Showing papers by "Houbing Song published in 2017"
TL;DR: An SDN-enabled network architecture assisted by MEC, which integrates different types of access technologies, is proposed, which can decrease data transmission time and enhance quality of user experience in latency-sensitive applications.
Abstract: Connected vehicles provide advanced transformations and attractive business opportunities in the automotive industry. Presently, IEEE 802.11p and evolving 5G are the mainstream radio access technologies in the vehicular industry, but neither of them can meet all requirements of vehicle communication. In order to provide low-latency and high-reliability communication, an SDN-enabled network architecture assisted by MEC, which integrates different types of access technologies, is proposed. MEC technology with its on-premises feature can decrease data transmission time and enhance quality of user experience in latency-sensitive applications. Therefore, MEC plays as important a role in the proposed architecture as SDN technology. The proposed architecture was validated by a practical use case, and the obtained results have shown that it meets application- specific requirements and maintains good scalability and responsiveness.
289 citations
TL;DR: Recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data are reviewed.
Abstract: The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.
288 citations
TL;DR: This paper proposes a security and privacy preservation scheme to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution, and implements a prototype system to evaluate the influence of security scheme on system performance.
Abstract: Face identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on face identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based face identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based face identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.
196 citations
TL;DR: The theoretical analysis proves the convergence of a proposed algorithm and efficient convergence during the first and second steps of the algorithm to effectively prevent parking navigation from a gridlock situation and demonstrates that the proposed algorithm performs more efficiently than existing algorithms.
169 citations
TL;DR: Two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores are studied and a new metric—collaborative reputation scores—is introduced to expand this definition.
Abstract: Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metric—collaborative reputation scores—to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control.
126 citations
TL;DR: A three-tier system architecture is proposed and mathematically characterize each tier in terms of energy consumption and latency so that the transmission latency and bandwidth burden caused by cloud computing can be effectively reduced.
121 citations
TL;DR: The authors' develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT) method that has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods.
Abstract: With the rapid development of computer science, problems with digital products piracy and copyright dispute become more serious; therefore, it is an urgent task to find solutions for these problems. In this study, the authors' develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT). The proposed method combines fractal encoding method and DCT method for double encryptions to improve traditional DCT method. The image is encoded by fractal encoding as the first encryption, and then encoded parameters are used in DCT method as the second encryption. First, the fractal encoding method is adopted to encode a private image with private scales. Encoding parameters are applied as digital watermarking. Then, digital watermarking is added to the original image to reversibly using DCT, which means the authors can extract the private image from the carrier image with private encoding scales. Finally, attacking experiments are carried out on the carrier image by using several attacking methods. Experimental results show that the presented method has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods.
114 citations
13 Nov 2017
TL;DR: This book provides an in-depth look at security and privacy, two of the most critical challenges facing both the CPS research and development community and ICT professionals.
Abstract: Written by a team of experts at the forefront of the cyber-physical systems (CPS) revolution, this book provides an in-depth look at security and privacy, two of the most critical challenges facing both the CPS research and development community and ICT professionals. It explores, in depth, the key technical, social, and legal issues at stake, and it provides readers with the information they need to advance research and development in this exciting area.
107 citations
TL;DR: This paper conducts the first study on applying runtime verification to cooperate with current DSS based on real-time data and outperforms the only use of DSS or human inspection, and improves the quality of clinical health care of hospital.
Abstract: Wireless medical cyber-physical systems are widely adopted in the daily practices of medicine, where huge amounts of data are sampled by the wireless medical devices and sensors, and is passed to the decision support systems (DSSs). Many text-based guidelines have been encoded for work-flow simulation of DSS to automate health care based on those collected data. But for some complex and life-critical diseases, it is highly desirable to automatically rigorously verify some complex temporal properties encoded in those data, which brings new challenges to current simulation-based DSS with limited support of automatical formal verification and real-time data analysis. In this paper, we conduct the first study on applying runtime verification to cooperate with current DSS based on real-time data. Within the proposed technique, a user-friendly domain specific language, named DRTV, is designed to specify vital real-time data sampled by medical devices and temporal properties originated from clinical guidelines. Some interfaces are developed for data acquisition and communication. Then, for medical practice scenarios described in DRTV model, we will automatically generate event sequences and runtime property verifier automata. If a temporal property violates, real-time warnings will be produced by the formal verifier and passed to medical DSS. We have used DRTV to specify different kinds of medical care scenarios and have applied the proposed technique to assist existing wireless medical cyber-physical system. As presented in experiment results, in terms of warning detection, it outperforms the only use of DSS or human inspection, and improves the quality of clinical health care of hospital.
