Deep Learning Based Inference of Private Information Using Embedded Sensors in Smart Devices IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Yi Liang; Zhipeng Cai; Jiguo Yu; Qilong Han; Yingshu Li
Smart mobile devices and mobile apps have been rolling out at swift speeds over the last decade, turning these devices into convenient and general-purpose computing platforms. Sensory data from smart devices are important resources to nourish mobile services, and they are regarded as innocuous information that can be obtained without user permissions. In this article, we show that this seemingly innocuous information could cause serious privacy issues. First, we demonstrate that users' tap positions on the screens of smart devices can be identified based on sensory data by employing some deep learning techniques. Second, it is shown that tap stream profiles for each type of apps can be collected, so that a user's app usage habit can be accurately inferred. In our experiments, the sensory data and mobile app usage information of 102 volunteers are collected. The experiment results demonstrate that the prediction accuracy of tap position inference can be at least 90 percent by utilizing convolutional neural networks. Furthermore, based on the inferred tap position information, users' app usage habits and passwords may be inferred with high accuracy.
Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Bo Feng; Qiang Fu; Mianxiong Dong; Dong Guo; Qiang Li
While mobile social networks (MSNs) enrich people's lives, they also bring many security issues. Many attackers spread malicious URLs through MSNs, which causes serious threats to users' privacy and security. In order to provide users with a secure social environment, many researchers make great efforts. The majority of existing work is aimed at deploying a detection system on the server and classifying messages or users in MSNs through graph-based algorithms, machine learning or other methods. However, as a kind of instant messaging service, MSNs continually generate a large amount of user data. Without affecting the user experience, with existing detection mechanisms it is difficult to implement real-time detection in practical applications. In order to realize real-time message detection in MSNs, we can build more powerful server clusters or improve the utilization rate of computing resources. Assuming that computing resources of servers are limited, we use edge computing to improve the utilization rate of computing resources. In this article, we propose a multistage and elastic detection framework based on deep learning, which sets up a detection system at the mobile terminal and the server, respectively. Messages are first detected on the mobile terminal, and then the detection results are forwarded to the server along with the messages. We also design a detection queue, according to which the server can detect messages elastically when computing resources are limited, and more computing resources can be used for detecting more suspicious messages. We evaluate our detection framework on a Sina Weibo dataset. The results of the experiment show that our detection framework can improve the utilization rate of computing resources and can realize real-time detection with a high detection rate at a low false positive rate.
A Dropconnect Deep Computation Model for Highly Heterogeneous Data Feature Learning in Mobile Sensing Networks IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Qingchen Zhang; Laurence T. Yang; Zhikui Chen; Peng Li
Deep computation model, as a tensor deep learning model, outperforms multi-modal deep learning models for feature learning on heterogenous data. However, deep computation model is limited in generalization to small heterogeneous data sets since it typically requires many training objects to learn the parameters. In this article, we propose a dropconnect deep computation model (DDCM) for highly heterogeneous data feature learning in mobile sensing networks. Specifically, the dropconnect technique is used to generalize the large fully-connected layers in the deep computation model for small heterogeneous data sets. Furthermore, the rectified linear units (ReLU) are used as the activation function to reduce computation and prevent overfitting. Finally, we compare the classification accuracy and execution time for learning the parameters between our model and the traditional deep computation model on two highly heterogeneous data sets. Results illustrate that our model achieves 2 percent higher classification accuracy and performs more efficiently than the deep computation model, proving the potential of our proposed model for highly heterogeneous data learning in mobile sensing networks.
Vehicle Safety Improvement through Deep Learning and Mobile Sensing IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Zhe Peng; Shang Gao; Zecheng Li; Bin Xiao; Yi Qian
Information about vehicle safety, such as the driving safety status and the road safety index, is of great importance to protect humans and support safe driving route planning. Despite some research on driving safety analysis, the accuracy and granularity of driving safety assessment are both very limited. Also, the problem of precisely and dynamically predicting road safety throughout a city has not been sufficiently studied and remains open. With the proliferation of sensor-equipped vehicles and smart devices, a huge amount of mobile sensing data provides an opportunity to conduct vehicle safety analysis. In this article, we first discuss mobile sensing data collection in VANETs and then identify two main challengs in vehicle safety analysis in VANETs, i.e., driving safety analysis and road safety analysis. In each issue, we review and classify the state-of-theart vehicle safety analysis techniques into different categories. For each category, a short description is given followed by a discussion of limitations. In order to improve vehicle safety, we propose a new deep learning framework (DeepRSI) to conduct real-time road safety prediction from the data mining perspective. Specifically, the proposed framework considers the spatio-temporal relationship of vehicle GPS trajectories and external environment factors. The evaluation results demonstrate the advantages of our proposed scheme over other methods by utilizing mobile sensing data collected in VANETs.
