Fixed-Mobile Convergence in the 5G Era: From Hybrid Access to Converged Core IEEE Netw. (IF 7.197) Pub Date : 2019-02-07 Massimo Condoluci; Stephen H. Johnson; Vicknesan Ayadurai; Maria A. Lema; Maria A. Cuevas; Mischa Dohler; Toktam Mahmoodi
The availability of different paths to communicate to a user or device introduces several benefits, from boosting end-user performance to improving network utilization. Hybrid access is a first step in enabling convergence of mobile and fixed networks; however, despite traffic optimization, this approach is limited as fixed and mobile are still two separate core networks inter-connected through an aggregation point. On the road to 5G networks, the design trend is moving toward an aggregated network, where different access technologies share a common anchor point in the core. This enables further network optimization in addition to hybrid access; examples are user-specific policies for aggregation and improved traffic balancing across different accesses according to user, network, and service context. This article aims to discuss the ongoing work around hybrid access and network convergence by the Broadband Forum and 3GPP. We present some testbed results on hybrid access and analyze some primary performance indicators such as achievable data rates, link utilization for aggregated traffic and session setup latency. We finally discuss future directions for network convergence to enable future scenarios with enhanced configuration capabilities for fixed and mobile convergence.
Cyber Security Game for Intelligent Transportation Systems IEEE Netw. (IF 7.197) Pub Date : 2019-01-25 Hichem Sedjelmaci; Makhlouf Hadji; Nirwan Ansari
ITSs are attracting much attention from the industry, academia and government in staging the new generation of transportation. Meanwhile, cyber security concerns are growing since cyber-attacks are prevalent and their behaviors are becoming more complex. Recently, game theory has been used to model the behaviors of these complex attacks and predict their future behaviors. In this article, we present a survey on how game theory can be used to protect ITSs from attacks and assess the pros and cons in terms of security level and required cost. Moreover, we propose a new cyber security Stackelberg game based framework to identify a relevant attack's features and subsequently improve the efficiency of detection.
Seamless Handoffs in Wireless HetNets: Transport-Layer Challenges and Multi-Path TCP Solutions with Cross-Layer Awareness IEEE Netw. (IF 7.197) Pub Date : 2019-01-14 Hassan Sinky; Bechir Hamdaoui; Mohsen Guizani
Complications and performance issues resulting from handoffs have widely been overlooked by transport layer protocols. In mobile scenarios, layer 2 protocols begin to see issues, especially when multiple handoffs are imminent. Differentiating between delay caused by actual packet loss and congestion on the current link and delay simply caused by handoffs is an important distinction where transport layer protocols fall short. With current advancements in technology, traditional TCP reform is needed to accommodate a growing mobile culture. MPTCP is a new evolution of TCP that enables multiple paths or subflows and connections to be used transparently to applications. This is essential in a dynamically changing network as each subflow runs independently, allowing the connection to be maintained. In this article, we present our findings on transport layer handoff issues in currently deployed networks. We then discuss the use of MPTCP as a potential solution to address handoff-related and mobility-related service continuity issues. Finally, we propose cross-layer techniques for potential solutions to consider when designing a handoff-aware MPTCP protocol.
Cooperative Transmission in Integrated Terrestrial-Satellite Networks IEEE Netw. (IF 7.197) Pub Date : 2019-01-14 Xiangming Zhu; Chunxiao Jiang; Linling Kuang; Ning Ge; Song Guo; Jianhua Lu
Satellite networks are able to provide seamless services for ground users with wide coverage, which is intrinsically suitable for providing broadcasting or multicasting services. While terrestrial networks have experienced rapid development in recent years, in next generation communications, the architecture of integrated terrestrial-satellite networks is promising to enable users to seamlessly access all types of services. In this article, we investigate the problem of cooperative transmission in integrated terrestrial-satellite networks, where two cases of unicast transmission and multicast transmission are discussed separately. Overall, this article aims to provide a comprehensive discussion and also propose a general framework of cooperative transmission in future terrestrial-satellite networks.
An Edge-Computing Based Architecture for Mobile Augmented Reality IEEE Netw. (IF 7.197) Pub Date : 2019-01-14 Jinke Ren; Yinghui He; Guan Huang; Guanding Yu; Yunlong Cai; Zhaoyang Zhang
In order to mitigate the long processing delay and high energy consumption of mobile augmented reality (AR) applications, mobile edge computing (MEC) has been recently proposed and is envisioned as a promising means to deliver better Quality of Experience (QoE) for AR consumers. In this article, we first present a comprehensive AR overview, including the indispensable components of general AR applications, fashionable AR devices, and several existing techniques for overcoming the thorny latency and energy consumption problems. Then we propose a novel hierarchical computation architecture by inserting an edge layer between the conventional user layer and cloud layer. Based on the proposed architecture, we further develop an innovative operation mechanism to improve the performance of mobile AR applications. Three key technologies are also discussed to further assist the proposed AR architecture. Simulation results are finally provided to verify that our proposals can significantly improve latency and energy performance as compared to existing baseline schemes.
Orchestrating 5G Network Slices to Support Industrial Internet and to Shape Next-Generation Smart Factories IEEE Netw. (IF 7.197) Pub Date : 2019-01-14 Tarik Taleb; Ibrahim Afolabi; Miloud Bagaa
Industry 4.0 aims at shaking the current manufacturing landscape by leveraging the adoption of smart industrial equipment with increased connectivity, sensing, and actuation capabilities. By exploring access to real-time production information and advanced remote control features, servitization of manufacturing firms promise novel added value services for industrial operators and customers. On the other hand, industrial networks would face a transformation process in order to support the flexibility expected by the next-generation manufacturing processes and enable inter-factory cooperation. In this scenario, 5G systems can play a key role in enabling Industry 4.0 by extending the network slicing paradigm to specifically support the requirements of industrial use cases over heterogeneous domains. We present a novel 5G-based network slicing framework that aims at accommodating the requirements of Industry 4.0. To interconnect different industrial sites up to the extreme edge, different slices of logical resources can be instantiated on-demand to provide the required end-to-end connectivity and processing features. We validate our proposed framework in three realistic use cases that enabled us to highlight the envisioned benefits for industrial stakeholders.
SI-STIN: A Smart Identifier Framework for Space and Terrestrial Integrated Network IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Su Yao; Jianfeng Guan; Zhiwei Yan; Ke Xu
Currently, many hybrid satellite-aerial-terrestrial networks are under construction. The problem is how to coordinate heterogeneous communication networks and platforms to provide more effective services, which is seriously hindering the further development of networks. To mitigate this and other problems, a novel heterogeneous wireless network architecture named the Smart Identifier for Space and Terrestrial Integrated Network (SI-STIN) is designed. Based on the Smart Identifier Network (SINET), SI-STIN aims to solve the problem of the heterogeneous convergence of the integrated network layer, break through the triple binding of the traditional network layer design, and ultimately achieve high efficiency for resource integration and interconnection. The SI-STIN includes three layers and two domains: the smart pervasive service layer, dynamic resource adaption layer, and collaborative network component layer, and the entity domain and behavior domain, in which many separate identifiers and behavior descriptions are applied. Then the details, workflow, applications, and challenges of the STIN are presented. Experimental analysis verifies that the SI-STIN improves the performance in regard to network security and transmission efficiency and thus has great potential to satisfy various network demands.