106 citations
TL;DR: This research has proposed some data-driven methodologies to increase data validity and provided some insights into the influence of IoT level data loss on public transportation systems for smart cities.
Abstract: Efficient urban transportation systems are widely accepted as essential infrastructure for smart cities, and they can highly increase a city�s vitality and convenience for residents. The three core pillars of smart cities can be considered to be data mining technology, IoT, and mobile wireless networks. Enormous data from IoT is stimulating our cities to become smarter than ever before. In transportation systems, data-driven management can dramatically enhance the operating efficiency by providing a clear and insightful image of passengers� transportation behavior. In this article, we focus on the data validity problem in a cellular network based transportation data collection system from two aspects: internal time discrepancy and data loss. First, the essence of time discrepancy was analyzed for both automated fare collection (AFC) and automated vehicular location (AVL) systems, and it was found that time discrepancies can be identified and rectified by analyzing passenger origin inference success rate using different time shift values and evolutionary algorithms. Second, the algorithmic framework to handle location data loss and time discrepancy was provided. Third, the spatial distribution characteristics of location data loss events were analyzed, and we discovered that they have a strong and positive relationship with both high passenger volume and shadowing effects in urbanized areas, which can cause severe biases on passenger traffic analysis. Our research has proposed some data-driven methodologies to increase data validity and provided some insights into the influence of IoT level data loss on public transportation systems for smart cities.
92 citations
19 Mar 2017
TL;DR: A network traffic prediction method based on a deep belief network and a Gaussian model that outperforms three existing methods for wireless mesh backbone network prediction.
Abstract: Wireless mesh network is prevalent for providing a decentralized access for users. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep belief network and a Gaussian model. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then a prediction model is built by learning a deep belief network from the extracted low-pass component. Otherwise, for the rest high-pass component that expresses the gusty and irregular fluctuations of network traffic, a Gaussian model is used to model it. We estimate the parameters of the Gaussian model by the maximum likelihood method. Then we predict the high-pass component by the built model. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
TL;DR: The core functionality of NDN is discussed followed by the new architecture proposed for ITS in smart cities and the current and future research challenges for NDN-enabled ITS in the context of smart cities are highlighted.
Abstract: A smart city enhances the quality of its citizens’ lives by providing ease of access to ubiquitous services through integration using communication systems at the foundation. Additionally, ITS plays a major role in making a metropolitan area into a smart city. The current IP-based solutions for ITS have slanted the performance due to high demand for data on the move, especially when the consumers become the producers. Meanwhile, NDN has evolved as a promising future Internet architecture and is being investigated extensively. In this article, we discuss the core functionality of NDN followed by our new architecture proposed for ITS in smart cities. Also, we highlight the current and future research challenges for NDN-enabled ITS in the context of smart cities.
TL;DR: A new packet scheduling scheme named LOES is proposed, which first combines the priority-based packets scheduling scheme with local optimization, and shows that LOES outperforms these previous scheduling schemes.
Abstract: With the widespread applications of Internet of Things (IoT), the emergency response performance for large-scale network packets is facing serious challenge, especially for renewable distributed energy resources monitoring in a smart grid. Therefore, how to improve the real-time performance of the emergency data packets has been a critical issue. Traditional packet scheduling schemes and topology optimization strategies are not suitable for a large-scale IoT-based smart grid. To address this problem, this paper proposes a new packet scheduling scheme named LOES, which first combines the priority-based packet scheduling scheme with local optimization. We exchange local geographic information to reduce the hop counts and distance between distributed source nodes and sink nodes. Each destination node determines the packet scheduling sequence according to the received emergency information. Finally, we compare LOES with first come first serve, multilevel scheme, and dynamic multilevel priority packet scheduling scheme using packet loss rate, packet waiting time, and average packet end-to-end delay as metrics. The simulation results show that LOES outperforms these previous scheduling schemes.
