Recently, Internet of Things (IoT) drives a large variety of research, development, and new type of markets. All type of devices and sensors will be part of the Internet of Things and will be able to communicate not only plain data, but also audio-visual, olfactory, and haptic media data. In addition, as the devices and sensors getting smarter, it is highly probable that they can process acquired media and metadata to extract higher level of information (e.g., semantics). To support such enhanced functionalities, ISO/IEC SC29 WG11 (MPEG) starts a new standard project, ISO/IEC 23093, called Internet of Media Things (IoMT) to provide standard data formats and APIs for media things. This paper presents the standardization activities of IoMT focusing on explaining terms, standard scopes, and major media things with their use cases. One of the use cases, an IoT system for a blind pedestrian navigation assistance, is evaluated to prove its effectiveness.

The separation of control plane and data plane in Software Defined Network (SDN) makes it flexible to control the network behavior, while also causes some inconveniences to the link failure recovery due to the delay between fail point and the controller. To avoid delay and packet loss, pre-defined backup paths are used to reroute the disrupted flows when failure occurs. However, it may introduce large overhead to build and maintain these backup paths and is hard to dynamically construct backup paths according to the network status so as to avoid congestion during rerouting process. In order to realize congestion aware fast link failure recovery, this paper proposes a novel method which installs multi backup paths for every link via source routing and per-hop-tags and spread flows into different paths at fail point to avoid congestion. We carry out experiments and simulations to evaluate the performance of the method and the results demonstrate that our method can achieve congestion aware fast link failure recovery in SDN with a very low overhead.

In the last decade, the 2.4 GHz band of IEEE 802.11 WLANs has become heavily congested due to the explosive increase in demand of Wi-Fi connectivity. With the current deployment of enterprise WLANs, channel switching mechanism continues to exhibit inefficiencies because it cannot adapt to real-time channel condition and the inability to support seamless channel switching. Software Defined Networking (SDN) as an emerging architecture is promising to introduce flexibility and programmability for wireless network management. Leveraging SDN to existing enterprise WLANs, channel switching method can be improved significantly. This paper presents a software-defined enterprise WLAN framework with a load aware automatic channel switching solution, which utilizes AP load and channel interference factor (CIF) to provide seamless channel switching. Two automatic channel switching algorithms named Single Switch (SS) and Double Switch (DS) are proposed to improve the overall user experience and the experience of users with highest traffic load respectively. Experiment results demonstrate that our solution can efficiently improve user experience in terms of jitter, transmission delay and network throughout when compared to the conventional channel switching mechanism.

Data center networks provide critical bandwidth for the continuous growth of cloud computing, multimedia storage, data analysis and other businesses. The problem of low link bandwidth utilization in data center network is gradually addressed in more hot fields. However, the current scheduling strategies applied in data center network do not adapt to the real-time dynamic change of the traffic in the network. Thus, they fail to distribute resources due to the lack of intelligent management. In this paper, we present an improved adaptive traffic scheduling strategy utilizing the simulated annealing genetic algorithm (SAGA). Inspired by the idea of software defined network, when a flow arrives, our strategy changes the bandwidth demand dynamically to filter out the flow. Then, SAGA distributes the path for the flow by considering the scheduling of the different pods as well as the same pod. It is implemented through software defined network technology. Simulation results show that the bisection bandwidth of our strategy is higher than state-of-the-art mechanisms.

Multihop relay-based cellular networks are attracting much interest because of their throughput enhancement, coverage extension, and low infrastructure cost. In these networks, relay stations (RSs) between a base station (BS) and mobile stations (MSs) drastically increase the overall spectral efficiency, with improved channel quality for MSs located at the cell edge or in shadow areas, and enhanced throughput of MSs in hot spots. These relay-based networks require an advanced radio resource management scheme because the optimal amount of radio resource for a BS-to-RS link should be allocated according to the MS channel quality and distribution, considering the interference among RSs and neighbor BSs. In this paper, we propose optimal resource planning algorithms that maximize the overall utility of relay-based networks under a proportional fair scheduling policy. In the first phase, we determine an optimal scheduling policy for distributing BS-to-RS link resources to RSs. In the second phase, we determine the optimal amount of the BS-to-RS link resources using the results of the first phase. The proposed algorithms efficiently calculate the optimal amount of resource without exhaustive searches, and their accuracy is verified by comparison with simulation results, in which the algorithms show a perfect match with simulations.

