This paper analyzes the threat of TCG Software Stack (TSS)/TCM Service Module (TSM) deadlock in multi-user environment such as cloud and discusses its causes and mechanism. In addition, this paper puts forward a dynamic priority task scheduling strategy based on value evaluation to handle this threat. The strategy is based on the implementation features of trusted hardware and establishes a multi-level ready queue. In this strategy, an algorithm for real-time value computing is also designed, and it can adjust the production curves of the real time value by setting parameters in different environment, thus enhancing its adaptability, which is followed by scheduling and algorithm description. This paper also implements the algorithm and carries out its performance optimization. Due to the experiment result from Intel NUC, it is shown that TSS based on advanced DPTSV is able to solve the problem of deadlock with no negative influence on performance and security in multi-user environment.

An Efficient and flexible implementation of block ciphers is critical to achieve information security processing. Existing implementation methods such as GPP, FPGA and cryptographic application-specific ASIC provide the broad range of support. However, these methods could not achieve a good tradeoff between high-speed processing and flexibility. In this paper, we present a reconfigurable VLIW processor architecture targeted at block cipher processing, analyze basic operations and storage characteristics, and propose the multi-cluster register-file structure for block ciphers. As for the same operation element of block ciphers, we adopt reconfigurable technology for multiple cryptographic processing units and interconnection scheme. The proposed processor not only flexibly accomplishes the combination of multiple basic cryptographic operations, but also realizes dynamic configuration for cryptographic processing units. It has been implemented with 0.18µmCMOS technology, the test results show that the frequency can reach 350MHz, and power consumption is 420mw. Ten kinds of block and hash ciphers were realized in the processor. The encryption throughput of AES, DES, IDEA, and SHA-1 algorithm is 1554Mbps, 448Mbps, 785Mbps, and 424Mbps respectively, the test result shows that our processor’s encryption performance is significantly higher than other designs.

Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS (Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.

Many websites use verification codes to prevent users from using the machine automatically to register, login, malicious vote or irrigate but it brought great burden to the enterprises involved in internet marketing as entering the verification code manually. Improving the verification code security system needs the identification method as the corresponding testing system. We propose an anisotropic heat kernel equation group which can generate a heat source scale space during the kernel evolution based on infinite heat source axiom, design a multi-step anisotropic verification code identification algorithm which includes core procedure ofbuilding anisotropic heat kernel, settingwave energy information parameters, combing outverification codecharacters and corresponding peripheral procedure of gray scaling, binarizing, denoising, normalizing, segmenting and identifying, give out the detail criterion and parameter set. Actual test show the anisotropic heat kernel identification algorithm can be used on many kinds of verification code including text characters, mathematical, chinese, voice, 3D, programming, video, advertising, it has a higher rate of 25% and 50% than neural network and context matching algorithm separately for Yahoo site, 49% and 60% for Captcha site, 20% and 52% for Baidu site, 60% and 65% for 3DTakers site, 40% and 51% for MDP site.

Human body posture recognition has attracted considerable attention in recent years in wireless body area networks (WBAN). In order to precisely recognize human body posture, many recognition algorithms have been proposed. However, the recognition rate is relatively low. In this paper, we apply back propagation (BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude (SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4 postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.

In this paper, we propose a Packet Cache-Forward (PCF) method based on improved Bayesian outlier detection to eliminate out-of-order packets caused by transmission path drastically degradation during handover events in the moving satellite networks, for improving the performance of TCP. The proposed method uses an access node satellite to cache all received packets in a short time when handover occurs and forward them out in order. To calculate the cache time accurately, this paper establishes the Bayesian based mixture model for detecting delay outliers of the entire handover scheme. In view of the outliers’ misjudgment, an updated classification threshold and the sliding window has been suggested to correct category collections and model parameters for the purpose of quickly identifying exact compensation delay in the varied network load statuses. Simulation shows that, comparing to average processing delay detection method, the average accuracy rate was scaled up by about 4.0%, and there is about 5.5% cut in error rate in the meantime. It also behaves well even though testing with big dataset. Benefiting from the advantage of the proposed scheme in terms of performance, comparing to conventional independent handover and network controlled synchronized handover in simulated LEO satellite networks, the proposed independent handover with PCF eliminates packet out-of-order issue to get better improvement on congestion window. Eventually the average delay decreases more than 70% and TCP performance has improved more than 300%.

