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Vehicular Ad Hoc Network (VANET) is an important foundation of intelligent transportation system and is widely used in traffic management, automatic driving, and road optimization. With the gradual popularization and further development of VANET, a large amount of VANET data has been produced. However, it poses huge challenges to the security and privacy when using VANET data provides services for users. In this paper, combining the technologies of blockchain and ciphertext-based attribute encryption (CP-ABE), we propose a fine-grained access control scheme for VANET data based on blockchain (FADB). In FADB, we employ the blockchain to replace the third-party service providers for user identity management and data storage. And different VANET data access rights can be established according to user attribute. By improving the CP-ABE, the lightweight VANET devices can outsource complex encryption and decryption operations to powerful RSUs and further improve the efficiency of data access. Final, we carry out a series of simulation tests and security analysis, proving that the FADB can provide effective data security and low performance overhead.

Vehicular social networks (VSNs) are moving from infancy to maturity. However, the characteristics of wireless channel availability and the high mobility of vehicles in the VSNs create new challenges for privacy preservation; the corresponding solutions require a combination of technologies. A common theme in this paper is the combination of crowdsensing (CS) and the VSNs. We first design a three-layer CS-based VSN architecture that includes a CS layer, mobile management layer, and core control layer. Then, we propose a two-stage privacy-preserving (TSPP) algorithm to protect identity and location privacy for the CS-based VSN. In TSPP algorithm, to protect identity privacy, we design a way of identity management. Simultaneously, by correlation analysis, we design a suppression strategy to satisfy $\varepsilon $ -privacy with no information loss for location privacy protection. The simulation results show that our proposed TSPP algorithm better protects identity and location privacy than current algorithms. The results are based on four metrics: average processing time, identity privacy, location privacy, and information loss.

Most studies on network function virtualization are based on cloud computing environments. Fog computing has been proposed as a supplement to cloud computing. When deploying the service function chain (SFC), the consumption of network resources can be effectively reduced by taking advantage of a combination of cloud and fog computing. However, few SFC studies are based on fog computing environments. Moreover, the problem of combining SFCs for the support of live online services to reduce network congestion and save network resources has not been considered. To effectively take advantage of cloud-fog computing and thus achieve the goal of saving resources and reducing network congestion, in this paper, we study the SFC combination and deployment problem in cloud-fog computing environments. To solve this problem, we present an efficient SFC combination and deployment algorithm. Finally, we conduct extensive simulations to evaluate the performance of our proposed algorithm. The results show that our proposed algorithm can effectively reduce network resource consumption and effectively resolve network congestion caused by live online services.

This study attempts to establish a relationship between EEG signals and depression under induced emotions using VR. The VR scenes were obtained from third party sources as a trigger. Due to the immersion and visualization of the whole environment, it makes this method different and in some case superior to the traditional music or video simulations. In this study, EEG signals of 463 participants were recorded as each watched two VR scenes chosen from our VR database with two 60s resting state before each scene. The results demonstrated that the power of alpha frequency band is related to depression and beta frequency band of high risking depression group is higher than the no depression group. The feedback will be future used in the next stage of machine learning of detecting depressive group.

Various of methods have been proposed for emotional induction including affective pictures, audios, and video clips. However, significant limitations have been exhibited such as low emotion elicitation efficiency and high susceptibility to ambient interferences. Considering virtual reality (VR) provides a more immersive and authentic audio-visual sense, this paper puts forward an emotion elicitation system using VR named Affective Virtual Reality System (AVRS). Firstly, the emotional multi-features were extracted and classified from the existing standard affective systems. Then the VR scenes were designed by writing scripts, building 3D modeling and scenes using Unreal Engine 4.12. Through Self-Assessment Manikin (SAM) evaluation, it was verified that the emotions inducted by the VR scenes were evenly distributed in the three-dimensional space of emotion, which reached the standardization of affective system in field of psychology. Then we further validated the advantages of AVRS by putting the four VR scenes into the LCD and VR device respectively. We tested the arousal level with the heart rate data and the SAM scale. The result shows that the arousal of other scenes have no significant difference from the videos in the VR environment, except the fear scene. We found that there are some conditions for the VR scenes to achieve a good emotional effect. Enhancing the interactivity and plot design is an important aspect.