Multi-hop cellular networks (MCNs) reduce the transmit power and improve the system performance. Recently, several research studies have been conducted on MCNs. The mobile relay selection scheme is a rising issue in the design of MCNs that achieves these advantages. The conventional opportunistic relaying (OR) is performed on the single factor for maximum signal-to-interference-plus-noise ratio (SINR). In this paper, a comprehensive OR scheme based on Bi-Target is proposed to improve the system throughput and reduce the relay handover by constraining the amount of required bandwidth and SINR. Moreover, the proposed algorithm captures the variability and the mobility that makes it more suitable for dynamic real scenarios. Numerical and simulation results show the superiority of the proposed algorithm in both enhancing the overall performance and reducing the handover.

Small cell deployment offers a low-cost solution for the boosted traffic demand in heterogeneous cellular networks (HCNs). Besides improved spatial spectrum efficiency and energy efficiency, future HCNs are also featured with the trend of network architecture convergence and feasibility for flexible mobile applications. To achieve these goals, dual connectivity (DC) is playing a more and more important role to support control/user-plane splitting, which enables maintaining fixed control channel connections for reliability. In this paper, we develop a tractable framework for the downlink SINR analysis of DC assisted HCN. Based on stochastic geometry model, the data-control joint coverage probabilities under multi-frequency and single-frequency tiering are derived, which involve quick integrals and admit simple closed-forms in special cases. Monte Carlo simulations confirm the accuracy of the expressions. It is observed that the increase in mobility robustness of DC is at the price of control channel SINR degradation. This degradation severely worsens the joint coverage performance under single-frequency tiering, proving multi-frequency tiering a more feasible networking scheme to utilize the advantage of DC effectively. Moreover, the joint coverage probability can be maximized by adjusting the density ratio of small cell and macro cell eNBs under multi-frequency tiering, though changing cell association bias has little impact on the level of the maximal coverage performance.

In this paper, we investigate the feasibility of interference alignment(IA) in signal space in the scenario of multiple cell and multiple user cellular networks, as the feasibility issue is closely related to the solvability of a multivariate polynomial system, we give the mathematical analysis to support the constraint condition obtained from the polynomial equations with the tools of algebraic geometry, and a new distribute IA algorithm is also provided to verify the accessibility of the constraint condition for symmetric system in this paper. Simulation results illustrate that the accessibility of the constraint condition is hold if and only if the degree of freedom(DoF) of each user can be divided by both the transmit and receive antenna numbers.

In-band full-duplex (IBFD) wireless communication supports symmetric dual transmission between two nodes and asymmetric dual transmission among three nodes, which allows improved throughput for distributed IBFD wireless networks. However, inter-node interference (INI) can affect desired packet reception in the downlink of three-node topology. The current Half-duplex (HD) medium access control (MAC) mechanism RTS/CTS is unable to establish an asymmetric dual link and consequently to suppress INI. In this paper, we propose a medium access control mechanism for use in distributed IBFD wireless networks, FD-DMAC (Full-Duplex Distributed MAC). In this approach, communication nodes only require single channel access to establish symmetric or asymmetric dual link, and we fully consider the two transmission modes of asymmetric dual link. Through FD-DMAC medium access, the neighbors of communication nodes can clearly know network transmission status, which will provide other opportunities of asymmetric IBFD dual communication and solve hidden node problem. Additionally, we leverage FD-DMAC to transmit received power information. This approach can assist communication nodes to adjust transmit powers and suppress INI. Finally, we give a theoretical analysis of network performance using a discrete-time Markov model. The numerical results show that FD-DMAC achieves a significant improvement over RTS/CTS in terms of throughput and delay.

In this paper, we study the cross-layer design for the AMC/HARQ-based wireless networks, and propose a new dynamic transmission-mode selection scheme to improve system spectrum efficiency. In the proposed scheme, dynamic thresholds for transmission-mode selection in each packet transmission and retransmission are jointly designed under the constraint of the overall packet error rate. Comparing with the existing schemes, the proposed scheme is inclined to apply higher modulation order at the first several (re)transmissions, which corresponds to higher-rate transmission modes thus higher average system spectrum efficiency. We also extend the cross-layer design to MIMO (Multi-input Multi-output) communication scenarios. Numerical results show that the proposed new dynamic transmission-mode selection scheme generally achieves higher average spectrum efficiency than the conventional and existing cross-layer design.

