Cyber-physical systems (CPSs) are an emerging discipline that involves engineered computing and communicating systems interfacing the physical world. The widespread applications of CPSs still face enormous challenges because of the lack of theoretical foundations. In this technical survey, we review state-of-the-art design techniques from various angles. The aim of this work is to provide a better understanding of this emerging multidisciplinary methodology. The features of CPSs are described, and the research progress is analyzed using the following aspects: energy management, network security, data transmission and management, model-based design, control technique, and system resource allocation. We focus on CPS resource optimization, and propose a system performance optimization model with resource constraints. In addition, some classic applications (e.g., integrating intelligent road with unmanned vehicle) are provided to show that the prospects of CPSs are promising. Furthermore, research challenges and suggestions for future work are outlined in brief.

Node cooperation during packet forwarding operations is critically important for fair resource utilization in Community Wireless Mesh Networks (CoWMNs). In a CoWMN, node cooperation is achieved by using fairness protocols specifically designed to detect and isolate malicious nodes, discourage unfair behavior, and encourage node participation in forwarding packets. In general, these protocols can be split into two groups: Incentive-based ones, which are managed centrally, and use credit allocation schemes. In contrast, reputation-based protocols that are decentralized, and rely on information exchange among neighboring nodes. Centrally managed protocols inevitably suffer from scalability problems. The decentralized, reputation-based protocols lacks in detection capability, suffer from false detections and error propagation compared to the centralized, incentive-based protocols. In this study, we present a new fairness protocol management scheme, called Hybrid FPMS that captures the superior detection capability of incentive-based fairness protocols without the scalability problems inherently expected from a centralized management scheme as a network's size and density grows. Simulation results show that Hybrid FPMS is more efficient than the current centralized approach and significantly reduces the network delays and overhead.

RFID technologies have attracted a lot of attention in recent years because of their cost/time-effectiveness in large-scale logistics, supply chain management (SCM) and other various potential applications. One of the most important issues of the RFID-based systems is how quickly tags can be identified. Tag collision arbitration plays a more critical role in determining the system performance especially for passive tag-based ones where tag collisions are dealt with rather than prevented. We present a novel tag collision arbitration protocol called Optimum Frame-Slotted Aloha (OFSA). The protocol has been designed to achieve time-optimal efficiency in tag identification through an analytic study of tag identification delay and tag number estimation. Results from our analysis and extensive simulations demonstrate that OFSA outperforms other collision arbitration protocols. Also, unlike most prior anti-collision protocols, it does not require any modification to the current standards and architectures facilitating the rollout of RFID systems.

Wireless Sensor Network (WSN) is made up of a number of sensor nodes and base stations. Traffic flow in WSN appears self-similar due to its data delivery process, and this impacts queue length greatly and makes queuing delay worse. Active queue management can be designed to improve QoS performance for WSN. In this paper, we propose self-similar traffic rate estimating algorithm named Power-Law Moving Averaging (PLMA) to regulate packet marking probability. This algorithm improves the availability of the rate estimation algorithm under the self-similar traffic condition. Then, we propose an adaptive Proportional Integral algorithm (SSPI) based on the estimation of the Self-Similar traffic rate by PLMA. Simulation results show that SSPI can achieve lower queue length jitter and smaller setting time than PI.

Adaptive Bit Loading (ABL) in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) is often used to achieve the desired Bit Error Rate (BER) performance in wireless systems. In this paper, we discuss some of the bit loading algorithms, compare them in terms of the BER performance, and present an effective and concise Adaptive Grouped Loading (AGL) algorithm. Furthermore, we propose a "weight factor" for loading algorithm to converge rapidly to the final solution for various data rate with variable Signal to Noise Ratio (SNR) gaps. In particular, we consider the bit loading in near optimal Singular Value Decomposition (SVD) based MIMO-OFDM system. While using SVD based system, the system requires perfect Channel State Information (CSI) of channel transfer function at the transmitter. This scenario of SVD based system is taken as an ideal case for the comparison of loading algorithms and to show the actual enhancement achievable by our AGL algorithm. Irrespective of the CSI requirement imposed by the mode of the system itself, ABL demands high level of feedback. Grouped Loading (GL) would reduce the feedback requirement depending upon the group size. However, this also leads to considerable degradation in BER performance. In our AGL algorithm, groups are formed with a number of consecutive sub-channels belonging to the same transmit antenna, with individual gains satisfying predefined criteria. Simulation results show that the proposed "weight factor" leads a loading algorithm to rapid convergence for various data rates with variable SNR gap values and AGL requires much lesser CSI compared to GL for the same BER performance.