TL;DR: This article presents an architecture that combines SDN functionalities within VNs to retrieve the required content using NDN and discusses a number of current research challenges and provides a precise roadmap that can be considered for the research community to jointly address such challenges.
Abstract: Named data networking and software defined networking share mutual courage in changing legacy networking architectures. In the case of NDN, IP-based communication has been tackled by naming the data or content itself, while SDN proposes to decouple the control and data planes to make various services manageable without physical interference with switches and routers. Both NDN and SDN also support communication via heterogeneous interfaces and have been recently investigated for vehicular networks. Na�ve VNs are based on the IP-based legacy, which is prone to several issues due to the dynamic network topology among other factors. In this article, we first see both SDN and NDN enabled VNs from a bird's eye view, and for the very first time, we present an architecture that combines SDN functionalities within VNs to retrieve the required content using NDN. Moreover, we discuss a number of current research challenges and provide a precise roadmap that can be considered for the research community to jointly address such challenges.
TL;DR: The results showed that active queue management algorithms, such as REM and RED, exhibited stronger defensive abilities than the passive queue management algorithm Drop-Tail under medium- and small-scale DDoS attacks; however, under large- scale DDoS attack, all three algorithms exhibited insufficient defensive capabilities.
Abstract: Concentrating on the influence of DDoS applied to ad hoc networks, we introduced three classic queue management algorithms: Drop-Tail, random early detection (RED), and random exponential marking (REM). We analyzed and compared the defensive abilities of these algorithms applied to ad hoc networks with NS2 under DDoS attack. The results showed that active queue management algorithms, such as REM and RED, exhibited stronger defensive abilities than the passive queue management algorithm Drop-Tail under medium- and small-scale DDoS attacks; however, under large-scale DDoS attack, all three algorithms exhibited insufficient defensive capabilities. This means that other defense schemes, such as network detection, must be integrated into security schemes to defeat DDoS attacks.
TL;DR: The extensive simulation results demonstrate that CASMOC could not only improve the quality of network coverage, but also mitigate rapid node energy consumption effectively, thereby extending the life cycle of the network significantly.
Abstract: Coverage is a significant performance indicator of wireless sensor networks. Data redundancy in k-coverage raises a set of issues including network congestion, coverage reduction, energy inefficiency, among others. To address these issues, this paper proposes a novel algorithm called complex alliance strategy with multi-objective optimization of coverage (CASMOC) which could improve node coverage effectively. This paper also gives the proportional relationship of the energy conversion function between the working node and its neighbors, and applies this relationship in scheduling low energy mobile nodes, thus achieving energy balance of the whole network, and optimizing network resources. The extensive simulation results demonstrate that CASMOC could not only improve the quality of network coverage, but also mitigate rapid node energy consumption effectively, thereby extending the life cycle of the network significantly.
TL;DR: A fully distributed charging management scheme with consideration of urban travel uncertainties, for example, traffic congestion and drivers' preferences is developed, and a guidance for the provisioning of a P/S communication framework is presented to improve EV drivers' experience.
Abstract: Charging management for EVs on the move has become an increasingly important research problem in smart cities. Major technical challenges include the selection of charging stations to guide charging plans, and the design of cost-efficient communication infrastructure between the power grid and EVs. In this article, we first present a brief review on state-of-the-art EV charging management schemes. Next, by incorporating battery switch technology to enable fast charging service, a publish/subscribe communication framework is provisioned to support the EV charging service. After that, we develop a fully distributed charging management scheme with consideration of urban travel uncertainties, for example, traffic congestion and drivers' preferences. This would benefit from low privacy sensitivity, as EVs' status information will not be released through management. Results demonstrate a guidance for the provisioning of a P/S communication framework to improve EV drivers' experience, for example, charging waiting time and total trip duration. Also, the benefit of a P/S communication framework is reflected in terms of the communication efficiency. Open research issues in this emerging area are also presented.
TL;DR: IoMT has considered both the issues and the comparative simulations in MATLAB have shown that it outperforms over the traditional protocols and presents the optimized solution for IoMT.
