Device-Free Wireless Sensing: Challenges, Opportunities, and Applications IEEE Netw. (IF 7.23) Pub Date : 2018-02-07 Jie Wang; Qinhua Gao; Miao Pan; Yuguang Fang
Recent developments on DFWS have shown that wireless signals can be utilized not only as a communication medium to transmit data, but also as an enabling tool for realizing non-intrusive device-free sensing. DFWS has many potential applications, for example, human detection and localization, human activity and gesture recognition, surveillance, elder or patient monitoring, emergency rescue, and so on. With the development and maturity of DFWS, we believe it will eventually empower traditional wireless networks with the augmented ability to sense the surrounding environment, and evolve wireless communication networks into intelligent sensing networks that could sense human-scale context information within the deployment area of the network. The research field of DFWS has emerged quickly recently. This article tries to provide an integrated picture of this emerging field and hopefully inspire future research. Specifically, we present the working principle and system architecture of the DFWS system, review its potential applications, and discuss research challenges and opportunities.
Security Threats in the Data Plane of Software-Defined Networks IEEE Netw. (IF 7.23) Pub Date : 2018-02-07 Shang Gao; Zecheng Li; Bin Xiao; Guiyi Wei
SDN has enabled extensive network programmability and speedy network innovations by decoupling the control plane from the data plane. However, the separation of the two planes could also be a potential threat to the whole network. Previous approaches pointed out that attackers can launch various attacks from the data plane against SDN, such as DoS attacks, topology poisoning attacks, and side-channel attacks. To address the security issues, we present a comprehensive study of data plane attacks in SDN, and propose FlowKeeper, a common framework to build a robust data plane against different attacks. FlowKeeper enforces port control of the data plane and reduces the workload of the control plane by filtering out illegal packets. Experimental results show that FlowKeeper could be used to efficiently mitigate different kinds of attacks (i.e., DoS and topology poisoning attacks).
QUOIN: Incentive Mechanisms for Crowd Sensing Networks IEEE Netw. (IF 7.23) Pub Date : 2018-02-07 Kaoru Ota; Mianxiong Dong; Jinsong Gui; Anfeng Liu
Crowd sensing networks play a critical role in big data generation where a large number of mobile devices collect various kinds of data with large-volume features. Although which information should be collected is essential for the success of crowd-sensing applications, few research efforts have been made so far. On the other hand, an efficient incentive mechanism is required to encourage all crowd-sensing participants, including data collectors, service providers, and service consumers, to join the networks. In this article, we propose a new incentive mechanism called QUOIN, which simultaneously ensures Quality and Usability Of INformation for crowd-sensing application requirements. We apply a Stackelberg game model to the proposed mechanism to guarantee each participant achieves a satisfactory level of profits. Performance of QUOIN is evaluated with a case study, and experimental results demonstrate that it is efficient and effective in collecting valuable information for crowd-sensing applications.
Reliable and Opportunistic Transmissions for Underwater Acoustic Networks IEEE Netw. (IF 7.23) Pub Date : 2018-02-07 Weiqi Chen; Hua Yu; Quansheng Guan; Fei Ji; Fangjiong Chen
Acoustic waves propagate slowly in water, and time-varying UACs result in inevitably high bit error rate and packet loss rate. The long propagation delay and the error-prone nature of UACs impose challenges on reliable transmissions in UANs. In this article, we identify the challenges for reliable acoustic transmissions and propose a CL-FEC scheme, which achieves opportunistic transmissions to overcome the frequent transmission failures in UACs. CL-FEC adopts fountain codes as a packet-level FEC and adopts channel codes as a bit-level FEC, to realize reliable transmissions over UACs without per-packet feedback. To further improve the throughput of CL-FEC, we formulate the transmissions over UACs into a stochastic throughput optimization problem. A discrete stochastic approximation based algorithm is then developed to achieve the optimal CL-FEC by online exploiting channel estimating and algorithm iterations. Simulation results show the asymptotic convergence and the iterative optimality of the algorithm.
A Robust Dynamic Edge Network Architecture for the Internet of Things IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Beatriz Lorenzo; Juan Garcia-Rois; Xuanheng Li; Javier Gonzalez-Castano; Yuguang Fang
A massive number of devices are expected to fulfill the missions of sensing, processing and control in cyber-physical IoT systems with new applications and connectivity requirements. In this context, scarce spectrum resources must accommodate high traffic volume with stringent requirements of low latency, high reliability, and energy efficiency. Conventional centralized network architectures may not be able to fulfill these requirements due to congestion in backhaul links. This article presents a novel design of an RDNA for IoT that leverages the latest advances of mobile devices (e.g., their capability to act as access points, storing and computing capabilities) to dynamically harvest unused resources and mitigate network congestion. However, traffic dynamics may compromise the availability of terminal access points and channels, and thus network connectivity. The proposed design embraces solutions at the physical, access, networking, application, and business layers to improve network robustness. The high density of mobile devices provides alternatives for close connectivity, reducing interference and latency, and thus increasing reliability and energy efficiency. Moreover, the computing capabilities of mobile devices project smartness onto the edge, which is desirable for autonomous and intelligent decision making. A case study is included to illustrate the performance of RDNA. Potential applications of this architecture in the context of IoT are outlined. Finally, some challenges for future research are presented.
Multiagent-Based Flexible Edge Computing Architecture for IoT IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Takuo Suganuma; Takuma Oide; Shinji Kitagami; Kenji Sugawara; Norio Shiratori
This article presents a proposal for FLEC architecture, which solves problems resulting from the rigidity of the traditional IoT architecture and edge computing. FLEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. We utilize COSAP, a system configuration platform based on a multiagent framework, as an implementation procedure for FLEC architecture. Furthermore, this article presents its application case study of a healthcare support system for a sports event with many participants. Finally, we demonstrate the contribution of this proposed architecture to problem solution in edge computing.
Edge Computing Gateway of the Industrial Internet of Things Using Multiple Collaborative Microcontrollers IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Ching-Han Chen; Ming-Yi Lin; Chung-Chi Liu
An Internet of Things gateway serves as a key intermediary between numerous smart things and their corresponding cloud networking servers. A typical conventional gateway system uses a high-level embedded microcontroller (MCU) as its core; that MCU performs low-level perception-layer device network management, upper-level cloud server functions, and remote mobile computation services. However, in edge computing, many factors need to be considered when designing an IoT gateway, such as minimizing the response time, the power consumption, and the bandwidth cost. Regarding system scalability, computational efficiency, and communication efficiency, solutions that use a single MCU cannot deliver IoT functionality such as big data collection, management, real-time communication, expandable peripherals, and various other services. Therefore, this article proposes an innovative multi-MCU system framework combining a field-programmable- gate-array-based hardware bridge and multiple scalable MCUs to realize an edge gateway of a smart sensor fieldbus network. Through distributed and collaborative computing, the multi-MCU edge gateway can efficiently perform fieldbus network management, embedded data collection, and networking communication, thereby considerably reducing the real-time power consumption and improving scalability compared to the existing industrial IoT solutions.
