuracy, TCQSA outperforms two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs. Additionally, the effectiveness of the attention mechanisms is quantitatively and qualitatively demonstrated.

Intelligent unmanned systems (IUSs) are distributed systems composed of multiple agents that share information or cooperate to accomplish specific complex tasks. Agents of the IUS are capable of perception, cognition, control, decision-making, and action. In some cases, the environmental situation and task objectives faced by the IUSs are constantly changing with time. Thus, IUSs are time-sensitive systems. To accelerate the task execution time and response speed, IUSs use artificial intelligence technology to increase the speed and quality of the `observation-orientation-decision-action' (OODA) cycle of task execution. IUSs will tend to decompose the system into different functional units in the future, and individuals take different task roles from the functional perspective of OODA. The system is evolving from a linear OODA cycle of individuals to a cooperative OODA (Co-OODA) with different node roles. At present, the reinforcement learning (RL) algorithm is the mainstream method to solve IUSs cooperation problems. However, it does not adapt to the Co-OODA with different roles; and cannot maximize the Co-OODA system's potential. This paper introduces the role-based Co-OODA system. Furthermore, we propose and design a role-based deep reinforcement learning framework and its corresponding information sharing mechanism.

Recently, the modeling and representation of multivariate time series has attracted much attention in the field of machine learning and data mining, due to its wide application potentials in biomedicine, finance, industry and so on. During the last decade, deep learning has achieved great success in many tasks. However, a large number of labeled data samples are needed to train a satisfactory model which has a huge amount of parameters, especially in cases that the inputs are multivariate time series (i.e., multi-dimension) and have complex relationships with the outputs. We propose a Multi-level attention-based prototype Network (MapNet) to model multivariate time series. Specifically, we first encode the time series based on deep learning and calculate the prototype for each class. Afterwards, we propose a multi-level attention mechanism to further optimize the prototype, including a short-term encoder as well as a long-term encoder. Experiments based on two public datasets demonstrate that MapNet outperforms state-of-the-art baseline models and is more applicable for few-shot dataset.

This paper puts forward a LSTM and CNN based assemble neural network framework to distinguish different types of arrhythmias by integrating stacked bidirectional long shot-term memory (SB-LSTM) network and two-dimensional convolutional neural network (TD-CNN). Particularly, SB-LSTM is used to mine the long-term dependencies contained in electrocardiogram (ECG) from two directions to model the overall variation trends of ECG, while TD-CNN aims at extracting local information of ECG to characterize the local features of ECG. Moreover, we design an ensemble empirical mode decomposition (EEMD) based signal decomposition layer and a support vector machine based intermediate result fusion layer, by which ECG can be analyzed more effectively, and the final classification results can be more accurate and robust. Experimental results on public INCART arrhythmia database show that our model surpasses three state-of-the-art methods, and obtains 99.1% of accuracy, 99.3% of sensitivity and 98.5% of specificity.

Electrocardiogram (ECG) signal based arrhythmias classification is an important task in healthcare field. Based on domain knowledge and observation results from large scale data, we find that accurately classifying different types of arrhythmias relies on three key characteristics of ECG: overall variation trends, local variation features and their relative location. However, these key factors are not yet well studied by existing methods. To tackle this problem, we design an attention-based hybrid LSTM-CNN model which is comprised of a stacked bidirectional LSTM (SB-LSTM) and a two-dimensional CNN (TD-CNN). Specifically, SB-LSTM and TD-CNN are utilized to extract the overall variation trends and local features of ECG, respectively. Furthermore, we add a trend attention gate (TAG) to SB-LSTM, meanwhile, add a feature attention mechanism (FAM) and a location attention mechanism (LAM) to TD-CNN. Thus, the effects of important trends and features at key locations in ECG can be enhanced, which is conducive to obtaining a better understanding of the fluctuation pattern of ECG. Experimental results on the MIT-BIH arrhythmias dataset indicate that our model outperforms three state-of-the-art methods, and achieve 99.3% of accuracy, 99.6% of sensitivity and 98.1% of specificity, respectively.

There are a lot of middle-class or small-class high-performance computing clusters at universities and research institutes, etc. Large volumes of job logs have been accumulated after many years of operation. In this paper, on the basis of accumulated job logs on a high-performance computing cluster, we examine and analyze the job logs. Then, we study machine learning based performance analysis and prediction methods for parallel jobs. Various machine learning methods such as multivariate linear fitting, artificial neural network are used to build performance prediction models. We compare the errors of each model, and select the optimal prediction model for different users. The experimental results show that we can obtain reasonable prediction accuracy using the selected machine learning algorithms.

