Electric vehicles (EVs) are a major load on the demand side. Flexible aggregation control of EVs can fully adjust clean energy consumption and promote the interaction between EVs and power grid. Due to hierarchical characteristic and differentiated quality of service (QoS) demands in EV aggregation control, a hierarchical edge network is needed to perform real-time, accurate and bi-directional data interaction. Therefore, this paper proposes a hierarchical edge communication network which featured “regional cloud-unmanned aerial vehicle (UAV) aggregator-EVs”. For the “EVs-UAV aggregator” layer, the UAV dynamic mobility deployment algorithm by Dueling deep Q-network (DQN) and the UAV static optimal position calculation method are combined to reduce transmission time delay and energy consumption. For the “UAV aggregator-regional cloud” layer, considering the differential requirements of time and accuracy while uploading load data and downloading control instructions, this paper established a transmission cost optimization problem with adjustable weights. Besides, the sub-channel resource allocation based on stable matching and the uploading decision based on game theory are designed to solve the problem iteratively. Ultimately, comparative simulations with different transmission strategies are provided to verify the effectiveness of the hierarchical transmission model in scenario of EVs aggregation control.

Sub-health is a state between health and disease conditions, which is common among people living with the fierce competition and rapid pace of modern life. At present, there are no unified approaches to diagnose the sub-health patients. Self-reporting, the use of questionnaires, is one of the most popular approaches to evaluate health conditions. While a questionnaire consists of as many as 400 questions, people are likely to lose patience. This paper presents a machine learning method to mine the sub-health related questions and then provide classification suggestion based on the self-reporting data collected from Sub-health Condition Identification and Classification Research project. To study the most effective mining approaches, four different feature selection methods were applied to discovery the internal relationship among questions and four different supervised learning classifiers were utilized to investigate the most related questions to the specific diagnostic tasks. Experimental results show that artificial neural network achieves the best performance and the final diagnostic accuracy reaches 84.07% with 20 most related questions.

In order to find the change laws of human meridian and to prove the laws' consistency with Traditional Chinese Medicine theory, conductance series data of 72 acupoints from 10 volunteers was collected for 2 years. Visualized analysis method is used in this paper to find the laws, as it a good way to find change laws before there's a definite research target. As it is a tough job to collect data form two years, this data is incomplete and has missing values. Traditionally, researches have to remove the incomplete samples. In this article, we put forward a novel method which estimates missing values in meridian dataset with Bayesian principal component analysis (BPCA) algorithm first and then visualize these values. With the proposed method, some useful characteristics of meridian conductance data were found.

[Objective]: To analyze dialectical thinking that three different prescriptions treating insomnia which Professor Zhimin Yang used. And to provide a reference for the research of Chinese medicine experts in dialectical thinking. [Methods]: First, we collected three empirical prescriptions that Professor Zhi-Min Yang used from her medical cases about insomnia. We also investigated the patients' demographic feature, historical feature, four diagnostic techniques of Traditional Chinese Medicine (TCM) feature, Pittsburgh Sleep Quality Index(PSQI), anxiety scale(SAS), depression scale(SDS). Second, we observed the whole treatment process, and defined the standard of curative effects by PSQI reduced rate between before-treatment and post-treatment. Third, to ensure that results have clinical utility, we selected the valid cases to analyze Filter preliminary features from three empirical prescriptions by chi-square test. And then, to analyze the relations among the preliminary indications by decision tree. Finally, to sum up three different prescriptions' dialectical thinking. [Results]: We had collected 78 valid cases of three prescriptions. We investigated 206 features of each case, and filtered 71 preliminary features from three empirical prescriptions by chi-square test. Decision-tree picked up 6 factors, 7 regulations. The accuracy rate of identification was 91.03%.[Conclusion]: The decision tree model can accurately and expeditiously to extract the characteristics from empirical prescriptions, and provide a reference for the research of Chinese medicine experts in dialectical thinking.

In this paper, we propose a new type of incentive-driven and privacy-preserving systems for large-scale message dissemination in mobile networks. To distribute incentives which encourage forwarding behaviors, such as monetary rewards, we want to keep track of the forwarder list. In our algorithms, we rely on a Probabilistic one-ownership forwarding algorithm to record the list, so that the exchanged messages can be kept short and privacy preserving. More specifically, only one hop of forwarder information, instead of the complete list, is recorded, and the information is updated probabilistically following two ownership flipping models, namely, One-Flip and Always-Flip models. We also use a Bluetooth Service Discovery Protocol (SDP) toolkit to enable fast, configuration-free message exchange. Throughout the paper, we use coupon as a typical type of message to illustrate the core ideas. We have implemented the coupon dissemination system in Java ME. Our experiments on real-world mobile phones, such as Nokia and Samsung phones, and large-scale simulations show that our system is efficient in peer-to-peer message distribution and capable of massive deployment. We believe our key methodology can serve as a general framework for facilitating information propagation on mobile phones, where incentives and privacy protection are both essential.