The present affective stimulation systems have shortages in terms of inefficient emotion evocation and poor immersion. This paper presents the design, instructions and ratings of a novel Affective Virtual Reality System (AVRS), which includes a large set of emotionally-evocative VR scenes and their affective ratings. It can provide more objective and direct affective stimuli of basic emotions (happiness, sadness, fear, relaxation, disgust, and rage) by shielding the environmental interferences. In this study, affective VR scenes have been designed by using various standard affective picture, video and audio materials as references. To assess the three dimensional emotion indices of valence, arousal and dominance, each scene of the system is rated and standardized by Self-Assessment Mainikin. AVRS is the first released VR version affect stimuli materials, which sets a precedent for future interdisciplinary work bridging the gap between VR and cognitive psychology.

In the learning process of children, status detection and interaction can improve their learning efficiency. The previous research of educational robots mainly focus on enriching the learning content and enhancing anthropomorphic communication. However, there is a lack of studies in the area of learning status detection of children. In this paper, a robot is designed to address the issue during children learning. It captures the facial features of children and analyzes the fatigue state based on multi-cue fusion. Moreover, the robot interacts with children via multi-channel interactions, such as voice, image and sound to relieve fatigue and improve learning efficiency. Experimental results proposed validate the effectiveness of the fatigue detection method.

In the process of dealing with the classification of text, because the text after the Chinese word segmentation, an article will have a large number of feature words, for this feature, the document vector dimension will reach tens of thousands or even hundreds of thousands of dimensions, although theoretically Speaking, a large number of feature words can better characterize a document, but a document contains a large number of features for the classification of the feature word, its value is quite low. So the need to screen out those who have the classification of the word, to reduce the operational dimension of the purpose. This paper studies the traditional feature selection algorithm, and according to the shortcomings of the chi-square test method, Based on the shortcomings of traditional chi-square test, this paper presents an improved method of chi-square test combined with frequency and interclass concentration. Experiments show that the method has a good effect on the traditional chi-square test method.

5G-based Vehicular Social Network (VSN) fiercely calls for an advanced requirement of vehicle's location and trajectory privacy preserving scheme. Since the VSN owns the characteristics of high mobility and multiple hops relay, we first construct an architecture of 5G-based VSN with the Mobile Femtocell (MFemtocell). Then we propose the Dynamic Group Division algorithm (DGD), which is suitable for the dynamic property of 5G and meets the real-time demand of VSN. For privacy preserving, the DGD algorithm increases the chance of pseudonym exchanging with the proposed group generating protocol and pseudonym exchanging protocol. Finally, we evaluate and validate the effectiveness of our proposed algorithm from three aspects: anonymity set size, distance deviation and pseudonym entropy. The simulation results show that the DGD algorithm can more effectively protect the vehicles' location and trajectory privacy than the existing privacy-preserving scheme.

Location-Based Service (LBS) is one of the fundamental and central functionalities of mobile social networks. Since users usually have to report their locations to the LBS providers while using services, the protection of user's location privacy poses a critical challenge. Although many existing approaches can preserve user's location privacy effectively, most of them must include and use the user's real location. In this paper, we first propose an efficient k-anonymity based Dummy Location and divided Circular Area (k-DLCA) approach to protect the user's location privacy. Different from existing studies, the k-DLCA algorithm adopts a greedy strategy to select dummy locations and considers the semantic location information of the location. Moreover, the user's real location may not be contained in the chosen dummy locations. We then show that k-DLCA algorithm can resist the attacks from adversaries, and has a low probability of exposing the user's real location. We conduct extensive simulations to evaluate the efficiency of the proposed scheme. The simulation results demonstrate that our proposed scheme is promising.

Preserving user location and trajectory privacy while using location-based service (LBS) is an important issue. To address this problem, we first construct three kinds of attack models that can expose a user's trajectory or path while the user is sending continuous queries to a LBS server. Then we propose the k- anonymity trajectory (KAT) algorithm, which is suitable for both single query and continuous queries. Different from existing works, the KAT algorithm selects k-1 dummy locations using the sliding widow based k- anonymity mechanism when the user is making single queries and selects k-1 dummy trajectories using the trajectory selection mechanism for continuous queries. We evaluate and validate the effectiveness of our proposed algorithm by conducting simulations for the single and continuous query scenarios.