Recent years have witnessed advances of Internet of Things technologies and their applications to enable contactless sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a minimum description length-based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids, and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one's back, on right/left side, and on one's stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal, and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m), and transmitted signal frequency variation.

Vehicularad hocnetworks (VANETs) have far-reaching application potentials in the intelligent transportation system (ITS) such as traffic management, accident avoidance and in-car infotainment. However, security has always been a challenge to VANETs, which may cause severe harm to the ITS. Sybil attack is considered as a serious security threat to VANETs since the adversary can disseminate false messages with multiple forged identities to attack various applications in the ITS. RSSI-based Sybil nodes detection is an efficient scheme against Sybil attacks, which adopts position estimation, distribution verification or similarity comparison to identify Sybil nodes. However, when Sybil nodes conduct power control to deliberately change transmission powers, the received RSSI values would change correspondingly, which leads to inaccurate localization or different RSSI time series of these Sybil nodes. Thus, it is very difficult to differentiate Sybil nodes from normal nodes via conventional RSSI-based methods. This paper first discusses potential power control models (PCMs) for launching Sybil attacks in VANETs, then presents two simple Sybil attack models and three sophisticated Sybil attack ones with or without power control in detail, finally proposes a power control identification Sybil attack detection (PCISAD) scheme to find anomalous variations in RSSI time series, which are then used to identify Sybil nodes via a linear SVM classifier. Extensive simulations and real-world experiments prove that the proposed scheme can effectively deal with Sybil attacks with power control.

Vehicular Ad Hoc Networks (VANETs) bring many benefits and conveniences to road safety and drive comfort in future transportation systems. However, VANETs suffer from almost all security issues as same as wireless networks. Sybil attack is one of the most risky threats since it violates the fundamental assumption of VANETs-based applications that all received information are correct and trusted. Sybil attacker can generate multiple fake identities to disseminate false messages. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. Unlike most of previous RSSI-based methods that compute the absolute position or relative distance according to RSSI values, or make statistic testing based on RSSI distributions, Voiceprint adopts RSSI time series as the vehicular speech and compares the similarity among all received series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without support of the centralized node. Moreover, we improve Voiceprint by allowing it to conduct detection on Service Channel (SCH) to shorten observation time. Furthermore, we extend Voiceprint with change-points detection to identify those illegitimate nodes performing power control. Extensive simulations and real-world experiments demonstrate that Voiceprint is an effective method considering the cost, complexity, and performance.

Hypertension is a common cardiovascular disease, which will lead to severe complications without timely treatment. Accurate hypertension identification is essential to preventing the condition deteriorated. However, the state of art hypertension identification methods only extract features from very few aspects, and hence have limited identification accuracy. Furthermore, they only can judge whether the subjects are hypertensive or not, more meaningful information (such as, why the subjects suffer from hypertension) that can help doctors to improve their diagnosis level are absent. In this paper, we propose a class association rules-based method to identify hypertension. Particularly, its key idea is to utilize the relationship existing in multi-dimensional features to characterize hypertension pattern more effectively, in order to improve the identification performance. In addition, it can also generate a set of class association rules (CARs), which can reflect the subjects' physiological status and are proved to be useful for doctors to analyze subject's condition deeply. Experiments based on 128 subjects (61 hypertension patients and 67 healthy subjects) shows that our method outperforms the baseline methods and the accuracy, precision and recall reach 85.2%, 85.0%, and 83.6%, respectively. Additionally, a user study based on five clinicians demonstrates the utility of the generated CARs.

Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.

Cyber physical systems (CPSs) incorporate computation, communication, and physical processes. The deep coupling and continuous interaction between such processes lead to a significant increase in complexity in the design and implementation of CPSs. Consequently, whereas developing CPSs from scratch is inefficient, developing them with the aid of CPS run-time sup-porting platforms can be efficient. In recent years, much research has been actively conducted on CPS run-time supporting plat-forms. However, few surveys have been conducted on these platforms. In this paper, we analyze and evaluate existing CPS run-time supporting platforms by first classifying them into three categories from the viewpoint of software architecture: com-ponent-based platforms, service-based platforms, and agent-based platforms. Then, for each type, we detail its design philosophy, key technical problems, and corresponding solutions with specific use cases. Subsequently, we compare existing platforms from two aspects: construction approaches for CPS tasks and support for non-functional properties. Finally, we outline several im-portant future research issues.