KID Model-Driven Things-Edge-Cloud Computing Paradigm for Traffic Data as a Service IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Bowen Du; Runhe Huang; Zhipu Xie; Jianhua Ma; Weifeng Lv
The development of intelligent traffic systems can benefit from the pervasiveness of IoT technologies. In recent years, increasing numbers of devices are connected to the IoT, and new kinds of heterogeneous data sources have been generated. This leads to traffic systems that exist in extended dimensions of data space. Although cloud computing can provide essential services that reduce the computational load on IoT devices, it has its limitations: high network bandwidth consumption, high latency, and high privacy risks. To alleviate these problems, edge computing has emerged to reduce the computational load for achieving TDaaS in a dynamic way. However, how to drive all edge servers' work and meet data service requirements is still a key issue. To address this challenge, this article proposes a novel three-level transparency-of-traffic-data service framework, that is, a KID-driven TEC computing paradigm. Its aim is to enable edge servers to cooperatively work with a cloud server. A case study is presented to demonstrate the feasibility of the proposed new computing paradigm with associated mechanisms. The performance of the proposed system is also compared to other methods.
A Cost-Efficient Cloud Gaming System at Scale IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Yiling Xu; Qiu Shen; Xin Li; Zhan Ma
This article proposes a transparent gaming (TG) cloud system that allows users to play any popular high-end desktop game on the fly over the Internet. Toward this goal, we have introduced the TG-SHARE technology to share the underlying hardware capabilities, particularly for the GPU and the dedicated compression acceleration unit (XCODER). TG-SHARE utilizes offthe- shelf consumer GPUs without resorting to expensive proprietary GPU virtualization technology (e.g., GRID from NVIDIA). XCODER adapts the compression based on the network dynamics, learned gaming behaviors, and hardware resources to significantly reduce bandwidth consumption. Google's webRTC protocol is integrated to offer real-time interaction and ubiquitous access from heterogeneous devices. Compared to the existing cloud gaming vendor using the GRID technology, our TG-SHARE not only reduces the expense per user (i.e., 75 percent hardware cost reduction, 20-40 percent network cost reduction), but also improves the quality of experience with higher rate of frames per second (i.e., 2 x FPS).
Multi-User Computation Offloading in Mobile Edge Computing: A Behavioral Perspective IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Ling Tang; Shibo He
By providing cloud computing capabilities at the network edge in proximity of mobile device users, mobile edge computing offers an effective solution to help mobile devices with computation- intensive and delay-sensitive tasks. In this article, we investigate the multi-user computation offloading problem in an uncertain wireless environment. Most of the existing works assume that mobile device users are rational and make offloading decisions to maximize their expected objective utilities. However, in practice, users tend to have subjective perceptions under uncertainty, such that their behavior deviates considerably from the conventional rationality assumption. Drawing on the framework of prospect theory (PT), we formulate users' decision making of whether to offload or not as a PT-based non-cooperative game. We propose a distributed computation offloading algorithm to achieve the Nash equilibrium of the game. Numerical results assess the impact of mobile device users' behavioral biases on offloading decision making.
Selective Offloading in Mobile Edge Computing for the Green Internet of Things IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Xinchen Lyu; Hui Tian; Li Jiang; Alexey Vinel; Sabita Maharjan; Stein Gjessing; Yan Zhang
Mobile edge computing provides the radio access networks with cloud computing capabilities to fulfill the requirements of the Internet of Things services such as high reliability and low latency. Offloading services to edge servers can alleviate the storage and computing limitations and prolong the lifetimes of the IoT devices. However, offloading in MEC faces scalability problems due to the massive number of IoT devices. In this article, we present a new integration architecture of the cloud, MEC, and IoT, and propose a lightweight request and admission framework to resolve the scalability problem. Without coordination among devices, the proposed framework can be operated at the IoT devices and computing servers separately, by encapsulating latency requirements in offloading requests. Then a selective offloading scheme is designed to minimize the energy consumption of devices, where the signaling overhead can be further reduced by enabling the devices to be self-nominated or self-denied for offloading. Simulation results show that our proposed selective offloading scheme can satisfy the latency requirements of different services and reduce the energy consumption of IoT devices.
ThriftyEdge: Resource-Efficient Edge Computing for Intelligent IoT Applications IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Xu Chen; Qian Shi; Lei Yang; Jie Xu
In this article we propose a new paradigm of resource-efficient edge computing for the emerging intelligent IoT applications such as flying ad hoc networks for precision agriculture, e-health, and smart homes. We devise a resource-efficient edge computing scheme such that an intelligent IoT device user can well support its computationally intensive task by proper task offloading across the local device, nearby helper device, and the edge cloud in proximity. Different from existing studies for mobile computation offloading, we explore the novel perspective of resource efficiency and devise an efficient computation offloading mechanism consisting of a delay-aware task graph partition algorithm and an optimal virtual machine selection method in order to minimize an intelligent IoT device's edge resource occupancy and meanwhile satisfy its QoS requirement. Performance evaluation corroborates the effectiveness and superior performance of the proposed resource-efficient edge computing scheme.
Collaborative Mobile Edge Computation Offloading for IoT over Fiber-Wireless Networks IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Hongzhi Guo; Jiajia Liu; Huiling Qin
Mobile edge computing is envisioned to be a promising paradigm to address the conflict between computationally intensive IoT applications and resource-constrained lightweight mobile devices. However, most existing research on mobile edge computation offloading has only taken the resource allocation between the mobile devices and the MEC servers into consideration, ignoring the huge computation resources in the centralized cloud computing center. To make full use of the centralized cloud and distributed MEC resources, designing a collaborative computation offloading mechanism becomes particularly important. Note that current MEC hosted networks, which mostly adopt the networking technology integrating cellular and core networks, face new challenges of single networking mode, long latency, poor reliability, high congestion, and high energy consumption. Hybrid fiber-wireless networks integrating both low-latency fiber optic and flexible wireless technologies should be a promising solution. Toward this end, we provide in this article a generic fiber-wireless architecture with coexistence of centralized cloud and distributed MEC for IoT connectivity. The problem of cloud-MEC collaborative computation offloading is defined, and a game-theoretic collaborative computation offloading scheme is proposed as our solution. Numerical results corroborate that our proposed scheme can achieve high energy efficiency and scales well as the number of mobile devices increases.
Joint Admission Control and Resource Allocation in Edge Computing for Internet of Things IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Shichao Li; Ning Zhang; Siyu Lin; Linghe Kong; Ajay Katangur; Muhammad Khurram Khan; Minming Ni; Gang Zhu
The IoT is a novel platform for making objects more intelligent by connecting to the Internet. However, mass connections, big data processing, and huge power consumption restrict the development of IoT. In order to address these challenges, this article proposes a novel ECIoT architecture. To further enhance the system performance, radio resource and computational resource management in ECIoT are also investigated. According to the characteristics of the ECIoT, we mainly focus on admission control, computational resource allocation, and power control. To improve the performance of ECIoT, cross-layer dynamic stochastic network optimization is studied to maximize the system utility, based on the Lyapunov stochastic optimization approach. Evaluation results are provided which demonstrate that the proposed resource allocation scheme can improve throughput, reduce end-to-end delay, and also achieve an average throughput and delay trade-off. Finally, the future research topics of resource management in ECIoT are discussed.