Heterogeneous Space and Terrestrial Integrated Networks for IoT: Architecture and Challenges IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Wei-Che Chien; Chin-Feng Lai; M. Shamim Hossain; Ghulam Muhammad
The number of IoT devices has been growing significantly, and mobile network traffic is also increasing explosively. In order to meet the human desire to explore unknown areas, edgeless communications have always been one of the directions for the development of wireless networks. H-STIN is a promising solution to face the problem of large bandwidth requirement, massive machine type communication, and edgeless communication requirements. However, it is difficult to integrate communication protocols, routing problems, and resource allocation in the large-scale and heterogeneous network architecture. Therefore, this article proposes a potential H-STIN architecture based on the development trends of the Internet of Things, mobile networks, and satellite networks. Since TN has adequate computation resource and routing architecture, the backbone network is given to support an entire network, and AS is adopted to achieve regional self-optimization. The SSTIS is composed of the perception layer, the cognition layer, and the intelligence layer. It integrates IoT, SDN, and network functions virtualization technologies to achieve self-monitoring, crisis forecasting, and optimal control. Finally, the promising technical challenge, including integrated route planning and large-scale resource allocation, is given.
Software Defined Space-Terrestrial Integrated Networks: Architecture, Challenges, and Solutions IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Yuanguo Bi; Guangjie Han; Shuang Xu; Xingwei Wang; Chuan Lin; Zhibo Yu; Peiyao Sun
A space and terrestrial integrated network (STIN) converges satellite communication networks, mobile wireless networking, and the Internet, has greatly extended the scope of cyberspace. The STIN is composed of diverse network elements and supports various access technologies, and the coordinations in the hybrid networks can better support complex and changeable communication tasks. Furthermore, the STIN takes advantage of the merits of low delay and large bandwidth in terrestrial networks, and achieves global coverage without the limitation of geographic conditions, which can support positioning and navigation, emergency relief, space exploration, and so on. However, the research on STIN is still confronted with some fundamental challenges including time-varying network topology, high satellite mobility, large end-to-end delay, scalability, and so on. In this article, we present a composite architecture that integrates space and terrestrial network components for providing anytime anywhere communications by utilizing the software defined networking and mobile edge computing paradigms, which not only facilitates the network management and increases network flexibility, but also provides improved quality of service for global multimedia services. Additionally, we identify a number of challenging issues associated with the proposed STIN architecture, including mobility management, resource management, routing, traffic steering, security, and so on, a
A Cross-Domain SDN Architecture for Multi-Layered Space-Terrestrial Integrated Networks IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Yongpeng Shi; Yurui Cao; Jiajia Liu; Nei Kato
The MLSTIN is considered as a powerful architecture to meet the heavy consumer demand for wireless data access in the coming 5G ecosystem. However, due to the inherent heterogeneity of MLSTIN, it is challenging to manage the diverse physical devices for large amounts of traffic delivery with optimal network performance. As promoted by the advantage of SDN, MLSTIN is expected to be a flexible paradigm to dynamically provision various applications and services. In light of this, a cross-domain SDN architecture that divides the MLSTIN into satellite, aerial, and terrestrial domains is proposed in this article. We discuss the design and implementation details of this architecture and then point out some challenges and open issues. Moreover, illustrative results validate that the proposed architecture can significantly improve the efficiencies of configuration updating and decision making in MLSTIN.
Security Analysis of a Space-Based Wireless Network IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Daojing He; Xuru Li; Sammy Chan; Jiahao Gao; Mohsen Guizani
With the gradual deployment of the spacebased wireless network, security risks in the data communication between satellites and even the internal structure of a satellite become extremely important. In this article, we use the satellite's internal communications security as an example to illustrate the space network security threats. We first provide a summary of the security requirements of space-based wireless networks. Then three typical attack approaches for satellite platforms based on MIL-STD-1553B bus are described. Subsequently, we present some attack simulation results and suggest some protective mechanisms.
Secure Emergent Data Protection Scheme for a Space-Terrestrial Integrated Network IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Jian Shen; Chen Wang; Sai Ji; Tianqi Zhou; Huijie Yang
With the continuous development and increasing popularity of satellite networks, more and more countries and researchers are focusing on proposing communication solutions for satellite networks. The space-terrestrial integrated network (STIN) concept is proposed for terrestrial users to communicate with satellite networks. STIN is mainly composed of a space-based backbone network, a space-based access network, and a terrestrial backbone network to provide services for all types of space-based users, aviation-based users, marine-based users, and land-based users. For instance, marine-based users often encounter sudden accidents in distant oceans, which may need emergency rescue with the help of STIN. The emergency data involved in the rescue is often users' private data and needs to be protected. In this article, we propose a novel secure emergency data protection scheme for such users in a STIN. The novel scheme is composed of three phases: emergency data transmission, group block-design- based key agreement, and secure satellite information acquisition. The performance analysis shows that the novel scheme is secure and efficient.
Modeling Space-Terrestrial Integrated Networks with Smart Collaborative Theory IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Fei Song; Yu-Tong Zhou; Liu Chang; Hong-Ke Zhang
Driven by the requirements of space exploration and terrestrial investigation, interplanetary network merging has been widely focused on by academia, industry, and the military. Although system architecture and layered workflow are updated endlessly, the pervasive methodology is still urgently needed to provide a unified solution for heterogeneous elements. In this article, we propose a modeling scheme named generalized logical sphere (GLS) for space-terrestrial networks based on smart collaborative theory. First, the coverage range, transmission mechanism, and practice guideline of integration evolution are fully analyzed via three phases to guarantee the comprehensive combinations. Then the specific approach as well as the advanced characteristics of GLS (i.e., load balancing, smart routing, cognitive healing, and hierarchical shielding) are presented and discussed. Finally, a prototype platform is established to validate the correctness. The results of two scenarios illustrate the functionality and practicability of our model.
Virtualized QoS-Driven Spectrum Allocation in Space-Terrestrial Integrated Networks IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Kai Lin; Di Wang; Long Hu; M. Shamim Hossain; Ghulam Muhammad
The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate.
A Tensor-Based Big-Data-Driven Routing Recommendation Approach for Heterogeneous Networks IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Xiaokang Wang; Laurence T. Yang; Liwei Kuang; Xingang Liu; Qingxia Zhang; M. Jamal Deen
Telecommunication networks are evolving toward a data-center-based architecture, which includes physical network functions, virtual network functions, as well as various types of management and orchestration systems. The primary purpose of this type of heterogeneous network is to provide efficient and convenient communication services for users. However, the diverse factors of a heterogeneous network such as bandwidth, delay, and communication protocol, bring great challenges for routing recommendations. In addition, the growing volume of big data and the explosive deployment of heterogeneous networks have started a new era of applying big data technologies to implement routing recommendations. In this article, a tensor-based big-data-driven routing recommendation framework, including the edge plane, fog plane, cloud plane, and application plane, is proposed. In this framework, a tensor-based, holistic, hierarchical approach is introduced to generate efficient routing paths using tensor decomposition methods. Also, a tensor matching method including the controlling tensor, seed tensor, and orchestration tensor is employed to realize routing recommendation. Finally, a case study is used to demonstrate the key processing procedures of the proposed framework.
Satellite Mobile Edge Computing: Improving QoS of High-Speed Satellite-Terrestrial Networks Using Edge Computing Techniques IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Zhenjiang Zhang; Wenyu Zhang; Fan-Hsun Tseng
The high-speed satellite-terrestrial network (STN) is an indispensable alternative in future mobile communication systems. In this article, we first introduce the architecture and application scenarios of STNs, and then investigate possible ways to implement mobile edge computing (MEC) technique for QoS improvement in STNs. We propose satellite MEC (SMEC), in which a user equipment without a proximal MEC server can also enjoy MEC services via satellite links. We propose a dynamic network virtualization technique to integrate the network resources, and furtherly design a cooperative computation offloading (CCO) model to achieve parallel computation in STNs. Task scheduling models in SMEC are discussed in detail, and an elemental simulation is conducted to evaluate the performance of the proposed CCO model in SMEC.