Multi-hop over-the-air reprogramming is essential for remote installation of software patches and upgrades in wireless sensor networks (WSNs). Several recent coding-based reprogramming protocols have been proposed to enable efficient code dissemination in high packet loss environments. An accurate and formal analysis of the performance of these protocols, however, has not been studied sufficiently in the literature. In this paper, we present a novel high-fidelity analytical model based on the shortest path algorithm to measure the completion time by incorporating overhearing and packet coding. This model can be applied to any coding-based reprogramming protocol by substituting the coding part with protocol specific operations. We conduct extensive testbed experiments to evaluate the performance of our proposed model. Based on the analytical and numerical experiments, we find that 1) overhearing causes significant reduction of the completion time in dense wireless sensor networks, particularly, it reduces 50-70 percent of the total completion time when the packet reception rate is 0.896; 2) coding delay plays a key role in the total completion time compared to the communication delay when the packet coding parameters are selected appropriately, for example, the communication delay is about 65 percent of the coding delay when the number of packets per page is 16 for the finite field size 28; 3) the total completion time can be minimized when the number of packets per page is close to 24 and the finite field size is close to 24.

Obstructive sleep apnea (OSA) is regarded as one of the most common sleep-related breathing disorders, which causes various diseases and affects people's daily life severely. Up to now, massive efforts have been devoted to identifying OSA events during sleep based on different signals (e.g., PSG, ECG, nasal airflow and EMG, etc.). However, there still are more or less shortcomings in current studies. In this paper, we propose a novel framework to improve the performance of identifying OSA events. Particularly, the key idea of our framework is to divide each potential event segment (i.e., a data segment that may or may not contain an OSA event) into different phases, from which we further extract fine-grained features to characterize respiratory pattern comprehensively. Concretely, we first automatically locate potential event segments from raw ballistocardiography (BCG) data by identifying arousals. Afterwards, each potential event segment is divided into three phases (i.e., Apnea Phase, Respiratory Effort Phase and Arousal Phase) by an adaptive threshold-based division algorithm. Based on these phases, we further extract and select efficient features that can characterize respiratory pattern from different aspects. Finally, these potential event segments are classified into OSA events or non-OSA events using BP neural network. Experimental results based on a real BCG dataset that contains 3,790 OSA events and 2,556 non-OSA events show that our framework outperforms the baselines and the precision, recall and AUC reach 94.6%, 93.1%, and 0.951, respectively.

Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages Wi-Fi signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding Wi-Fi signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of Wi-Fi. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform- based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.

The study of geo‐social behaviors has long been a scientific problem. In contrast to traditional social science, which suffers from the problems such as high data collection cost and imported user subjectivity, a new approach is presented to study social behaviors based on mobile phone sensing data. Different from other similar studies on mobile social sensing, three different types of geo‐social behaviors, including online interaction, offline interaction, and mobility patterns, are characterized based on a newly released Nokia mobile phone data set. We further discuss the impact factors to these behaviors as well as the correlation among them. The findings in this article are crucial for many different fields, ranging from urban planning, location‐based services, to social recommendation.

This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs.

Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.

Frequent route is an important individual outdoor behavior pattern that many trajectory-based applications rely on. In this paper, we propose a novel framework for extracting frequent routes from personal GPS trajectories. The key idea of our design is to accurately detect road corners and utilize these new metaphors to tackle the problem of frequent route extraction. Concretely, our framework contains three phases: 1) characteristic point (CP) extraction; 2) corner detection; and 3) trajectory mapping. In the first phase, we present a linear fitting-based algorithm to extract CPs. In the second phase, we develop a multiple density level DBSCAN (density-based spatial clustering of applications with noise) algorithm to locate road corners by clustering CPs. In the third phase, we convert each trajectory into an ordered sequence of road corners and obtain all routes that have been traversed by an individual for at least ${F}$ (frequency threshold) times. We evaluate the framework using real-world trajectory datasets of individuals for one year and the experimental results demonstrate that our framework outperforms the baseline approach by 7.8% on average in terms of precision and 21.9% in terms of recall.