Reinforcement Learning-based Content-Centric Services in Mobile Sensing IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Keke Gai; Meikang Qiu
The recent remarkable advancement of smart devices is enabling a higher-level flexibility of mobile sensing. Along with the rapid development of mobile devices and applications, a challenging issue is becoming more serious than ever before. A large number of mobility-based services have brought heavy workloads to mobile devices. Resource outsourcing via resource allocations is a type of method to mitigate local workloads. However, most current solutions are restricted by two issues, namely, the variety of inputs and the contradiction between optimal outputs and latency. In this article, we utilize the mechanism of Reinforcement Learning (RL) and propose a novel approach, named Smart Reinforcement Learning-based Resource Allocation (SRL-RA), to achieve optimal allocation through a self-learning process.
Urban Traffic Prediction from Mobility Data Using Deep Learning IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Zhidan Liu; Zhenjiang Li; Kaishun Wu; Mo Li
Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale.
Word-Fi: Accurate Handwrite System Empowered by Wireless Backscattering and Machine Learning IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Dong Ren; Yizhuo Zhang; Ning Xiao; Hao Zhou; Xiangyang Li; Jianwei Qian; Panlong Yang
Word-Fi is a handwriting input system, driven by wireless backscattering technology and machine learning methods. It could effectively mitigate the surrounding noise and extract the weak signals incurred by tiny writing gestures accurately. Leveraging our customized wireless backscattering system, Word-Fi could be noise tolerant across relatively complex environments, especially when multiple persons are presented around, which significantly differs from status quo wireless sensing systems that suffer from multi-user presentation. For weak signal extraction, Word- Fi incorporates an efficient feature selection scheme for classification and improves the classifier by fully exploiting the physical layer information. After using the word suggestion module, it could recognize writing words with fairly high accuracy (above 90 percent) across different volunteers (7-10).
Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Zhenyu Zhou; Haijun Liao; Bo Gu; Kazi Mohammed Saidul Huq; Shahid Mumtaz; Jonathan Rodriguez
The emergence of MCS technologies provides a cost-efficient solution to accommodate large-scale sensing tasks. However, despite the potential benefits of MCS, there are several critical issues that remain to be solved, such as lack of incentive-compatible mechanisms for recruiting participants, lack of data validation, and high traffic load and latency. This motivates us to develop robust mobile crowd sensing (RMCS), a framework that integrates deep learning based data validation and edge computing based local processing. First, we present a comprehensive state-of-the-art literature review. Then, the conceptual design architecture of RMCS and practical implementations are described in detail. Next, a case study of smart transportation is provided to demonstrate the feasibility of the proposed RMCS framework. Finally, we identify several open issues and conclude the article.
Privacy in Neural Network Learning: Threats and Countermeasures IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Shan Chang; Chao Li
Algorithmic breakthroughs, the feasibility of collecting huge amount of data, and increasing computational power, contribute to the remarkable achievements of NNs. In particular, since Deep Neural Network (DNN) learning presents astonishing results in speech and image recognition, the amount of sophisticated applications based on it has exploded. However, the increasing number of instances of privacy leakage has been reported, and the corresponding severe consequences have caused great worry in this area. In this article, we focus on privacy issues in NN learning. First, we identify the privacy threats during NN training, and present privacy-preserving training schemes in terms of using centralized and distributed approaches. Second, we consider the privacy of prediction requests, and discuss the privacy-preserving protocols for NN prediction. We also analyze the privacy vulnerabilities of trained models. Three types of attacks on private information embedded in trained NN models are discussed, and a differential privacy-based solution is introduced.