[Objective]: To explore the thoughts of the addition and subtraction of empirical formula of traditional Chinese medicine. [Methods]: To collect the clinical features of insomnia patients treated by the empirical formula of "si-ni and gui-gan-long-mu" prescription (hereinafter referred as to empirical formula) which contained cocta tuber, gingiberi, Zhigancao, carina, ostreae and Guizhi" with added and subtracted drugs from professor Zhimin Yang of Guangdong Province Hospital of Traditional Chinese Medical. We use application of association rules (GRI algorithm) to analysis the association rules between the clinical characteristics and addition and subtraction of drugs. [Results]: We collected 40 insomnia patients by the empirical formula of "si-ni and gui-gan-long-mu" with addition and subtraction of drugs from Professor Zhimin Yang, with a total of 106 clinical features. Using association rules (GRI algorithm), respectively analyzing association rules between the 28 drugs of addition and subtraction out of the prescription and 81 clinical characteristics, and we get 12 association rules, including 5 kinds of addition and subtraction drugs which contained white atractylodes rhizome, Poria cocos, Poria cocos with Rhizoma Pinellinae Praeparata, Fresh dogwood with fructus murne, equivalent Guizhi with Aglaophotis radix herbaceu. [Conclusion]: Using the association rules (GRI algorithm), to set the drugs of addition and subtraction as post-item, and the symptoms as pre-item which can meet the clinical characteristic of empirical formula. It is an effective and objective method to explore the empirical formula of addition and subtraction.

In this paper, an in-depth analysis on the statistical distribution characteristics of human tongue color that aims to propose a mathematically described tongue color space for diagnostic feature extraction is presented. Three characteristics of tongue color space, i.e., tongue color gamut that defines the range of colors, color centers of 12 tongue color categories, and color distribution of typical image features in the tongue color gamut, are elaborately investigated in this paper. Based on a large database, which contains over 9000 tongue images collected by a specially designed noncontact colorimetric imaging system using a digital camera, the tongue color gamut is established in the CIE chromaticity diagram by an innovatively proposed color gamut boundary descriptor using one-class SVM algorithm. Thereafter, centers of 12 tongue color categories are defined accordingly. Furthermore, color distributions of several typical tongue features, such as red points and petechial points, are obtained to build a relationship between the tongue color space and color distributions of various tongue features. With the obtained tongue color space, a new color feature extraction method is proposed for diagnostic classification purposes, with experimental results validating its effectiveness.

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish non-service-related information without establishing a connection. We propose a novel iterative Bloom filter protocol that encodes topics to fit in SDP attributes and achieves a low false-positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.

Classifying Web users based on multi-dimensional features is one of the foundations of realizing personalized Web applications. It could be used for user classification model, users' multi-dimensional data analysis, potential user group discovery and personalized recommendation and so forth. In this paper, a variable granularity user classification algorithm based on Web users' multidimensional features is proposed. Given a user feature model, the algorithm will mine all common feature categories and find the relationships between them. A series of experiments are conducted to analyze the performances of this algorithm with different condition. The experimental results indicate that this algorithm has good performance and can be deployed in Web applications with massive Web users.

Nowadays, wireless sensor networks are widely used to monitor real time events and answer the ad hoc queries from a certain member node. However, computing and maintaining the information of aggregate queries in event monitoring wireless sensor networks incurs high spatial and temporal overhead for storage and transmission where potentially high volumes of unnecessary data may run through with changing time. Failure of processing that data can lead to unsuccessful event detection which can be very dangerous and costly in real world application. In order to reduce the overhead caused by unnecessary data for aggregate, suppression techniques such as data fusion, data sharing, data prediction, lossless data compression and base station side query rewriting are widely discussed in the WSN research community. In this paper, a technique which makes use of the spatial data relationship of local sensor nodes collaboratively is proposed to rein the detection of composite events with data aggregate. An empirical study is carried out to show the efficiency of the new technique. In addition, the new algorithm is compared to the previous event detection algorithms without spatial data suppression technique to demonstrate the significant performance gains.

