The recent development of communication devices and wireless network technologies continues to advance the new era of the Internet and telecommunications. The various “things”, which include not only communication devices but also every other physical object on the planet, are also going to be connected to the Internet, and controlled through wireless networks. This concept, which is referred to as the “Internet of Things (IoT)”, has attracted much attention from many researchers in recent years. The concept of IoT can be associated with multiple research areas such as body area networks, Device-to-Device (D2D) communications networks, home area networks, Unmanned Aerial Vehicle (UAV) networks, satellite networks, and so forth. Also, there are various kinds of applications created by using IoT technologies. Thus, the concept of the IoT is expected to be integrated into our society and support our daily life in the near future. In this paper, we introduce different classifications of IoT with examples of utilizing IoT technologies. In addition, as an example of a practical system using IoT, a tsunami detection system (which is composed of a satellite, sensor terminals, and an active monitoring system for real-time simultaneous utilization of the devices) is introduced. Furthermore, the requirements of the next generation systems with the IoT are delineated in the paper.

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Oblivious RAM (ORAM) schemes, the concept introduced by Goldreich and Ostrovsky, are very useful technique for protecting users' privacy when storing data in remote untrusted servers and running software on untrusted systems. However they are usually considered impractical due to their huge overhead. In order to reduce overhead, many improvements have been presented. Thanks to these improvements, ORAM schemes can be considered practical on cloud environment where users can expect huge storage and high computational power. Especially for private information retrieval (PIR), some literatures demonstrated they are usable. Also dedicated PIRs have been proposed and shown that they are usable in practice. Yet, they are still impractical for protecting software running on untrusted systems. We first survey recent researches on ORAM and PIR. Then, we present a practical software-based memory protection scheme applicable to several environments. The main feature of our scheme is that it records the history of accesses and uses the history to hide the access pattern. We also address implementing issues of ORAM and propose practical solutions for these issues.

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To secure a wireless sensor and actuator network (WSAN) in cyber-physical systems, trust management framework copes with misbehavior problem of nodes and stimulate nodes to cooperate with each other. The existing trust management frameworks can be classified into reputation-based framework and trust establishment framework. There, however, are still many problems with these existing trust management frameworks, which remain unsolved, such as frangibility under possible attacks. To design a robust trust management framework, we identify the attacks to the existing frameworks, present the countermeasures to them, and propose a hybrid trust management framework (HTMF) to construct trust environment for WSANs in the paper. HTMF includes second-hand information and confidence value into trustworthiness evaluation and integrates the countermeasures into the trust formation. We preform extensive performance evaluations, which show that the proposed HTMF is more robust and reliable than the existing frameworks.

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As the development of Internet-of-Things is moving towards large scale industry, such as logistic and manifacturing, there is a need for end-users to get involved in the process of creating IoT easily. In this paper, we introduce PaperIO, a paper-based 3D I/O interface, in which a single piece of paper can be sensed and actuated at the same time in three dimensions using the technology of selective inductive power transmission. With this technology, paper material with multiple embedded receivers, can not only selectively receive inductive power to perform paper-computing behavior, but also work as input sensors to communicate with power transmitter wirelessly. This technology allows the creation of paper-based sensor and actuators, and forms an Interent of Embedded Paper-craft. This paper presents the detailed implementation of the system, results of the technical experiments, and a few sample applications of the presented paper-based 3D I/O interface, and finally discusses the future plan of this research.

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Mobile social networks (MSN) provides diverse services to meet the needs of mobile users, i.e., discovering new friends, and sharing their pictures, videos and other information among their common interest friends. On the other hand, Quality-of-Experience (QoE) is a new concept related to but differs from Quality-of-Service (QoS) perception. QoE is a subjective measure of a customer's experiences with a service focuses on the entire service experience, and is a more holistic evaluation. So far, QoS issues have been focused and mainly addressed in the literature of MSNs. To the best of our knowledge, this paper is the first article to address QoE issues in emerging MSNs. In this paper, we first present a comprehensive investigation on recent advances in MSNs as well as QoE issues addressed in various types of applications and networks. From the lessons learned from the literature, then we propose a future research direction of QoE in MSNs.

