ISSN 1000-1239 CN 11-1777/TP

Table of Content

15 June 2013, Volume 50 Issue 6
WInternet: From Net of Things to Internet of Things
Jianjia Wu and Wei Zhao
2013, 50(6):  1127-1134. 
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In recently years, Internet of things (IoT) has attracted great attentions from academic, industry, and the government. IoT is considered to be a network that can connect billions of physical objects together, and has great potential in extending human beings capability in monitoring, analyzing, and controlling the physical space using cyber technologies. Extensive IoT research has been done. Large number of IoT systems have been built and applied successfully. However, the development of IoT is facing several fundamental issues, such as what are the characteristic requirements of IoT? What is the current development stage of IoT, and what is the future direction? These are questions need further investigation. In this paper, we review the development history of Internet and summarize the lessons we learned from this development. Then we analyze the current status of IoT, and argue that most of the current IoT systems are “net of things”, i.e., small scale networks connecting physical physical objects. The future of IoT should be “Internet of Things”, i.e., inter-connected, large scale, and open network. With this judgment, we propose a novel IoT architecture, named WInternet, and disuse its design principals, network topology, connection scheme, protocol stack, as well the open issues in IoT and WInternet development.
Universal Compute Account and Personal Information Asset Algebra in Human-Cyber-Physical Ternary Computing
Xu Zhiwei, Xie Yi, Hai Mo, Li Xiaolin, and Yuan Zimu,
2013, 50(6):  1135-1146. 
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Invariant abstractions have played a fundamental role in knowledge accumulation and capital deepening in the field of computer science, of which an example in the past 20 years is the resource abstraction in RESTful Web services and cloud computing. An important direction in future computer science research is computing for the masses in a ternary universe of the human society, the cyberspace, and the physical world, whereby the resource-centric paradigm will shift to a user-centric paradigm, enabling personalization of future information products and services. This calls for a new invariant abstraction, i.e., a universal compute account (UCA) for each user to utilize human-cyber-physical resources in a tetherless and seamless way. This paper discusses the basic properties and organization of UCA, and presents a personal information asset algebra as well as a prototype implementation of the corresponding data asset management system. Analysis of experimental examples shows that the UCA and the asset algebra system facilitate data integration and sharing, thus alleviate the current problem that users personal data are scattered and locked in many vendors devices and services.
An Important Aspect of Big Data: Data Usability
Li Jianzhong and Liu Xianmin
2013, 50(6):  1147-1162. 
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With the rapid development of information technology, especially the great progresses of Internet, cyber physical system, Internet of things, cloud computing and social network, big data becomes ubiquitous. Big data brings not only great benefits but also crucial challenges. Improving the data usability is one of the most significant challenges. Dirty data accompanies the tremendous increase of data volume, degrades the data quality and data usability, and brings serious harm to the information societies. Fortunately, there has been widespread concern about the data usability in both industrial and academic communities, and the recent research efforts on data usability have yielded some impressive results. However, there are only few works focusing on the usability of big data. In this paper, the concepts of big data usability are introduced first, and then the challenges and research problems of the big data usability are discussed. Finally, the works related to the data usability are surveyed.
A Distributed Context-Aware Complex Event Processing Method for Internet of Things
Cao Kening, Wang Yongheng, Li Renfa, and Wang Fengjuan
2013, 50(6):  1163-1176. 
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The data produced by Internet of things has the characteristic of big-data and those data can hardly be processed by present data processing technologies. As the key part of Internet of things, complex event process (CEP) has two characteristics of big data: quantity, complexity and has the need of on-time processing. Context-awareness is an important feature of CEP engine. In this paper, a high-performance distributed context-aware CEP architecture and method is proposed for Internet of things. The method uses fuzzy ontology to create query model to support event query of uncertainty and fuzzy. Based on fuzzy ontology query and similarity based distributed reasoning, complex event query plans are generated and context-aware queries are rewritten into context independent sub-queries. The sub-queries are optimized and executed parallel based on data partition. The experimental results show that this method can support fuzzy context in CEP and has better performance and scalability than other methods.
Multi-Label Feature Selection Algorithm Based on Information Entropy
Zhang Zhenhai, Li Shining, Li Zhigang, and Chen Hao
2013, 50(6):  1177-1184. 