Toward Efficient Content Delivery for Automated Driving Services: An Edge Computing Solution IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Quan Yuan; Haibo Zhou; Jinglin Li; Zhihan Liu; Fangchun Yang; Xuemin Sherman Shen
Automated driving is coming with enormous potential for safer, more convenient, and more efficient transportation systems. Besides onboard sensing, autonomous vehicles can also access various cloud services such as high definition maps and dynamic path planning through cellular networks to precisely understand the real-time driving environments. However, these automated driving services, which have large content volume, are time-varying, location-dependent, and delay-constrained. Therefore, cellular networks will face the challenge of meeting this extreme performance demand. To cope with the challenge, by leveraging the emerging mobile edge computing technique, in this article, we first propose a two-level edge computing architecture for automated driving services in order to make full use of the intelligence at the wireless edge (i.e., base stations and autonomous vehicles) for coordinated content delivery. We then investigate the research challenges of wireless edge caching and vehicular content sharing. Finally, we propose potential solutions to these challenges and evaluate them using real and synthetic traces. Simulation results demonstrate that the proposed solutions can significantly reduce the backhaul and wireless bottlenecks of cellular networks while ensuring the quality of automated driving services.
A Tensor-Based Holistic Edge Computing Optimization Framework for Internet of Things IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Huazhong Liu; Laurence T. Yang; Man Lin; Dexiang Yin; Yimu Guo
Balancing the costs of different objectives in EC requires comprehensive and global analysis. This article investigates the holistic EC optimization problem for IoT. First, a triple-plane EC architecture for IoT is proposed including the edge device plane, edge server plane, and cloud plane, respectively, which is conducive to collaboratively accomplishing the EC applications. Then five tensor-based representation models are constructed to represent the complex relationships and resolve the heterogeneity of different devices. Afterward, we construct a generalized and holistic EC optimization model based on the constructed tensors including energy consumption, execution time, system reliability, and quality of experience. Finally, a customized optimization framework is proposed in which the optimization objectives can be arbitrarily combined according to practical applications. A case study is conducted to evaluate the performance of the proposed scheme; results demonstrate that it significantly outperforms the state-of-the-art cloud-assisted mobile computing scheme and holistic mobile cloud computing scheme.
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 He Li; Kaoru Ota; Mianxiong Dong
Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.
Consolidate IoT Edge Computing with Lightweight Virtualization IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Roberto Morabito; Vittorio Cozzolino; Aaron Yi Ding; Nicklas Beijar; Jorg Ott
Lightweight virtualization (LV) technologies have refashioned the world of software development by introducing flexibility and new ways of managing and distributing software. Edge computing complements today's powerful centralized data centers with a large number of distributed nodes that provide virtualization close to the data source and end users. This emerging paradigm offers ubiquitous processing capabilities on a wide range of heterogeneous hardware characterized by different processing power and energy availability. The scope of this article is to present an in-depth analysis on the requirements of edge computing from the perspective of three selected use cases that are particularly interesting for harnessing the power of the Internet of Things. We discuss and compare the applicability of two LV technologies, containers and unikernels, as platforms for enabling the scalability, security, and manageability required by such pervasive applications that soon may be part of our everyday lives. To inspire further research, we identify open problems and highlight future directions to serve as a road map for both industry and academia.
Hyperconnected Network: A Decentralized Trusted Computing and Networking Paradigm IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Hao Yin; Dongchao Guo; Kai Wang; Zexun Jiang; Yongqiang Lyu; Ju Xing
With the development of the Internet of Things, a complex CPS system has emerged and is becoming a promising information infrastructure. In the CPS system, the loss of control over user data has become a very serious challenge, making it difficult to protect privacy, boost innovation, and guarantee data sovereignty. In this article, we propose HyperNet, a novel decentralized trusted computing and networking paradigm, to meet the challenge of loss of control over data. HyperNet is composed of the intelligent PDC, which is considered as the digital clone of a human individual; the decentralized trusted connection between any entities based on blockchain as well as smart contract; and the UDI platform, enabling secure digital object management and an identifier-driven routing mechanism. HyperNet has the capability of protecting data sovereignty, and has the potential to transform the current communication-based information system to the future data-oriented information society.
MECPASS: Distributed Denial of Service Defense Architecture for Mobile Networks IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Van Linh Nguyen; Po-Ching Lin; Ren-Hung Hwang
Distributed denial of service is one of the most critical threats to the availability of Internet services. A botnet with only 0.01 percent of the 50 billion connected devices in the Internet of Things is sufficient to launch a massive DDoS flooding attack that could exhaust resources and interrupt any target. However, the mobility of user equipment and the distinctive characteristics of traffic behavior in mobile networks also limit the detection capabilities of traditional anti-DDoS techniques. In this article, we present a novel collaborative DDoS defense architecture called MECPASS to mitigate the attack traffic from mobile devices. Our design involves two filtering hierarchies. First, filters at edge computing servers (i.e., local nodes) seek to prevent spoofing attacks and anomalous traffic near sources as much as possible. Second, global analyzers located at cloud servers (i.e., central nodes) classify the traffic of the entire monitored network and unveil suspicious behaviors by periodically aggregating data from the local nodes. We have explored the effectiveness of our system on various types of application- layer DDoS attacks in the context of web servers. The simulation results show that MECPASS can effectively defend and clean an Internet service provider core network from the junk traffic of compromised UEs, while maintaining the false-positive rate of its detection engine at less than 1 percent.
Block-Stream as a Service: A More Secure, Nimble, and Dynamically Balanced Cloud Service Model for Ambient Computing IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Jackson He; Yaoxue Zhang; Ju Lu; Ming Wu; Fujin Huang
Cloud computing has become mainstream in the last few years. Diverse services based on IaaS, PaaS, SaaS, and app store models have been widely available to millions of users worldwide. At the same time, transparent computing (TC) has also gained strong interest in China. With the rapid development of IoT, increasing IoT devices will be deployed to provide information services for end users. As we are heading into the era of ambient computing, where end users are immersed in seamless computing devices and services, the boundary between cloud and devices is getting blurry, and more devices and services need to be securely managed. The existing service models that are defined for user-cloud interaction should be extended to serve more diverse and lightweight devices with nimble and fluid services. With this evolution trend, it is paramount for both cloud service providers and IoT service operators to manage the security and integrity of these services. In this article, we propose a new cloud service model, named block-stream as a service (BaaS), based on our previous study on TC. BaaS is nimbler than SaaS and has better security management than an app store. It is expected that this new cloud service model has great potential to support the vision of ambient computing and securely manage diverse applications on lightweight IoT devices.