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With the increase of the number of wireless sensing or metering devices, the collection of sensing data using wireless communication becomes an important part of a smart grid system. Cognitive radio technology can be used to facilitate the deployment of smart grid systems. In this paper, we propose a data collection and dissemination framework for cognitive radio smart grid systems to fully utilize wireless resources while maintaining a reliably connected and efficient topology for each channel. In the proposed framework, each sensor node selects a channel considering the primary user (PU) channel utilization and network connectivity. In this way, the data collection and dissemination can be performed with a high reliability and short delay while avoiding a harmful effect on primary users. We use computer simulations to evaluate the proposed framework.

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Understanding the structure and evolution of spatial-temporal networks is crucial for different fields ranging from urbanism to epidemiology. As location based technologies are pervasively used in our daily life, large amount of sensing data has brought the opportunities to study human activities and city dynamics. Ubiquitous cell phones can be such a sensor to analyze the social connection and boundaries of geographical regions. In this paper, we exploit user mobility based on large-scale mobile phone records to study urban areas. We collect the call data records from 1 million anonymous subscribers of 8 weeks and study the user mobility flux between different regions. First we construct the urban areas as a spatial network and use modularity detection algorithm to study the intrinsic connection between map areas. Second, another generative model which is widely used in linguistic context is adopted to explore the functions of regions. Based on mobile call records we are able to derive the partitions which match boundaries of the administrative districts. Our results can also catch the dynamics of urban area as the basis for city planning and policy making.

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Provisioning multiple paths can improve fault tolerance and transport capability of multi-routing in wireless networks. Disjoint paths can improve the diversity of paths and further reduce the risk of simultaneous link failure and network congestion. In this paper we first address a many-to-one disjoint-path problem (MOND) for multi-path routing in a multi-hop wireless network. The objective of this problem is to maximize the minimum number of disjoint paths of every source to the destination. We prove that it is NP-hard to obtain k disjoint paths for every source when k ≥ 3. To solve this problem efficiently, we propose a heuristic algorithm called TOMAN based on network flow theory. Experimental results demonstrate that it outperforms three related algorithms.

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Cloud computing, a novel distributed paradigm to provide powerful computing capabilities, is usually adopted by developers and researchers to execute complicated IoT applications such as complex workflows. In this scenario, it is fundamentally important to make an effective and efficient workflow application scheduling and execution by fully utilizing the advantages of the cloud (as virtualization and elastic services). However, in the current stage, there is relatively few research for workflow scheduling in cloud environment, where they usually just bring the traditional methods directly into cloud. Without considering the features of cloud, it may raise two kinds of problems: (1) The traditional methods mainly focus on static resource provision, which will cause the waste of resources; (2) They usually ignore the performance fluctuation of virtual machines on the physical machines, therefore it will lead to the estimation error of task execution time. To address these problems, a novel mechanism which can estimate the probability distribution of subtask execution time based on background VM load series over physical machines is proposed. An elastic performance fluctuations-aware stochastic scheduling algorithm is introduced in this paper. The experiments show that our proposed algorithm can outperform the existing algorithms in several metrics and can relieve the influence of performance fluctuations brought by the dynamic nature of cloud.

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More recently, there has been a growing interest in the study of wireless sensor network (WSN) technologies for Interest of Things (IoT). To improve the positioning accuracy of mobile station under the non-line-of-sight (NLOS) environment, a localization algorithm based on the single-hidden layer feedforward network (SLFN) using extreme learning machine (ELM) for WSN is proposed in this paper. Optimal reduction in the time difference of arrival (TDOA) measurement error is achieved using SLFN optimized by ELM. Compared with those traditional learning algorithms, ELM has its unique feature of a higher generalization capability at a much faster learning speed. After utilizing the ELM by randomly assigning the parameters of hidden nodes in the SLFN, the competitive performance can be obtained on the optimization task for TDOA measurement error. Then, based on that result, Taylor algorithm is implemented to deal with the position problem of mobile station. Experimental results show that the effect of NLOS propagation is reduced based on our proposed algorithm by introducing the ELM into Taylor algorithm. Moreover, in the simulation, the proposed approach, called Taylor-ELM, provides better performance compared with some traditional algorithms, such as least squares, Taylor, backpropagation neural network based Taylor, and Chan positioning methods.