Fog of Social IoT: When the Fog Becomes Social IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Enzo Baccarelli; Michele Scarpiniti; Paola G. Vinueza Naranjo; Leticia Vaca-Cardenas
SIoT and FC are two stand-alone technological paradigms under the realm of the Future Internet. SIoT relies on the self-establishment and self-management of inter-thing social relationships, in order to guarantee scalability to large IoT networks composed of both human and non-human agents. FC extends cloud capabilities to the access network, in order to allow resource-poor IoT devices to support delay-sensitive applications. Motivated by these complementary features of the SIoT and FC models, in this article we propose their integration into the novel paradigm of the SoFT. Specifically, we provide the following three main contributions. After describing the SoFT paradigm, we discuss its introduction through a number of exemplary use cases. We describe the architecture and the main resource-management functions of the resulting virtualized SoFT technological platform. It merges the physical things at the IoT layer and their virtual clones at the Fog layer into a cyber-physical overlay network of social clones. As a proof-of-concept, we present the simulated performance of a small-scale SoFT prototype, and compare its energy-vs.-delay performance with the corresponding one of a state-of-the-art virtualization-free technological platform, which relies only on device-to-device (D2D) inter-thing communication.
Protocol Architectures for IoT Domains IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Jelena Misic; M. Zulfiker Ali; Vojislav B. Misic
In this work we discuss architectural alternatives for the design of a proxy that interconnects IoT domains running CoAP with the rest of the Internet including micro datacenters and other domains building scalable hierarchical architectures. We assume that the CoAP domain is terminated by an IoT proxy with cache, and we investigate several design alternatives, assuming that the proxy autonomously maintains data freshness. Our analysis indicates that multicast and observe-based proxies perform better than the default POST/GET approach in terms of successful data transmission, round trip delay and energy consumption, with the multicast option having a slight advantage.
Wireless Network Optimization via Physical Layer Information for Smart Cities IEEE Netw. (IF 7.197) Pub Date : 2018-08-03 Fu Xiao; Xiaohui Xie; Zhetao Li; Qingyong Deng; Anfeng Liu; Lijuan Sun
In recent years, the rapid development of urbanization has posed enormous challenges to transportation, security management, quality of life, and so on, which makes the research and development of smart city important. As an information-driven project, strong communication infrastructures are required for connecting smart objects, people, and sensors together. As a consequence, the optimization of the wireless network is the primary premise to support and improve the quality of smart services. The challenge lies in the difficulty to achieve the quantitative description of networks due to the complexity and variability of wireless environments. It is too coarse-grained to express characteristics of networks simply by the strength of received signals through mobile devices. To extract more fine-grained network characteristics, we dig into the PHY layer collecting CSI for network descriptions and extract three signal characteristics, i.e., Rician-K, delay spread and spectral width, from real-world wireless channels. The K-means clustering algorithm is implemented in this article for good performance of network partitioning, and experiments are conducted based on the data sets collected from real scenarios. Simulation results verify the feasibility of our scheme.
Reliable and Opportunistic Transmissions for Underwater Acoustic Networks IEEE Netw. (IF 7.197) 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.
Task Assignment in Mobile Crowdsensing: Present and Future Directions IEEE Netw. (IF 7.197) Pub Date : 2018-03-13 Wei Gong; Baoxian Zhang; Cheng Li
Mobile crowdsensing has wide application perspectives and tremendous advantages over traditional sensor networks due to its low cost, extensive coverage, and high sensing accuracy properties. Task assignment is a crucial issue in mobile crowdsensing systems which is intended to achieve a good tradeoff between task quality and task cost. The design of efficient task assignment mechanisms has attracted a lot of attention and much work has been carried out. In this article, we present a comprehensive survey of state-of-the-art task assignment mechanisms in mobile crowdsensing systems. We will first introduce several fundamental issues in task assignment and classify existing mechanisms based on different design criteria. Then we introduce how each of the existing mechanisms works and discuss their merits and deficiencies. Finally, we discuss challenging issues and point out some future directions in this area.
Security Threats in the Data Plane of Software-Defined Networks IEEE Netw. (IF 7.197) 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).