Gait is usually a crucial indicator for recognizing and evaluating the progression of Parkinson's disease (PD). To assess gait variability in the elderly with PD in a continuous and natural way, we pay more attention to their feet pressure variability while walking, and try to obtain the varying patterns of center of pressure (CoP). In this paper, we propose a framework based on the bimodal distribution probability density functions (BDPDFs) to recognize gait patterns of PD patients. The result evaluated with the 10-fold cross-validation method demonstrates that the Bagging classifier is able to provide classification precision of 82.4% with AUC of 88.3%. The features and the classifiers used in the present study can also be used to study the effect of dopamine and rehabilitation in PD patients.

Internet of Things (IoT) invasions a future in which digital and physical entities (e.g., mobile devices, wearable devices) can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this paper, we study the dissemination of content over a mobile social network, which has become an attractive proxy for investigating human behaviors due to the rapid development of mobile phones. One of the most interesting and challenging problems about content dissemination is that how much attention of a specific post from a user can ultimately gain? Hence, in other words, can we forecast the crowds׳ concern in the mobile social networking environment, and how? We try to tackle this issue by exploring approaches to predict the amount of reposts any given post will obtain in Sina Weibo, a well-known mobile social networking service in China. We examine several novel implicit factors impacting the popularity of content, such as Modality, MaxMediaWeight and Activeness. Furthermore, we propose a RepostsTree based method to model the reposting process in a temporal dynamic manner. Experimental results over the collected data from Sina Weibo indicate that our method is effective on content diffusion prediction in mobile social networks.

Human memory often fails. People are frequently beset with questions like “Who is that person? I think I met him in Tokyo last year.” Existing memory aid tools cannot well support the recall of names effectively. This paper explores the memory recall enhancement issue from the perspective of memory cue extraction and associative search, and proposes a generic methodology to extract memory cues from heterogeneous, multimodal, physical/virtual data sources. Specifically, we use the contact name recall in the academic community as the target application to showcase our proposed methodology. We further develop an intelligent social contact manager that supports 1) autocollection of rich contact data from a combination of pervasive sensors and Web data sources, and 2) associative search of contacts when human memory fails. The system is validated by testing the performance of contact data collection techniques. An empirical user study on contact memory recall is also conducted, through which several findings about contact memorizing and recall are presented. Classic cognitive psychology theories are used to interpret these findings.

With the recent surge of location-based social networks (LBSNs), such as Foursquare and Facebook Places, huge digital footprints of people's locations, profiles, and online social connections become accessible to service providers. Unlike social networks (e.g., Flickr, Facebook) that have explicit groups for users to subscribe to or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection and profiling approaches are needed. In the meantime, the diversity of people's interests and behaviors when using LBSNs suggests that their community structures overlap. In this paper, based on the user check-in traces at venues and user/venue attributes, we come out with a novel multimode multi-attribute edge-centric coclustering framework to discover the overlapping and hierarchical communities of LBSNs users. By employing both intermode and intramode features, the proposed framework is not only able to group like-minded users from different social perspectives but also discover communities with explicit profiles indicating the interests of community members. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset.

Cyber-Physical Systems (CPS) tightly integrate cyber and physical components and transcend discrete and continuous domains. It is greatly desired that the physical components being controlled and the software implementation of control algorithms can be verified together. We present an efficient approach to reachability analysis of Hybrid Automata Pushdown System (HAPS) models for cyber/physical co-verification of CPS. We have realized this approach and applied it to real-world control systems. The evaluation has shown that HAPS is an effective model for co-verification of CPS and our approach has major potential in verifying system-level properties of CPS, therefore improving the reliability of CPS.

Cyber-Physical Systems (CPSs) are interactive, intelligent and distributed-hybrid systems which have computing units embedded in physical environment and widely applied in the safety-critical field. Compared with the traditional embedded hybrid system, the problems of safety, reliability and uncertainty, caused by constant interaction between computing and physical process, are more prominent than ever before. An astronautic case has been taken for example in this paper. Correspondingly, the Discrete Hybrid Automata (DHA) modeling frame and Hybrid System Description Language (HYSDEL) are adopted to build and analyze its behavior model. Besides, combined with the hybrid toolbox, the trajectories of the continuous states and the reachability of system are simulated and analyzed. The usage of the approach to modeling and analysis of CPS has been applied in the scene of lunar rover autonomous walking, which lay a model foundation for the further safety verification.