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Multi-label classification is the learning problem where each instance is associated with a set of labels. Feature selection is capable of eliminating redundant and irrelevant features in multi-label classification, which leads to performance improvement of multi-label classifiers. However the existing feature selection methods have high computation complexity and are not able to give a reasonable feature subset. Hence a novel multi-label feature selection algorithm based on information entropy is proposed in this paper. It assumes that features are independent of each other. Its main ideas are: 1) The information gain between the feature and label set is derived from the information gain between the feature and the label, and employed to measure the correlation degree between them; 2) An threshold selection method is used to choose a reasonable feature subset from original features. The proposed algorithm firstly computes the information gain between each feature and label set, and then removes the irrelevant and redundant features according to the selected information gain value determined by threshold selection method. The experiment is conducted on four different datasets and two different classifiers. The experimental results and their analysis show that the proposed algorithm can effectively promote the performance of multi-label classifiers in multi-label classification.
PMDA: A Physical Model Driven Software Architecture for Internet of Things
Xie Kaibin, Chen Haiming, and Cui Li
2013, 50(6):  1185-1197. 
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It is a basic method to establish Internet of Things(IoT) by interconnecting all existing physical applications. However, there has not yet been an effective architecture to guide horizontal interconnections of physical applications. To address the problem, this paper proposes a Physical Model Driven software Architecture for Internet of Things (PMDA), which supports the horizontal interconnections of physical applications. PMDA is composed of three models, namely, Physical Model, Sense-Execute Model and Application Model. The connections and interactions between models, and interactions among components in the models are realized by connectors, which satisfies some certain constraints. The composition of the three models in PMDA and the interactions among the models are formally described by an Architecture Description Language named Wright. The effectiveness of horizontal interconnections of physical applications based on PMDA is verified by PAT, in terms of deadlock-free,divergence-free and nonterminating. Besides that, those properties of IoT application systems developed under guidance of PMDA are proven by mathematical induction.
An Approach to Building Systems and Applications of Internet of Things with Smart Things
Kong Junjun, Guo Yao, Chen Xiangqun, and Shao Weizhong
2013, 50(6):  1198-1209. 
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With the rapid development and application of pervasive computing and Internet of Things, more and more artificial things are augmented by seamlessly embedding new capabilities such as sensing, actuating, communication and calculation. Compared with their traditional counterparts, the augmented artifacts can perform complex tasks more intelligently, automatically, and even collaboratively. These augmented artifacts are referred to as smart things (or smart objects), which are becoming basic building blocks of Internet of Things and driving the emergence of novel pervasive applications. However, programming smart things faces challenges because they are dynamic and inherently heterogeneous in terms of their built-in functionalities, computational capabilities, network interfaces, etc. This paper proposes an interaction mechanism and programming abstraction to support smart things-based application development. A middleware system is implemented to realize the interaction mechanism and programming abstraction while providing runtime support for heterogeneous smart things. With the help of the proposed programming abstraction and interfaces, application developers can easily program smart household appliances such as smart TV, smart air conditioner, smart projector, smart light, and so on. Case studies and experimental results show that the proposed smart things based approach can be utilized to develop systems and applications of the Internet of Things flexibly and effectively.
Cooperative Localization for Vehicular Ad Hoc Networks
Peng Xin, Li Renfa, Wang Dong, and Li Zhetao
2013, 50(6):  1210-1216. 
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A novel cooperative localization scheme for vehicular ad hoc metworks(VANETs) is proposed based on the semi-definite programming. Firstly, vehicles broadcast its movement information and exchange the range and angle data. The range and angle data are used to deduce simi-definite relaxation constraint condition for inter-vehicle distance matrix within a short time interval. And then the semi-definite programming method is employed to determine vehicles’ coordinate. Finaly, gradient descent optimization can be incorporated into our algorithm to further improve the estimating accuracy at the expense of additional cost. During simulations, the proposed algorithm is shown to provide preferable localization performance, and perform well on distance error.
Data Dissemination Based on System Utility in Cooperative Delay Tolerant Networks
Zhao Guangsong, Chen Ming, and Zuo Qingyun
2013, 50(6):  1217-1226. 