COAST: A Cooperative Storage Framework for Mobile Transparent Computing Using Device-to-Device Data Sharing IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Jiahui Jin; Junzhou Luo; Yunhao Li; Runqun Xiong
TC is a promising network computing paradigm that offers an efficient way to make lightweight terminals more powerful, convenient, and secure. TC's execution model separates data storage and application execution, letting terminals load applications from TC servers on demand via the Internet. With this approach, the network's performance significantly affects the TC applications' performance. To enhance TC applications' performance, existing research typically deploys many cache servers on the Internet. However, such caching techniques are not ideal in a mobile environment, where the wireless networks that mobile terminals use for Internet access are expensive and have limited bandwidth. To address this problem, we propose COAST, a cooperative storage framework for MTC. Based on a deviceto- device data-sharing technique, COAST enables a mobile terminal to fetch applications from nearby terminals without accessing the Internet. In this article, we introduce COAST's design, explore the opportunities and challenges of cooperative storage in MTC environments, and identify future research directions.
A Multi-Level Cache Framework for Remote Resource Access in Transparent Computing IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Di Zhang; Yuezhi Zhou; Yaoxue Zhang
With the increasing demand for high performance of remote resource access in transparent computing, there is a requirement to design a multi-level cache framework to alleviate the network latency. Existing cache frameworks in CPU and web systems cannot be applied simply because the remote resource access architecture needs to be extended to support multi-level cache, and the ways that resources are accessed in transparent computing require specific designs. In this article, we propose a multi-level cache framework for remote resource access in transparent computing. Based on the low latency feature of edge computing, we extend the remote resource access architecture to an architecture with multi-level caches by setting caches on the edge devices with low network latency. Then we design a hybrid multi-level cache hierarchy and make corresponding cache policies. Through a case study, we show the effectiveness of our design. Finally, we discuss several future research issues for deploying the proposed multi-level cache framework.
Transparent Learning: An Incremental Machine Learning Framework Based on Transparent Computing IEEE Netw. (IF 7.23) Pub Date : 2018-01-26 Kehua Guo; Zhonghe Liang; Ronghua Shi; Chao Hu; Zuoyong Li
In the Internet of Things environment, the capabilities of various clients are being developed in the direction of networking and intellectualization. How to develop the clients' capability from that of only collecting and displaying data to that of possessing intelligence has been a critical issue. In recent years, machine learning has become a representative technology in client intellectualization and is now attracting growing interest. In machine learning, massive computing, including data preprocessing and training, requires substantial computing resources; however, lightweight clients usually do not have strong computing capability. To solve this problem, we introduce the advantage of transparent computing (TC) for the client intellectualization framework and propose an incremental machine learning framework named transparent learning (TL), where training tasks are moved from lightweight clients to servers and edge devices. After training, test models are transmitted to clients and updated with incremental training. In this study, a cache strategy is designed to divide the training set in order to optimize the performance. We choose deep learning as the performance evaluation case, and conduct several TensorFlow-based experiments to demonstrate the efficiency of the framework.
Anomaly Detection and Array Diagnosis in Wireless Networks with Multiple Antennas: Framework, Challenges and Tools IEEE Netw. (IF 7.23) Pub Date : 2017-11-28 Bo Wang; Fengye Hu; Yanping Zhao; Terry N. Guo
Anomaly detection and array diagnosis in wireless networks are both important technologies and have many applications ranging from discovering malicious traffic and identifying abnormal nodes, to detecting faulty antennas and so on. In general, anomaly detection mainly depends on relational data, which denotes the links between nodes of the networks, to decide whether abnormal networks caused by intentional attack or array failure are embedded in large wireless networks. Additionally, the typical scheme of array diagnosis is to measure signals radiating from the array antennas under test to detect the faulty elements by using a centralized method. However, in largescale wireless networks, a centralized strategy results in a communication bottleneck because of transmitting all signals to a center node. Moreover, since faulty elements are only a tiny proportion for the whole networks, the method that all antennas are under test is unnecessary and also causes huge computational complexity to identify the failure of elements. Aiming to mitigate these problems, this article provides a novel framework to monitor networks and detect faulty antennas by fusing relational data and measured signals. In this article, we first review the algorithms related to anomaly detection and survey the array diagnosis problem. In particular, we discuss the relationship between anomaly detection and array diagnosis in the new framework and highlight the importance of data fusion. Finally, the main challenges are presented and mathematical tools are introduced to solve the corresponding problems.
Unlocking the Door to Mobile Social VR: Architecture, Experiments and Challenges IEEE Netw. (IF 7.23) Pub Date : 2017-10-03 Lanshan Zhang; Linhui Sun; Wendong Wang; Jiangchuan Liu
VR redefines the well known social network by introducing impressive environment sharing and revolutionary face-to-face interaction. Benefiting from better mobility and affordability, mobile social VR is gaining popularity; however, very little is known about how and how well it performs. In this article, we characterize the workflow of mobile social VR, propose a tentative architecture by drawing lessons from well known techniques, and implement this architecture as a prototype. To analyze mobile social VR's performance and identify potential bottlenecks, we conduct experiments to evaluate and decouple the prototype. We observe that mobile social VR's performance mainly depends on the scene it renders, and existing high-end mobile devices exhaust most GPU resources but still offer poor experience for complicated scenes. Finally, we highlight the major challenges and prospects for future work.
Toward Secure Outsourced Middlebox Services: Practices, Challenges, and Beyond IEEE Netw. (IF 7.23) Pub Date : 2017-08-15 Cong Wang; Xingliang Yuan; Yong Cui; Kui Ren
Modern enterprise networks heavily rely on ubiquitous network middleboxes for advanced traffic processing such as deep packet inspection, traffic classification, and load balancing. Recent advances in NFV have pushed forward the paradigm of migrating in-house middleboxes to third-party providers as software-based services for reduced cost yet increased scalability. Despite its potential, this new service model also raises new security and privacy concerns, as traffic is now redirected and processed in an untrusted environment. In this article, we survey recent efforts in the direction of enabling secure outsourced middlebox functions, and identify open challenges for researchers and practitioners to further investigate solutions toward secure middlebox services.
Fine-Grained Scheduling in Cloud Gaming on Heterogeneous CPU-GPU Clusters IEEE Netw. (IF 7.23) Pub Date : 2017-11-28 Wei Zhang; Xiaofei Liao; Peng Li; Hai Jin; Li Lin; Bing Bing Zhou
Cloud gaming is a promising approach to provide high-quality gaming services to mobile devices. However, existing cloud gaming systems fail to fully exploit hardware resources due to coarse-grained resource scheduling based on virtual machine migration. In this article, we propose a novel cloud gaming design, referred to as FGCG, with fine-grained scheduling to maximize resource utilization on a heterogeneous CPU-GPU cluster. Specifically, we decompose game workloads into small and independent render tasks that can be freely dispatched to different machines. Trace-driven simulation results show that FGCG can significantly improve resource utilization compared to existing cloud gaming systems.