Copy-Move Forgery (CMF) is one of the simple and effective operations to create forged digital images. Recently, techniques based on Scale Invariant Features Transform (SIFT) are widely used to detect CMF. Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance. However, for some CMF images, these approaches cannot produce satisfactory detection results. For instance, the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result. Sometimes these approaches may even produce error results. According to our observations, one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences. These values are only applicable to a few images, which limits their application. To solve the problem, a novel approach named as CMF Detection with Particle Swarm Optimization (CMFD-PSO) is proposed in this paper. CMFD-PSO integrates the Particle Swarm Optimization (PSO) algorithm into the SIFT-based framework. It utilizes the PSO algorithm to generate customized parameter values for images, which are used for CMF detection under the SIFT-based framework. Experimental results show that CMFD-PSO has good performance.

Security and privacy issues are magnified by velocity, volume, and variety of big data. User’s privacy is an even more sensitive topic attracting most people’s attention. While XcodeGhost, a malware of iOS emerging in late 2015, leads to the privacy-leakage of a large number of users, only a few studies have examined XcodeGhost based on its source code. In this paper we describe observations by monitoring the network activities for more than 2.59 million iPhone users in a provincial area across 232 days. Our analysis reveals a number of interesting points. For example, we propose a decay model for the prevalence rate of XcodeGhost and we find that the ratio of the infected devices is more than 60%; that a lot of popular applications, such as Wechat, railway 12306, didi taxi, Youku video are also infected; and that the duration as well as the traffic volume of most XcodeGhost-related HTTP-requests is similar with usual HTTP-request which makes it difficult to be found. Besides, we propose a heuristic model based on fingerprint and its web-knowledge to identify the infected applications. The identifying result shows the efficiency of this model.

Due to limited volume, weight and power consumption, micro-satellite has to reduce data transmission and storage capacity by image compression when performs earth observation missions. However, the quality of images may be unsatisfied. This paper considers the problem of recovering sparse signals by exploiting their unknown sparsity pattern. To model structured sparsity, the prior correlation of the support is encoded by imposing a transformed Gaussian process on the spike and slab probabilities. Then, an efficient approximate message-passing algorithm with structured spike and slab prior is derived for posterior inference, which, combined with a fast direct method, reduces the computational complexity significantly. Further, a unified scheme is developed to learn the hyperparameters using expectation maximization (EM) and Bethe free energy optimization. Simulation results on both synthetic and real data demonstrate the superiority of the proposed algorithm.

Network virtualization is an enabling technology of running multiple virtual networks on a shared substrate network. It aims to deal with the ossification of current network architecture. As a crucial component of network virtualization, virtual network embedding (VNE) can efficiently and effectively allocates the substrate resource to proposed virtual network requests. According to the optimization strategy, VNE approaches can be classified into three categories: exact, heuristic and meta-heuristic solution. The VNE exact solution is the foundation of its corresponding heuristic and meta-heuristic solutions. This paper presents a survey of existing typical VNE exact solutions, and open problems for the future research of VNE exact solutions are proposed.

With rapid advances of solar blind ultraviolet LED and ultraviolet detecting technology in recent years, ultraviolet communication gradually becomes a research hotspot due to its inherent advantages: low solar background noise, non-line-of-sight(NLOS) and good secrecy. The strong scattering characteristics in atmospheric render ultraviolet waveband the ideal choice for achieving NLOS optical communication. This paper reviews the research history and status of ultraviolet communication both in China and abroad, and especially introduces three main issues of ultraviolet communication: channel model, system analysis and design, light sources and detectors. For each aspect, current open issues and prospective research directions are analyzed.