Virtual Local-Hub: A Service Platform on the Edge of Networks for Wearable Devices IEEE Netw. (IF 7.197) Pub Date : 2018-05-17 Hsin-Peng Lin; Yuan-Yao Shih; Ai-Chun Pang; Chun-Ting Chou
With the rapid development of sensing and communication capacities, wearable technology, one of the most significant trends in the mobile computing evolution, has been changing our daily life. Wearable devices generally require a powerful local hub to replenish computing capacities for advanced features. However, it is inconvenient to carry the local hub in many situations, even though more and more wearable devices are equipped with a WiFi/cellular interface, enabling them to exchange data with the local hub through the Internet, However, this results in long response time and functional limitations. To overcome the restriction of a physical local hub, we propose a VLH solution, which utilizes network equipment nearby (e.g., a WiFi hotspot or cellular base station) as the local hub. In this article, we first describe the operating mechanism of a local hub and give an overview of the VLH system. Then we describe the system design of the VLH, including the container-based virtualization and the modified microservice architecture which enables remote function module sharing in the fog-computing environment. We then propose an algorithm to deal with function module allocation and sharing decisions. Finally, we demonstrate and verify the effectiveness and practicality of VLH via both simulations under large-scale network setting and real-world prototype implementation.
Efficient Coastal Communications with Sparse Network Coding IEEE Netw. (IF 7.197) Pub Date : 2018-03-13 Ye Li; Jue Wang; Shibing Zhang; Zhihua Bao; Jiangzhou Wang
The demand for wideband communication in the coastal area (i.e., ≤ 100 km from the coastline) has been rapidly increasing in recent years. Compared to the terrestrial scenario, the coastal environment has long-distance and highly dynamic channels, and the communication devices are more strictly constrained by energy supplies. While the RLNC has the fountain erasure-correction property and is suitable for transmissions over the long-distance dynamic channels, it suffers from high coding coefficient delivery cost and decoding complexity. In this article, we look into the application of sparse network coding in coastal communication systems. We identify two typical multicast scenarios that may appear in coastal communications, namely the relay-aided multicast and multicast from a shore-based base station with D2D communication enabled among the subscribers. We provide a detailed comparison of existing sparse network coding schemes. Based on that, we demonstrate through simulations that an appropriate choice of sparse codes is critical to meet the unique requirements in coastal communication systems. We show that batched sparse code is suitable for relay-aided multicast, and subset-based sparse codes are preferable for D2D-enabled multicast.
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.
REMT: A Real-Time End-to-End Media Data Transmission Mechanism in UAV-Aided Networks IEEE Netw. (IF 7.197) Pub Date : 2018-04-13 Jiajie Zhang; Jian Weng; Weiqi Luo; Jia-Nan Liu; Anjia Yang; Jiancheng Lin; Zhijun Zhang; Hailiang Li
In recent years, UAVs have received much attention in both the military and civilian fields for monitoring, emergency relief and searching tasks. UAVs are considered a new technology to obtain data at high altitudes when equipped with sensors. This technology is vital to the success of next-generation monitoring systems, which are expected to be reliable, real-time, efficient and secure. However, due to the bandwidth limitations in UAV-aided networks, the size of the transmitted data is a crucial factor for real-time media data transmission requirements, especially for national defense. To address this issue, in this article, we propose a real-time end-to-end media data transmission mechanism with an unsupervised deep neural network. The proposed mechanism transmutes the media data captured by UAVs into latent codes with a predefined constant size and transmits the codes to the ground console station (GCS) for further reconstruction. We use a real-word dataset containing millions of samples to evaluate the proposed mechanism which achieves a high transmission ratio, low resource usage and good visual quality.
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.