Multi-hop over-the-air reprogramming is essential for the remote installation of software patches and upgrades in wireless sensor networks (WSNs). Recently, coding-based reprogramming protocols are proposed to address efficient code dissemination in environments with high packet loss rate. The problem of analyzing the performance of these protocols, however, has not been explored in the literature. In this paper, we present a high-fidelity analytical model based on Dijkstra's shortest path algorithm to measure the completion time of coding-based reprogramming protocols. Our model takes into account not only page pipelining and negotiation, but also coding computation. Results from extensive simulations of a representative coding-based reprogramming protocol called Rateless Deluge are in good agreement with the performance predicted by our model, thus validating our approach. Our analytical results show both the number of packets per page and the finite field size have significant impact on completion time. Most notably, the time overhead of coding computation exceeds that of communication when the number of packets per page is 24 and the finite field size is at least 24.

As a key enabling technology for the next generation inter-vehicle safety communications, The IEEE 802.11p protocol is currently attracting much attention. Many inter-vehicle safety communications have stringent real-time requirements on broadcast messages to ensure drivers have enough reaction time toward emergencies. Most existing studies only focus on the average delay performance of IEEE 802.11p, which only contains very limited information of the real capacity for inter-vehicle communication. In this paper, we propose an analytical model, showing the performance of broadcast under IEEE 802.11p in terms of the mean, deviation and probability distribution of the MAC access delay. Comparison with the NS-2 simulations validates the accuracy of the proposed analytical model. In addition, we show that the exponential distribution is a good approximation to the MAC access delay distribution. Numerical analysis indicates that the QoS support in IEEE 802.11p can provide relatively good performance guarantee for higher priority messages while fails to meet the real-time requirements of the lower priority messages.

Networked cooperation is one primarily problem for autonomous Cyber-Physical Systems(CPS) that feature cyber-physical fusion, and smart behavior. After analyzing current contributions, a three-level task model, with which missions can be recognized and mapped to tasks, is studied firstly in this paper. And then, based on a designed uniform model of the surroundings, external missions and internal computational resources, two hybrid models, which are autonomously cooperative and real-time reactive by taking advantages of agent and adaptive control, are illuminated respectively. Further, a hierarchal cooperation mechanism of CPS fleet is put forward. With this mechanism, the dynamic topologies of a fleet will be more flexible and dependable, and by adopting an intelligent algorithm, all tasks mapped from a mission can be autonomously assigned to suitable members under decentralized consultation or centralized allocation mode. In this paper, an optimized genetic algorithm is employed to illustrate the process of proposed mechanisms.

With more and more elders in most countries, the health and safety issues become the primary concern for elderly people because ageing is often associated with physical and cognitive impairments. In order to provide intelligent assistive services for elders, in this paper, we propose an integrated service platform that can accommodate safety assurance, daily activity assistance, and health support services. Specifically, the platform takes care of anomalous events detection, daily activities tracking, and health status monitoring leveraging stationary sensors deployed in living environments and mobile sensing artifacts carried by elders. The services in this integrated platform can ensure safety and independency of elders, they can also help them sustain and/or improve health condition with personalized health support. Based on the proposed platform, we present a case study of assistive safety assurance service wandering detection, which monitors an elder's outdoor movements by using GPS sensor embedded in smart phone and recognizes the lapping or pacing movement pattern to find potential wandering behavior. The experimental results show that our method works well in detecting wandering behavior in terms of detection rate and false alarm rate.

The Internet of Things (IoT) is a technical revolution that represents the future of computing and communications. Under its vision, the next-generation Internet will promote the harmonious interaction between human, society, and smart things. The current research in IoT is mainly from the perspective of connecting and managing things. The humanized, social side of IoT, however, is still not explored. In this article, we intend to present the IoT from the human-centric perspective. By analyzing the tight-coupled relationship between human and opportunistic connection of smart things (e.g., mobile phones, vehicles), we propose Opportunistic IoT. It enables information sharing and dissemination within/among opportunistic communities that are formed with the movement and opportunistic contact nature of human. We characterize the bi-directional effect between human and opportunistic IoT, present the innovative application areas, and discuss the challenges raised by this new computing paradigm.