Processors have been playing important roles in both communication infrastructure systems and terminals. In this paper, both application specific and general purpose processors for communications are discussed including the roles, the history, the current situations, and the trends. One trend is that ASIPs (Application Specific Instruction-set Processors) are taking over ASICs (Application Specific Integrated Circuits) because of the increasing needs both on performance and compatibility of multi-modes. The trend opened opportunities for researchers crossing the boundary between communications and computer architecture. Another trend is the serverlization, i.e., more infrastructure equipments are replaced by servers. The trend opened opportunities for researchers working towards high performance computing for communication, such as research on communication algorithm kernels and real time programming methods on servers.

Plenty of multimedia contents such as traffic images, surveillance video, music and movie will flood into vehicular ad hoc networks. However, content distribution over VANETs is not a easy task, due to the high mobility of vehicles and intermittent connectivity. Infrastructure-based scheme can relieve the problem, but with a large amount of investment. In this paper, we propose a mobile content distribution scheme based on roadside parking cloud (RPC), which is formed by the parked car on the roadside, and mobile cloud (MC), which is formed by moving cars on the road. According to a trip history model, a mobile car can estimate its following trajectory. When it wants to download the content, gateway node of the RPC will work out a downloading schedule, which tells it how much chunks it can download from which RPCs. Moreover, the helper of the mobile car in mobile cloud would deliver specified chunks to it when there is lack of RPC in the following trip. Simulation results show that cloud-based scheme performs better than inter-vehicle communication approach and cluster-based scheme.

The initial cell search plays an important role during the process of downlink synchronization establishment between the User Equipment (UE) and the base station. In particular, the uncertainty of the synchronization signals on the frequency domain and the flexibility of frame structure configuration have brought great challenges to the initial cell search for the fifth-generation (5G) new radio (NR). To solve this problem, firstly, we analyze the physical layer frame structure of 5G NR systems. Then, by focusing on the knowledge of synchronization signals, the 5G NR cell search process is designed, and the primary synchronization signal (PSS) timing synchronization algorithm is proposed, including a 5G-based coarse synchronization algorithm and conjugate symmetry-based fine synchronization algorithm. Finally, the performance of the proposed cell search algorithm in 5G NR systems is verified through the combination of Digital Signal Processing (DSP) and personal computer (PC). And the MATLAB simulation proves that the proposed algorithm has better performance than the conventional cross-correlation algorithm when a certain frequency offset exists.

Recently, virtualization technologies have been widely used in industry. In order to monitor the security of target systems in virtualization environments, conventional methods usually put the security monitoring mechanism into the normal functionality of the target systems. However, these methods are either prone to be tempered by attackers or introduce considerable performance overhead for target systems. To address these problems, in this paper, we present a concurrent security monitoring method which decouples traditional serial mechanisms, including security event collector and analyzer, into two concurrent components. On one hand, we utilize the SIM framework to deploy the event collector into the target virtual machine. On the other hand, we combine the virtualization technology and multi-core technology to put the event analyzer into a trusted execution environment. To address the synchronization problem between these two concurrent components, we make use of Lamport’s ring buffer algorithm. Based on the Xen hypervisor, we have implemented a prototype system named COMO. The experimental results show that COMO can monitor the security of the target virtual machine concurrently within a little performance overhead.

To deal with the dynamic and imbalanced traffic requirements in Low Earth Orbit satellite networks, several distributed load balancing routing schemes have been proposed. However, because of the lack of global view, these schemes may lead to cascading congestion in regions with high volume of traffic. To solve this problem, a Hybrid-Traffic-Detour based Load Balancing Routing (HLBR) scheme is proposed, where a Long-Distance Traffic Detour (LTD) method is devised and coordinates with distributed traffic detour method to perform self-adaptive load balancing. The forwarding path of LTD is acquired by the Circuitous Multipath Calculation (CMC) based on prior geographical information, and activated by the LTD- Shift-Trigger (LST) through real-time congestion perception. Simulation results show that the HLBR can mitigate cascading congestion and achieve efficient traffic distribution.

