To improve the quality of multimedia services and stimulate secure sensing in Internet of Things applications, such as healthcare and traffic monitoring, mobile crowdsensing (MCS) systems must address security threats such as jamming, spoofing and faked sensing attacks during both sensing and information exchange processes in large-scale dynamic and heterogeneous networks. In this article, we investigate secure mobile crowdsensing and present ways to use deep learning (DL) methods, such as stacked autoencoder, deep neural networks, convolutional neural networks, and deep reinforcement learning, to improve approaches to MCS security, including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS. We discuss the performance gain of these DL-based approaches compared to traditional security schemes and identify the challenges that must be addressed to implement these approaches in practical MCS systems.

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users’ quality of experience (QoE) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users’ real-time video QoE. First, to measure and assess video QoE, we provide a continuous QoE prediction engine modeled by RNN recurrent neural network. Different from traditional QoE models which consider the QoE-aware factors separately or incompletely, our RNN-QoE model accounts for three descriptive factors (video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-QoE can follow the subjective QoE quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time QoE compared with classical rate adaption methods.

The explosive growth of data volume in mobile networks makes fast online diagnose a pressing search problem. In this paper, an object-oriented detection framework with a two-step clustering, named as Hourglass Clustering, is given. Where three object parameters are chosen as Synthetical Quality of Experience (SQoE) Key Quality Indicators (KQIs) to reflect accessibility, integrality, and maintainability of networks. Then, we choose represented Key Performance Indicators (rKPIs) as cause parameters with correlation analysis. For these two kinds of parameters, a hybrid algorithm combining the self-organizing map (SOM) and k-medoids is used for clustering them into different types. We apply this framework to online anomaly detection in Cellular Networks, named SQoE-driven Anomaly Detection and Cause Location System (SQoE-ADCL). Our experiments with real 4G data show that besides fast online detection, SQoE-ADCL makes a better soft decision instead of a traditional hard decision. Furthermore, it is also a general way of being applied to other similar applications in big data.

In the network virtualization environments, one of the most challenges is how to map the virtual networks (VNs) onto a shared substrate network managed by an infrastructure provider (InP), which is termed as virtual network embedding problem. Prior studies on this issue only emphasize on maximizing the revenue or minimizing the energy consumption while ignoring the reliability requirements of end-users. In our work, we incorporate the reliability probability into the virtual network embedding process with an aim to improve the QoS/QoE of end users from a new perspective. We devised two novel reliable virtual network embedding algorithms called RRW-MaxMatch and RDCC-VNE based on RW-MaxMatch and DCC-VNE, respectively. Extensive simulations demonstrated that the efficiency of our proposed algorithms is better than those of two primitive algorithms in terms of the reliability demands, the acceptance ratio of virtual networks and the long-term average revenue.

Quality of experience (QoE), which is very critical for the experience of users in wireless networks, has been extensively studied. However, due to different human perceptions, quantifying the effective capacity of wireless network subject to diverse QoE is very difficult, which leads to many new challenges regarding QoE guarantees in wireless networks. In this paper, we formulate the QoE guarantees model for cellular wireless networks. Based on the model, we convert the effective capacity maximization problem into the equivalent convex optimization problem. Then, we develop the optimal QoE-driven power allocation scheme, which can maximize the effective capacity. The obtained simulation results verified our proposed power allocation scheme, showing that the effective capacity can be significantly increased compared with that of traditional QoE guarantees based schemes.

In remote terrestrial-satellite networks, caching is a very promising technique to alleviate the burden of space cloudlet (e.g., cache-enabled satellite user terminal) and to improve subscribers’ quality of experience (QoE) in terms of buffering delay and achievable video streaming rate. In this paper, we studied a QoE-driven caching placement optimization problem for video streaming that takes into account the required video streaming rate and the social relationship among users. Social ties between users are used to designate a set of helpers with caching capability, which can cache popular files proactively when the cloudlet is idle. We model the utility function of QoE as a logarithmic function. Then, the caching placement problem is formulated as an optimization problem to maximize the user’s average QoE subject to the storage capacity constraints of the helpers and the cloudlets. Furthermore, we reformulate the problem into a monotone submodular optimization problem with a partition matroid constraint, and an efficient greedy algorithm with approximation ratio is proposed to solve it. Simulation results show that the proposed caching placement approach significantly outperforms the traditional approaches in terms of QoE, while yields about the same delay and hit ratio performance compare to the delay-minimized scheme.

In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience (QoE) has received much attention and has become a key performance measurement for the application and service. In order to meet the users’ expectations, the management of the resource is crucial in wireless network, especially the QoE based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing QoE for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic dataa, and the experiments verify the effectiveness of our method.

The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5G era, service based architecture is introduced into mobile networks. The monolithic network elements (e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5G core network are still big challenges. In this paper, we propose a novel management architecture for 5G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.

Network function virtualization is a new network concept that moves network functions from dedicated hardware to software-defined applications running on standard high volume severs. In order to accomplish network services, traffic flows are usually processed by a list of network functions in sequence which is defined by service function chain. By incorporating network function virtualization in inter-data center (DC) network, we can use the network resources intelligently and deploy network services faster. However, orchestrating service function chains across multiple data centers will incur high deployment cost, including the inter-data center bandwidth cost, virtual network function cost and the intra-data center bandwidth cost. In this paper, we orchestrate SFCs across multiple data centers, with a goal to minimize the overall cost. An integer linear programming (ILP) model is formulated and we provide a meta-heuristic algorithm named GBAO which contains three modules to solve it. We implemented our algorithm in Python and performed side-by-side comparison with prior algorithms. Simulation results show that our proposed algorithm reduces the overall cost by at least 21.4% over the existing algorithms for accommodating the same service function chain requests.