From Software-Defined to Human-Defined Networking: Challenges and Opportunities IEEE Netw. (IF 7.23) Pub Date : 2017-10-27 Elisa Rojas
The SDN paradigm is still in an early stage of development. Considering full automatization and effortless management as the main objective of these networks, we believe diverse challenges need to be tackled. For this purpose, this article reviews the SDN architecture from top to bottom, paying attention to components yet under standardization or that demand enhancement from a network operator's perspective. The main conclusion is that the SDN area requires a significant amount of research to reach its full potential, which we consider a huge opportunity to innovate toward a truly human-defined networking.
SSDNet: Small-World Super-Dense Device-to-Device Wireless Networks IEEE Netw. (IF 7.23) Pub Date : 2017-10-03 Wei Cheng; Jiguo Yu; Feng Zhao; Xiuzhen Cheng
In this article, we propose a novel networking paradigm called Small-world SSDNet, servicing applications such as public safety, proximity based services, and fog computing based on device-todevice multi-hop wireless communications. The "small-world" feature is determined by the service area, whose size is usually within a community level, and the well known small-world properties existing in SSDNets; the "super-dense" feature comes from the fact that the increased direct communication range and the popularity of 5G and IoT devices jointly result in a large number of devices within a single-hop communication range. This article first formally defines SSDNet. Then the challenges and the opportunities brought by the design and the implementation of the SSDNet protocols and applications are addressed. Finally, the broader discussions on issues relevant to modeling, engineering, and dissemination are provided.
Drone Assisted Vehicular Networks: Architecture, Challenges and Opportunities IEEE Netw. (IF 7.23) Pub Date : 2018-01-10 Weisen Shi; Haibo Zhou; Junling Li; Wenchao Xu; Ning Zhang; Xuemin Shen
This article introduces the DAVN, which provides ubiquitous connections for vehicles by efficiently integrating the communication and networking technologies of drones and connected vehicles. Specifically, we first propose a comprehensive architecture of the DAVN and outline its potential services. By cooperating with vehicles and infrastructures, drones can improve vehicle-to-vehicle connectivity, infrastructure coverage, network information collection ability, and network interworking efficiency. We then present the challenges and research opportunities of DAVNs. In addition, a case study is provided to demonstrate the effectiveness of DAVNs by leveraging our designed simulation platform. Simulation results demonstrate that the performance of vehicular networks can be significantly enhanced with the proposed DAVN architecture.
Scalable Spectrum Access System for Massive Machine Type Communication IEEE Netw. (IF 7.23) Pub Date : 2018-01-10 Beeshanga Abewardana Jayawickrama; Ying He; Eryk Dutkiewicz; Markus Dominik Mueck
Future 5G networks aspire to enable new services with vastly different data rate, latency and scalability requirements. The consensus is that these new services will fall into three categories: eMBB, URLLC, and mMTC. Due to unique characteristics of these services and the limited availability of finite spectrum resources, 5G will need to carefully map appropriate bands and spectrum usage models for each service. The SAS is an emerging spectrum sharing model that is gaining momentum in the U.S. SAS presents an opportunity for operators to access the 3.5 GHz military radar band for commercial use. This article discusses the feasibility of the current SAS model in the context of mMTC. We propose a scalable SAS framework that can manage the mMTC uplink interference to the incumbent with less overhead. The simulation setup models the interference levels in New York City and its surrounding counties. The results show that mMTC uplink transmission can be enabled using our framework even on the coast of New York, where mMTC density is high, without causing a harmful level of interference to the incumbent.
Narrowband IoT Data Transmission Procedures for Massive Machine-Type Communications IEEE Netw. (IF 7.23) Pub Date : 2017-11-27 Pilar Andres-Maldonado; Pablo Ameigeiras; Jonathan Prados-Garzon; Jorge Navarro-Ortiz; Juan M. Lopez-Soler
Large-scale deployments of massive machine type communications involve several challenges on cellular networks. To address the challenges of massive machine-type communications, or more generally, the Internet of Things (IoT), the 3GPP has developed Narrowband IoT (NB-IoT) as part of Release 13. NB-IoT is designed to provide better indoor coverage, support of a massive number of low-throughput devices, with relaxed delay requirements, and lower energy consumption. NB-IoT reuses Long Term Evolution functionality with simplifications and optimizations. Particularly for small data transmissions, NB-IoT specifies two procedures to reduce the required signaling: one of them based on the control plane and the other on the user plane (UP). In this work, we provide an overview of these procedures as well as an evaluation of their performance. The results of the energy consumption show both optimizations achieve a battery lifetime extension of more than two years for a large range in the considered cases, and up to eight years for CP with good coverage. In terms of cell capacity relative to Service Request, CP achieves gains from 26 to 224 percent, and UP ranges from 36 to 165 percent. The comparison of CP and UP optimizations yields similar results, except for some specific configurations.
Overview of 3GPP Release 14 Enhanced NB-IoT IEEE Netw. (IF 7.23) Pub Date : 2017-11-27 Andreas Hoglund; Xingqin Lin; Olof Liberg; Ali Behravan; Emre A. Yavuz; Martin Van Der Zee; Yutao Sui; Tuomas Tirronen; Antti Ratilainen; David Eriksson
In 3GPP LTE Release 13, Narrowband Internet of Things (NB-IoT) was standardized for providing wide-area connectivity for massive machine-type communications for IoT. In LTE Release 14, NB-IoT was further developed to deliver enhanced user experience in selected areas through the addition of features such as increased positioning accuracy, increased peak data rates, the introduction of a lower device power class, improved non-anchor carrier operation, multicast, and authorization of coverage enhancements. In this article, we provide an overview of these features introduced for NB-IoT in LTE Release 14. An analysis is given on the applicability of these features and their benefits to enhance the NB-IoT radio access technology.
Locally and Temporary Shared Spectrum as Opportunity for Vertical Sectors in 5G IEEE Netw. (IF 7.23) Pub Date : 2017-11-27 Maria Dolores Perez Guirao; Andreas Wilzeck; Axel Schmidt; Konstantin Septinus; Christoph Thein
The fifth generation of cellular mobile communication networks is on the horizon and aims to integrate use cases of vertical sectors in its holistic ecosystem of technologies, frameworks, and paradigms. To integrate vertical sectors successfully, their key requirements should be communicated, properly discussed, and finally reflected within the design and standardization process of 5G technologies. In this contribution, we analyze the needs and communication requirements of selected vertical sectors - industrial automation, utilities, and program making and special events (PMSE) - which can be covered by a mixed use of massive machine-type communication (mMTC) and mission-critical communications (MCC), including ultra-reliable low latency communications. Based on this analysis, we discuss local high-quality wireless networks aiming at local, self-sustaining, private, and temporary deployment. Considering the current discussion on radio spectrum scarcity and exclusive radio spectrum rights, we suggest license-based spectrum sharing as the enabling spectrum access technology for such networks. In this respect, we propose three functional extensions of licensed shared access (LSA) architectures that may allow for locally confined and temporally flexible licensed shared spectrum arrangements. Furthermore, the adoption of our proposed architectures would enable vertical sectors to roll out private networks by themselves and meet their local demands.
Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE IEEE Netw. (IF 7.23) Pub Date : 2017-11-27 Dong Min Kim; Rene Brandborg Sorensen; Kashif Mahmood; Olav Norvald Osterbo; Andrea Zanella; Petar Popovski
In cellular massive machine-type communications, a device can transmit directly to the BS or through an aggregator (intermediate node). While direct device-BS communication has recently been the focus of 5G/3GPP research and standardization efforts, the use of aggregators remains a less explored topic. In this article we analyze the deployment scenarios in which aggregators can perform cellular access on behalf of multiple MTC devices. We study the effect of packet bundling at the aggregator, which alleviates overhead and resource waste when sending small packets. The aggregators give rise to a trade-off between access congestion and resource starvation, and we show that packet bundling can minimize resource starvation, especially for smaller numbers of aggregators. Under the limitations of the considered model, we investigate the optimal settings of the network parameters in terms of number of aggregators and packet bundle size. Our results show that, in general, data aggregation can benefit the uplink massive MTC in LTE by reducing the signaling overhead.
Pinpointing Anomaly RFID Tags: Situation and Opportunities IEEE Netw. (IF 7.23) Pub Date : 2017-08-15 Xiulong Liu; Xin Xie; Kun Wang; Heng Qi; Jiannong Cao; Song Guo; Keqiu Li
RFID is one of the most important automatic identification techniques in the future IoT world. Compared with the other techniques, RFID has various advantages such as no requirement of line of sight, the capability of wireless communication, low cost, small size, and so on. This article discusses two types of anomaly tags, i.e., missing tags and unknown tags, which usually appear due to theft or product misplacement. Generally, there are two categories of anomaly tag pinpointing protocols: boolean detection, which aims at probabilistically determining whether there exist anomaly tags or not; and exact identification, which aims at identifying all anomaly tag IDs. This article first conducts a comprehensive survey of the existing anomaly tag pinpointing protocols, particularly emphasizing their evolution road. After that, we also conduct a taxonomy of the existing protocols and summarize their pros and cons. Finally, we discuss some technical challenges and problems that have not been covered yet, and point out the potential opportunities in RFID research and applications.
Green Femtocells in the IoT Era: Traffic Modeling and Challenges -- An Overview IEEE Netw. (IF 7.23) Pub Date : 2017-11-27 Fadi Al-Turjman; Enver Ever; Hadi Zahmatkesh
The rapid increase in numbers of communicating devices, such as smartphones, PDAs, and notebooks, is causing the demand for mobile data traffic to grow significantly. In recent years, mobile operators have been trying to find solutions to increase the network capacity in order to satisfy mobile users' requests and meet the requirements in terms of various quality of service measures in the case of high mobile data traffic. With ever increasing demand from mobile users and implementations in the area of IoT, femtocells have proved to be a promising solution for network operators to enhance coverage and capacity, and they provide high data rate services in a less expensive manner. This article describes possible femtocell applications and traffic modeling approaches in the IoT environment, and highlights potentials and challenges for IoT-femtocell-based applications.
Q-Charge: A Quadcopter-Based Wireless Charging Platform for Large-Scale Sensing Applications IEEE Netw. (IF 7.23) Pub Date : 2017-11-27 Jiming Chen; Songyuan Li; Shuo Chen; Shibo He; Zhiguo Shi
Wireless charging technologies have attracted much attention from both academia and industry due to their great potential of freeing devices from wires and batteries, becoming one of the key research areas in recent years. Many platforms have been designed based on different charging technologies for a wide range of scenarios, including structural monitoring, habitat monitoring, wearable devices, volcano monitoring, and so on. However, these existing platforms cannot be deployed quickly and flexibly in many harsh environments, resulting in high deployment cost. In this article, we propose Q-Charge, a wireless charging platform integrated with a quadcopter, which is able to provide desirable energy for large-scale sensing applications. Q-Charge is composed of three components: an energy transfer module, a programmable quadcopter unit, and a cloud server. A wireless charger is installed on a quadcopter to achieve remote energy delivery, and a sink node retrieves data packets from sensors and forwards them to the cloud via a service gateway. Our platform has been successfully implemented for building structure and farmland monitoring, which demonstrates Q-Charge to be effective and efficient.
Toward the Evolution of Wireless Powered Communication Networks for the Future Internet of Things IEEE Netw. (IF 7.23) Pub Date : 2017-07-19 Parisa Ramezani; Abbas Jamalipour
With the ever-increasing speed of technological advances, more and more objects are being connected to the Internet every day and the world is moving toward the next generation of the Internet, the so called Internet of Things (IoT). However, numerous challenges need to be addressed for the widespread adoption of the IoT paradigm. Energy scarcity is one of these challenges. Recently, the advent of energy harvesting has relaxed energy limitation concerns. Specifically, RF energy harvesting has attracted a large amount of research due to its clear advantages over other energy harvesting methods. Lately, the extensive research in the RF energy harvesting domain has led to the emergence of wireless powered communication networks (WPCNs), where energy-constrained users are powered by wireless energy transfer of an integrated energy and information access point referred to as a hybrid access point (HAP). In this article, we review the research conducted in the field of WPCN and discuss opportunities for further extensions of these networks. Looking from the IoT point of view, we also present some of the points that must be taken into account for impeccable operation of WPCN in the IoT environment.
Interference Management in Ultra-Dense Networks: Challenges and Approaches IEEE Netw. (IF 7.23) Pub Date : 2017-08-15 Junyu Liu; Min Sheng; Lei Liu; Jiandong Li
Capable of enhancing spatial multiplexing and greatly expanding network capacity, network densification has undoubtedly become a general trend in future wireless networks. However, in addition to the expected advantages, the overuse of spatial resources would result in unpredictable and overwhelming interference as well due to the limited spectrum resources and highly disorganized network infrastructures. Hence, a critical issue arises: are conventional IM techniques still applicable and effective to tackle the interference in UDN? To shed light on the problem, in this article we first investigate the characteristics of interference in UDN and then provide an overview of the existing dominant IM techniques. It is found that, due to the complicated interference, most IM techniques designed for sparse networks would lose their merits when applied in UDN. To reap their potential benefits, we design an IM entity for UDN, aided by which an adaptive on-off power control method is proposed. Results reveal that network capacity could be significantly improved using the proposed method, especially in UDN. In addition, open issues and challenges for IM co-design with stateof- the-art communications technologies are highlighted to provide insight on the potential direction in future wireless networks.