Artificial Intelligence Empowered Mobile Sensing for Human Flow Detection IEEE Netw. (IF 7.197) Pub Date : 2018-12-04 Fu Xiao; Zhengxin Guo; Yingying Ni; Xiaohui Xie; Sabita Maharjan; Yan Zhang
Intelligent human detection based on WiFi is a technique that has recently attracted a significant amount of interest from research communities. The use of ubiquitous WiFi to detect the number of queuing persons can facilitate dynamic planning and appropriate service provisioning. In this article, we propose HFD, one of the first schemes to leverage WiFi signals to estimate the number of queuing persons by employing classifiers from machine learning in a device-free manner. In the proposed HFD scheme, we first utilize the sliding window method to filter and remove the outliers. We extract two characteristics, skewness and kurtosis, as the identification features. Then, we use the support vector machine (SVM) to classify these two features to estimate the number of people in the current queue. Finally, we combine our scheme with the latest Fresnel Zone model theory to determine whether someone is in or out, and thus dynamically adjust the detected value. We implement a proof-of-concept prototype upon commercial WiFi devices and evaluate it in both conference room and corridor scenarios. The experimental results show that the accuracy of our proposed HFD detection can be maintained at about 90 percent with high robustness.
Smart Service-Aware Wireless Mixed-Area Networks IEEE Netw. (IF 7.197) Pub Date : 2018-12-04 Feng Ye; Yi Qian; Rose Qingyang Hu
Smart services such as smart home, smart grid, smart transportation, and so on, are enabled with advanced communications networks, data analysis and control. However, challenges exist in designing a supporting network with optimal resource management while achieving quality- of-experience for a smart service. In this article, we propose a unified SA-WMN architecture that can be applied to different smart services. The core of the SA-WMN architecture is a smart service-aware network controller. The mega network controller has a service adaptation module, network cognition module and resource provisioning module that can achieve cost-effective wireless-mixed area network deployment, and efficient network resource management of SA-WMN. The proposed SA-WMN architecture is expected to close the gap on wireless network design, improve energy efficiency and enhance spectrum efficiency and access, in a smart service- aware paradigm. It will pave the way for realizing next-generation wireless network based smart services.
In-Vehicle Networking: Protocols, Challenges, and Solutions IEEE Netw. (IF 7.197) Pub Date : 2018-03-13 Jun Huang; Mingli Zhao; Yide Zhou; Cong-Cong Xing
Fuel utilization efficiency and cost reduction are two major goals in designing in-vehicle networks. Aiming to address these two issues, we investigate in-vehicle networking protocols from both wired and wireless perspectives by first presenting representative solutions in each area, then identifying the challenges to current solutions, and finally advocating the use of the automotive Ethernet. Also, we propose a priority-based scheduler for the automotive Ethernet. Our preliminary experiments show that the proposed scheduler is effective and flexible, and thus is applicable to next-generation in-vehicle networks. We hope that further studies in this area can be inspired by our work and will be forthcoming in years to come.
Channel Precoding Based Message Authentication in Wireless Networks: Challenges and Solutions IEEE Netw. (IF 7.197) Pub Date : 2018-05-17 Dajiang Chen; Ning Zhang; Rongxing Lu; Nan Cheng; Kuan Zhang; Zhiguang Qin
Due to the broadcast characteristic of the wireless medium, message impersonation and substitution attacks can possibly be launched by an adversary with low cost in wireless communication networks. As an ingenious solution, physical layer based message authentication can achieve perfect security by leveraging channel precoding techniques to meet high level security requirements. In this article, we focus on channel-precoding- based message authentication (CPC-based authentication) over a binary-input wiretap channel (BIWC). Specifically, message authentication with physical layer techniques is first reviewed. Then, a CPC-based authentication framework and its security requirements are presented. Based on the proposed framework, an authentication scheme with polar codes over a binary symmetric wiretap channel (BSWC) is developed. Moreover, a case study is provided as an example of message authentication with polar codes over BSWC. Finally, open research topics essential to CPC-based authentication are discussed.
MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices IEEE Netw. (IF 7.197) Pub Date : 2018-09-24 Jose M. Jimenez; Juan R. Diaz; Jaime Lloret; Oscar Romero
Multimedia cloud computing has appeared as a very attractive environment for the business world in terms of providing cost-effective services with a minimum of entry costs and infrastructure requirements. There are some architecture proposals in the related literature, but there is no multimedia cloud computing architecture with hybrid features specifically designed for mobile devices. In this article, we propose a new multimedia hybrid cloud computing architecture and protocol. It merges existing private and public clouds and combines IaaS, SaaS and SECaaS cloud computing models in order to find a common platform to deliver real time traffic from heterogeneous multimedia and social networks for mobile users. The developed protocol provides suitable levels of QoS, while providing a secure and trusted cloud environment.
Joint Opportunistic Routing and Intra-Flow Network Coding in Multi-Hop Wireless Networks: A Survey IEEE Netw. (IF 7.197) Pub Date : 2018-09-24 Chen Zhang; Cheng Li; Yuanzhu Chen
Opportunistic routing and network coding are two promising techniques that have been proposed for wireless networks. These techniques have significantly improved the performance of multi-hop wireless networks by utilizing the broadcast nature of wireless media and optimizing the capacity of lossy wireless networks. Recent research has shown that the combination of opportunistic routing and network coding in a single joint protocol outperforms each of them individually. This article explains the motivation and interaction effect of the joint protocols. We provide a taxonomy of joint protocols and illustrate the benefit and cost by highlighting their fundamental components and comparing different solutions. We also present a conclusion along with the outline of future research direction.
Delay-Aware Application Protocol for Internet of Things IEEE Netw. (IF 7.197) Pub Date : 2018-12-04 Yanyan Han; Dale Seed; Chonggang Wang; Xu Li; Quang Ly; Zhuo Chen
Existing CoAP primarily focuses on communication (i.e., client/server-based request/response interaction) between constrained devices in a constrained network. Constrained networks may have large latencies and constrained devices may not be able to respond in a timely manner. Therefore, incorporating delay awareness in the CoAP protocol may prove beneficial for communications between devices. This article presents a new method to support a delay-aware CoAP messaging model, where a Delay- Indicator is proposed to be contained in messages to reflect delay tolerance information. Furthermore, the DelayIndicator can be leveraged by CoAP Clients and CoAP Servers to improve their operations of entering sleep mode to save energy while not violating delay tolerances. With this new enabler, a timeout mechanism for CoAP Responses is put forward to enhance CoAP performance. Simulation results show that incorporating delay awareness in CoAP significantly improves messaging performance.
Resource Mobility in Space Information Networks: Opportunities, Challenges, and Approaches IEEE Netw. (IF 7.197) Pub Date : 2018-12-04 Min Sheng; Di Zhou; Runzi Liu; Yu Wang; Jiandong Li
Space information networks (SINs) have been designed to achieve agile acquisition, transmission, and processing of space information. Compared to traditional terrestrial communication networks comprised of mobile users and fixed resources, SINs are characterized by resource mobility, specifically in terms of resource migration, interchange, and aggregation. The resource mobility of SIN provides opportunities to deal with the resource scarcity problem. However, due to complex multi-dimensional resource mobility management, making full use of the opportunities brought by resource mobility is a daunting challenge for network performance improvement. This article provides an overview of open issues with regard to resource mobility in SINs. We first propose the concept of resource mobility and further elaborate on the opportunities it provides. Then we introduce the key research challenges of resource mobility management from three aspects. Furthermore, an optimal resource mobility utilization strategy is devised to achieve high network performance. Overall, resource mobility opens new niches for the exploitation of efficient resource utilization in SINs.
DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization IEEE Netw. (IF 7.197) Pub Date : 2018-08-22 Liangzhi Li; Kaoru Ota; Mianxiong Dong
Traditional Network Functions Virtualization (NFV) implementations are somehow too heavy and do not have enough functionality to conduct complex tasks. In this work, we propose a lightweight NFV framework named DeepNFV, which is based on the Docker container running on the network edge, and integrates state-of-the-art deep learning models with NFV containers to address some complicated problems, such as traffic classification, link analysis, and so on. We compare the DeepNFV framework with several existing works, and detail its structures and functions. The most significant advantage of DeepNFV is its lightweight design, resulting from the virtualization and low-cost nature of the container technology. Also, we design this framework to be compatible with edge devices, in order to decrease the computational overhead of the central servers. Another merit is its strong analysis ability brought by deep learning models, which make it suitable for many more scenarios than traditional NFV approaches. In addition, we also describe some typical application scenarios, regarding how the NFV container works and how to utilize its learning ability. Simulations demonstrate its high efficiency, as well as the outstanding recognition performance in a typical use case.
Service Delivery Models for Converged Satellite-Terrestrial 5G Network Deployment: A Satellite-Assisted CDN Use-Case IEEE Netw. (IF 7.197) Pub Date : 2019-01-11 Michele Luglio; Simon Pietro Romano; Cesare Roseti; Francesco Zampognaro
The current shift toward the virtualization of network infrastructure components enables a dynamic instantiation, deployment and configuration of virtual network functions (VNFs), which can be offered "as-a-service" to multiple tenants, thus enabling 5G architectures. Simultaneously, the recent high throughput satellite (HTS) systems can play an important role in the 5G era thanks to their characteristics, such as their large coverage, fast deployment of the ground infrastructure and native broadcast/multicast broadband capabilities. In this context, this paper proposes a review of the satellite service delivery models in order to identify viable alternatives to deploy converged satellite-terrestrial services. This objective is pursued by taking as a reference a satellite-assisted IP streaming service for the enhancement of current Content Delivery Network (CDN) infrastructures, as tackled by the European Space Agency within the SHINE ("Secure Hybrid In Network caching Environment") project. SHINE aims at efficiently extending terrestrial CDN services to satellite-enabled scenarios, by designing innovative mechanisms for the secure distribution of real-time multimedia information across hybrid channels, leveraging both unicast and multicast communication paradigms. The original contribution of the paper is the analysis of satellite architectures and configurations tailored to efficiently support the SHINE solution, together with a high-level applicability assessment taking into account different satellite-enabled service models.
BGP with BGPsec: Attacks and Countermeasures IEEE Netw. (IF 7.197) Pub Date : 2018-12-28 Qi Li; Jiajia Liu; Yih-Chun Hu; Mingwei Xu; Jianping Wu
The BGP suffers from numerous security vulnerabilities, for example, fake routing updates incurring traffic hijacking and interception. The BGPsec protocol is supposed to fix these vulnerabilities by attesting routing updates. Although the BGP security problem has been extensively studied, the security of BGP with BGPsec is not well studied yet. We argue that even secured with BGPsec, BGP still has inherent security vulnerabilities. In particular, traffic can still be hijacked. In this article, we systematically study the vulnerabilities of BGP with BGPsec. We find that the protocol still cannot achieve the desired security guarantee of inter-domain routing. In particular, it is unable to ensure correct packet delivery on the Internet. We measure the impacts of the vulnerabilities by using a real data trace, and discuss enhancements to the design and the implementation of the secure BGP protocol, which allows BGP to achieve strong secure inter-domain routing.
When Traffic Flow Prediction and Wireless Big Data Analytics Meet IEEE Netw. (IF 7.197) Pub Date : 2018-12-28 Yuanfang Chen; Mohsen Guizani; Yan Zhang; Lei Wang; Noel Crespi; Gyu Myoung Lee; Ting Wu
In this article, we verify whether or not prediction performance can be improved by fitting the actual data to optimize the parameter values of a prediction model. Traffic flow prediction is an important research issue for solving the traffic congestion problem in an ITS. Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is possible by analyzing the real-time transportation data from correlative roads and vehicles. The verification in this article is conducted by comparing the optimized and the normal time series prediction models. With the verification, we can learn that the era of big data is here and will become an important aspect for the study of traffic flow prediction to solve the congestion problem. Experimental results of a case study are provided to verify the existence of the performance improvement in the prediction, while the research challenges of this data-analytics-based prediction are presented and discussed.
Impact of Lossy Forwarding on MAC and Routing Design in Wireless Ad Hoc Networks IEEE Netw. (IF 7.197) Pub Date : 2018-12-04 Szymon Szott; Sebastian Kuhlmorgen; Valtteri Tervo; Olayinka Adigun; Marek Natkaniec; Katarzyna Kosek-Szott
Multi-hop ad hoc networks may employ cooperative communication, in which opportunistic relays use the spatial diversity of radio channels to increase communication reliability. An emerging variant of traditional relaying and forwarding schemes is the lossy forwarding and joint decoding (LF-JD) concept. In theory, LF-JD has shown the potential to improve spectral efficiency, reduce transmit power, and decrease outage probability by exploiting route diversity and the broadcast nature of radio transmissions. To be applied in real devices, a lower-layer protocol stack embracing the LF-JD concept needs to be designed. In this article, we show how LF-JD impacts the design of MAC and routing protocols and whether its benefits translate to performance improvements. We show that, in comparison to currently available solutions, LF-JD can provide gains in scenarios with high interference or poor signal strength such as in V2V networks.
Label-less Learning for Traffic Control in an Edge Network IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Min Chen; Yixue Hao; Kai Lin; Zhiyong Yuan; Long Hu
With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.
AI-Based Malicious Network Traffic Detection in VANETs IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Nikita Lyamin; Denis Kleyko; Quentin Delooz; Alexey Vinel
Inherent unreliability of wireless communications may have crucial consequences when safety-critical C-ITS applications enabled by VANETs are concerned. Although natural sources of packet losses in VANETs such as network traffic congestion are handled by decentralized congestion control (DCC), losses caused by malicious interference need to be controlled too. For example, jamming DoS attacks on CAMs may endanger vehicular safety, and first and foremost are to be detected in real time. Our first goal is to discuss key literature on jamming modeling in VANETs and revisit some existing detection methods. Our second goal is to present and evaluate our own recent results on how to address the real-time jamming detection problem in V2X safety-critical scenarios with the use of AI. We conclude that our hybrid jamming detector, which combines statistical network traffic analysis with data mining methods, allows the achievement of acceptable performance even when random jitter accompanies the generation of CAMs, which complicates the analysis of the reasons for their losses in VANETs. The use case of the study is a challenging platooning C-ITS application, where V2X-enabled vehicles move together at highway speeds with short inter-vehicle gaps.