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Data dissemination in delay tolerant networks (DTNs) has been extensively studied in recent years. The end-to-end path between any two nodes in DTNs suffers intermittence frequently, so the nodes exploit the store-carry-forward routing paradigm to communicate with others. When two nodes come into contact with each other, how to select data objects for buffering is a critical problem for each node, which affects the data dissemination performance of the system. Nodes in DTNs usually lack the global network information, so they cannot make the global optimal selection of data objects for buffering. In order to solve this problem, the global optimization problem is converted to an optimization problem under each contact, and the new problem can be formulated as 0-1 knapsack problem in this paper. A heuristic algorithm is proposed to solve this 0-1 knapsack problem and instructs nodes to selectively buffer data objects for maximizing the gain in system utility even when nodes maintain local network information. Furthermore, this paper investigates the relationship between the decisions made by the nodes and the scope of network information they maintain. Extensive trace-driven simulations based on MIT trace are conducted to evaluate the performance of our heuristic algorithm. The results demonstrate that our heuristic algorithm can achieve better performance than SocialCast algorithm. And the simulation results also show that the larger scope of network information the nodes maintain, the better performance our heuristic algorithm can achieve.
A Classification Prediction Mechanism Based on Comprehensive Assessment for Wireless Link Quality
Guo Zhiqiang, Wang Qin, Wan Yadong, and Li Mohan
2013, 50(6):  1227-1238. 
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In the applications of wireless sensor networks (WSN), it is a fundamental issue to effectively estimate and predict the quality of wireless links for the network protocol design, such as reliable WSN deployment, routing policy and resource management protocol, especially in respect of the reliability of data delivery. In this paper, we characterize the quality of wireless links from a perspective of multiple dimensions and propose a comprehensive quality index of wireless links (referred as fuzzy-logic based link quality index, FLI), which overcomes the defects of the single link quality indicator. FLI takes the link reliability, the link vibration and the burstiness of packet loss into consideration, which affects the reliable data delivery. Further, we design a mechanism based on Bayesian classifier to classify and predict wireless links based on the FLI metric. Taking the limited computing and storage resources in the WSN into account, the prediction mechanism uses offline model training and online classification prediction. Then it is tested and verified in the wireless link databases from three real WSN research testbeds, and the results show that our classifier achieves an average prediction accuracy of 85%. In comparison with the 4C approach, it avoids the sudden drop of prediction accuracy on intermediate quality links shown in the 4C, while maintaining a higher average accuracy. In other words, the distribution of prediction accuracy is uniform.
Privacy-Preserving Top-k Query Processing in Two-Tiered Wireless Sensor Networks
Dai Hua,Yang Geng, Qin Xiaolin, and Liu Liang
2013, 50(6):  1239-1252. 
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Privacy preservation in wireless sensor networks has attracted more and more attentions recently. Computing top-k query result while preserving data privacy is a challenge in wireless sensor networks. This paper proposes a prefix membership verification (PMV) based privacy-preserving top-k query processing in two-tiered wireless sensor networks, which is denoted as PPTQ. In PPTQ, each sensor node in the query aera firstly converts its collected data into encoded ones by using prefix encoding and hashed message authentication encoding mechanism, and encrypts all the collected data items, then submits the encoded data and the ciphertext to the corresponding storage node. According to the numerical comparison theory of PMV, the minimum candidate ciphertext sets containing the query result will be generated by the covered storage nodes, without knowing the actual values of the collected data items. Then, the storage nodes send the candidate ciphertext to sink, and the final top-k result will be computed by sink after decryption. To reduce the power consumption of sensor nodes, an energy optimization method based on a Hash function is proposed. The theoretical analysis and experimental results show that the PPTQ can ensure the privacy of the collected data and the query result, and it has better performance than the current work in query processing.
Secure Range Query in Two-Tiered Wireless Sensor Networks
Dou Yi, Huang Haiping,, Wang Ruchuan, and Qin Xiaolin
2013, 50(6):  1253-1266. 
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In the process of wireless sensor networks query, an adversary may violate the privacy of sensitive data and manipulate compromised storage sensors to reply incomplete or incorrect query results to sink. Based on the two-tiered wireless sensor network model, a secure range query protocol, named ZOSR, is proposed in this paper. ZOSR enables storage sensor to process queries correctly while preserving the privacy of sensed data and the integrity of query result. Firstly, it transfers the range judgment into the comparison between data distance with query range median and query range radius. Which enables the predication only once. To preserve privacy, it converts above comparison into the judgment on whether there is an intersection between two Z-O encoding data sets. Moreover it combines hashed message authentication code mechanism with proposed approach, so that it can prevent other sensitive sensed data from being disclosed and under conspiracy attack. To preserve the query result integrity, it generates Hash value of the shared key as the verification code of sensed data which is not satisfied with the query range. At last, it analyzes the security and conducts performance simulations of ZOSR protocol which shows greater security and less power consumption.