With the increasing popularity of Internet services and cloud computing, both energy efficiency and performance guarantees are major concerns for data center operators. However, only a few of previous works elaborate the dynamic system working procedure while using specific mathematical methods to study on the relationship between the two characters. This paper addresses how to guarantee performance requirement in terms of waiting time so as to minimize power consumption by means of dynamic management policies that switch on/off a certain group of servers. We develop a mathematical model using queuing theory to determine activation thresholds of servers. Our scheme provides an analytical approach for studying the power-performance tradeoff in large data centers.

In this paper, we investigate the problem of online multicast virtual network embedding (MVNE) by introducing the strategies of waiting-tolerant and load-prediction into our VN provisioning framework for reducing the blocking ratio. We propose a heuristic algorithm called MVNE-WL (MVN Embedding with the strategies of Waiting-tolerant and Load-prediction) for solving this problem efficiently. The numerical results obtained from extensive simulations demonstrate the effectiveness of the proposed approach and show that MVNE-WL is better than traditional solutions in terms of the MVN mapping blocking ratio, the total net revenue, the unit cost efficiency, the node utilization and the link utilization in the long term.

In this paper, a network-based service management framework is proposed. Our framework places programmable nodes, distributes services among these nodes and directs traffic through them in accordance to a predefined sequence. Based on standard shortest path routing, it is impossible to represent all paths as shortest paths with only one set of link weights, i.e., partial traffic cannot be directed through required service nodes. In order to solve this problem, multi-topology routing (MTR) is introduced to provide multiple optional paths over multiple sets of link weights. One mixed integer linear programming model is proposed for the joint optimization problem. Numerical results show that our optimization scheme is feasible and effective in network with standard shortest path routing.

Recent research on virtualization has focused on developing various solutions for the problem of mapping a virtual network (VN) onto the substrate network. However, these solutions and associated algorithms are only efficient for constructing unicast service-oriented virtual networks, and generally not applicable to the cases of multicast service-oriented virtual networks (MVNs). Furthermore, there has been very limited work on the survivable MVN mapping (SMVNM) problem, which is important while considering multicast traffic. In this research, we discuss SMVNM problem while considering regional failures of the substrate network and propose an efficient algorithm for solving this problem. We validate and evaluate our framework and algorithms by conducting extensive simulations on realistic network under various scenarios, and by comparing with existing approaches. The simulation results show that our approach outperforms existing solutions.

Virtual Network Mapping (VNM) has been a key issue for network virtualization need to be addressed. A traditional substrate network is usually managed by multicast InPs, and many applications in the substrate network can be characterized by hybrid virtual networks with both unicast and multicast requests. However, to the best of our knowledge, few researches focus on the problem of hybrid virtual network mapping across multiple domains (HVNMMD). In this paper, we investigate the HVNMMD problem and propose two algorithms to solve this problem efficiently: i) the decomposition-based algorithm, HVNMMD_D; and ii) the spectral clustering based algorithm, HVNMMD_SC. We evaluate the performance of our algorithms through conducting simulation experiments. The simulation results show that the proposed algorithms perform better than existing approaches.

Network virtualization technology wherein multiple virtual infrastructures (VIs) are supported on the same underlying infrastructure is the key enabler of the cloud computing paradigm. However, due to the resource sharing, even a single server failure in the substrate will affect all the VIs mapped on to it. Thus, backup resources should be reserved intelligently to provide cost-effective VI reliability. In this paper, we study the problem of reliable VI mapping over multiple clusters. We propose a local protection scheme to minimize the total reliable mapping cost, i.e., each VI node is protected in a local cluster so that the high backup bandwidth cost on the wide-area network can be avoided. We present our local protection based reliable VI mapping algorithm (LP-RVIM) to minimize the mapping cost for VIs with protection. Through simulations we show that our LP-RVIM algorithm can efficiently reduce the mapping costs when compared to other VI mapping algorithms.