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According to the match-degree between lightpaths, an HC-sharing approach is proposed to assign wavelength for an arriving transmission request for dynamic traffic in LOBS-based datacenter networks. The simulation results demonstrate that the proposed approach can provide lower block probability than other approaches for both unicast and multicast transmissions.

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Operating system (OS) reboots are an essential part of updating kernels and applications on laptops and desktop PCs. Long downtime during OS reboots severely disrupts users' computational activities. This long disruption discourages the users from conducting OS reboots, failing to enforce them to conduct software updates. Although the dynamic updatable techniques have been widely studied, making the system “reboot-free” is still difficult due to their several limitations. As a result, users cannot benefit from new functionality or better performance, and even worse, unfixed vulnerabilities can be exploited by attackers. This paper presents ShadowReboot, a virtual machine monitor (VMM)-based approach that shortens downtime of OS reboots in software updates. ShadowReboot conceals OS reboot activities from user's applications by spawning a VM dedicated to an OS reboot and systematically producing the rebooted state where the updated kernel and applications are ready for use. ShadowReboot provides an illusion to the users that the guest OS travels forward in time to the rebooted state. ShadowReboot offers the following advantages. It can be used to apply patches to the kernels and even system configuration updates. Next, it does not require any special patch requiring detailed knowledge about the target kernels. Lastly, it does not require any target kernel modification. We implemented a prototype in VirtualBox 4.0.10 OSE. Our experimental results show that ShadowReboot successfully updated software on unmodified commodity OS kernels and shortened the downtime of commodity OS reboots on five Linux distributions (Fedora, Ubuntu, Gentoo, Cent, and SUSE) by 91 to 98%.

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Strongly deterministic multithreading provides determinism for multithreaded programs even in the presence of data races. A common way to guarantee determinism for data races is to isolate threads by buffering shared memory accesses. Unfortunately, buffering all shared accesses is prohibitively costly. We propose an approach called DRDet to efficiently make data races deterministic. DRDet leverages the insight that, instead of buffering all shared memory accesses, it is sufficient to only buffer memory accesses involving data races. DRDet uses a sound data-race detector to detect all potential data races. These potential data races, along with all accesses which may access the same set of memory objects, are flagged as data-race-involved accesses. Unsurprisingly, the imprecision of static analyses makes a large fraction of shared accesses to be data-race-involved. DRDet employs two optimizations which aim at reducing the number of accesses to be sent to query alias analysis. We implement DRDet on CoreDet, a state-of-the-art deterministic multithreading system. Our empirical evaluation shows that DRDet reduces the overhead of CoreDet by an average of 1.6X, without weakening determinism and scalability.

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Presentation slide composition is an important job for knowledge workers. Instead of starting from scratch, users tend to make new presentation slides by reusing existing ones. A primary challenge in slide reuse is to select desired materials from a collection of existing slides. The state-of-the-art solution utilizes texts and images in slides as well as file names to help users to retrieve the materials they want. However, it only allows users to choose an entire slide as a query but does not support the search for a single element such as a few keywords, a sentence, an image, or a diagram. In this paper, we investigate content-based search for a variety of elements in presentation slides. Users may freely choose a slide element as a query. We propose different query processing methods to deal with various types of queries and improve the search efficiency. A system with a user-friendly interface is designed, based on which experiments are performed to evaluate the effectiveness and the efficiency of the proposed methods.