Efficient Management and Fast Handovers in Software Defined Wireless Networks Using UAVs IEEE Netw. (IF 7.23) Pub Date : 2017-11-27 Vishal Sharma; Fei Song; Ilsun You; Han-Chieh Chao
Compared to traditional networking, SDN has better controllability and visibility for network components, which enable better management by using the common controller. In this article, the standard architecture of SDN is enhanced to utilize UAVs as on-demand forwarding switches. The proposed approach can achieve efficient management and fast handovers by decreasing the handover latency, E2E delay, and signaling overheads. The illustrated scenarios will help in understanding the impact of existing handover approaches in the next generation wireless networks, especially the upcoming 5G, which includes small cells, UAVs, UEs, and so on. The simulation study shows that scenarios with both UAVs and small cells perform better than scenarios with only small cells. The results in this article show that the proposed SDNbased handover scenarios perform better than the existing 4G-LTE handover for UAVs.
HetNet: A Flexible Architecture for Heterogeneous Satellite-Terrestrial Networks IEEE Netw. (IF 7.23) Pub Date : 2017-09-11 Bohao Feng; Huachun Zhou; Hongke Zhang; Guanwen Li; Haifeng Li; Shui Yu; Han-Chieh Chao
As satellite networks have played an indispensable role in many fields, how to integrate them with terrestrial networks (e.g., the Internet) has attracted significant attention in academia. However, it is challenging to efficiently build such an integrated network, since terrestrial networks are facing a number of serious problems, and since they do not provide good support for heterogeneous network convergence. In this article, we propose a flexible network architecture, HetNet, for efficient integration of heterogeneous satellite-terrestrial networks. Specifically, the HetNet synthesizes Locator/ID split and Information-Centric Networking to establish a general network architecture. In this way, it is able to achieve heterogeneous network convergence, routing scalability alleviation, mobility support, traffic engineering, and efficient content delivery. Moreover, the HetNet can further improve its network elasticity by using the techniques of Software-Defined Networking and Network Functions Virtualization. In addition, to evaluate the HetNet performance, we build a proof-of-concept prototype system and conduct extensive experiments. The results confirm the feasibility of the HetNet and its advantages.
Distributed Gateway Selection for M2M Communication in Cognitive 5G Networks IEEE Netw. (IF 7.23) Pub Date : 2017-08-03 Muhammad Naeem; Waleed Ejaz; L. Karim; Syed Hassan Ahmed; A. Anpalagan; Minho Jo; Houbing Song
M2M communication is an important component for future wireless networks. M2M systems consist of a large number of devices that can operate with minimum or no human intervention. However, spectrum demand rises exponentially with the increase in the number of connected devices. Cognitive 5G networks are key to address the issue of spectrum scarcity. Further, use of multiple gateways in cognitive 5G networks for M2M communication can increase system throughput, coverage, and energy efficiency. Nevertheless, using multiple gateways for the secondary M2M devices may cause interference to the primary M2M devices. Existing gateway selection protocols for cognitive M2M communication mostly use single channel CSMA, and thus are not efficient in terms of reducing the interference. Thus, in this article, we propose a DGAP based on multi-channel CSMA for M2M communication in 5G networks. Further, we propose a Lo-DGAP, where each gateway transmits only the worst primary M2M device information rather than transmitting all neighboring primary M2M device information. The proposed Lo-DGAP increases the throughput of the system by reducing the message header payload and is also energy- efficient. Simulation results demonstrate the effectiveness of the proposed schemes in terms of network lifetime and energy consumption.
A Framework for Searching Internet-Wide Devices IEEE Netw. (IF 7.23) Pub Date : 2017-08-15 Qiang Li; Xuan Feng; Lian Zhao; Limin Sun
Today, millions of physical devices are visible on the Internet through IP addresses, such as wearables, residential routers, cameras and industrial control devices. Users can access and control online devices, while attackers also target them, bringing potential security concerns. In this article, we propose a conceptual framework for discovering, recognizing and managing these online devices at the Internet-wide scale. It is the first work of Internet-wide device search, including three modules: network measurement, fingerprinting, and applications. We have implemented a prototype system and real-world experiments to illustrate the effectiveness of the framework. Furthermore, we explore the use and application of the system through searching camera devices in the whole IPv4 space. We deployed the system on Amazon EC2 to search for webcams and ICS devices nine times from April 2015 to April 2016, and we found 1.3 million webcams in total. In a comprehensive analysis of our dataset, we have characterized the use of online physical devices on the Internet.
Ultra-Dense Heterogeneous Networks with Full-Duplex Small Cell Base Stations IEEE Netw. (IF 7.23) Pub Date : 2017-08-15 Guanding Yu; Zihan Zhang; Fengzhong Qu; Geoffrey Ye Li
The FD communication technique has been recently proposed as a new paradigm to increase the spectral efficiency of wireless systems. In this article, we investigate the application of FD communications to ultra-dense heterogeneous networks where small base stations can work in the FD mode. In such networks, it is a challenging task to coordinate various kinds of interference caused by FD communications, including self-interference, intra-cell and inter-cell uplink-todownlink interference. To deal with it, we have developed a novel ICIC scheme, which consists of four steps: base station association, user pairing and mode selection, power control, and resource block allocation. The implementation issue of the proposed scheme is also discussed. Simulation results demonstrate that our proposed ICIC scheme can effectively harvest the FD gain for ultra-dense heterogeneous networks. Finally, several important future directions and open challenges are identified in this article.
Prospective Positioning Architecture and Technologies in 5G Networks IEEE Netw. (IF 7.23) Pub Date : 2017-08-03 Ying Liu; Xiufang Shi; Shibo He; Zhiguo Shi
Accurate and real-time positioning is highly demanded by location-based services in 5G networks, which are currently being standardized and developed to achieve significant performance improvement over existing cellular networks. In 5G networks, many new envisioned technologies, for example, massive Multiple Input Multiple Output (MIMO), millimeter Wave (mmWave) communication, ultra dense network (UDN), and device-to-device (D2D) communication, are introduced to not only enhance communication performance but also offer the possibility to increase positioning accuracy. In this article, we provide an extensive overview of positioning architectures in previous generations of cellular networks to show a road map of how positioning technologies have evolved in past decades. With this insight, we then propose a general positioning architecture for 5G networks, by exploiting the new features of emerging technologies wherein. We also investigate positioning technologies that have great potential in achieving sub-meter accuracy in 5G networks, and discuss some of the new challenges and open issues.
Win-Win Security Approaches for Smart Grid Communications Networks IEEE Netw. (IF 7.23) Pub Date : 2017-10-03 Daojing He; Sammy Chan; Mohsen Guizani
The smart grid introduces communication capability into various components of the power system to facilitate information exchange. This also makes the smart grid more vulnerable to cyber attacks ranging from hacking of customer details to disrupting the operation of the power grid. When the provision of security functions does not lead to a "win-win" situation, it means that at least one participant does not gain anything or its profit is less than its loss. As a result, the participant has no incentive to provide security functions. We believe that smart grid security will be fully successful if and only if it makes all participants winners. In this article, we point out that existing smart grid security approaches lead to zero-sum or win-lose situations. Then, we study and suggest how to change these non-win-win situations to win-win situations. We also use network forensics in smart grids as an example to illustrate how to design effective cyber security mechanisms with a win-win objective.