With the rapidly ageing population in most countries, the health and safety issues become the primary concern for elderly people because ageing is often associated with physical and cognitive impairments. In order to provide intelligent assistive health services for elders, in this paper, we propose an integrated OSGi-based service platform that can aggregate a variety of health assistive services, including health status monitoring, automated diagnosis and prognosis, and personalized health instruction by leveraging either stationary biosensors deployed in domestic settings or mobile sensing artifacts carried by elders. The assistive services in this integrated platform are able to help elders sustain and enhance health condition and quality of life with personalized health support. Our proposed platform is highly scalable and reusable by exploiting the component-based architecture of OSGI service framework, new services can be easily added into the platform according to individuals' needs and requirements. An implemented prototype system and the relevant performance evaluation are also presented.

Wandering is among the most frequent, problematic, and dangerous behaviors for elders with dementia. Frequent wanderers likely suffer falls and fractures, which affect the safety and quality of their lives. In order to monitor outdoor wandering of elderly people with dementia, this paper proposes a real-time method for wandering detection based on individuals' GPS traces. By representing wandering traces as loops, the problem of wandering detection is transformed into detecting loops in elders' mobility trajectories. Specifically, the raw GPS data is first preprocessed to remove noisy and crowded points by performing an online mean shift clustering. A novel method called θ_WD is then presented that is able to detect loop-like traces on the fly. The experimental results on the GPS datasets of several elders have show that the θ_WD method is effective and efficient in detecting wandering behaviors, in terms of detection performance (AUC > 0.99, and 90% detection rate with less than 5 % of the false alarm rate), as well as time complexity.

Over the last few years, many efforts have been devoted to revealing human mobility patterns. However, the regularity and variability of human mobility from a microscopic view, i.e., what factors affect human mobility patterns, has yet not been investigated. In this paper, we aim to study the impact factors that may affect the regularity and variability of human mobility patterns using social network analysis. Specifically, we introduce the spatial interaction matrix to represent the interaction strength and interaction semantics among spatial regions. Based on the spatial interaction matrix, we investigate the factors that impact the mobility patterns, including temporal factors, occupational factors and age factors. Our experimental results demonstrate that lots of factors such as environmental, temporal and age factors contribute to the shape of human mobility patterns.

Discovering semantic knowledge is significant for understanding and interpreting how people interact in a meeting discussion. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Human interaction flow in a discussion session is represented as a tree. Tree-based interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results show that we can successfully extract several interesting patterns that are useful for the interpretation of human behavior in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships between different types of interactions.

Due to the strict requirements of reliabilities in safety-critical domains, this paper researches and proposes an embedded adaptive real-time software fault-tolerant method based on hierarchical and multi-strategy structure. This paper mainly researches on the key issues and appropriate solutions of checkpoints rollback recovery and adaptive task migration. Based on this fault-tolerant method, this paper gives the algorithms of fault monitoring period, checkpoints number and a constraint condition of setting checkpoints. The test result shows that the method improves embedded software reliability effectively under the premise of no additional redundant hardware resources and ensuring the system real-time requirements.

Currently, SAAS (Software as a Service) solutions are usually provided for business, such as salesforce.com. Few work focus on the application software for high performance scientific computing. However, in the high performance cloud computing environment, traditional application software is not intrinsically service oriented. And the limitation of traditional software licenses is a bottleneck problem for large scale of dynamic users. To enable on-demand services for applications, we propose a solution: Application Software as a Service (ASAAS). It provides a web services portal, an on-demand software license service for the users. Application software is wrapped as web services on the basis of underlying computational resources. With a pay-for-use mode, there is no limitation for the licenses any more. The instant service rate, average job response time, and cost are analyzed for an evaluation. A case of implementation and the evaluation show that ASAAS can bring a much better effect than traditional mechanism.

This paper explores the problem of user experience evaluation, in particular the quantitative evaluation of group user experience, in smart spaces. First, the classification and definition of four different categories of user groups are proposed, and the notion of group user experience is introduced. Second, we analyze the quantitative evaluation of group user experience for different types of user groups and establish an evaluation model for group user experience. Particularly, we employ two quantitative social metrics, user rating and user attention duration, as the main criteria for evaluating user experience. Other social factors, such as group interaction and the diversity of group members, are also taken into account to form a general quantitative evaluation model of group user experience for different user groups. Finally, we evaluate the effectiveness of the proposed model with preliminary experiments in a smart museum.