In this paper, we propose a new scheme to suppress the narrowband interference (NBI) in OFDM-based systems. The scheme utilizes code division multiple access (CDMA) and weighted-type fractional Fourier transform (WFRFT) domain preprocessing technologies. Through setting the WFRFT order, the scheme can switch into a single carrier (SC) or a multi-carrier (MC) frequency division multiple access block transmission system. The residual NBI can be eliminated to the maximum extent when the WFRFT order is selected properly. Final simulation results show that the proposed system can outperform MC and SC with CDMA and frequency domain preprocessing in terms of the narrowband interference suppression.

The primary aim of this paper is to propose a non-linear regression based technique for mapping different network Quality of Service (QoS) factors to an integrated end-user Quality of Experience (QoE) or Mean Opinion Score (MOS) value for an online video streaming service on a mobile phone. We use six network QoS factors for finding out the user QoE. The contribution of this paper is threefold. First, we investigate the impact of the network QoS factors on the perceived video quality. Next, we perform an individual mapping of the significant network QoS parameters obtained in stage 1 to the user QoE based upon a non-linear regression method. The optimal QoS to QoE mapping function is chosen based upon a decision variable. In the final stage, we evaluate the integrated QoE of the system by taking the combined effect of all the QoS factors considered. Extensive subjective tests comprising of over 50 people across a wide variety of video contents encoded with H.265/HEVC and VP9 codec have been conducted in order to gather the actual MOS data for the purpose of QoS to QoE mapping. Our proposed hybrid model has been validated against unseen data and reveals good prediction accuracy.

The construction of panoramic images on smartphones and low-powered devices is a challenging task. In this paper, we propose a new approach for smoothly stitching images on mobile phones in the presence of moving objects in the scene. Our main contributions include handling moving object problems, reducing processing time, and generating rectangular panoramic images. First, unique and robust feature points are extracted using fast ORB method and a feature matching technique is applied to match the extracted feature points. After obtaining good matched feature points, we employ the non-deterministic RANSAC algorithm to discard wrong matches, and the hommography transformation matrix parameters are estimated with the algorithm. Afterward, we determine precise overlap regions of neighboring images and calculate their absolute differences. Then, thresholding operation and noise removal filtering are applied to create a mask of possible moving object regions. Sequentially, an optimal seam is estimated using dynamic programming algorithm, and a combination of linear blending with the mask information is applied to avoid seam transition and ghosting artifacts. Finally, image-cropping operation is utilized to obtain a rectangular boundary image from the stitched image. Experiments demonstrate that our method is able to produce panoramic images quickly despite the existence of moving objects.

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

Recently, a tracking algorithm called the spatial-temporal context model has been proposed to locate a target by using the contextual information around the target. This model has achieved excellent results when the target undergoes slight occlusion and appearance changes. However, the target location in the current frame is based on the location in the previous frame, which will lead to failure in the presence of fast motion because of the lack of a prediction mechanism. In addition, the spatial context model is updated frame by frame, which will undoubtedly result in drift once the target is occluded continuously. This paper proposes two improvements to solve the above two problems: First, four possible positions of the target in the current frame are predicted based on the displacement between the previous two frames, and then, we calculate four confidence maps at these four positions; the target position is located at the position that corresponds to the maximum value. Second, we propose a target reliability criterion and design an adaptive threshold to regulate the updating speed of the model. Specifically, we stop updating the model when the reliability is lower than the threshold. Experimental results show that the proposed algorithm achieves better tracking results than traditional STC and other algorithms.

Although two-dimensional principal component analysis (2DPCA) has been shown to be successful in face recognition system, it is still very sensitive to illumination variations. To reduce the effect of these variations, texture-based techniques are used due to their robustness to these variations. In this paper, we explore several texture-based techniques and determine the most appropriate one to be used with 2DPCA-based techniques for face recognition. We also propose a new distance metric computation in 2DPCA called Row Assembled Matrix Distance (RowAMD). Experiments on Yale Face Database, Extended Yale Face Database B, AR Database and LFW Database reveal that the proposed RowAMD distance computation method outperforms other conventional distance metrics when Local Line Binary Pattern (LLBP) and Multi-scale Block Local Binary Pattern (MB-LBP) are used for face authentication and face identification, respectively. In addition to this, the results also demonstrate the robustness of the proposed RowAMD with several texture-based techniques.