An Improved Stacked Auto-Encoder for Network Traffic Flow Classification IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Peng Li; Zhikui Chen; Laurence T. Yang; Jing Gao; Qingchen Zhang; M. Jamal Deen
Network flow classification plays a very important role in various network applications and is a fundamental task in network flow control. However, the innovations in the multi-source network application and the elastic network architecture with the network flows of high volume, velocity, variety, and veracity pose unprecedented challenges on accurate network flow classification. In this article, an improved stacked auto-encoder is proposed to learn the complex relationships over the multi-source network flows by stacking several basic Bayesian auto-encoders. Specifically, to model the uncertainty contained in the network flows, the Bayesian auto-encoder is trained on the objects using the unsupervised learning strategy. Furthermore, the stacked auto-encoder is trained by the back-propagation algorithm using the supervised learning strategy to capture the complex relationships over the network flows. Finally, to assess the performance of the improved model, extensive experiments are conducted on two synthetic datasets based on the representative network flow datasets, that is, MAWI and DARPA 99. The results demonstrate that the improved stacked auto-encoder outperforms the traditional one in terms of classification accuracy.
A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Yibo Zhou; Zubair Md. Fadlullah; Bomin Mao; Nei Kato
Recently, deep learning has emerged as a state-of-the-art machine learning technique with promising potential to drive significant breakthroughs in a wide range of research areas. The application of deep learning for network traffic control, however, remains immature due to the difficulty in uniquely characterizing the network traffic features as an appropriate input and output dataset to the learning structures. The network traffic features are anticipated to be even more dynamic and complex in the UDNs of the emerging 5G networks with high traffi c demands coupled with beamforming and massive MIMO technologies. Therefore, it is critical for 5G network operators to carry out radio resource control in an efficient manner instead of adopting the simple conventional F/TDD. This is because the conventional uplink-downlink configuration change in the existing dynamic TDD method, typically used for resource assignment in beamforming and massive-MIMO-based UDNs, is prone to repeated congestion. In this article, we address this issue and discuss how to leverage the deep LSTM learning technique to make localized prediction of the traffic load at the UDN base station (i.e., the eNB). Based on localized prediction, our proposed algorithm executes the appropriate action policy a priori to avoid/alleviate the congestion in an intelligent fashion. Simulation results demonstrate that our proposal outperforms the conventional method in terms of packet loss rate, throughput, and MOS.
Deep Reinforcement Learning for Multimedia Traffic Control in Software Defined Networking IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Xiaohong Huang; Tingting Yuan; Guanhua Qiao; Yizhi Ren
Software Defined Networking (SDN) is a promising paradigm to provide centralized traffic control. Multimedia traffic control based on SDN is crucial but challenging for Quality of Experience (QoE) optimization. It is very difficult to model and control multimedia traffic because solutions mainly depend on an understanding of the network environment, which is complicated and dynamic. Inspired by the recent advances in artificial intelligence (AI) technologies, we study the adaptive multimedia traffic control mechanism leveraging Deep Reinforcement Learning (DRL). This paradigm combines deep learning with reinforcement learning, which learns solely from rewards by trial-and-error. Results demonstrate that the proposed mechanism is able to control multimedia traffic directly from experience without referring to a mathematical model.
Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Imad Alawe; Adlen Ksentini; Yassine Hadjadj-Aoul; Philippe Bertin
5G is expected to provide network connectivity to not only classical devices (i.e., tablets, smartphones, etc.) but also to the IoT, which will drastically increase the traffic load carried over the network. 5G will mainly rely on NFV and SDN to build flexible and on-demand instances of functional networking entities via VNFs. Indeed, 3GPP is devising a new architecture for the core network, which replaces point-to-point interfaces used in 3G and 4G by producer/consumer-based communication among 5G core network functions, facilitating deployment over a virtual infrastructure. One big advantage of using VNFs is the possibility of dynamic scaling, depending on traffic load (i.e., instantiate new resources to VNFs when the traffic load increases and reduce the number of resources when the traffic load decreases). In this article, we propose a novel mechanism to scale 5G core network resources by anticipating traffic load changes through forecasting via ML techniques. The traffic load forecast is achieved by using and training a neural network on a real dataset of traffic arrival in a mobile network. Two techniques were used and compared: RNN, more specifically LSTM; and DNN. Simulation results show that the forecast-based scalability mechanism outperforms the threshold-based solutions, in terms of latency to react to traffic change, and delay to have new resources ready to be used by the VNF to react to traffic increase.
Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Hao Zhu; Yang Cao; Wei Wang; Tao Jiang; Shi Jin
Mobile edge caching is a promising technique to reduce network traffic and improve the quality of experience of mobile users. However, mobile edge caching is a challenging decision making problem with unknown future content popularity and complex network characteristics. In this article, we advocate the use of DRL to solve mobile edge caching problems by presenting an overview of recent works on mobile edge caching and DRL. We first examine the key issues in mobile edge caching and review the existing learning- based solutions proposed in the literature. We also discuss the unique features in the application of DRL in mobile edge caching, and illustrate an example of DRL-based mobile edge caching with trace-data-driven simulation results. This article concludes with a discussion of several open issues that call for substantial future research efforts.
Artificial Intelligence to Manage Network Traffic of 5G Wireless Networks IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Yu Fu; Sen Wang; Cheng-Xiang Wang; Xuemin Hong; Stephen McLaughlin
The deployment of 5G wireless communication systems is projected to begin in 2020. With new scenarios, new technologies, and new network architectures, the traffic management for 5G networks will present significant technical challenges. In recent years, AI technologies, especially ML technologies, have demonstrated significant success in many application domains, suggesting their potential to help solve the problem of 5G traffic management. In this article, we investigate the new characteristics of 5G wireless network traffic and discuss challenges they present for 5G traffic management. Potential solutions and research directions for the management of 5G traffic, including distributed and lightweight ML algorithms and a novel AI assistant content retrieval algorithm framework, are discussed.
SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Anish Jindal; Gagangeet Singh Aujla; Neeraj Kumar; Rajat Chaudhary; Mohammad S. Obaidat; Ilsun You
The rapid growth in the transportation sector has led to the emergence of smart vehicles that are equipped with ICT. These modern smart vehicles are connected to the Internet to access various services such as road condition information, infotainment, and energy management. This kind of scenario can be viewed as a vehicular cyber-physical system (VCPS) where the vehicles are at the physical layer and services are at the cyber layer. However, network traffic management is the biggest issue in the modern VCPS scenario as the mismanagement of network resources can degrade the quality of service (QoS) for end users. To deal with this issue, we propose a software defined networking (SDN)-enabled approach, named SeDaTiVe, which uses deep learning architecture to control the incoming traffic in the network in the VCPS environment. The advantage of using deep learning in network traffic control is that it learns the hidden patterns in data packets and creates an optimal route based on the learned features. Moreover, a virtual-controller-based scheme for flow management using SDN in VCPS is designed for effective resource utilization. The simulation scenario comprising 1000 vehicles seeking various services in the network is considered to generate the dataset using SUMO. The data obtained from the simulation study is evaluated using NS-2, and proves that the proposed scheme effectively handles real-time incoming requests in VCPS. The results also depict the improvement in performance on various evaluation metrics like delay, throughput, packet delivery ratio, and network load by using the proposed scheme over the traditional SDN and TCP/IP protocol suite.