The growing trend of network virtualization results in a widespread adoption of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in OpenFlow-based software defined networks. This paper first takes an insight into packet classification in virtual OpenFlow switching, and points out that its performance bottleneck is dominated by flow table traversals of multiple failed mask probing for each arrived packet. Then we are motivated to propose an efficient packet classification algorithm based on counting bloom filters. In particular, counting bloom filters are applied to predict the failures of flow table lookups with great possibilities, and bypass flow table traversals for failed mask probing. Finally, our proposed packet classification algorithm is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed algorithm outperforms the classical one in Open vSwitch in terms of average search length, and contributes to promote virtual OpenFlow switching performance.

Software Defined Networking (SDN) provides flexible network management by decoupling control plane from data plane. And multiple controllers are deployed to improve the scalability and reliability of the control plane, which could divide the network into several subdomains with separate controllers. However, such deployment introduces a new problem of controller load imbalance due to the dynamic traffic and the static configuration between switches and controllers. To address this issue, this paper proposes a Distribution Decision Mechanism (DDM) based on switch migration in the multiple subdomains SDN network. Firstly, through collecting network information, it constructs distributed migration decision fields based on the controller load condition. Then we choose the migrating switches according to the selection probability, and the target controllers are determined by integrating three network costs, including data collection, switch migration and controller state synchronization. Finally, we set the migrating countdown to achieve the ordered switch migration. Through verifying several evaluation indexes, results show that the proposed mechanism can achieve controller load balancing with better performance.

Scalable video coding (SVC) is a powerful tool to solve the network heterogeneity and terminal diversity in video applications. However, in related works about the optimization of SVC-based video streaming over Software Defined Network (SDN), most of the them are focused either on the number of transmission layers or on the optimization of transmission path for specific layer. In this paper, we propose a noval optimization algorithm for SVC to dynamically adjust the number of layers and optimize the transmission paths simultaneously. We establish the problem model based on the 0/1 knapsack model, and then solve it with Artificial Fish Swarm Algorithm. Additionally, the simulations are carried out on the Mininet platform, which show that our approach can dynamically adjust the number of layers and select the optimal paths at the same time. As a result, it can achieve an effective allocation of network resources which mitigates the congestion and reduces the loss of non-SVC stream.

In order to solve the problem that traditional energy efficiency power allocation algorithms usually require the assumption of constant or perfect channel state information in cognitive radio networks (CRNs), which may lead to performance degradation in real systems with disturbances or uncertainties, we propose a robust energy efficiency power allocation algorithm for underlay cognitive radio (CR) systems with channel uncertainty in consideration of interference power threshold constraint and minimum target SINR requirement constraint. The ellipsoid sets are used to describe the channel uncertainty, and a constrained fractional programming for the allocation is transformed to a convex optimization problem by worst-case optimization approach. A simplified version of robust energy efficiency scheme by a substitutional constraint having lower complexity is presented. Simulation results show that our proposed scheme can provide higher energy efficiency compared with capacity maximization algorithm and guarantee the signal to interference plus noise ratio (SINR) requirement of each cognitive user under channel uncertainty.

Massive multiple input and multiple output (MIMO) is a key technology of the fifth generation (5G) wireless communication systems, which brings various advantages, such as high spectral efficiency and energy efficiency. In MIMO system, spatial modulation (SM) has recently emerged as a new transmission method. In this paper, in order to improve the security in SM-MIMO, a physical layer encryption approach named chaotic antenna-index three-dimensional modulation and constellation points rotated (CATMCPR) encryption scheme is proposed, which utilizes the chaotic theory and spatial modulation techniques. The conventional physical-layer encryption in SM-MIMO suffers from spectral efficiency (SE) performance degradation and usually needs a preshared key, prior channel state information (CSI) or excess jamming power. By contrast, we show that the CATMCPR scheme can not only achieve securely communication but also improve above drawbacks. We evaluate the performances of the proposed scheme by an analysis and computer simulations.

Recently, Mao, Zhang, Wu et al. constructed two key exchange (KE) protocols based on tensor ergodic problem (TEP). Although they conjectured that these constructions can potentially resist quantum computing attack, they did not provide a rigorous security proof for their KE protocols. In this paper, applying the properties of ergodic matrix, we first present a polynomial time algorithm to solve the TEP problem using O(n6) arithmetic operations in the finite field, where n is the security parameter. Then, applying this polynomial time algorithm, we generate a common shared key for two TEP-based KE constructions, respectively. In addition, we also provide a polynomial time algorithm with O(n6) arithmetic operations that directly recovers the plaintext from a ciphertext for the KE-based encryption scheme. Thus, the TEP-based KE protocols and their corresponding encryption schemes are insecure.

Skyline query processing has recently received a lot of attention in database and data mining communities. However, most existing algorithms consider how to efficiently process skyline queries from base tables. Obviously, when the data size and the number of skyline queries increase, the time cost of skyline queries will increase exponentially, which will seriously influence the query efficiency. Motivated by the above, in this paper, we consider improving the query efficiency via skyline views and propose a cost-based algorithm (abbr. CA) to efficiently select the optimal set of skyline views for storage. The CA algorithm mainly includes two phases: (i) reduce the skyline views selection to the minimum steiner tree problem and obtain the approximate optimal set AOS of skyline views, and (ii) adjust AOS and produce the final optimal set FOS of skyline views based on the simulated annealing. Moreover, in order to improve the extendibility of the CA algorithm, we implement it based on the map/reduce distributed computation model in cloud computing environments. The detailed theoretical analyses and extensive experiments demonstrate that the CA algorithm is both efficient and effective.

UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games (EPG) and have Nash equilibrium (NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play (MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.

Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model (GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform (PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.