Network virtualization allows the coexistence of multiple virtual networks on a shared substrate network. A virtual network created by a service provider (SP) typically spans across multiple domains that may be managed by different infrastructure providers (InPs). The topology and resource information on each domain is confidential and kept private by the InP. However, a domain-level view is required to achieve quality of service (QoS)-aware VN mapping across multiple domains. In this paper, we present a framework for the QoS-aware VN mapping across multiple domains. In the framework, the VN mapping is accomplished by the mapping manager that is the broker between the SPs and the InPs. The mapping manager collects the mapping candidates for the VN request from the InPs, and then establishes the abstracted domain-level graph. Finally the candidates are selected on the domain-level graph with QoS consideration. We formulate the problem of candidate selection as an integer linear programming (ILP) and also present a heuristic algorithm to solve it. The simulation results show that our proposed framework can achieve low resource allocation cost and good QoS performance for the VN request.

In the cloud computing paradigm users access applications/services hosted and run on virtual machines in interconnected datacenters. Applications from the same user may need to interact and change data or information, thus, we may abstract the applications/services request from same user as a virtual network (VN). To improve performance and resource efficiency, it is critical that the VN request be optimally provisioned given the current resource state of the datacenters. However, all of the existing research about optimal VN provisioning conducted for deterministic services. In this work, we formulate the problem of optimal provisioning for elastic service oriented VN request as a mixed integer programming (MIP) model with the objective of maximizing the total revenue of infrastructure provider (InP), and propose a genetic algorithm based heuristic (GAH) algorithm for addressing the optimal provisioning for virtual network request with un-splittable flow (OPVNUF) and optimal provisioning for virtual network request with splittable flow (OPVNSF) problems of elastic services. We demonstrate the effectiveness of our approach for improving the total revenue, by conducting extensive simulations on real substrate network.

In P2P file sharing systems over mobile cellular networks, the bottleneck of file transfer speed is usually the downlink bandwidth of the receiver rather than the uplink bandwidth of the senders. In this paper we consider the impact of downlink bandwidth limitation on file transfer speed and propose two novel peer selection algorithms named DBaT-B and DBaT-N, which are designed for two different cases of the requesting peer's demand respectively. Our algorithms take the requesting peer's downlink bandwidth as the target of the sum of the selected peers' uplink bandwidth. To ensure load balance on cells, they will first choose a cell with the lowest traffic load before choosing each peer. We also provide a Fuzzy Cognitive Map that can be used to estimate peers' service ability in P2P systems over mobile cellular networks. Simulation results show that in respective cases DBaT-B and DBaT-N can both achieve much better load balance on cells than some traditional algorithms while ensuring favorable file transfer speed.

We propose a novel segmentation algorithm TSESS and a novel chunk scheduling algorithm TSESCS for SVC based P2P streaming systems. TSESS splits each layer into equal sized chunks and tags each chunk with a time-stamp. TSESCS is based on TSESS and adopts two scheduling windows with adaptive strategies. Compared with traditional segmentation algorithm and chunk scheduling algorithms, our TSESS and TSESCS algorithms can achieve better video quality on clients in heterogeneous network environments.

Cloud based datacenters provide on demand services and resources, both transparently and cost effectively. These services and applications are typically hosted and run on the servers located in interconnected datacenters. The task or application request from users can be abstracted as a virtual network (VN) request. How to efficiently accommodate VN requests by mapping them onto the substrate network is a challenging problem. Current research only considers static VN requests, where the VN request and the demand for resources is fixed and does not change over time. However, most of the application requests submitted to datacenters present dynamic changing characteristics. In this paper, we address the issue of how to optimally reconfigure and map an existing VN while this VN request changes. As the VN provisioning problem is NP-hard, we propose heuristic algorithms for solving it efficiently. We evaluate the performance and effectiveness our algorithms by conducting simulations on realistic network. The simulation results show that our approach solves this problem efficiently and performs well than existing approaches.