Intelligent Context-Aware Communication Paradigm Design for IoVs Based on Data Analytics IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Feng Lyu; Nan Cheng; Hongzi Zhu; Haibo Zhou; Wenchao Xu; Minglu Li; Xuemin Sherman Shen
IoVs have been envisioned to improve road safety and efficiency, and provide Internet access on the move, by providing a myriad of safety and infotainment applications to drivers and passengers. However, with limited spectrum resource, harsh wireless channel, and variable vehicle density, IoV communication faces severe challenges to achieve scalability, efficiency, and reliability. In this article, we propose a context-aware IoV paradigm design to enhance the communication performance, where the high-level contextual information is utilized to bring intelligence in the design. Specifically, through big data analytics on large-scale IoV communication traces collected from an extensive experiment conducted in Shanghai, we investigate the impacts of different contextual information on V2V communication performance. We reveal that among many types of contextual information, the NLoS link condition is a major one that significantly affects V2V link performance. Based on that observation, we discuss three critical but challenging communication paradigm designs with context awareness of V2V link conditions: smart medium resource allocation, efficient routing establishment, and reliable safety message broadcasting. Furthermore, we present a case study of a cooperative beaconing scheme, where machine learning methods are utilized to learn the real-time link contextual information, and vehicles in deep NLoS condition choose helpers to enhance the overall beaconing reliability.
Artificial Intelligence Inspired Multi-Dimensional Traffic Control for Heterogeneous Networks IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Jian Shen; Tianqi Zhou; Kun Wang; Xin Peng; Li Pan
The heterogeneous network is the foundation of next-generation networks. It aims to explore the existing network resources effectively, and providing better QoS for every kind of traffic flow as far as possible. However, the diversity and dynamic nature of heterogeneous networks will bring a huge burden and big data to the network traffic control. Therefore, how to achieve efficient and intelligent network traffic control becomes the key problem of heterogeneous networks. In this article, an AI-inspired traffic control scheme is proposed. In order to realize fine-grained traffic control in heterogeneous networks, multi-dimensional (i.e., inter-layer, intra-layer, and caching and pushing) network traffic control is introduced. It is worth noting that backpropagation in deep recurrent neural networks is applied in the intra-layer such that an intelligent traffic control scheme can be derived efficiently when facing the huge traffic load in heterogeneous networks. Moreover, DBSCAN is adopted in the inter-layer, which supports efficient classification in the inter-layer. In addition, caching and pushing is adopted to make full use of network resources and provide better QoS. Simulation results demonstrate the effectiveness and practicability of the proposed scheme.
Living with Artificial Intelligence: A Paradigm Shift toward Future Network Traffic Control IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Jun Xu; Kaishun Wu
Future Internet is expected to meet explosive traffic growth and extremely complex architecture, which tend to make the traditional NTC strategies inefficient and even ineffective. Inspired by the latest breakthroughs of AI and its power to address large-scale and complex difficulties, the network community has begun to consider shifting the NTC paradigm from legacy rule-based to novel AI-based. As an applied inter-discipline, design and implementation are important. Although there have been some preliminary explorations along this frontier, they are either limited by only envisioning the prospects, or too scattered to provide high-level insight into a general methodology. To this end, we start with the domain knowledge relationships of AI and NTC, summarizing a baseline workflow toward deep reinforcement learning, which will be the dominant method for the AI-NTC paradigm. On top of that, we argue that AI-NTC training and running must be carried out in online environments in closed-loop fashion for the purpose of putting ti into practice. A series of challenges and opportunities are discussed from a realistic viewpoint, and a set of new architecture and mechanism to enable the online and closed-loop AI-NTC paradigm are proposed. Hopefully, this work can help the AI community to better understand NTC and the NTC community to better live with AI.
Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Alejandro Canovas; Jose Miguel Jimenez; Oscar Romero; Jaime Lloret
Nowadays, there is high interest in modeling the type of multimedia traffic with the purpose of estimating the network resources required to guarantee the quality delivered to the user. In this work we propose a multimedia traffic classification model based on patterns that allows us to differentiate the type of traffic by using video streaming and network characteristics as input parameters. We show that there is low correlation between network parameters and the delivered video quality. Because of this, in addition to network parameters, we also add video streaming parameters in order to improve the efficiency of our system. Finally, it should be noted that, based on the objective video quality received by the user, we have extracted traffic patterns that we use to perfor
DeepTP: An End-to-End Neural Network for Mobile Cellular Traffic Prediction IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Jie Feng; Xinlei Chen; Rundong Gao; Ming Zeng; Yong Li
The past 10 years have witnessed the rapid growth of global mobile cellular traffic demands due to the popularity of mobile devices. While accurate traffic prediction becomes extremely important for stable and high-quality Internet service, the performance of existing methods is still poor due to three challenges: complicated temporal variations including burstiness and long periods, multi-variant impact factors such as the point of interest and day of the week, and potential spatial dependencies introduced by the movement of population. While existing traditional methods fail in characterizing these features, especially the latter two, deep learning models with powerful representation ability give us a chance to consider these from a new perspective. In this article, we propose Deep Traffic Predictor (DeepTP), a deep-learning-based end-toend model, which forecasts traffic demands from spatial-dependent and long-period cellular traffic. DeepTP consists of two components: a general feature extractor for modeling spatial dependencies and encoding the external information, and a sequential module for modeling complicated temporal variations. In the general feature extractor, we introduce a correlation selection mechanism for a spatial modeling and embedding mechanism to encode external information. Moreover, we apply a seq2seq model with attention mechanism to build the sequential model. Extensive experiments based on large-scale mobile cellular traffic data demonstrate that our model outperforms the state-of-the-art traffic prediction models by more than 12.31 percent.
Providing Low Latency Guarantees for Slicing-Ready 5G Systems via Two-Level MAC Scheduling IEEE Netw. (IF 7.197) Pub Date : 2018-11-29 Adlen Ksentini; Pantelis A. Frangoudis; Amogh PC; Navid Nikaein
5G comes with the promise of sub-millisecond latency, which is critical for realizing an array of emerging URLLC services, including industrial, entertainment, telemedicine, automotive, and tactile Internet applications. At the same time, slicing-ready 5G networks face the challenge of accommodating other heterogeneous coexisting services with different and potentially conflicting requirements. Providing latency and reliability guarantees to URLLC service slices is thus not trivial. We identify transmission scheduling at the RAN level as a significant contributor to end-toend latency when considering network slicing. In this direction, we propose a two-level MAC scheduling framework that can effectively handle uplink and downlink transmissions of network slices of different characteristics over a shared RAN, applying different per-slice scheduling policies, and focusing on reducing latency for URLLC services. Our scheme offers the necessary flexibility to dynamically manage radio resources to meet the stringent latency and reliability requirements of URLLC, as demonstrated by our simulation results.
NDN Construction for Big Science: Lessons Learned from Establishing a Testbed IEEE Netw. (IF 7.197) Pub Date : 2018-11-20 Huhnkuk Lim; Alexander Ni; Dabin Kim; Young-Bae Ko; Susmit Shannigrahi; Christos Papadopoulos
NDN is one instance of ICN, which is a cleanslate approach that promises to reduce inefficiencies in the current Internet. NDN provides intelligent data retrieval using the principles of name-based symmetrical forwarding of Interest/ Data packets and in-network caching. The continually increasing demand for the rapid dissemination of large-scale scientific data is driving the use of NDN in big science experiments. In this article, we establish the first intercontinental NDN testbed to offer complete insight into NDN construction for big science. In the testbed, an NDN-based application that targets climate science as an example big-science application is designed and implemented with differentiated features compared to previous works on NDNbased application design for big science. We first attempt to systematically address detailed analysis of why or how NDN benefits fit in big science and issues that must be resolved to improve each advantage, mostly based on lessons learned from establishing the NDN testbed for climate science. We extensively justify the needs of using NDN for large-scale scientific data in the intercontinental network, through experimental performance comparisons between classical deliveries and NDNbased climate data delivery, and detailed analysis of why or how NDN benefits fit in big science.