Network reprogramming is a crucial service in wireless sensor networks (WSNs) that relies on epidemic strategy for spreading software updates by just having a local view of the networks. Securing the process of network reprogramming is essential in some certain WSNs applications, state-of-the-art secure network reprogramming protocols for WSNs aim for the efficient source authentication and integrity verification of code image, however, due to the resource constrains of WSNs, existing secure network reprogramming protocols are vulnerable to Denial of Service (DoS) attacks when sensor nodes can be compromised (insider DoS attacks). In this paper, we identify different types of DoS attacks exploiting the epidemic propagation strategies used by Deluge and propose corresponding analysis models to attempt to quantify the cost of these attacks damage. Simulation further shows the impact of insider DoS attacks on network reprogramming in WSNs.

Software reliability models are used to estimation and prediction of software reliability. Existing models either use black-box approach that based on test data during software test phase or white-box approach that based on software architecture and individual component reliability, which is more suited to assess the reliability of modern software system. However, most of the reliability models based on architecture assumed that a failure occurring within one component will not cause any other component to fail, which is inconsistent with the facts. This paper introduces a reliability model and a reliability analysis technique for architecture-based reliability evaluation. Our approach extend existing reliability model by considering fault propagation. We believe that this model can be used to effectively improve software quality.

User-defined preferences in a natural style is useful in the pervasive computing environment, but bring a great challenge to understand. People often express conditional as well as independent preferences. We propose an ontology-based quantitative model for conditional preferences that aims to enhance the inference capacity of conditional preference statements and thus reduce users' workload. Different interpretations of the statements of our model are depicted and compared, including the inheritance property of the concept hierarchy in ontology, the connotation of sufficient and necessary conditions, and the bipolar property of preferences in human thinking. An experiment in the trip domain is conducted and shows the feasibility of our conditional preference model.

Large scale parameter sweep application (PSA) is one of the main grid applications, which may have different characteristics and demands. In this paper, we describe how to use swift to enable the on-demand execution of large scale PSA on open science grid (OSG). The basic on-demand concept means providing appropriate grid resources for the application, which is decided by the characteristics and demands of the application. So we can get high reliability, efficiency, and scalability for large scale independent PSA jobs on OSG. The main on-demand policies include: trust based site selection and pre-selection; scheduling policy on-demand configuration; clustering for small jobs; adaptive execution and automatic data staging; divide and conquer for the scalability. Some usage examples of swift for executing large scale PSA are presented, such as dock, blast. The experimental results for the performance of different policies are presented, with a benchmarking workload size of 10,000 jobs.

The scheduling and execution for grid application is an important problem in the grid environment. To get the high reliability and efficiency, we propose a runtime reputation based grid resource selection algorithm. According to the accumulated raw score, the runtime reputation degree for a grid resource is quantified as an evaluating score in the runtime of an application. Instead of being dependent on the historical experiences, it is dynamically adaptive to the runtime availability, load, and performance of the grid resources. The execution framework on the grid is based on Globus Toolkit and Swift system. In a real production grid, Open Science Grid (OSG), a typical grid application with large scale independent jobs was experimented, which was based on BLAST application. The experimental results for the performance of different policies are presented, with a benchmarking workload size of 10,000 jobs. The runtime reputation and behavior statistics for the grid resources are also presented.

Resource performance monitoring is among the most active research topics in distributed computing. In this paper, we propose an adaptive resource monitoring method for applications in heterogeneous computing environment. According to the operating environment of distributed heterogeneous system and the changes of system resource workload, the method combines periodic pull mode with event-driven push mode to adaptively publish and retrieve system resource information. Preliminary experiments reveal that, by using our adaptive monitoring method, the efficiency of system monitoring is improved over that accrued by using regular monitoring approaches.

In this paper, we propose an approach to handle conditional preferences in recommender systems. A quantitative conditional preference model based on domain knowledge is introduced. The inheritance property in concept trees and bipolar property in preference statements are adopted when interpreting conditional preference rules. Group preferences are merged from personal preferences with consideration of manipulability. A graphical user interface is developed for visualization of domain knowledge, conditional preference rules, personal and group preferences.