Cognitive radio (CR) MAC protocol provides access control of unused spectrum resources without causing interference to primary users. To achieve this goal, in this paper a TDMA based cooperative multi-channel cognitive radio MAC (MCR-MAC) protocol is proposed for wireless ad hoc networks to provide reliable protection for primary users by achieving cooperative detection of incumbent system signals around the communication pair. Each CR node maintains transmission opportunity schedules and a list of available channels that is employed in the neighbor discovery period. To avoid possible signal collision between incumbent systems and cognitive radio ad hoc users, we propose a simple but efficient emergency notification message exchanging mechanism between neighbor CR nodes with little overhead. Our simulation results show that the proposed MCR-MAC can greatly reduce interference with primary users and remarkably improve the network throughput.

In this paper, we propose the enhanced network-based mobility management protocol, called enhanced proxy mobile ipv6 (E-PMIPv6), which can provide mobile nodes (MNs) with a fast and efficient mobility service in PMIPv6 domain. The proposed scheme can provide a fast and efficient mobility service to MNs and also the strength of network scalability and stability to an access network by proposing the dynamic virtual hierarchical network architecture. In addition, the pre-authentication procedure for an MN, based on the information of neighbor mobile access gateway (MAG) list in the enhanced-policy server (E-PS), is proposed to support seamless handover by reducing MN's handover latency. Through performance evaluations of numerical analyses and simulations, we have confirmed and verified the superiority of the proposed scheme compared to the conventional proxy mobile ipv6 (PMIPv6).

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

Theoretical Bit Error Rate (BER) and channel capacity analysis are always of great interest to the designers of wireless communication systems. At the center of such analyses people are often encountered with a high-dimensional multiple integrals with quite complex integrands. Conventional Gaussian quadrature is inefficient in handling problems like this, as it tends to entail tremendous computational overhead, and the principal order of its error term increase rapidly with the dimension of the integral. In this paper, we propose a new approach to calculate complex multi-fold integrals based on the number theory. In contrast to Gaussian quadrature, the proposed approach requires less computational effort, and the principal order of its error term is independent of the dimension. The effectiveness of the number theory based approach is examined in BER and capacity analyses for practical systems. In particular, the results generated by numerical computation turn out in good match with that of Monte-Carlo simulations.

Complementary trees are two spanning trees rooted at the sink node satisfying that any source node's two paths to the sink node on the two trees are node-disjoint. Complementary trees routing strategy is a special node-disjoint multi-path routing approach. Several complementary trees routing algorithms have been proposed, in which path discovery methods based on depth first search (DFS) or Dijkstra's algorithm are used to find a path for augmentation in each round of path augmentation step. In this paper, a novel path discovery method based on multi-tree-growing (MTG) is presented for the first time to our knowledge. Based on this path discovery method, a complementary trees routing algorithm is developed with objectives of low average path length on both spanning trees and low complexity. Measures are employed in our complementary trees routing algorithm to add a path with nodes near to the sink node in each round of path augmentation step. The simulation results demonstrate that our complementary trees routing algorithm can achieve low average path length on both spanning trees with low running time, suitable for wireless sensor networks in industrial scenarios.

Based on the quincunx sub-sampling grid, the New Interleaved Hierarchical INTerpolation (NIHINT) method is recognized as a superior pyramid data structure for the lossless and progressive coding of natural images. In this paper, we propose a new image interpolation algorithm, Edge Adaptive Hierarchical INTerpolation (EAHINT), for a further reduction in the entropy of interpolation errors. We compute the local variance of the causal context to model the strength of a local edge around a target pixel and then apply three statistical decision rules to classify the local edge into a strong edge, a weak edge, or a medium edge. According to these local edge types, we apply an interpolation method to the target pixel using a one-directional interpolator for a strong edge, a multi-directional adaptive weighting interpolator for a medium edge, or a non-directional static weighting linear interpolator for a weak edge. Experimental results show that the proposed algorithm achieves a better compression bit rate than the NIHINT method for lossless image coding. It is shown that the compression bit rate is much better for images that are rich in directional edges and textures. Our algorithm also shows better rate-distortion performance and visual quality for progressive image transmission.