Distributed and Efficient Object Detection in Edge Computing: Challenges and Solutions IEEE Netw. (IF 7.197) Pub Date : 2018-04-13 Ju Ren; Yundi Guo; Deyu Zhang; Qingqing Liu; Yaoxue Zhang
In the past decade, it was a significant trend for surveillance applications to send huge amounts of real-time media data to the cloud via dedicated high-speed fiber networks. However, with the explosion of mobile devices and services in the era of Internet-of-Things, it becomes more promising to undertake real-time data processing at the edge of the network in a distributed way. Moreover, in order to reduce the investment of network deployment, media communication in surveillance applications is gradually changing to be wireless. It consequently poses great challenges to detect objects at the edge in a distributed and communication-efficient way. In this article, we propose an edge computing based object detection architecture to achieve distributed and efficient object detection via wireless communications for real-time surveillance applications. We first introduce the proposed architecture as well as its potential benefits, and identify the associated challenges in the implementation of the architecture. Then, a case study is presented to show our preliminary solution, followed by performance evaluation results. Finally, future research directions are pointed out for further studies.
Querying in Internet of Things with Privacy Preserving: Challenges, Solutions and Opportunities IEEE Netw. (IF 7.197) Pub Date : 2018-03-13 Hao Ren; Hongwei Li; Yuanshun Dai; Kan Yang; Xiaodong Lin
IoT is envisioned as the next stage of the information revolution, enabling various daily applications and providing better service by conducting a deep fusion with cloud and fog computing. As the key mission of most IoT applications, data management, especially the fundamental function-data query, has long been plagued by severe security and privacy problems. Most query service providers, including the big ones (e.g., Google, Facebook, Amazon, and so on) are suffering from intensive attacks launched by insiders or outsiders. As a consequence, processing various queries in IoT without compromising the data and query privacy is an urgent and challenging issue. In this article, we propose a thing-fog-cloud architecture for secure query processing based on well studied classical paradigms. Following with a description of crucial technical challenges in terms of functionality, privacy and efficiency assurance, we survey the latest milestone-like approaches, and provide an insight into the advantages and limitations of each scheme. Based on the recent advances, we also discuss future research opportunities to motivate efforts to develop practical private query protocols in IoT.
Enabling Efficient Service Function Chaining by Integrating NFV and SDN: Architecture, Challenges and Opportunities IEEE Netw. (IF 7.197) Pub Date : 2018-08-29 Jiao Zhang; Zenan Wang; Ningning Ma; Tao Huang; Yunjie Liu
In legacy networks, network functions (e.g., firewalls, NAT, QoS) are highly dependent on dedicated hardware. It is sophisticated and costly for network operators to determine the order of network functions and stitch them together to create service chains for end users, especially with the explosive growth of end-users and Internet services. The recent emergence of NFV and SDN technologies provide benefits for service function chaining and reduce OPEX and CAPEX for network operators. This article presents a typical framework to construct service chains by combining SDN and NFV, and describes several critical issues in this field, expecting readers to catch current research progress and make their new contributions in this field.
Big Data Driven Vehicular Networks IEEE Netw. (IF 7.197) Pub Date : 2018-08-29 Nan Cheng; Feng Lyu; Jiayin Chen; Wenchao Xu; Haibo Zhou; Shan Zhang; Xuemin Sherman Shen
VANETs enable information exchange among vehicles, other end devices and public networks, which plays a key role in road safety/infotainment, intelligent transportation systems, and self-driving systems. As vehicular connectivity soars, and new on-road mobile applications and technologies emerge, VANETs are generating an ever-increasing amount of data, requiring fast and reliable transmissions through VANETs. On the other hand, a variety of VANETs related data can be analyzed and utilized to improve the performance of VANETs. In this article, we first review VANETs technologies to efficiently and reliably transmit big data. Then, the methods employing big data for studying VANETs characteristics and improving VANETs performance are discussed. Furthermore, we present a case study where machine learning schemes are applied to analyze VANETs measurement data for efficiently
Assessing the Limits of Mininet-Based Environments for Network Experimentation IEEE Netw. (IF 7.197) Pub Date : 2018-10-30 David Muelas; Javier Ramos; Jorge E. Lopez de Vergara
Virtualization and emulation have become worthy approaches to save significant amounts of money related to physical resource acquisition expenditures. In light of this, the development of network emulation platforms has led a revolution in the research and testing of novel services and applications, as they provide a cost-effective, flexible and reproducible environment for experimentation. However, they present some practical issues, in particular, their scalability is one of the limiting factors as it links the emulated networks that can be successfully deployed on a given hardware. We address this matter by testing the consumption and exploitation of physical resources of one popular network emulation platform, Mininet. We follow a methodology based on the isolation of the threads associated to the operating system, the virtual hosts, and the monitoring tasks. In such a manner, this approach can measure the effect of the placement of threads in the available cores, and help optimize bottlenecks that jeopardize the results of network emulations. Additionally, we monitor several key performance indicators for general-purpose Mininet deployments in different network topologies, varying the number of active elements, links and network conditions such as packet loss or delay. Our results show that Mininet presents performance bounds in commodity servers that suffice a wide range of general network tests. It achieves aggregated bandwidths above 10 Gb/s and median roundtrip time values around 1 ms, even in demanding scenarios where more than a thousand hosts, up to 64-hop paths and 64
Socially-Motivated Cooperative Mobile Edge Computing IEEE Netw. (IF 7.197) Pub Date : 2018-05-17 Xu Chen; Zhi Zhou; Weigang Wu; Di Wu; Junshan Zhang
In this article we propose a novel paradigm of socially-motivated cooperative mobile edge computing, where the social tie structure among mobile and wearable device users is leveraged for achieving effective and trustworthy cooperation for collaborative computation task executions. We envision that a combination of local device computation and networked resource sharing empowers the devices with multiple flexible task execution approaches, including local mobile execution, D2D offloaded execution, direct cloud offloaded execution, and D2D-assisted cloud offloaded execution. Specifically, we propose a system model for cooperative mobile edge computing where a device social graph model is developed to capture the social relationship among the devices. We then devise a socially- aware bipartite matching based cooperative task offloading algorithm by integrating the social tie structure into the device computation and network resource sharing process. We evaluate the performance of socially-motivated cooperative mobile edge computing using both Erdos-Renyi and real-trace based social graphs, which corroborates the superior performance of the proposed socially-aware mechanism.
A Blockchain-Based Privacy-Preserving Payment Mechanism for Vehicle-to-Grid Networks IEEE Netw. (IF 7.197) Pub Date : 2018-04-16 Feng Gao; Liehuang Zhu; Meng Shen; Kashif Sharif; Zhiguo Wan; Kui Ren
As an integral part of V2G networks, EVs receive electricity from not only the grid but also other EVs and may frequently feed the power back to the grid. Payment records in V2G networks are useful for extracting user behaviors and facilitating decision-making for optimized power supply, scheduling, pricing, and consumption. Sharing payment and user information, however, raises serious privacy concerns in addition to the existing challenge of secure and reliable transaction processing. In this article, we propose a blockchain-based privacy preserving payment mechanism for V2G networks, which enables data sharing while securing sensitive user information. The mechanism introduces a registration and data maintenance process that is based on a blockchain technique, which ensures the anonymity of user payment data while enabling payment auditing by privileged users. Our design is implemented based on Hyperledger to carefully evaluate its feasibility and effectiveness.