Abstract: The futuristic trend is toward the merging of cyber world with physical world leading to the development of Internet of Things (IoT) framework. Current research is focused on the scalar data-based IoT applications thus leaving the gap between services and benefits of IoT objects and multimedia objects. Multimedia IoT (IoMT) applications require new protocols to be developed to cope up with heterogeneity among the various communicating objects. In this paper, we have presented a cross-layer protocol for IoMT. In proposed methodology, we have considered the cross communication of physical, data link, and routing layers for multimedia applications. Response time should be less, and communication among the devices must be energy efficient in multimedia applications. IoMT has considered both the issues and the comparative simulations in MATLAB have shown that it outperforms over the traditional protocols and presents the optimized solution for IoMT.
TL;DR: This work proposes Heap-based BellmanFord algorithm to find the shortest path in a dynamically changing traffic graph and it works efficiently in practical implementations and proves the correctness of the algorithms and discusses their time complexity.
TL;DR: The impacts of access control and multimedia security are analyzed, and a secure hybrid cloud storage architecture is presented, showing that the various possible attacks can be mitigated via the proposed system.
01 Apr 2017
TL;DR: An online workload scheduling algorithm CECM based on the Lyapunov optimization framework is proposed, which is able to tradeoff between the electricity cost and the performance of delay tolerant workloads without any future information about the time-varying system states.
Abstract: In order to simultaneously power and cool hundreds of thousands of servers, large-scale data centers usually consume several to tens of megawatts of electricity This enormous electricity consumption leads to considerable concerns in the electricity cost including both electricity bills and carbon tax To achieve a sustainable data center, many Internet service providers begin to build their own on-site renewable energy plants to help reduce the electricity cost However, considering the performance constraint of delay tolerant workloads and the lack of future information about the time-varying electricity price, carbon emission rate, and available on-site renewable energy, it is a fairly challenging problem that how to schedule the delay tolerant workloads to reduce the electricity cost of a sustainable data center To address this challenging optimization problem, this paper proposes an online workload scheduling algorithm CECM based on the Lyapunov optimization framework, which is able to tradeoff between the electricity cost and the performance of delay tolerant workloads without any future information about the time-varying system states With extensive simulations based on the real-life traces, we show that CECM is able to reduce the electricity cost by 926 percent, while still guaranteeing the performance constraint of delay tolerant workloads
TL;DR: This paper presents a novel criterion by standard deviation, Kullback–Leibler distance, and correlation coefficient for feature selection, and proposes an ensemble learning framework, which applies the boosting technique to learn multiple kernel classifiers for classification problems.
01 Feb 2017
TL;DR: This work proposes a mechanism termed ICMDS (Inter-Cluster Multiple Key Distribution Scheme for Wireless Sensor Networks), which enables the securing of the entire network and uses two phases of security implementations for the sensor node's authenticity while communicating with the CH.
Abstract: In wireless sensor networks (WSNs), a large number of nodes are densely deployed in an open environment to gather some useful required information. These nodes are small in size, operating on limited processing capabilities with scarce working memory and battery life and not very powerful radio transceivers. They can only communicate with each other through wireless media. Radio waves are insecure in nature; therefore, by using such waves for communication there are always opportunities for different attacks on the network. Most wireless techniques are founded on the cluster-based sensor network. Forwarding cluster head's (CHs) data in a secure manner is very important because CHs collect data from the cluster members and send it to the sink node or base station. For securing CH's data, we propose a mechanism termed ICMDS (Inter-Cluster Multiple Key Distribution Scheme for Wireless Sensor Networks), which enables the securing of the entire network. In ICMDS, we use two phases of security implementations for the sensor node's authenticity while communicating with the CH. A recovery phenomena is also stated at the time when a CH ceases to function due to its high energy consumption.
TL;DR: It is argued that a big data network joint SDN, together with cloud and fog computing platforms, can build a service chain network.
Abstract: This article argues that a big data network joint SDN, together with cloud and fog computing platforms, can build a service chain network. In SDN, the purpose is to reduce a large amount of redundant data and response time. We propose a novel Big Data Orchestration as a Service (BDOaaS) as the networking framework, which can dynamically orchestrate big data into services in SDN. In BDOaaS networking, the data center distributes software to all devices in the distributed network, which can orchestrate big data into services in the distributed network; the services- oriented network model is formed. Thus, the network load and response time is reduced. The BDOaaS framework and various components of BDOaaS as well as operation mechanisms are discussed in detail. Simulation results are presented to show the effectiveness of the proposed BDOaaS framework. In addition, we discuss a number of challenges in implementing the proposed framework in next generation networks.