A Hierarchical Access Control Scheme for Perceptual Layer of IoT
Ma Jun, Guo Yuanbo, Ma Jianfeng, Xiong Jinbo, and Zhang Tao
2013, 50(6):  1267-1275. 
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The perceptual layer is at the most front-end of information collection, which plays a fundamental role in the Internet of Things (IoT). In the perceptual layer, mass perceptual nodes are required to sense a vast range of different data types for authorized users in accordance with privacy, security, and customization. This leads to the problem that traditional access control schemes (IBAC, RBAC etc.) fail to meet the requirements of users who want secure and efficient access resources on-demand. In this paper, a hierarchical access control scheme for perceptual layer of the IoT is presented. In the scheme, every hierarchical node, representing a class in the access hierarchy, is composed of perceptual nodes which provide information with the same levels of security. More hierarchical nodes can be modeled as a set of partially ordered classes. Besides, the scheme considers the limited computational and storage capacity of mass perceptual nodes. Compared with previous proposals, the scheme has the following advantages: Every user and perceptual node possesses a single key material to get some keys by a deterministic key derivation algorithm, and obtains the resources at the presented class and all descendant classes in the hierarchy. This increases the security of hierarchical node and reduces much storage costs. Due to supporting full-dynamic changes to the access hierarchy and replacement of key material, the presented scheme not only provides security of hierarchical data access, but also efficiently reduces much communication cost greatly. Furthermore, the scheme is provably secure without random oracle model and meets other security features. Further analysis show that our scheme adapts to access control requirement of perceptive layer of the IoT.
Scalable RFID Mutual Authentication Protocol with Backward Privacy
Wang Shaohui, Liu Sujuan, and Chen Danwei
2013, 50(6):  1276-1284. 
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Anonymous authentication mechanisms can be used in RFID systems to preserve the privacy of the RFID tags. Scalability problem and backward privacy problem are two important issues considered in practice. In this paper, security analysis is presented on three recently proposed RFID authentication protocols satisfying scalability and backward privacy. The research shows that ACP protocol can not provide the property of backward privacy; the G-I protocol can not resist dysynchronization attack, i.e. the adversary can make the secrets stored in tag and reader unmatched, which results in the tag and the reader in a desynchronized state and renders future authentication impossible; and the MMR protocol can not resist active attack, because the adversary can extract tags all secrets via querying the tag and analyzing the messages sent by the tag. In addition, we present a modified scalable hash-based mutual authentication protocol with less storage and computation requirements than G-I. And we prove our scheme can provide the property of backward privacy and resist the desynchronization attack.
Health Status Detection via Temporal-Spatial Factor Graph Model in Medical Social Networks
Gong Jibing, Wang Rui, Wang Xiaofeng,and Cui Li
2013, 50(6):  1285-1296. 
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Applications of Social Networks is very popular in various fields including health and medical area. At the same time, Wireless Sensor Networks(WSNs) has some new development situations. In real world, people’s health status detection/prediction is influenced by various factors such as social relationships, history health statuses and people’s personal condition. However, few publications systematically study how health statuses evolve in a dynamic social network and to what extent different factors affect the user health status. In this paper, we first describe a novel Medical Social Networks(MSNs) which is a classic kind of Medical Internet of Things(Medical IoTs). Then combining these above factors together, we propose a unified model, namely TS-FGM, based on Probability Factor Graph Model, and thus present a novel health status prediction method based on TS-FGM in MSNs. More specifically, users’ health statuses at time t are influenced by their private attributes, their own health statuses at time t-1 and their neighbors’ health statuses at both time t and t-1. At last, we present an efficient decision-fusion-oritented algorithm to learn the model. Finally, we validate the model on real-world data sets in Twitter. And we compare our method with baseline algorithm SVM on a real clinic medical data set for pulse diagnosis. Experimental results show that the model is effective and the proposed method partly outperforms the baseline method for disease prediction.
Key Technologies of Vessel Segmentation and Reconstruction of the Cerebrovascular Disease Detection E-Health Platform Based on the Internet of Things
Wang Xingce, Wu Zhongke, Zhou Mingquan, Luo Yanlin, Shui Wuyang, and Liu Xinyu
2013, 50(6):  1297-1312. 