The purpose of this study is to develop and present a strategic framework for mobile Internet marketing in the $Web^2$ environment. The $Web^2$ mobile Internet marketing is characterized by services, such as augmented reality and social network services. Considering the changes in the effects and types of advertisements in the mobile Internet, few studies of mobile advertisements have been conducted thus far in the $Web^2$ environment, including the cloud computing environment. Accordingly, this research aims to identify the relationships between importance and satisfaction and to uncover the characteristics of mobile advertisements through smart phones using the IPA (Importance-performance Analysis) methodology in the $Web^2$ environment. To induce the minimum required characteristics of a mobile advertisement in terms of the importance and satisfaction of IPA, Kano's model is applied to this analysis. The study also probes the relationships between the overall satisfaction and factors of each dimension of IPA through a regression analysis. As a result, this study presents four types of $Web^2$ mobile Internet marketing strategies. It was also confirmed that the maintenance reinforcement factors of the IPA dimension affect the degree of overall satisfaction. This study has implications for businesses and researchers preparing $Web^2$ mobile services and marketing.

In this research, we consider the replication strategy for the applications of video streaming in wireless mesh networks (WMNs). In particular, we propose a closed-form of optimal replication densities for a set of frames of a video streaming by exploiting not only the skewed access probability of each frame but also the skewed loss probability and skewed encoding rate-distortion information. The simulation results demonstrate that our method improves the replication performance in terms of user-perceived quality (UPQ) which includes: 1) minimum average maximum reconstructed distortion for high peak signal-to-noise ratio (PSNR), 2) small reconstructed distortion fluctuation among frames for smooth playback, and 3) reasonable average maximum transmission distance for continuous playback. Furthermore, the proposed strategy consumes smaller storage capacity compared to other existing optimal replication strategies. More importantly, the effect of encoding rate is carefully investigated to show that high encoding rate does not always gain high performance of replication for video streaming.

In this paper, we propose an e-Learning process control model that aims to graphically describe and automatically generate the manifest of sequencing prerequisites in packaging SCORM's content aggregation models. In specifying the e-Learning activity sequencing, SCORM provides the concept of sequencing prerequisites to be manifested on each e-Learning activity of the corresponding tree-structured content organization model. However, the course developer is required to completely understand the SCORM's complicated sequencing prerequisites and other extensions. So, it is necessary to achieve an efficient way of packaging for the e-Learning content organization models. The e-Learning process control model proposed in this paper ought to be an impeccable solution for this problem. Consequently, this paper aims to realize a new concept of process-driven e-Learning content aggregating approach supporting the e-Learning process control model and to implement its e-Learning process modeling system graphically describing and automatically generating the SCORM's sequencing prerequisites. Eventually, the proposed model becomes a theoretical basis for implementing a SCORM-based e-Learning process management system satisfying the SCORM's sequencing prerequisite specifications. We strongly believe that the e-Learning process control model and its modeling system achieve convenient packaging in SCORM's content organization models and in implementing an e-Learning management system as well.

The hybrid architecture of content delivery networks (CDN) and peer-to-peer overlay networks (P2P) is a promising technology enables effective real-time streaming services. It complements the advantages of quality control and reliability in a CDN, and the scalability of a P2P system. With real-time streaming services, however, high connection setup and media delivery latency are becoming the critical issues in deploying the CDN-P2P system. These issues result from biased peer selection without location awareness or content awareness, and can lead to significant service disruption. To reduce service disruption latency, we propose a group-based CDN-P2P hybrid architecture (iCDN-P2P) with a location/content-aware selection of peers. Specifically, a SuperPeer network makes a location-aware peer selection by employing a content addressable network (CAN) to distribute channel information. It also manages peers with content awareness, forming a group of peers with the same channel as the sub-overlay. Through a performance evaluation, we show that the proposed architecture outperforms the original CDN-P2P hybrid architecture in terms of connection setup delay and media delivery time.

In this paper, a new technique for color reproduction based on color variation characteristics of digital camera under a dim light condition is proposed. Generally, photographers should adjust a camera exposure properly for obtaining an image with real color tone of subjects. Thus, in case of taking a picture under a dim light condition, the exposure time of a camera has to be relatively longer than the one under a bright light condition. Although images with real color tone of the subject are obtained, the images may get blurred due to the shaky hands of photographer holding the camera. In order to avoid the blur effect, an input image is captured from a camera set as a short exposure time under a dim light condition. Then we propose a method to reproduce color tone of the dim input image. To this end, color variation characteristics which represent color variations of a digital camera are first extracted by analyzing the Macbeth color checker images taken under various exposure values. Then, a color reproduction is performed by an estimation based on the color variation characteristics. Experimental results have shown that the proposed method has achieved better performance of color reproduction, compared with existing methods.