Full-Duplex for Multi-Channel Cognitive Radio Ad Hoc Networks IEEE Netw. (IF 7.197) Pub Date : 2018-08-29 Wenchi Cheng; Wei Zhang; Liping Liang; Hailin Zhang
Cognitive radio ad hoc network (CRAHN) has been considered an efficiently spectrum-aware communication paradigm in wireless networks because of its intrinsic properties of cognition and self-organization. In practice, because primary and secondary ad hoc networks are usually associated with two different types of wireless networks, the synchronization between primary users and secondary users is hardly guaranteed. The multi-channel asynchronous CRAHNs referred to as multi-channel non-time-slotted CRAHNs impose many challenging problems that severely degrade system performance. In this article, we review these challenging issues in multi-channel non-time-slotted CRAHNs, including reactivation-failure, frequently unexpected hand-offs, non-real-time spectrum aggregation, inefficient power allocation, and frequent re-routing problems. Then, we develop a full-duplex based framework to resolve these issues. Future research directions are discussed to improve system performance of multi-channel non-time-slotted CRAHNs.
Attack Provenance Tracing in Cyberspace: Solutions, Challenges and Future Directions IEEE Netw. (IF 7.197) Pub Date : 2018-08-22 Cheng Tan; Qian Wang; Lina Wang; Lei Zhao
With the increasing damage of APT attacks, the modern world has moved from individual hackers for fun to nation-wide cybercriminals for strategic advantage or profit. These APT attacks are often prolonged and have multiple stages, and they usually utilize zero-day or one-day exploits to be penetrating and stealthy. As a result, there is an urgent need to detect and investigate APT attacks. Among all kinds of security techniques, provenance tracing is regarded as a promising and important approach for attack investigation, as it discloses the root cause, the path, and the results of attacks. However, the existing techniques either suffer from the limitation of only focusing on the log type, or have non-trivial space and runtime overhead, which hinder their wide applications in practice. In this article, we provide a comprehensive survey of provenance tracing technologies in the most recent literature. Following the overview of each scheme, we present the key technical features of them and then compare the state-of-the-art solutions in terms of both security and performance. Finally, we propose and discuss several potential future research directions.
Ultra-Low Latency Mobile Networking IEEE Netw. (IF 7.197) Pub Date : 2018-11-14 Kwang-Cheng Chen; Tao Zhang; Richard D. Gitlin; Gerhard Fettweis
Mobile networking to achieve the ultra-low latency goal of 1 msec enables massive operation of autonomous vehicles and other intelligent mobile machines, and emerges as one of the most critical technologies beyond 5G mobile communications and state-of-the-art vehicular networks. Introducing fog computing and proactive network association, realizing virtual cell by integrating open-loop radio transmission and error control, and innovating anticipatory mobility management through machine learning, opens a new avenue toward ultra-low latency mobile networking.
A Friendly and Low-Cost Technique for Capturing Non-Cooperative Civilian Unmanned Aerial Vehicles IEEE Netw. (IF 7.197) Pub Date : 2018-11-14 Daojing He; Yinrong Qiao; Shiqing Chen; Xiao Du; Wenjie Chen; Sencun Zhu; Mohsen Guizani
As a result of continuous cost reduction and device miniaturization, small UAVs are now more easily accessible to the public. Consequently, numerous new applications in the civilian and commercial domains have emerged. However, despite regulations, non-cooperative UAVs have started to abuse low-altitude airspace with potential security and safety problems. In this work, we present a new GNSS spoofing based counter-UAV defense system, which is able to flexibly, friendly, and remotely control a non-cooperating UAV to fly to a location we specify for capture. Our simulation and field study show the effectiveness of such a defense technique.
How Much of Wireless Rates Can Smartphones Support in 5G Networks? IEEE Netw. (IF 7.197) Pub Date : 2018-11-14 Jing Yang; Xiaohu Ge; Yi Zhong
Due to the higher wireless transmission rates in the 5G cellular networks, higher computation overhead is incurred in smartphones, which can cause the wireless transmission rates to be limited by the computation capability of wireless terminals. In this case, is there a maximum receiving rate for smartphones to maintain stable wireless communications in 5G cellular networks? The main objective of this article is to investigate the maximum receiving rate of smartphones and its influence on 5G cellular networks. Based on Landauer’s principle and the safe temperature bound on the smartphone surface, a maximum receiving rate of the smartphone is proposed for 5G cellular networks. Moreover, the impact of the maximum receiving rate of smartphones on the link adaptive transmission schemes has been investigated. Numerical analyses imply that the maximum receiving rate of smartphones cannot always catch up with the downlink rates of future 5G cellular networks. Therefore, the link adaptive transmission scheme for future 5G cellular networks has to take the maximum receiving rate of smartphones into account.
A Coverage-Aware Hierarchical Charging Algorithm in Wireless Rechargeable Sensor Networks IEEE Netw. (IF 7.197) Pub Date : 2018-11-14 Guangjie Han; Xuan Yang; Li Liu; Sammy Chan; Wenbo Zhang
Constant energy supply for sensor nodes is essential for the development of the green Internet of Things (IoT). Recently, WRSNs have been proposed to resolve the energy limitations of nodes, aiming to realize continuous functioning. In this article, a coverage-aware hierarchical charging algorithm in WRSNs is proposed, considering energy consumption and the degree of node coverage. The algorithm first performs network clustering using the K-means algorithm. In addition, nodes are classified into multiple levels in each cluster to calculate respective anchor points based on the energy consumption rate and coverage degree of nodes. Then, the anchor points converge to an optimized anchor point in each cluster. To reduce charging latency, the optimized anchor points form two disjoint internal and external polygons. Next, mobile chargers travel along the internal and external polygons, respectively. Experimental results indicate that the proposed algorithm can improve charging efficiency and reduce charging latency substantially.
Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering IEEE Netw. (IF 7.197) Pub Date : 2018-10-30 Raniere Rocha Guimaraes; Leandro A. Passos; Raimir Holanda Filho; Victor Hugo C. de Albuquerque; Joel J. P. C. Rodrigues; Mikhail M. Komarov; Joao Paulo Papa
Distinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the aforementioned context. The results are compared against one-class support vector machines and multivariate Gaussian distribution. Additionally, we also propose to employ meta-heuristic optimization techniques to fine-tune the OPF classifier in the context of anomaly detection in wireless sensor networks.
Satellite Networking Integration in the 5G Ecosystem: Research Trends and Open Challenges IEEE Netw. (IF 7.197) Pub Date : 2018-09-27 Luca Boero; Roberto Bruschi; Franco Davoli; Mario Marchese; Fabio Patrone
The envisioned 5G ecosystem will be composed of heterogeneous networks based on different technologies and communication means, including satellite communication networks. The latter can help increase the capabilities of terrestrial networks, especially in terms of higher coverage, reliability, and availability, contributing to the achievement of some of the 5G KPIs. However, technological changes are not immediate. Many current satellite communication networks are based on proprietary hardware, which hinders the integration with future 5G terrestrial networks as well as the adoption of new protocols and algorithms. On the other hand, the two main paradigms that are emerging in the networking scenario -- software defined networking (SDN) and network functions virtualization -- can change this perspective. In this respect, this article presents first an overview of the main research works in the field of SDN satellite networks in order to understand the already proposed solutions. Then some open challenges are described in light of the network slicing concept by 5G virtualization, along with a possible roadmap including different network virtualization levels. The remaining unsolved problems are related to the development and deployment of a complete integration of satellite components in the 5G ecosystem.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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