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In the application of Internet of things (IoT), the cerebrovascular disease detection e-health platform can realize the long care and long telemedicine of cerebrovascular health. The four levels architecture of the platform is designed and realized in the paper. The segmentation and reconstruction of brain vessel is deeply researched here. The double Gauss mixture model is put forward to realize the cerebrovascular segmentation and the stochastic estimation maximization (SEM) algorithm is adopted to estimate the parameters of it. It could be easily used for the common user of the platform without initial contour, high dimensional evolution equation and the iteration terminal condition. The cerebrovascular vessels have low proportion (<5%) in the brain tissue. Its angiography gray is non-uniform. Geometry is complex and individual differences are quite large. The segmentation method in our paper can get the good result. Ball B-Spline curve (BBSC) has the characters of strict mathematic foundation, less dataset, stable smoothness and continuity, good interactivity. It suits to transfer the data in the IoT platform. Combing these technologies, a CUDA (compute unified device architecture) based ray-casting volume rendering, the interactive and automatic virtual wandering are realized. The platform can realize the diagnosis of the cerebrovascular disease, the medical plan design and monitor in the treatment, which can also be widely used in the teaching. The research is an interesting try to extend the fine tissues e-health platform based on the internet of things.
Multiple Particle Swarms Coevolutionary Algorithm for Dynamic Multi-Objective Optimization Problems and Its Application
Hu Chengyu, Yao Hong, and Yan Xuesong
2013, 50(6):  1313-1323. 
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Most of multi-objective optimization problems in the real-world are dynamic, so optimization algorithms are required to continuously track time-varying Pareto optimal set (POS) or Pareto optimal front (POF) rapidly with high accuracy. To meet this requirement, an improved variant based on particle swarm optimization (PSO) is proposed, in which competitive and cooperative models are combined. The competitive model is used to explore the search space, and when it fails, this model is adaptively switched to the cooperation model to exploit the search space. Co-evolution probability analysis indicates that searching solution using multiple swarms is much more efficient than using a single one. Numerical simulation also shows that the proposed algorithm is an excellent alternative for solving dynamic multi-objective optimization problems. Finally, the proposed algorithm is applied to the PID controller parameter tuning for a dynamic system and gets a satisfactory control.
CEclipse: A Services Composition Based Online IDE
Wu Ling, Liang Guangtai, and Wang Qianxiang
2013, 50(6):  1324-1334. 
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With the introduction of the concept of cloud computing, many desktop applications have been migrated into the cloud. The desktop Integrated Development Environment (IDE) has become a hot topic in recent years. This paper introduces CEclipse which is an online integrated development environment. CEclipse encapsulates the functions of local IDE into Web services, and then uses the services composition technology to combine all the services to form the core functions of online IDE. Besides, CEclipse utilizes the program static analysis and program dynamic analysis technologies to handle with the specific security issues of online IDE. In order to fully take advantage of online IDE, CEclipse proposes to use the data mining technology to explore the development behavior of the programmers, and use the mining result to give instructions to the programmers in their development process. Finally, CEclipse makes use of single sign on technology to integrate the existing Web application into online IDE, and proposes a suite of approach to semi-automatically migrate the function of eclipse plugin to the online IDE, so that it can improve the expansibility of the online IDE.
State-of-the-Art on Texture-Based Well Logging Image Classification
Wang Huafeng, Wang Yuting, and Chai Hua
2013, 50(6):  1335-1348. 
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Imaging well logging can easily and effectively determine the existing locations of the reservoirs, because of its ability of showing the stratal lithologies and geometric changes in the form of image. Recently, imaging log is used widely for its higher discernibility and has become a research hotspot in the field of well logging technology. How to fully utilize image processing, pattern recognition and other related theoretical methods for both more precise quantitative evaluation of imaging logging and the interpretation of images is the focus. Texture analysis plays an important role in the field of computer vision and pattern recognition. This paper surveys the research background and current situation of the imaging well logging, and then reviews most existing typical algorithms for texture analysis. It focuses on the grey level co-occurrence matrices (GLCM) algorithm, local binary patterns algorithm (LBP), Gabor transform, wavelet transform, as well as Contourlet transform, and analyzes their respective pros and cons. Based on this, and considering the features of the logging images, this paper gives a method for the classification of the well logging images, and proposes a system model of logging image recognition and classification. The problems, prospects for future development and suggestions for further research works are put forward at the end of the paper.