Going Fast and Fair: Latency Optimization for Cloud-Based Service Chains IEEE Netw. (IF 7.23) Pub Date : 2017-11-29 Yuchao Zhang; Ke Xu; Haiyang Wang; Qi Li; Tong Li; Xuan Cao
State-of-the-art microservices have been attracting more attention in recent years. A broad spectrum of online interactive applications are now programmed to service chains on the cloud, seeking better system scalability and lower operating costs. Different from the conventional batch jobs, most of these applications consist of multiple stand-alone services that communicate with each other. These step-by-step operations unavoidably introduce higher latency to the delay-sensitive chained services. In this article, we aim at designing an optimization approach for reducing the latency of chained services. Specifically, presenting the measurement and analysis of chained services on Baidu's cloud platform, our real-world trace indicates that these chained services are suffering from significantly high latency because they are mostly handled by different queues on cloud servers for multiple times. However, such a unique feature introduces significant challenges to optimize a microservice's overall queueing delay. To address this problem, we propose a delay-guaranteed approach to accelerate the overall queueing of chained services while obtaining fairness across all the workloads. Our evaluations on Baidu servers shows that the proposed design can successfully reduce the latency of chained services by 35 p
Analyzing and Detecting Money-Laundering Accounts in Online Social Networks IEEE Netw. (IF 7.23) Pub Date : 2017-11-28 Yadong Zhou; Ximi Wang; Junjie Zhang; Peng Zhang; Lili Liu; Huan Jin; Hongbo Jin
Virtual currency in OSNs plays an increasingly important role in supporting various financial activities such as currency exchange, online shopping, and paid games. Users usually purchase virtual currency using real currency. This fact motivates attackers to instrument an army of accounts to collect virtual currency unethically or illegally with no or very low cost and then launder the collected virtual money for massive profit. Such attacks not only introduce significant financial loss of victim users, but also harm the viability of the ecosystem. It is therefore of central importance to detect malicious OSN accounts that engage in laundering virtual currency. To this end, we extensively study the behavior of both malicious and benign accounts based on operation data collected from Tencent QQ, one of the largest OSNs in the world. Then, we devise multi-faceted features that characterize accounts from three aspects: account viability, transaction sequences, and spatial correlation among accounts. Finally, we propose a detection method by integrating these features using a statistical classifier, which can achieve a high detection rate of 94.2 percent at a very low false positive rate of 0.97 percent.
Machine Learning for Networking: Workflow, Advances and Opportunities IEEE Netw. (IF 7.23) Pub Date : 2017-11-28 Mowei Wang; Yong Cui; Xin Wang; Shihan Xiao; Junchen Jiang
Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the networking and distributed computing system is the key infrastructure to provide efficient computational resources for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of MLN, which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations of their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities in networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help motivate researchers to develop innovative algorithms, standards and frameworks.
Challenges and Solutions in Fog Computing Orchestration IEEE Netw. (IF 7.23) Pub Date : 2017-11-28 Yuxuan Jiang; Zhe Huang; Danny H. K. Tsang
Fog computing, complementary to cloud computing, has recently emerged as a new paradigm that extends the computing infrastructure from the center to the edge of the network. This article explores the design of a fog computing orchestration framework to support IoT applications. In particular, we focus on how the widely adopted cloud computing orchestration framework can be customized to fog computing systems. We first identify the major challenges in this procedure that arise due to the distinct features of fog computing. Then we discuss the necessary adaptations of the orchestration framework to accommodate these challenges.
Toward Secure Crowd Sensing in Vehicle-to-Everything Networks IEEE Netw. (IF 7.23) Pub Date : 2017-11-28 Kaigui Bian; Gaoxiang Zhang; Lingyang Song
V2X communication facilitates information sharing between a vehicle and the infrastructure, pedestrians, devices, or any other entity that may affect the vehicle, which is known as a critical component in 5G that promises to realize the vision of connected and autonomous vehicles. Crowd sensing, a.k.a. collective perception, is one of the essential concepts of V2X networks, where vehicles share their information collected by local perception sensors about the environment for improving safety, saving energy, optimizing traffic, and so on. Although the operational aspects of V2X networks are being studied actively, its security aspect has received little attention. In this article, we discuss security issues that may pose serious threats to crowd sensing in V2X networks, and we focus on V2X-specific threats that are unique in V2X networks, e.g. platoon disruption and perception data falsification. We also discuss countermeasures against these threats and the technical challenges that must be overcome to implement such methods.
Recent Advances of LTE/WiFi Coexistence in Unlicensed Spectrum IEEE Netw. (IF 7.23) Pub Date : 2017-10-27 Yan Huang; Yongce Chen; Y. Thomas Hou; Wenjing Lou; Jeffrey H. Reed
U-LTE is a new wireless technology that is currently being developed by industry and academia to offer LTE service in unlicensed spectrum. U-LTE addresses spectrum shortage from 4G LTE cellular networks by allowing them to operate in unlicensed bands. To ensure fair spectrum sharing among different wireless technologies (LTE and WiFi in particular), a number of coexistence mechanisms have been proposed. These mechanisms operate in the time, frequency, or power domains to minimize potential adverse effects from LTE. Based on these mechanisms, a number of U-LTE standards are being developed by industry. In this article, we present recent advances in this exciting area by reviewing the state-of-the-art LTE/WiFi coexistence mechanisms and show how they are incorporated into industry standards. We also point out several key challenges and open problems for future research.
Reliable Formation Protocol for Bluetooth Hybrid Single-hop and Multi-hop Networks IEEE Netw. (IF 7.23) Pub Date : 2017-10-03 Chih-Min Yu; En-Li Lin
There are presently many applications where a non-uniform distribution of devices needs to be established for Bluetooth scatternets. Under the scenario of one dense zone and multiple sparse zones, the dense area has a high probability of generating a single-hop scenario since most devices are within radio range, whereas most devices are out of radio range under the multi-hop scenario in other sparse areas. Thus, both situations have to be considered in the formation of an algorithm design for most real-life situations. This work proposes a reliable formation protocol, called Dual-Ring Tree, for hybrid single-hop/ multi-hop instances. To benefit from the advantages of the hybrid scenarios, a dual-ring subnet is presented as a single-hop solution for dense areas, while a tree-shaped subnet is designed as a multi-hop solution for sparse areas. To the best of the authors' knowledge, this is the first time such an algorithm has been designed to deal with both single-hop and multi-hop scenarios. The computer simulation results suggest that the reliable Dual- Ring Tree outperforms conventional BlueHRT in terms of routing efficiency and network reliability for Bluetooth multi-hop networks.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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