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Internet traffic classification is an essential step for stable service provision. The payload signature classifier is considered a reliable method for Internet traffic classification but is prohibitively computationally expensive for real-time handling of large amounts of traffic on high-speed networks. In this paper, we describe several design techniques to minimize the search space of traffic classification and improve the processing speed of the payload signature classifier. Our suggestions are (1) selective matching algorithms based on signature type, (2) signature reorganization using hierarchical structure and traffic locality, and (3) early packet sampling in flow. Each can be applied individually, or in any combination in sequence. The feasibility of our selections is proved via experimental evaluation on traffic traces of our campus and a commercial ISP. We observe 2 to 5 times improvement in processing speed against the untuned classification system and Snort Engine, while maintaining the same level of accuracy.

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The applicability of at-speed scan-based logic built-in self-test (BIST) is being severely challenged by excessive capture power that may cause erroneous test responses even for good circuits. Different from conventional low-power BIST, this paper is the first to explicitly focus on achieving capture power safety with a novel and practical scheme, called capture-power-safe logic BIST (CPS-LBIST). The basic idea is to identify all possibly-erroneous test responses caused by excessive capture power and use the well-known approach of masking (bit-masking, slice-masking,vector-masking) to block them from reaching the multiple-input signature register(MISR). Experiments with large benchmark circuits and a large industrial circuit demonstrate that CPS-LBIST can achieve capture power safety with negligible impact on test quality and circuit overhead.

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With IC design entering the nanometer scale integration, the reliability of VLSI has declined due to small-delay defects, which are hard to detect by traditional delay fault testing. To detect small-delay defects, on-chip delay measurement, which measures the delay time of paths in the circuit under test (CUT), was proposed. However, our pre-simulation results show that when using on-chip delay measurement method to detect small-delay defects, test generation under the single-path sensitization is required. This constraint makes the fault coverage very low. To improve fault coverage, this paper introduces techniques which use segmented scan and test point insertion (TPI). Evaluation results indicate that we can get an acceptable fault coverage, by combining these techniques for launch off shift (LOS) testing under the single-path sensitization condition. Specifically, fault coverage is improved 27.02∼47.74% with 6.33∼12.35% of hardware overhead.

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Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.

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With the development of the Internet, there are more and more shared resources on the Web. Personalized search becomes increasingly important as users demand higher retrieval quality. Personalized search needs to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags (features) and thus provide a powerful way for organizing, retrieving and sharing different types of social resources. To capture and understand user preferences, a user is typically modeled as a vector of tag: value pairs (i.e., a tag-based user profile) in collaborative tagging systems. In such a tag-based user profile, a user's preference degree on a group of tags (i.e., a combination of several tags) mainly depends on the preference degree on every individual tag in the group. However, the preference degree on a combination of tags (a tag-group) cannot simply be obtained from linearly combining the preference on each tag. The combination of a user's two favorite tags may not be favorite for the user. In this article, we examine the limitations of previous tag-based personalized search. To overcome their problems, we model a user profile based on combinations of tags (tag-groups) and then apply it to the personalized search. By comparing it with the state-of-the-art methods, experimental results on a real data set shows the effectiveness of our proposed user profile method.

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The bilateral filter (BF) is a nonlinear and low-pass filter which can smooth an image while preserving detail structures. However, the filer is time consuming for real-time processing. In this paper, we bring forward a fresh idea that bilateral filtering can be accelerated by a multigrid (MG) scheme. Our method is based on the following two facts. a) The filtering result by a BF with a large kernel size on the original resolution can be approximated by applying a small kernel sized (3×3) version on the lower resolution many times on the premise of visual acceptance. Early work has shown that a BF can be viewed as nonlinear diffusion. The desired filtering result is actually an intermediate status of the diffusion process. b) Iterative linear equation techniques are sufficiently mature to cope with the nonlinear diffusion equation, which can be accelerated by the MG scheme. Experimental results with both simulated data sets and real sets are provided, and the new method is demonstrated to achieve almost twice the speed of the state-of-the-art. Compared with previous efforts for finding a generalized representation to link bilateral filtering and nonlinear diffusion by adaptive filtering, a novel relationship between nonlinear diffusion and bilateral filtering is explored in this study by focusing attention on numerical calculus.