In this paper, we propose P3-coupon, a Prompt and Privacy-preserving electronic coupon distribution system based on a Probabilistic one-ownership forwarding algorithm. In this algorithm, only one hop of forwarder (coupon owner) information instead of the complete forwarder list is recorded to keep the coupon short and privacy-preserving. Such information is updated probabilistically following two ownership flipping models, namely, One-Flip and Always-Flip models. We also use a Bluetooth Service Discovery Protocol (SDP) toolkit to enable fast, configuration-free coupon exchange. We have implemented the system in Java ME. Our experiments on real world mobile phones, such as Nokia and Samsung phones, and large scale simulations show that our system is efficient in peer to peer coupon distribution and capable of massive deployment. We believe our key methodology can serve as a general framework for facilitating information propagation on mobile phones, which requires promptness and privacy protection.

Nowadays, all of library management systems for CDs attached to books are running independently: independent devices, independent systems, and making CD images independently. That brings huge waste of investment and labor. In this paper, we redesign the management system for CDs attached to books based on Cloud Computing. By comparing and analyzing the two systems, we can see that the system based on Cloud Computing saves the cost of the library greatly. Then we discuss the workflow of database design for the system, management module, release module, and the computing way of the expense.

In this paper, the existing trust models and firewall technology are studied. Then we develop an approach based on Cloud Computing to estimate dynamic context and present the definition of risk signal. A collaborative trust model of firewall-through based on Cloud Environment is proposed. The model has three advantages: Firstly, there are different security policies for different domains. Secondly, the model considers the transaction context, the historical data of entity influences and the measurement of trust value dynamically. Thirdly, the trust model is compatible with the firewall and does not break the firewall's local control policies. To verify the reliability and effectiveness of the coordinated trust model, a simulation is carried out. The simulation result shows that this trust model is robust and out-performances traditional trust models based on domains because it successfully blocks malicious access under Cloud Computing environment.

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish non-service-related information without establishing a connection. We propose a novel iterative Bloom filter (IBF) protocol that encodes topics to fit in SDP attributes and achieves a low false positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.

Malicious shellcodes are segments of binary code disguised as normal input data. Such shellcodes can be injected into a target process's virtual memory. They overwrite the process's return addresses and hijack control flow. Detecting and filtering out such shellcodes is vital to prevent damage. In this paper, we propose a new malicious shellcode detection methodology in which we take snapshots of the process's virtual memory before input data are consumed, and feed the snapshots to a malicious shellcode detector. These snapshots are used to instantiate a runtime environment that emulates the target process's input data consumption to monitor shellcodes' behaviors. The snapshots can also be used to examine the system calls that shellcodes invoke, these system call parameters, and the process's execution flow. We implement a prototype system in Debian Linux with kernel version 2.6.26. Our extensive experiments with real traces and thousands of malicious shellcodes illustrate our system's performance with low overhead and few false negatives and few false positives.

Null data frames are a special but important type of frames in IEEE 802.11 WLANs. They are widely used in 802.11 WLANs for control purposes such as power management, channel scanning, and association keeping alive. The wide applications of null data frames come from their salient features such as lightweight frame format and implementation flexibility. However, such features can be taken advantage of by malicious attackers to launch a variety of attacks on 802.11 WLANs. In this paper, we identify potential security vulnerabilities in current null data frame applications in 802.11 WLANs. We then study two types of attacks taking advantage of these vulnerabilities in detail that are functionality-based Denial-of-Service attack and implementation-based fingerprinting attack. We also evaluate their effectiveness based on extensive experiments. Furthermore, we design and implement novel defense mechanisms against the attacks, and evaluate their effectiveness based on extensive experiments. Although our proposed defenses help alleviate the vulnerabilities, completely eliminating the vulnerabilities brought by null data frames remains an open issue. Finally, we point out that our work has broader impact in that similar vulnerabilities exist in many other networks due to the adoption of simple and lightweight messages for control purpose.

Smartphones have been widely used in recent years due to their capabilities of supporting many applications from simple Short Message Service messages to complicated Location-based services. It is challenging for smartphones to enable their end users to manage all applications in all possible use cases to protect privacy or sensitive data. However, the security model for smartphone users is still a two-state model in which they can do anything or absolutely nothing, and it is no longer suitable. In this paper, we propose DiffUser, a differentiated user access control model to enhance smartphone security and user privacy. DiffUser classifies smartphone users based on certain sets of user access privileges. We implement a prototype of DiffUser on real-world T-Mobile G1 smartphones. The evaluation results show that our system is lightweight and flexible.

This paper presents a cross-layer iterative decoder for irregular low-density parity-check (LDPC) codes which uses cyclic redundancy check (CRC) codes. The key idea of the decoder is to use correctly decoded frames as an aid for correcting the remaining erroneous frames. To accomplish this, the decoder exchanges the relevant information between layers by using the cross-layer design method and an iterative decoding architecture. Moreover, the unequal-error protection (UEP) property of irregular LDPC is exploited and both the multiple-error detection and single-error correction capabilities of the CRC code are used. Simulation results show that the proposed decoder outperforms the pure sum-product algorithm (SPA) decoder by a considerable gain while the increase in complexity is moderate. Furthermore, the error floor of irregular LDPC codes in the high Eb/NO regime can be lowered effectively. The proposed cross-layer iterative decoder can be used for any irregular LDPC coded wireless system to boost the performance and lower the error floor.