In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks. First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow. Then, according to the mobility classification, network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.

In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.

In cellular networks, the proximity devices may share files directly without going through the eNBs, which is called Device-to-Device communications (D2D). It has been considered as a potential technological component for the next generation of communication. In this paper, we investigate a novel framework to distribute video files from some other proximity devices through users’ media cloud assisted D2D communication. The main contributions of this work lie in: 1) Providing an efficient algorithm Media Cloud Cluster Selecting Scheme (MCCSS) to achieve the reasonable cluster; 2) Distributing the optimum updating files to the cluster heads, in order to minimize the expected D2D communication transmission hop for files; 3) Proposing a minimum the hop method, which can ensure the user obtain required file as soon as possible. Extensive simulation results have demonstrated the efficiency of the proposed scheme.

This paper presents an efficient image feature representation method, namely angle structure descriptor (ASD), which is built based on the angle structures of images. According to the diversity in directions, angle structures are defined in local blocks. Combining color information in HSV color space, we use angle structures to detect images. The internal correlations between neighboring pixels in angle structures are explored to form a feature vector. With angle structures as bridges, ASD extracts image features by integrating multiple information as a whole, such as color, texture, shape and spatial layout information. In addition, the proposed algorithm is efficient for image retrieval without any clustering implementation or model training. Experimental results demonstrate that ASD outperforms the other related algorithms.

To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-Defined Networking (SDN) provides a promising solution to manage the underlying network. In this paper, we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement, request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost. We use a utility function to capture the two aspects of user experience: the level of satisfaction and average latency, and formulate the joint optimization problem as a mixed integer programming problem. We develop an optimal algorithm based on dual decomposition and prove its optimality. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.

There are a lot of security issues in block cipher algorithm. Security analysis and enhanced design of a dynamic block cipher was proposed. Firstly, the safety of ciphertext was enhanced based on confusion substitution of S-box, thus disordering the internal structure of data blocks by four steps of matrix transformation. Then, the diffusivity of ciphertext was obtained by cyclic displacement of bytes using column ambiguity function. The dynamic key was finally generated by using LFSR, which improved the stochastic characters of secret key in each of round of iteration. The safety performance of proposed algorithm was analyzed by simulation test. The results showed the proposed algorithm has a little effect on the speed of encryption and decryption while enhancing the security. Meanwhile, the proposed algorithm has highly scalability, the dimension of S-box and the number of register can be dynamically extended according to the security requirement.

Simultaneous dimming controlling and data transmission are usually required in a white LED based indoor visible light communication system. However, the diming controlling of LED normally interferes the data transmission due to the modulation nonlinearity of LED. In order to solve this problem, a scheme by separating the LEDs for the functions of dimming control and data transmission respectively is proposed in this paper. In the scheme, the LEDs used for dimming control function are driven by a dc amplified circuit, and the LEDs for data transmission are driven by a digital modulation circuit respectively. In this way, the modulation distortion to the data signal caused by the modulation nonlinearity can be avoided even if the dimming is at high level dc driven current. The proof-of-concept experiment of a 2.5Mbit/s visible light communication system demonstrates that the dimming controlling and data transmission can be realized simultaneously in a simple way, and the data transmission is not affected by the dimming controlling function. Compared to previous methods, the scheme in this paper is simpler and cost effective, and makes sense when high rate data is transmitted in a visible light communication system.

In the trust management scheme of the distributed cognitive radio networks, the absence of the central control devices cause many problems such as a lack of standardized control for trust computation, and the absence of the decision makers in trust evaluation and collaborative decision making. A trust management mechanism based on the jury system for distributed cognitive radio networks is proposed in this paper. The “jury user” is designed to collaboratively examine the reputation of the cognitive user in the networks and to perform data fusion and spectrum allocation for distributed cognitive radio networks. Simulation analysis results show that the proposed scheme can ensure accuracy and fairness in trust evaluation and improve effectiveness and flexibility of spectrum allocation.