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The Hough voting framework is a popular approach to parts based pedestrian detection. It works by allowing image features to vote for the positions and scales of pedestrians within a test image. Each vote is cast independently from other votes, which allows for strong occlusion robustness. However this approach can produce false pedestrian detections by accumulating votes inconsistent with each other, especially in cluttered scenes such as typical street scenes. This work aims to reduce the sensibility to clutter in the Hough voting framework. Our idea is to use object segmentation and object pose parameters to enforce votes' consistency both at training and testing time. Specifically, we use segmentation and pose parameters to guide the learning of a pedestrian model able to cast mutually consistent votes. At test time, each candidate detection's support votes are looked upon from a segmentation and pose viewpoints to measure their level of agreement. We show that this measure provides an efficient way to discriminate between true and false detections. We tested our method on four challenging pedestrian datasets. Our method shows clear improvements over the original Hough based detectors and performs on par with recent enhanced Hough based detectors. In addition, our method can perform segmentation and pose estimation as byproducts of the detection process.

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In this paper, we propose a texture descriptor based on amplitude distribution and phase distribution of the discrete Fourier transform (DFT) of an image. One dimensional DFT is applied to all the rows and columns of an image. Histograms of the amplitudes and gradients of the phases between adjacent rows/columns are computed as the feature descriptor, which is called aggregated DFT (ADFT). ADFT can be easily combined with completed local binary pattern (CLBP). The combined feature captures both global and local information of the texture. ADFT is designed for isotropic textures and demonstrated to be effective for roughness classification of castings. Experimental results show that the amplitude part of ADFT is also discriminative in describing anisotropic textures and it can be used as a complementary descriptor of local texture descriptors such as CLBP.

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In Still-to-Video (S2V) face recognition, only a few high resolution images are registered for each subject, while the probe is video clips of complex variations. As faces present distinct characteristics under different scenarios, recognition in the original space is obviously inefficient. Thus, in this paper, we propose a novel discriminant analysis method to learn separate mappings for different scenario patterns (still, video), and further pursue a common discriminant space based on these mappings. Concretely, by modeling each video as a manifold and each image as point data, we form the scenario-oriented mapping learning as a Point-Manifold Discriminant Analysis (PMDA) framework. The learning objective is formulated by incorporating the intra-class compactness and inter-class separability for good discrimination. Experiments on the COX-S2V dataset demonstrate the effectiveness of the proposed method.

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This paper provides a prototype neural prosthesis system dedicated to restoring continence and micturition function for patients with lower urinary tract diseases, such as detrusor hyperreflexia and detrusor-sphincter dyssynergia. This system consists of an ultra low-noise electroneurogram (ENG) signal recording module, a bi-phasic electrical stimulator module and a control unit for closed-loop bladder monitoring and controlling. In order to record extremely weak ENG signal from extradural sacral nerve roots, the system provides a programmable gain from 80 dB to 117 dB. By combining of advantages of commercial-off-the-shelf (COTS) electronics and custom designed IC, the recording front-end acquires a fairly low input-referred noise (IRN) of 0.69 μV_rms under 300 Hz to 3 kHz and high area-efficiency. An on-chip multi-steps single slope analog-to-digital converter (ADC) is used to digitize the ENG signals at sampling rate of 10 kSPS and achieves an effective number of bits (ENOB) of 12.5. A bi-phasic current stimulus generator with wide voltage supply range (±0.9 V to ±12.5 V) and variable output current amplitude (0-500 μA) is introduced to overcome patient-depended impedance between electrode and tissue electrolyte. The total power consumption of the entire system is 5.61 mW. Recording and stimulation function of this system is switched by control unit with time division multiplexing strategy. The functionality of this proposed prototype system has been successfully verified through in-vivo experiments from dogs extradural sacral nerve roots.