Hierarchical CORD for NFV Datacenters: Resource Allocation with Cost-Latency Tradeoff IEEE Netw. (IF 7.197) Pub Date : 2018-04-13 Ying-Dar Lin; Chih-Chiang Wang; Chien-Ying Huang; Yuan-Cheng Lai
Network Function Virtualization (NFV) allows datacenters to consolidate network appliance functions onto commodity servers and devices. Currently telecommunication carriers are re-architecting their central offices as NFV datacenters that, along with SDN, help network service providers to speed deployment and reduce cost. However, it is still unclear how a carrier network shall organize its NFV datacenter resources into a coherent service architecture to support global network functional demands. This work proposes a hierarchical NFV/SDN-integrated architecture in which datacenters are organized into a multi-tree overlay network to collaboratively process user traffic flows. The proposed architecture steers traffic to a nearby datacenter to optimize user-perceived service response time. Our experimental results reveal that the 3-tier architecture is favored over others as it strikes a good balance between centralized processing and edge computing, and the resource allocation should be decided based on traffic’s source-destination attributes. Our results indicate that when most traffic flows within the same edge datacenter, the strategy whereby resources are concentrated at the carrier’s bottom-tier datacenters is preferred, but when most traffic flows across a carrier network or across different carrier networks, a uniform distribution over the datacenters or over the tiers, respectively, stands out from others.
Fog-Aided Verifiable Privacy Preserving Access Control for Latency-Sensitive Data Sharing in Vehicular Cloud Computing IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Kaiping Xue; Jianan Hong; Yongjin Ma; David S. L. Wei; Peilin Hong; Nenghai Yu
VCC is an emerging computing paradigm developed for providing various services to vehicle drivers, and has attracted more and more attention from researchers and practitioners over the last few years. However, privacy preserving and secure data sharing has become a very challenging and important issue in VCC. Unfortunately, existing secure access control schemes consume too many computation resources, which prevents them from being performed on computing resource constrained vehicle onboard devices. Also, these cloud-based schemes suffer large latency and jitter due to their centralized resource management, and thus may not be suitable for real-time applications in VANETs. In this article, we thus propose a novel fog-to-cloud-based architecture for data sharing in VCC. Our scheme is a cryptography-based mechanism that conducts fine-grained access control. In our design, the complicated computation burden is securely outsourced to fog and cloud servers with confidentiality and privacy preservation. Meanwhile, with the prediction of a vehicle's mobility, pre-pushing data to specific fog servers can further reduce response latency with no need to consume more resources of the fog server. In addition, with the assumption of no collusion between different providers for the cloud and fog servers, our scheme can provide verifiable auditing of fog servers' reports. The scheme is proved secure against existing adversaries and newborn security threats. Experimental test shows significant performance improvement in edge devices' overhead saving and response delay reduction. Introduction
A Secure and Efficient Transmission Method in Connected Vehicular Cloud Computing IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Yixian Yang; Xinxin Niu; Lixiang Li; Haipeng Peng
Connected vehicular cloud computing (CVCC) and the VANET can realize real-time monitoring and intelligent adjustment of traffic conditions. With the data collected by vehicles and the guidance provided by cloud computing platforms, the current traffic is facing new opportunities. CVCC is a mobile computing model, which extends the fixed nodes of traditional cloud infrastructure into mobile nodes composed of vehicles. Thus, compared to the traditional cloud infrastructures, CVCC requires a more complex mechanism to ensure the secure and efficient information transmission in both cloud platform-to-vehicle communication and vehicle-to-vehicle communication. Channel capacity, as the key parameter to measure the channel utilization, plays an important role in ensuring the reliability of CVCC service and the integrity of transmission data. In recent decades, the existing calculation methods could not solve the channel capacities problem in multi-participant VANETs. Different from the traditional calculation methods, we propose a novel calculation method in this article, which combines the core concepts of game theory and information theory, to calculate the channel capacities of multiple vehicular networks. The proposed method refreshes the concept of communication, which has potential applications in different services of CVCC.
RTSense: Providing Reliable Trust-Based Crowdsensing Services in CVCC IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Liehuang Zhu; Chuan Zhang; Chang Xu; Kashif Sharif
CVCC has garnered significant attention in recent years as a special cloud computing platform capable of broadening network service provisioning in mobile computing. Vehicular crowdsensing is a prime candidate for CVCC applications as connected vehicles can provide tremendous sensing, computing, and storage resources. Truthfulness of sensing data is very important, as malicious vehicles may create inaccuracy in sensing results. In this work, we propose RTSense, which enables trust-based crowdsensing services in CVCC. The architecture divides the system into control and data planes, where the trust authority and service providers sit in the control plane, and vehicles and fogs exist in the data plane. We provide solutions for anonymous vehicle authentication, interactive filtering truth discovery, and trust management for reliable crowdsensing. The experimental analysis shows that RTSense can effectively segregate malicious and trustworthy vehicles. We also identify interesting future directions along with possible solutions.