Network virtualization is known as a promising technology to tackle the ossification of current Internet and will play an important role in the future network area. Virtual network embedding(VNE) is a key issue in network virtualization. VNE is NP-hard and former VNE algorithms are mostly heuristic in the literature. VNE exact algorithms have been developed in recent years. However, the constraints of exact VNE are only node capacity and link bandwidth. Based on these, this paper presents an exact VNE algorithm, ILP-LC, which is based on Integer Linear Programming(ILP), for embedding virtual network request with location constraints. This novel algorithm is aiming at mapping virtual network request(VNR) successfully as many as possible and consuming less substrate resources. The topology of each VNR is randomly generated by Waxman model. Simulation results show that the proposed ILP-LC algorithm outperforms the typical heuristic algorithms in terms of the VNR acceptance ratio, at least 15%.

In this paper, we consider a wireless ad hoc network consisting of multiple source nodes transmitting to their respective destinations, where an eavesdropper attempts to intercept their transmissions. We propose an optimal transmission scheduling scheme to defend against the eavesdropper, where a source node having the highest secrecy rate is scheduled to access the wireless medium for transmitting to its destination in an opportunistic manner. To be specific, the secrecy rate between a pair of the source and destination in the presence of an eavesdropper varies temporally due to the wireless fading effect. The proposed optimal transmission scheduling scheme opportunistically selects a source node with the highest secrecy rate to transmit its data for the sake of maximizing the security of the ad hoc network against eavesdropping attacks. For comparison purposes, we also consider the conventional round-robin scheduling as a benchmark, where multiple source nodes take turns in accessing their shared wireless medium for transmitting to their respective destinations. We derive closed-form secrecy outage probability expressions of both the round-robin scheduling and the proposed optimal scheduling schemes over Rayleigh fading environments. Numerical results show that the proposed transmission scheduling scheme outperforms the conventional round-robin method in terms of its secrecy outage probability. Additionally, upon increasing the number of source-destination pairs, the secrecy outage probability of the round-robin scheme keeps unchanged, whereas the secrecy outage performance of the proposed transmission scheduling significantly improves, showing the security benefits of exploiting transmission scheduling for protecting wireless ad hoc networks against eavesdropping.

Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations. Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.

Spectrum sharing for efficient reuse of licensed spectrum is an important concept for cognitive radio technologies. In a spectrum-sharing system (SSS), deploying the antennas in a distributed manner can offer a new spatial dimension for the efficient reuse of licensed frequency bands. To improve the whole performance of multiple secondary users (SUs), this paper addresses the problem of coordinated multi-SU spectrum sharing in a distributed antenna-based SSS. By adopting the Hungarian method, the primal decomposition method and pricing policy, we propose a coordinated multi-user transmission scheme, so as to maximize the sum-rate of SUs. Simulation results show that the proposed method can significantly enhance the system performance, and the computational complexity is low.

In cellular network, users with same demand and in proximity to each other form the mobile cloud, in which the short-range D2D technology is employed by users to improve the data dissemination efficiency. In view of the fact that the D2D links with the poor channel conditions are likely to be the bottleneck of resource utilization improvement, aiming at the differentiation of link quality, this paper proposes a intra-cloud D2D multicast retransmission algorithm based on SINR constraint to meet the minimum requirement of D2D retransmission for QoS. In the proposed algorithm, the model of system link cost is built, the number of multicast retransmission times is restricted and each link quality matrix is traversed to reasonably select the multicast transmitter as well as its routing, which further reduces the link cost consumption, and in turn improves the bandwidth efficiency. Simulation results show that the proposed algorithm is more efficient to improve the bandwidth utilization when the ratio between normal user and non-normal user is small in mobile cloud.