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In this letter, we present a new method of alleviating the deterioration in the quality of real-time video service during vertical handover (VHO). The proposed method stochastically delays the starting time of the service disruption of VHO in order to reduce the number of lost frames caused by the inter-frame dependency of multi-layered video traffic. The results show that the proposed method significantly decreases the average frame loss time at the sacrifice of an increased handover execution time by one half of the group of picture (GOP) interval of the video traffic.

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The goal of dimension reduction is to represent high-dimensional data in a lower-dimensional subspace, while intrinsic properties of the original data are kept as much as possible. An important challenge in unsupervised dimension reduction is the choice of tuning parameters, because no supervised information is available and thus parameter selection tends to be subjective and heuristic. In this paper, we propose an information-theoretic approach to unsupervised dimension reduction that allows objective tuning parameter selection. We employ quadratic mutual information (QMI) as our information measure, which is known to be less sensitive to outliers than ordinary mutual information, and QMI is estimated analytically by a least-squares method in a computationally efficient way. Then, we provide an eigenvector-based efficient implementation for performing unsupervised dimension reduction based on the QMI estimator. The usefulness of the proposed method is demonstrated through experiments.

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In measuring TV ratings, some features can be significant at a certain time, whereas they can be meaningless in other time periods. Because the importance of features can change, a model capturing the time changing relevance is required in order to estimate TV ratings more accurately. Therefore, we focus on the time-awareness of features, particularly the time when the words of tweets are used. We develop a correlation-based, time-aware feature selection algorithm which finds the optimal time period of each feature, and the estimation method using e-SVR based on top-n-features that are ordered by correlation. We identify that the correlation values between features and TV ratings vary according to the time of postings - before and after the broadcast time. This implies that the relevance of features can change according to the time of the tweets. Experimental results indicate that the proposed method has better performance compared with the method based on count-based features. This result implies that understanding the time-dependency of features can be helpful in improving the accuracy of measuring TV ratings.

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This paper deals with the problem of reconstructing a high-resolution digital image from a single low-resolution digital image and proposes a new intra-frame super-resolution algorithm based on the mixed l_p/l₁ norm minimization. Introducing some assumptions, this paper formulates the super-resolution problem as a mixed l₀/l₁ norm minimization and relaxes the l₀ norm term to the l_p norm to avoid ill-posedness. A heuristic iterative algorithm is proposed based on the iterative reweighted least squares (IRLS). Numerical examples show that the proposed algorithm achieves super-resolution efficiently.

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This paper proposes a novel method to segment the object of a specific class based on a rough detection window (such as Deformable Part Model (DPM) in this paper), which is robust to the positions of the bounding boxes. In our method, the DPM is first used to generate the root and part windows of the object. Then a set of object part candidates are generated by randomly sampling windows around the root window. Furthermore, an undirected graph (the minimum spanning tree) is constructed to describe the spatial relationships between the part windows. Finally, the object is segmented by grouping the part proposals on the undirected graph, which is formulated as an energy function minimization problem. A novel energy function consisting of the data term and the smoothness term is designed to characterize the combination of the part proposals, which is globally minimized by the dynamic programming on a tree. Our experimental results on challenging dataset demonstrate the effectiveness of the proposed method.

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A discriminative reference-based method for scene image categorization is presented in this letter. Reference-based image classification approach combined with K-SVD is approved to be a simple, efficient, and effective method for scene image categorization. It learns a subspace as a means of randomly selecting a reference-set and uses it to represent images. A good reference-set should be both representative and discriminative. More specifically, the reference-set subspace should well span the data space while maintaining low redundancy. To automatically select reference images, we adapt affinity propagation algorithm based on data similarity to gather a reference-set that is both representative and discriminative. We apply the discriminative reference-based method to the task of scene categorization on some benchmark datasets. Extensive experiment results demonstrate that the proposed scene categorization method with selected reference set achieves better performance and higher efficiency compared to the state-of-the-art methods.

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