TL;DR: This examination is the first attempt to provide a thorough survey of studies on multimedia streaming in ICN and identifies the gray areas and provides a road map for the research community working in the same domain.
TL;DR: A privacy-enhanced wave form design approach aided by artificial noise (AN) to enhance the communication secrecy in a wireless environment with multipath receptions and develops a robust waveform design method and obtains the lower bound of the achievable secrecy rate.
Abstract: Cyber-physical system (CPS), regarded as the next generation of engineered system, has the capability to interact with the real physical world. Applications of CPS span various fields such as medical monitoring, traffic control, and smart grid. With such widespread applications, privacy assurance is becoming more and more important since what the CPS connects are people and the real world. Any leakage of private information will cause serious consequences. In this paper, we focus on enhancing the secrecy of wireless communications in CPS by use of physical layer security techniques. Specifically, we study an amplify and forward (AF) relay network where all devices are equipped with a single antenna. We propose a privacy-enhanced waveform design approach aided by artificial noise (AN) to enhance the communication secrecy in a wireless environment with multipath receptions. First, we consider the case with perfect eavesdropper’s channel state information (CSI). We optimize the AF coefficient for forwarding the information-bearing signal and the AN covariance to maximize the achievable secrecy rate. The optimal solution is obtained by solving a series of semidefinite programs. Then, a more practical scenario with imperfect eavesdropper’s CSI is studied. We develop a robust waveform design method and obtain the lower bound of the achievable secrecy rate. Numerical results are presented to show the effectiveness of our proposed algorithms.
TL;DR: A feature learning algorithm based on the overcomplete AISA to apply on big data in parallel computing and shows that the classification accuracy is mostly higher than those obtained from the other ICA related features and two other sparse representation features with a small number of training samples via nearest neighbor (NN) classification method.
TL;DR: Simulation results indicated that the model can represent cyclist crossing behavior at unsignalized intersection with heterogeneous traffic as in the real world.
Abstract: Cycling is a typical green traffic mode, and takes a growing part of urban traffic volume. Yet limited cyclist behavior models shed light on cases at unsignalized intersections with heterogeneous traffic, where bicycle behavior is characterized by frequent confrontations with other road users (vehicles, bicycles, and pedestrians). This study developed a microscopic simulation model for cyclist behavior analysis at unsignalized intersection with heterogeneous traffic. The cyclist crossing model applied fuzzy logic and social force theory for this purpose. The parameters are either estimated directly based on empirical data or derived indirectly through maximum likelihood estimation. Finally model performance was confirmed through comparisons between estimations and observations on individual trajectory, minimum distances, and average riding speeds of collision avoidance behaviors with different conflicting road users. Simulation results indicated that the model can represent cyclist crossing behavior at unsignalized intersection with heterogeneous traffic as in the real world.
TL;DR: A full reference stereo image quality assessment (SIQA) framework which focuses on the innovation of binocular visual properties and applications of low-level features is proposed, and a novel binocular modulation function in spatial domain is adopted into the overall quality prediction of amplitude and phase.
Abstract: With widespread applications of three-dimensional (3-D) technology, measuring quality of experience for 3-D multimedia content plays an increasingly important role. In this paper, we propose a full reference stereo image quality assessment (SIQA) framework which focuses on the innovation of binocular visual properties and applications of low-level features. On one hand, based on the fact that human visual system understands an image mainly according to its low-level features, local phase and local amplitude extracted from phase congruency measurement are employed as primary features. Considering the less prominent performance of amplitude in IQA, visual saliency is applied into the modification on amplitude. On the other hand, by fully considering binocular rivalry phenomena, we create the cyclopean amplitude map and cyclopean phase map. With this method, both image features and binocular visual properties are mutually combined with each other. Meanwhile, a novel binocular modulation function in spatial domain is also adopted into the overall quality prediction of amplitude and phase. Extensive experiments demonstrate that the proposed framework achieves higher consistency with subjective tests than relevant SIQA metrics.
TL;DR: An aggregated IOS detector based on a semi-CRF (semi-Markov conditional random fields) algorithm is designed that holds the potential to realize the seamless localization from indoor to outdoor, and vice versa.