Cloud data sharing service, which allows a group of people to work together to access and modify the shared data, is one of the most popular and efficient working styles in the enterprises. However, the cloud server is not completely trusted, and its security could be compromised by monetary reasons or caused by hacking and hardware errors. Therefore, despite of having advantages of scalability and flexibility, cloud storage service comes with privacy and the security concerns. A straightforward method to protect the user’s privacy is to encrypt the data stored at the cloud. To enable the authenticated users to access the encrypted cloud data, a practical group key management algorithm for the cloud data sharing application is highly desired. The existing group key management mechanisms presume that the server is trusted. But, the cloud data service mode does not always meet this condition. How to manage the group keys to support the scenario of the cloud storage with a semi-trusted cloud server is still a challenging task. Moreover, the cloud storage system is a large-scale and open application, in which the user group is dynamic. To address this problem, we propose a practical group key management algorithm based on a proxy re-encryption mechanism in this paper. We use the cloud server to act as a proxy to re-encrypt the group key to allow authorized users to decrypt and get the group key by their private key. To achieve the hierarchical access control policy, our scheme enables the cloud server to convert the encrypted group key of the lower group to the upper group. The numerical analysis and experimental results further validate the high efficiency and security of the proposed scheme.

Traditional cellular network requires that a user equipment (UE) should associate to the same base station (BS) in both the downlink (DL) and the uplink (UL). Based on dual connectivity (DC) introduced in LTE-Advanced R12, DL/UL decouple access scheme has been proposed, which is especially suitable for heterogeneous networks (HetNets). This paper is the pioneer to take the DL/UL decouple access scheme into consideration and develop a novel resource allocation algorithm in a two-tier HetNet to improve the total system throughput in the UL and ease the load imbalance between macro base stations (MBSs) and pico base stations (PBSs). A model is formulated as a nonlinear integer programming, and the proposed algorithm is a sub-optimal algorithm based on the graph theory. First, an undirected and weighted interference graph is obtained. Next, the users are grouped to let users with large mutual interferences to be assigned to different clusters. Then, the users in different clusters are allocated to different resource blocks (RBs) by using the Hungarian algorithm. Simulation results show that the proposed algorithm can provide great promotions for both the total system throughput and the average cell edge user throughput and successfully ease the load imbalance between MBSs and PBSs.

In power communication networks, it is a challenge to decrease the risk of different services efficiently to improve operation reliability. One of the important factor in reflecting communication risk is service route distribution. However, existing routing algorithms do not take into account the degree of importance of services, thereby leading to load unbalancing and increasing the risks of services and networks. A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems. First, the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices, service load, and service characteristics. Second, service weights are determined with modified relative entropy TOPSIS method, and a balanced service routing determination algorithm is proposed. Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.

In this paper, an energy-harvesting cognitive radio (CR) is considered, which allows the transmitter of the secondary user (SU) to harvest the primary signal energy from the transmitter of the primary user (PU) when the presence of the PU is detected. Then the harvested energy is converted into the electrical power to supply the transmission of the SU at the detected absence of the PU. By adopting the periodic spectrum sensing, the average total transmission rate of the SU is maximized through optimizing the sensing time, subject to the constraints of the probabilities of false alarm and detection, the harvested energy and the interference rate control. The simulation results show that there deed exists an optimal sensing time that maximizes the transmission rate, and the maximum transmission rate of the energy-harvesting CR can better approach to that of the traditional CR with the increasing of the detection probability.

To lower the cross-tier inter-cell interference(ICI) between macrocell and microcell ,three user selection algorithms for the heterogeneous network were proposed in this paper, assuming full knowledge of channel-state information at the transmitter. Algorithm 1 chooses microcell users whose interference channel matrix is parallel to that of a known user and targets at increasing user SINR. Algorithm 2 takes effect of chordal distance-channel norm balance on the system into account and predetermines the available user set from which it can choose service users. With comprehensive considerations to effect of interference signal and useful signal on system, Algorithm 3 set a weighting function as the objective function of user selection. Simulation results demonstrated that all three proposed algorithms could achieve user diversity gain while lowering cross-tier interference.