Use of the Gaussian inverse Wishart PHD (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets. However, the partitioning approaches used in the GIW-PHD filter, such as distance partition with sub-partition (DP-SP), prediction partition (PP) and expectation maximization partition (EMP), fails to provided accurate partition results when targets are spaced closely together and performing maneuvers. In order to improve the performance of a GIW-PHD filter, this paper presents a cooperation partitioning (CP) algorithm to solve the partitioning issue when targets are spaced closely together. In the GIW-PHD filter, the DP-SP is insensitive to target maneuvers but sensitive to the differences in target sizes, while EMP is the opposite. The proposed CP algorithm is a fusion approach of DP-SP and EMP, which employs EMP as a sub-partition approach after DP. Therefore, the CP algorithm will be sensitive to neither target maneuvers nor differences in target sizes. The simulation results show that the use of the proposed CP algorithm will improve the performance of the GIW-PHD filter when targets are spaced closely together.

Three-dimensional video (3DV), as the new generation of video format standard, can provide the viewers with a vivid screen sense and a realistic stereo impression. Meanwhile the view synthesis has become an important issue for 3DV application. Differently from the conventional methods based on depth, in this paper we propose a new view synthesis algorithm, which can employ the correlation among views and warp in the image domain only. There are mainly two contributions. One is the incorporation of sobel edge points into feature extraction and matching, which can obtain a better stable homography and then a visual comfortable synthesis view compared to SIFT points only. The other is a novel image blending method proposed to obtain a better synthesis image. Experimental results demonstrate that the proposed method can improve the synthesis quality both in subjectivity and objectivity.

In this paper, a video based smoke detection method using dynamic texture feature extraction with volume local binary patterns is studied. Block based method was used to distinguish smoke frames in high definition videos obtained by experiments firstly. Then we propose a method that directly extracts dynamic texture features based on irregular motion regions to reduce adverse impacts of block size and motion area ratio threshold. Several general volume local binary patterns were used to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP and CVLBP, to study the effect of the number of sample points, frame interval and modes of the operator on smoke detection. Support vector machine was used as the classifier for dynamic texture features. The results show that dynamic texture is a reliable clue for video based smoke detection. It is generally conducive to reducing the false alarm rate by increasing the dimension of the feature vector. However, it does not always contribute to the improvement of the detection rate. Additionally, it is found that the feature computing time is not directly related to the vector dimension in our experiments, which is important for the realization of real-time detection.

Having the characteristics of unlinkability, anonymity, and unforgeability, blind signatures are widely used for privacy-related applications such as electronic cash, electronic voting and electronic auction systems where to maintain the anonymity of the participants. Among these applications, the blinded message is needed for a certain purpose by which users delegate signing operation and communicate with each other in a trusted manner. This application leads to the need of proxy blind signature schemes. Proxy blind signature is an important type of cryptographic primitive to realize the properties of both blind signature and proxy signature. Over the past years, many proxy blind signature algorithms have been adopted to fulfill such task based on the discrete logarithm problem (DLP) and the elliptic curve discrete log problem (ECDLP), and most of the existing studies mainly aim to provide effective models to satisfy the security requirements concerning a single blinded message. Unlike many previous works, the proposed scheme applies the signcryption paradigm to the proxy blind signature technology for handling multiple blinded messages at a time based on elliptic curve cryptography (ECC). This innovative method thus has a higher level of security to achieve the security goals of both blind signature and proxy signature. Moreover, the evaluation results show that this proposed protocol is more efficient, consuming low communication overhead while increasing the volume of digital messages compared to the performance from other solutions. Due to these features, this design is able to be implemented in small low-power intelligent devices and very suitable and easily adoptable for e-system applications in pervasive mobile computing environment.

As a main component of Internet of Things (IoTs), the wireless sensor networks (WSNs) have been widely applied to various areas, including environment monitoring, health monitoring of human body, farming, commercial manufacture, reconnaissance mission in military, and calamity alert etc. Meanwhile, the privacy concerns also arise when the users are required to get the real-time data from the sensor nodes directly. To solve this problem, several user authentication and key agreement schemes with a smart card and a password have been proposed in the past years. However, these schemes are vulnerable to some attacks such as offline password guessing attack, user impersonation attack by using attacker's own smart card, sensor node impersonation attack and gateway node bypassing attack. In this paper, we propose an improved scheme which can resist a wide variety of attacks in WSNs. Cryptanalysis and performance analysis show that our scheme can solve the weaknesses of previously proposed schemes and enhance security requirements while maintaining low computational cost.