Due to the characteristics of the DCF model in 802.11 and P2P users' requirements of both downloading and uploading, P2P traffic frequently causes congestion in WLAN. However, traditional P2P cache cannot alleviates the congestion caused by P2P traffic in WLAN effectively. In this paper, we propose a novel solution called bidirectional cache for this problem. A bidirectional cache which contains a reverse cache as well as a forward cache can be deployed at the AC of a WLAN. Being a novel design, the reverse cache can cache release the uplink bandwidth resource at each AP can be and the congestion can be alleviated effectively. Meanwhile, the forward cache can still act as the traditional P2P cache to reduce the cross domain traffic. Simulation results show that, compared with traditional P2P cache, our bidirectional cache can shorten the file transfer time of P2P applications by at least 42% and improve the throughput of the other Internet applications by at least 28%.

In the paradigm of cloud computing, multiple users can share the cloud infrastructure resources. The application or service requests from a user can be abstracted as a virtual network (VN) request and submitted to the cloud-based data centers. How to map a virtual network onto the cloud infrastructure network is a challenging issue in cloud resource provisioning. Current researches about virtual network mapping problem mainly focus on resource efficiency, which can be classified into resource efficient VN mapping or cost efficient VN mapping. However, the amount of power or energy consumed by data centers takes a large part of all of the power consumption. This may not only leads to a higher expenditure on the operation of data centers but also contribute to carbon emissions and the greenhouse effect. In this work we study the problem of power efficient virtual network mapping and propose a framework and algorithm for solving this problem. The simulation results show that our approach performs well.

The inertial navigation system (INS) computer is a key part in the inertial navigation application. Traditional PC104 computer is of many disadvantages such as fixed board layout and large power dissipation, which makes the system impossible to integrate the functions of signal collecting and navigation computing for reducing the power dissipation and size of board. In this paper, a new INS computer is designed using DSP TMS320C6713 and FPGA XC3S400AN. The computer integrates the signal collecting work by employing pulse subdivision method to avoid ±1 quantization error. Experimental results show that the new INS computer gets good performance on signal collecting (RMS of 10−3) and inertial navigation computing, brings decreased power dissipation (1.5W) as well, which makes it useful in INS application.

The frequency stability is of great importance to DILAG's performance. Based on physical principle of frequency stabilizing, a fuzzy PID controller is designed by combined fuzzy control with conventional PID control. Simulated and experimental results show that the fuzzy PID controller is superior to the conventional PID controller in dynamic steady performance, and the fuzzy PID controller features stronger null stability.

Topological dynamics and periodicity are two of the major differences between LEO satellite networks and terrestrial networks. In this work, we first propose MAPSS (Method to Analyze Periodicity of Satellite System) to obtain the system period of satellite networks based on spectral analysis. Then the topological dynamics of satellite networks, including the number of snapshots in one system period, is characterized by using this new method. Our method could be used in the development of routing algorithms as well as the design of satellite constellation.

In this paper, we propose an effective upgrading algorithm in incremental planning for optical networks. This algorithm integrates making full use of existent available resource with upgrading capacity. Our objective is to minimize the newly added resource in the network. The Integer Linear Programming (ILP) formulation is presented. Since the ILP formulation is not adapted for large-scale network, a novel fast heuristic algorithm called Minimal Upgrading Resource (MUR) is proposed and evaluated. Computer simulation results show the MUR can achieve the performance near to the ILP results, and performs better than other two proposed approaches.

In terrestrial-satellite hybrid networks, traditional TCP performance degrades a lot due to long propagation delays and high bit error rates link. Performance enhancing proxy(PEP) can shorten round trip time loop time and Peach can enhance the performance over high bit error rates link. In this paper, we propose a new scheme that combines PEP and TCP Peach for terrestrial-satellite hybrid networks. The scheme could reduce the negative impact on performance (in terms of throughput). To obtain an analysis result, we also introduce a mathematic model for our PEP. The analysis and simulation results demonstrate that our scheme could significantly improve the network performance than traditional TCP protocols.

Scalability is one of the key issues of the researches on next-generation Packet Switching Fabrics (PSF). The torus network becomes a promising technology for next-generation PSF, since it has good scalability as well as high capacity and reliability. In this paper, the principles on how to determine the values of critical design parameters for constructing PSF of Torus topology (PSF-T) are discussed. Then a modular switching subnetwork that employs the torus network technology, Configurable Board (CB), is proposed. A scheme to construct scalable PSF-T with CB modules is also given. The number of ports and the capacity of the PSF can grow smoothly up to hundreds of times, and its performance does not degrade during scaling. Simulation results are presented to show the validity of our scheme.