Integrated Authentication and Key Agreement Framework for Vehicular Cloud Computing IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Qi Jiang; Jianbing Ni; Jianfeng Ma; Li Yang; Xuemin Shen
VCC leverages the underutilized storage and computing resources of vehicles to collaboratively provide traffic management, road safety, and infotainment services to end users, such as drivers and passengers. It is a hybrid technology that improves the resource utilization on vehicles and is able to perform complex computing tasks that cannot be handled by a single vehicle. Despite the appealing advantages, security and privacy threats are severe in VCC due to the sharing of resources among unfamiliar vehicles. In this article, we identify security goals for the interoperability with VCC and provide an AKA framework for VCC. Specifically, we first present the research challenges and open problems for designing a reliable AKA with strong security guarantees for VCC. Then we propose an integrated AKA framework that integrates the single-server 3-factor AKA protocol and the non-interactive identity-based key establishment protocol, and evaluate its performance based on a simulated experimental platform. Finally, several interesting issues are discussed to light up the further research directions on AKA for VCC.
Secure Outsourced Computation in Connected Vehicular Cloud Computing IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Jun Shao; Guiyi Wei
CVCC, a hybrid technology to exploit the computing resources among the cloud, roadside infrastructure, and vehicles, has received considerable attention in recent years. Different from traditional cloud computing, CVCC additionally makes use of underutilized vehicular resources, which can make rich applications and services possible even when neither cloud nor roadside infrastructure is available. CVCC is popular today; however, the security and privacy issues are still the main obstacle in CVCC for its full implementation. To deal with security and privacy challenges, advanced cryptographic primitives have become indispensable parts of CVCC. Among them, the pairing-based cryptographic primitive is of particular interest. However, the pairing computation is usually time-consuming. Therefore, it is beneficial for vehicles to outsource pairing computations especially when the underlying pairing computations are massively needed. In this article, we devote our attention toward the security challenges and efficiency requirements of outsourcing pairing from vehicles to vehicles in CVCC. Specifically, we give a pairing outsourcing protocol for illustration, which indicates that secure and effective pairing outsourcing in CVCC is possible. Furthermore, we also identify some future research directions in secure CVCC.
UAV-Empowered Edge Computing Environment for Cyber-Threat Detection in Smart Vehicles IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Sahil Garg; Amritpal Singh; Shalini Batra; Neeraj Kumar; Laurence T. Yang
Over the last few years, we have witnessed an exponential increase in the computing and storage capabilities of smart devices that has led to the popularity of an emerging technology called edge computing. Compared to the traditional cloud-computing- based infrastructure, computing and storage facilities are available near end users in edge computing. Moreover, with the widespread popularity of unmanned aerial vehicles (UAVs), huge amounts of information will be shared between edge devices and UAVs in the coming years. In this scenario, traffic surveillance using UAVs and edge computing devices is expected to become an integral part of the next generation intelligent transportation systems. However, surveillance in ITS requires uninterrupted data sharing, cooperative decision making, and stabilized network formation. Edge computing supports data processing and analysis closer to the deployed machines (i.e., the sources of the data). Instead of simply storing data and missing the opportunity to capitalize on it, edge devices can analyze data to gain insights before acting on them. Transferring data from the vehicle to the edge for real-time analysis can be facilitated by the use of UAVs, which can act as intermediate aerial nodes between the vehicles and edge nodes. However, as the communication between UAVs and edge devices is generally done using an open channel, there is a high risk of information leakage in this environment. Keeping our focus on all these issues, in this article, we propose a data-driven transportation optimization model where cyber-threat detection in smart vehicles is done using a probabilistic data structure (PDS)- based approach. A triple Bloom filter PDS- based scheduling technique for load balancing is initially used to host the real-time data coming from different vehicles, and then to distribute/collect the data to/from edges in a manner that minimizes the computational effort. The results obtained show that the proposed system requires comparatively less computational time and storage for load sharing, authentication, encryption, and decryption of data in the considered edge-computing-based smart transportation framework.