This paper presents the effects of depth map quantization for multiview intermediate image generation using depth image-based rendering (DIBR). DIBR synthesizes multiple virtual views of a 3D scene from a 2D image and its associated depth map. However, it needs precise depth information in order to generate reliable and accurate intermediate view images for use in multiview 3D display systems. Previous work has extensively studied the pre-processing of the depth map, but little is known about depth map quantization. In this paper, we conduct an experiment to estimate the depth map quantization that affords acceptable image quality to generate DIBR-based multiview intermediate images. The experiment uses computer-generated 3D scenes, in which the multiview images captured directly from the scene are compared to the multiview intermediate images constructed by DIBR with a number of quantized depth maps. The results showed that there was no significant effect on depth map quantization from 16-bit to 7-bit (and more specifically 96-scale) on DIBR. Hence, a depth map above 7-bit is needed to maintain sufficient image quality for a DIBR-based multiview 3D system.

In this paper, we propose a fast and robust algorithm for fighting behavior detection based on Motion Vectors (MV), in order to solve the problem of low speed and weak robustness in traditional fighting behavior detection. Firstly, we analyze the characteristics of fighting scenes and activities, and then use motion estimation algorithm based on block-matching to calculate MV of motion regions. Secondly, we extract features from magnitudes and directions of MV, and normalize these features by using Joint Gaussian Membership Function, and then fuse these features by using weighted arithmetic average method. Finally, we present the conception of Average Maximum Violence Index (AMVI) to judge the fighting behavior in surveillance scenes. Experiments show that the new algorithm achieves high speed and strong robustness for fighting behavior detection in surveillance scenes.

Without providing a proper security measure to the handover procedure in Mobile WiMAX, several security attacks can be mounted. Even though security schemes have been previously proposed for this purpose, they are still vulnerable to several security attacks due to fatal design flaws. A newly proposed security scheme in this paper is based on the framework of authentication domain and concept of handover ticket. A method of establishing security associations within the authentication domain is proposed, and a lightweight security measure to protect the management messages associated with the handover is also proposed. Especially, using the handover ticket, the new security scheme can defend against a Redirection Attack arising from a compromised base station. The new security scheme is comparatively analyzed with the previous security schemes in terms of Replay, Session Hijacking, Man-In-The-Middle, and Redirection attacks.

The fuzzy vault scheme has emerged as a promising solution to user privacy and fingerprint template security problems. Recently, however, the fuzzy vault scheme has been shown to be susceptible to a correlation attack. This paper proposes a novel scheme for one-time templates for fingerprint authentication based on the fuzzy vault scheme. As in one-time passwords, the suggested method changes templates after each completion of authentication, and thus the compromised templates cannot be reused. Furthermore, a huge number of chaff minutiae can be added by expanding the size of the fingerprint image. Therefore, the proposed method can protect a user's fingerprint minutiae against the correlation attack. In our experiments, the proposed approach can improve the security level of a typical approach against brute-force attack by the factor of $10^{34}$.

Digital document file such as Adobe Acrobat or MS-Office is encrypted by its own ciphering algorithm with a user password. When this password is not known to a user or a forensic inspector, it is necessary to recover the password to open the encrypted file. Password cracking by brute-force search is a perfect approach to discover the password but a time consuming process. This paper presents a new method of speeding up password recovery on Graphic Processing Unit (GPU) using a Compute Unified Device Architecture (CUDA). PDF files are chosen as a password cracking target, and the Abode Acrobat password recovery algorithm is examined. Experimental results show that the proposed method gives high performance at low cost, with a cluster of GPU nodes significantly speeding up the password recovery by exploiting a number of computing nodes. Password cracking performance is increased linearly in proportion to the number of computing nodes and GPUs.

Access control in dynamic environments needs the ability to provide more access opportunities of information to users, while also ensuring protection information from malicious users. Trust and risk are essential factors and can be combined together in access control decision-making to meet the above requirement. In this paper, we propose the combination of the trust and risk in access control to balance information accessibility and protection. Access control decision is made on the basis of trustworthiness of users and risk value of permissions. We use potential relations between users and relations between permissions in access control. Our approach not only provides more access opportunities for trustworthy users in accessing permissions, but also enforces traditional access control constraints such as Chinese Wall policy and Separation of Duty (SoD) of Role-Based Access Control (RBAC) model in an effective way.