The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the networks. Energy-efficient clustering algorithm should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new cross-layer energy efficient clustering (CLEEC) algorithm for heterogeneous wireless sensor networks with renewable energy source. In CLEEC, the cluster heads are elected by probabilities based on cross-layer optimal estimation, considering energy replenishment. The cross-layer optimal estimation uses cross-layer information, such as energy and replenishment rate, to theoretically compute the election probabilities. Finally, the simulation results demonstrate that our clustering approach outperforms current important clustering algorithms in the efficiency of prolonging the network lifetime.

In this paper, we address the problem of resource allocation with efficiency and quality of service (QoS) support in uplink for a wireless CDMA network supporting real-time (RT) and nonreal-time (NRT) communication services. For RT and NRT users, there are different QoS requirements. We introduce and describe a new scheme, namely the traffic aided uplink opportunistic scheduling (TAUOS). While guaranteeing the different QoS requirements, TAUOS exploits the channel condition to improve the system throughput. In TAUOS, the cross-layer information, file size information, is used to improve the fairness of NRT users. Extensive simulation results show that our scheme can achieve high system throughput in uplink wireless CDMA system, while guaranteeing the QoS requirements.

In this paper, we focus on the problem with the objective to maximize the system performance, while guaranteeing multiple QoS (quality of service) constraints for wireless data networks accommodating multiclass services with different quality requirements. First, we formulate and solve the opportunistic scheduling problem with multiple general long-term QoS constraints. Then, we generalize this problem to include short-term QoS constraints for real-time multimedia users and long-term QoS constraints for non-real-time data users simultaneously in multiclass services networks. Simulation results illustrate that the proposed scheduling schemes guarantee the different QoS constraints, and achieve high system performance.

In this paper, we focus on the problem with the objective to maximize the system performance, while guaranteeing specified QoS constraints for multiple user classes in wireless data networks. First, we propose two opportunistic scheduling algorithms that exploit time-varying channel conditions for the special two-constraint case, and then propose an opportunistic scheduling algorithm for the general case. Simulation results illustrate that the proposed scheduling algorithms guarantee the different constraints, and achieve high system performance that is close to the true optimal value using a known general-purpose optimization package, lingo

In this paper, we study the fundamental problem of allocating the available resources with efficiency and fairness in a wireless CDMA network. In particular, we first introduce an ideal fair scheduling (IFS) scheme. The allocation of resources in IFS is strictly fair. Then we introduce the proportional compensation based fair scheduling (PCFS) algorithm. PCFS distinguishes the services in different application levels. In PCFS, a channel-adaptive resource allocation policy is introduced to improve the system throughput and a compensation model is introduced to achieve fair services. Based on the assignment of PCFS, we propose the traffic aided PCFS (T-PCFS) algorithm that incorporates traffic information into compensation model to achieve better fairness. Extensive simulation results show that our scheme can operate well in wireless CDMA networks.

Fair queuing has long been a popular paradigm for providing bounded delay channel access and separation between users in wired networks. However, adapting fair queuing to the wireless domain is not a trivial task because of the unique problems in wireless channels such as location dependent and bursty channel error. In this paper, we incorporate traffic information into wireless fair scheduling using compensation scheme. In particular, we first establish general properties for fair scheduling using proportional compensation scheme in CDMA cellular networks. It distinguishes the services in different application levels. A compensation model and a reallocation method in the scheme were introduced to achieve wireless fair services. Then, we develop a new traffic aided fair scheduling scheme based on compensation model by making use of file size information. Extensive simulation results show that our scheme can operate well in CDMA cellular networks.

We first discuss the need for computational electromagnetics (CEM) in high-frequency IC design. We then review recent advances in CEM, present chip design challenges to CEM analysis, and analyze the shortcomings of current CEM methodology. We then emphasize the importance of chip-design-driven CEM development, and finally present some promising full-wave electromagnetic-based solutions that can achieve full-chip analysis and design.