The current 802.11i standard can provide data confidentiality, integrity and mutual authentication in enterprise Wireless Local Area Networks (WLANs). However, secure communication can only be provided after successful authentication and a robust security network association is established. In general, the wireless link layer is not protected by the current standard in WLANs, which leads to many possible attacks, especially in public open-access wireless networks. We argue that regardless of the type of network under consideration, link-layer protection and data confidentiality are of great importance in wireless applications. In this paper, we first identify and analyze the security issues ignored by the current 802.11 security standard. Then we propose our solution to patch the current 802.11i standard and address all those issues with a new dummy authentication key-establishment algorithm. Dummy means no real authentication for a user. In dummy authentication, we apply public-key cryptography's key-establishment technique to the 802.11 MAC protocol. Our solution can provide link-layer data encryption in open-access wireless networks, separate session encryption keys for different users, and protection for important frames such as management and null data frames as well as Extensible Authentication Protocol (EAP) messages.

Current methods for querying XML data streams are mostly based on events filtering techniques. It is unavoidable for the query systems to adopt an event filter to process the incoming XML tags. It is well known that during the filtering, some data items have to be buffered before the filter can make the proper decision for adopting strategies to deal with them. Furthermore, for a single filter system, the buffer size often increases exponentially in the real application. Lots of work both theoretically and empirically, has been done to dissolve or depress that exponential explosion, but it still remains an active research topic. Considering the fast development of computer supported collaboration, we propose a new multi-filters collaborative querying technique to process XML data streams. We show that the multi-filters collaboration can effectively share and reduce the filtering space consumption if some XML document and query constraints can be satisfied. Deploying our multi-filters collaboration technique, the querying systems together can break the limitation of the concurrency lower bound, an important theoretic lower bound within XML data streams research field. The empirical study shown in this paper demonstrates that our multi-filters collaboration outperforms the limitation of the concurrency lower bound.

Null data frames are a special but important type of frames in IEEE 802.11 based wireless local area networks (e.g., 802.11 WLANs). They are widely used for power management, channel scanning and association keeping alive. The wide applications of null data frames come from their salient features such as lightweight frame format and implementation flexibility. However, such features can be taken advantage of by malicious attackers to launch a variety of attacks. In this paper, we identify the potential security vulnerabilities in the current applications of null data frames. We then study two types of attacks taking advantage of these vulnerabilities in detail, and evaluate their effectiveness based on extensive experiments. Finally, we point out that our work has broader impact in that similar vulnerabilities exist in many other networks.

CSCW is a platform which is established to support the cooperation. Mobile agent (MA) technique provides a new way to implement network affairs pick up information and migrate the task. A distributed environment which presents group, interaction and cooperation is set up by using MA technique in CSCW. By the supporting of this environment, a system making agent coordinator (AC) as a core is set up by migrating task and choosing route. Finally the P2P cooperation was proved efficiently in CSCW.

In this paper, an anti-sensor network system is proposed, aiming to protect an important area from being under surveillance by an adversary's sensor nodes. The major components of the system are a set of observing points (monitors) deployed in the area of importance. The observers try to localize sensor positions using antenna arrays to measure direction of arrival (DoA) and received signal strength of the signals emitted by sensors. Once sensors are localized, additional measures are taken to physically remove or disable localized sensors. The proposed anti-sensor network system is designed to handle additional counter-measures that can be employed by sensors, including message encryption and non-uniform transmission power levels. The simulation results show the effectiveness of the proposed system and effects of counter-measures on sensor localization performance.

To reduce the risk of intentional destruction in CSCW systems, an intelligent agent based intrusion detection and protection system solution is presented. The system uses rules to check intrusion and adopts machine learning algorithms to recognize unknown actions and to update rules or create new ones automatically. Cooperating with other security products is another useful characteristic for more integrated protection, and the final test shows its feasibility

Based on the information system of Shanhua Company, this paper discusses the structure and function of intrusion detection system based on RBF, the steps and method of intrusion detection. In the experiment of network simulation, through continuous training of input normal samples and abnormal sample, keeping an eye on if the RBF neural networks can distinguish the known intrusion behavior character among the training samples with high exactness and distinguish new intrusion behavior character and mutation of known intrusion behavior character with some probability. The result of experiment proves that RBF network is better than BP network in its property of optimal approximation, classify ability and the rapidity of study, RBF can improve the detection performances of IDS.