Orthogonal Time Frequency and Space (OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio (SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator (RBFSD) based on the pseudo-noise (PN) sequence. The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about $1/D$ times less complex than the existing PN pilot sequence algorithm, where $D$ is the resolution of the fractional Doppler.

Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/WiFi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.

With the growing trend toward using cloud storage, the problem of efficiently checking and proving data integrity needs more consideration. Many cryptography and security schemes, such as PDP (Provable Data Possession) and POR (Proofs of Retrievability) were proposed for this problem. Although many efficient schemes for static data have been constructed, only a few dynamic schemes exist, such as DPDP (Dynamic Provable Data Possession). But the DPDP scheme falls short when updates are not proportional to a fixed block size. The FlexList-based Dynamic Provable Data Possession (FlexDPDP) was an optimized scheme for DPDP. However, the update operations (insertion, remove, modification) in Flex DPDP scheme only apply to single node at a time, while multiple consecutive nodes operation is more common in practice.To solve this problem, we propose optimized algorithms for multiple consecutive nodes, which including MultiNodes Insert and Verification, MultiNodes Remove and Verification, MultiNodes Modify and Verification. The cost of our optimized algorithms is also analyzed. For m consecutive nodes, an insertion takes O(m)+O(logN)+O(logm), where N is the number of leaf nodes of FlexList, a remove takes O(logN), and a modification is the same as the original algorithm. Finally, we compare the optimized algorithms with original FlexList through experiences, and the results show that our scheme has the higher efficiency of time and space.

The varying trajectory of Doppler frequency under changing speed motion conditionsare investigated in HighSpeed Railway (HSR) scenarios. Based on the geometrical physical parameters, instantaneous Doppler trajectories and expression forms of the change rate arededuced, including acceleration and deceleration cases.These modified models provide more accurate and realisticapproximations in modeling rapidly fading channels.

The paper proposes a low power non-volatile baseband processor with wake-up identification (WUI) receiver for LR-WPAN transceiver. It consists of WUI receiver, main receiver, transmitter, non-volatile memory (NVM) and power management module. The main receiver adopts a unified simplified synchronization method and channel codec with proactive Reed-Solomon Bypass technique, which increases the robustness and energy efficiency of receiver. The WUI receiver specifies the communication node and wakes up the transceiver to reduce average power consumption of the transceiver. The embedded NVM can backup/restore the states information of processor that avoids the loss of the state information caused by power failure and reduces the unnecessary power of repetitive computation when the processor is waked up from power down mode. The baseband processor is designed and verified on a FPGA board. The simulated power consumption of processor is 5.1μW for transmitting and 28.2μW for receiving. The WUI receiver technique reduces the average power consumption of transceiver remarkably. If the transceiver operates 30 seconds in every 15 minutes, the average power consumption of the transceiver can be reduced by two orders of magnitude. The NVM avoids the loss of the state information caused by power failure and energy waste caused by repetitive computation.

Data sharing is a main application of cloud computing. Some existing solutions are proposed to provide flexible access control for outsourced data in the cloud. However, few attentions have been paid to group-oriented data sharing when multiple data owners want to share their private data for cooperative purposes. In this paper, we put forward a new paradigm, referred to as secure, scalable and efficient multi-owner (SSEM) data sharing in clouds. The SSEM integrates identity-based encryption and asymmetric group key agreement to enable group-oriented access control for data owners in a many-to-many sharing pattern. Moreover, with SSEM, users can join in or leave from the group conveniently with the privacy of both group data and user data. We proposed the key-ciphertext homomorphism technique to construct an SSEM scheme with short ciphertexts. The security analysis shows that our SSEM scheme achieves data security against unauthorized accesses and collusion attacks. Both theoretical and experimental results confirm that our proposed scheme takes users little costs to share and access outsourced data in a group manner.