Data fusion or In-network processing methods were often adopted in Wireless Sensor Networks (WSNs) to reduce data communication and prolong network lifetime, which made WSNs application can be described as a set of tasks (sensing, processing) and dependencies among them. Task assignment has become a

The event-driven nature of wireless sensor networks (WSNs) leads to unpredictable network load. As a result, congestion may occur at sensors that receive more data than they can forward, which causes energy waste, throughput reduction and packet loss. In this paper, we propose a rate-based fairness-aware congestion control protocol (FACC), which controls congestion and achieves approximately fair bandwidth allocation for different flows. In FACC, we categorize intermediate relaying sensor nodes into near-source nodes and near-sink nodes. Near-source nodes maintain per-flow state, and allocate an approximately fair rate to each passing flow. On the other hand, near-sink nodes do not need to maintain per-flow state and use a light-weight probabilistic dropping algorithm based on queue occupancy and hit frequency. Our simulation results and analysis show that FACC provides better performance than previous approaches in terms of throughput, packet loss, and fairness.

Now, autonomous tasks planning and allocating (TPA) in Multi Agent System (MAS) has been one key and fundamental problem to promote the intelligent level of such system. Autonomous TPA means that, all tasks should be (re)planned and (re)allocated automatically according to the synthesis constraints and the dynamic environment aspects, such as the changing mission, status of each member, and topology, etc. In this article, the formal descriptions of hierarchical tasks and models of logic constraints are studied firstly. And then, some new methods are proposed to evaluate the efficiency of synthesis constraints. Moreover, the key elements, e.g. task allocation vector (TAV), are designed with the theory of genetic algorithm (GA), and a TPA problem can be mapped to the solving model of GA. Based on above, the crossover and mutation operators of GA are optimized with the domain knowledge to perfect the solving efficiency and quality while ensuring the randomicity of evolution. The simulation results show that the solving quality and velocity are improved with studied methods.

In large-scale grid applications, relationship between VOs is not simple information aggregating for autonomy and visiting security. This paper presents a virtual grid information database (VGID) which implements a hierarchical and distributed information management mechanism. An information model is presented with extensible resource metadata management and hierarchical information organization that manages storage-independent resources into a global information structure according to logical relations between resources. Local information database is built within a domain (VO), which includes information collection, storage, and query, as lots of information service has done. Based on dynamical domain information management with topology service and distributed query mechanism, local information databases are integrated to provide a user-transparent global information view. VGID supports standard XPath statement, and index-based query process is tested to be efficient. VGID has been applied to Information Service of ChinaGrid.

The emerging computational grid infrastructure consists of heterogeneous resource in widely distributed autonomous domains, which makes resource scheduling even more challenging. In this paper, we propose an adaptive resource scheduling algorithm for computational grid called BLBD (based on load balancing and demand). According to the computational grid job's personal resource requirements, the system load of computational grid nodes and the load balancing of computational grid system, the algorithm choose an appropriate node self adaptively for computational grid job from the candidates. It has been successfully tested in NPU campus computational grid environment. The experimental result shows that the new task scheduling algorithm can lead to significant performance gain for a variety of applications

The formal description of distributed supervisory and controlling systems can promote development efficiency and decline cost during the procedure of system design and implementation. The goal of our research is to develop a formal modeling methodology for supervisory and controlling systems that have artificially intelligent features. This approach is agent-based and central to the development of the model with mobility agents considering reactivity for real-time purpose and deliberation for optimal realization and other special problems for critical systems like intelligent transportation systems by a Petri nets based formal modeling language. By using nets within nets, a special high-level Petri nets, we investigate the concurrency of the system and the agent behaviors in one model without losing the needed abstraction. The synchronous channels are introduced to denote the communication and coordination among agents. Finally an example is given to illustrate that the formal method is feasible and valid

Event sequence arises naturally in many applications. Episode mining can discovery the knowledge hidden in the event sequence. Currently, the most influential algorithm for episode mining is WINEPI. However, it is likely to suffer from the tendency of generating too many of candidate episodes. In this paper, a novel algorithm named DRE for mining frequent episodes is presented. It studied the conditions for the events which can be pruned from the database, so the size of database is reduced gradually. The performance of algorithm DRE was evaluated and compared with WINEPI algorithm. The results demonstrate that the DRE has better performance

As network-centric computing becomes more pervasive and applications become more distributed, the demand for flexible and efficient message delivery is increasing. Messaging middleware is a key technique of message delivery between distributed computing applications. The main communication models of current messaging middleware are publish/subscribe and point-to-point. The two communication models have their own characteristics. However, neither of them can achieve efficiency and flexibility simultaneously. In this paper, we proposed and implemented a hybrid communication model based messaging middleware (HCM3), which takes full advantages of two models. Performance result shows that HCM3 can deliver message in heterogeneous environment with the features of high efficiency, flexibility and reliability.