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms.

A novel scheme based on polarization modulation and the weighted fractional Fourier transform (PM-WFRFT) is proposed in this paper to enhance the physical layer security of dual-polarized satellite systems. This scheme utilizes the amplitude and phase of the carrier as information-bearing parameters to transmit the normal signal and conceals the confidential information in the carrier's polarization state (PS). After being processed by WFRFT, the characteristics of the transmit signal (including amplitude, phase and polarization state) vary randomly and in nearly Gaussian distribution. This makes the signal very difficult for an eavesdropper to recognize or capture. The WFRFT parameter is also encrypted by a pseudo-random sequence and updated in real time, which enhances its anti-interception performance. Furthermore, to prevent the polarization-based impairment to PM-WFRFT caused by depolarization in the wireless channel, two components of the polarized signal are transmitted respectively in two symbol periods; this prevents any mutual interference between the two orthogonally polarized components. Demodulation performance in the system was also assessed, then the proposed scheme was validated with a simulated dual-polarized satellite system.

Certificateless public key cryptography resolves the certificate management problem in traditional public key cryptography and the key escrow problem in identity-based cryptography. In recent years, some good results have been achieved in speeding up the computation of bilinear pairing. However, the computation cost of the pairing is much higher than that of the scalar multiplication over the elliptic curve group. Therefore, it is still significant to design cryptosystem without pairing operations. A multi-signer universal designated multi-verifier signature scheme allows a set of signers to cooperatively generate a public verifiable signature, the signature holder then can propose a new signature such that only the designated set of verifiers can verify it. Multi-signer universal designated multi-verifier signatures are suitable in many different practical applications such as electronic tenders, electronic voting and electronic auctions. In this paper, we propose a certificateless multi-signer universal designated multi-verifier signature scheme and prove the security in the random oracle model. Our scheme does not use pairing operation. To the best of our knowledge, our scheme is the first certificateless multi-signer universal designated multi-verifier signature scheme.

Antivirus is an important issue to the security of virtual machine (VM). According to where the antivirus system resides, the existing approaches can be categorized into three classes: internal approach, external approach and hybrid approach. However, for the internal approach, it is susceptible to attacks and may cause antivirus storm and rollback vulnerability problems. On the other hand, for the external approach, the antivirus systems built upon virtual machine introspection (VMI) technology cannot find and prohibit viruses promptly. Although the hybrid approach performs virus scanning out of the virtual machine, it is still vulnerable to attacks since it completely depends on the agent and hooks to deliver events in the guest operating system. To solve the aforementioned problems, based on in-memory signature scanning, we propose an agentless runtime antivirus system VirtAV, which scans each piece of binary codes to execute in guest VMs on the VMM side to detect and prevent viruses. As an external approach, VirtAV does not rely on any hooks or agents in the guest OS, and exposes no attack surface to the outside world, so it guarantees the security of itself to the greatest extent. In addition, it solves the antivirus storm problem and the rollback vulnerability problem in virtualization environment. We implemented a prototype based on Qemu/KVM hypervisor and ClamAV antivirus engine. Experimental results demonstrate that VirtAV is able to detect both user-level and kernel-level virus programs inside Windows and Linux guest, no matter whether they are packed or not. From the performance aspect, the overhead of VirtAV on guest performance is acceptable. Especially, VirtAV has little impact on the performance of common desktop applications, such as video playing, web browsing and Microsoft Office series.

With the rapid development of cloud computing, more and more data owners are motivated to outsource their data to cloud for various benefits. Due to serious privacy concerns, sensitive data should be encrypted before being outsourced to the cloud. However, this results that effective data utilization becomes a very challenging task, such as keyword search over ciphertexts. Although many searchable encryption methods have been proposed, they only support exact keyword search. Thus, misspelled keywords in the query will result in wrong or no matching. Very recently, a few methods extends the search capability to fuzzy keyword search. Some of them may result in inaccurate search results. The other methods need very large indexes which inevitably lead to low search efficiency. Additionally, the above fuzzy keyword search methods do not support access control. In our paper, we propose a searchable encryption method which achieves fuzzy search and access control through algorithm design and Ciphertext-Policy Attribute-based Encryption (CP-ABE). In our method, the index is small and the search results are accurate. We present word pattern which can be used to balance the search efficiency and privacy. Finally, we conduct extensive experiments and analyze the security of the proposed method.