Secure and Efficient Privacy-Preserving Ciphertext Retrieval in Connected Vehicular Cloud Computing IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Kai Fan; Xin Wang; Katsuya Suto; Hui Li; Yintang Yang
As vehicular equipment is becoming more and more intelligent, the vehicular information service, as the main means of capturing information, has been far from able to meet the needs of occupants [1, 2]. Cloud computing, with its powerful computing and storage capabilities, convenient network access, energy saving and excellent scalability, reliability, availability, and other advantages, can be an effective solution to the limitations of existing automotive information services. Connected vehicular cloud computing, which combines cloud computing and VANETs, has the characteristics of both a cloud platform and a mobile ad hoc network, including autonomy and no fixed structure, good scalability, and so on. However, during the information retrieval, high-density node distribution and high-speed mobile nodes may directly affect the information transmission capacity of a VANET by information tampering, transmission delay, and other issues. In this article, we propose a ciphertext-based search system that exploits RSUs as super peers for connected vehicular cloud computing. The proposed system supports ciphertext retrieval for related documents. In the proposed system, all the computations and retrieval operations are handled by super stationary peers, while documents are stored in the cloud to achieve high efficiency and security of the index structure. We can also reduce the impact of vehicle dynamics on the information retrieval process in this way. In our system, the indexing efficiency is also improved by utilizing a hybrid indexing structure in which binary trees are nested in a B+ tree. Through security analysis and performance evaluation, we demonstrate that our proposal can achieve acceptable security and efficiency.
Connected Vehicles' Security from the Perspective of the In-Vehicle Network IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Xiangxue Li; Yu Yu; Guannan Sun; Kefei Chen
Connected vehicles are generally equipped with many (dozens of, or even hundreds of) electronic and intelligent devices so that drivers can gain a more comfortable driving experience. Despite their numerous benefits, these technological developments have also created serious safety/security concerns. This article recapitulates the diversified attack surfaces of connected vehicles related to the technological developments from the perspective of the in-vehicle network. For each of the attacks, we discuss the rationale and the concrete methods presented in the literature. In particular, we illustrate how to launch successful attacks through the controlled area network (CAN) bus, electronic control units (ECUs), and in-vehicle infotainment system. The article also suggests some feasible solutions to the attacks demonstrated by the community. Considering the fact that vehicles are safety- critical, more practical and effective steps should be taken within the connected vehicle network toward securing connected vehicles and protecting drivers and passengers. Introduction
SVCC-HSR: Providing Secure Vehicular Cloud Computing for Intelligent High-Speed Rail IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Ping Dong; Tao Zheng; Xiaojiang Du; Hongke Zhang; Mohsen Guizani
VCC can bring many benefits to intelligent transportation systems. Meanwhile, HSR, an increasingly efficient means of transportation, faces several challenges in terms of high-frequency handover at speeds over 300 km/h. This includes large volumes of data of different types and different degrees of importance. Therefore, a secure and comprehensive cloud computing solution is attractive to improve the safety and efficiency of intelligent HSR. In this article, we present a novel and practical SVCC-HSR based on our long-term research and practice in this field. SVCC-HSR not only considers the various technical features of vehicular cloud computing, but also addresses several special demands in the HSR context. We perform extensive experiments using various scenarios, including frequent handover scenarios in high-speed trains running at 300 km/h with large-volume data transmission scenarios in locomotive depots. The real-world experimental results demonstrate that SVCC-HSR achieves better performance in fast authentication, hierarchical attribute-based data encryption, and transmission efficiency compared to its counterparts.
Collaborative Security in Vehicular Cloud Computing: A Game Theoretic View IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Abdulrahman Alamer; Yong Deng; Guiyi Wei; Xiaodong Lin
Connected vehicular cloud computing (CVCC) is a promising paradigm that utilizes the rich resources of connected cars. However, it also introduces new cyber-attack surfaces that may compromise the security or privacy of the vehicles. In reality, security in vehicular cloud computing lies in the willingness of vehicle owners who are concerned with their vehicles' protection from various threats. Increasing the participation of vehicles will improve the security in vehicular cloud computing as a whole. Therefore, for a CVCC service provider, it is very critical to encourage vehicle owners to invest in their own security for achieving deeper security of CVCC systems. In this article, we first present a CVCC architecture and its applications. Then we study several security issues in vehicular cloud computing. Afterward, we model a CVCC network by a two-phase heterogeneous public good game, and then investigate the influence of different incentive mechanisms and the structure of a complex network describing the vehicles' connectivity on the vehicles' investment rate. Finally, we present our conclusion.
Blockchain-Enabled Security in Electric Vehicles Cloud and Edge Computing IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Hong Liu; Yan Zhang; Tao Yang
EVCE computing is an attractive network paradigm involving seamless connections among heterogeneous vehicular contexts. It will be a trend along with EVs becoming popular in V2X. The EVs act as potential resource infrastructures referring to both information and energy interactions, and there are serious security challenges for such hybrid cloud and edge computing. Context-aware vehicular applications are identified according to the perspectives of information and energy interactions. Blockchain-inspired data coins and energy coins are proposed based on distributed consensus, in which data contribution frequency and energy contribution amount are applied to achieve the proof of work. Security solutions are presented for securing vehicular interactions in EVCE computing.
Secrecy-Driven Resource Management for Vehicular Computation Offloading Networks IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Yuan Wu; Li Ping Qian; Haowei Mao; Xiaowei Yang; Haibo Zhou; Xiaoqi Tan; Danny H. K. Tsang
The growing developments in vehicular networks and vehicular Internet services have yielded a variety of computation-intensive applications, resulting in great pressure on vehicles equipped with limited computation resources. The cloud/ edge-based service, which enables in-motion vehicles to actively offload computation tasks to cloud/ edge servers, has provided a promising approach to address the intensive computation burden. However, due to the possibility of disclosing private data, offloading computation tasks suffers from potential eavesdropping attacks. In this article, we focus on the eavesdropping attack when vehicular users (VUs) deliver computation tasks to cloud/edge servers over radio frequency channels. We take the tool of physical layer security and investigate resource management for secrecy provisioning when the VUs offload computation tasks. We then discuss three promising technologies, including non-orthogonal multiple access, multi-access assisted computation offloading, and mobility- and delay-aware offloading, which facilitate the enhancement of secrecy against the eavesdropping attack. Finally, as a detailed example of the multi-access assisted computation offloading, we present a case study on the optimal dual-connectivity- assisted computation task offloading with secrecy provisioning and show the performance of the proposed computation offloading.
On the Fundamental Characteristics of Ultra-Dense Small Cell Networks IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Ming Ding; David Lopez-Perez; Holger Claussen; Mohamed Ali Kaafar
In order to cope with the forecasted 1000 increase in wireless capacity demands by 2030, network operators will aggressively densify their network infrastructure to reuse the spectrum as much as possible. However, it is important to realize that these new ultra-dense small cell networks are fundamentally different from the traditional macrocell or sparse small cell networks, and thus UDNs cannot be deployed and operated in the same way as in the last 25 years. In this article, we systematically investigate and visualize the performance impacts of several fundamental characteristics of UDNs that mobile operators should consider when deploying UDNs. Moreover, we also provide new deployment and management guidelines to address the main challenges brought by UDNs in the future.
ETC-IoT: Edge-Node-Assisted Transmitting for the Cloud-Centric Internet of Things IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Wei Zhao; Jiajia Liu; Hongzhi Guo; Takahiro Hara
The promising emergence of cloud systems can bring significant benefits to cloud-centric IoT applications in terms of both their platform management and improvements to quality of service. Edge or fog computing, where edge nodes mainly contribute their computing resources voluntarily, has been attracting great attention for their ability to overcome scalability problems and to alleviate the load of cloud computing servers in the traditional cloud-centric IoT architecture. However, since high-data-rate devices have become ubiquitous in IoT and huge amounts of data need to be transmitted to end users, providing such a platform by renting a service from commercial clouds is costly and inefficient. Thus, this article describes a novel approach for edge-node-assisted data transmission in the cloud-centric IoT architecture (ETC-IoT) to overcome the problem of overwhelming bandwidth consumption in the cloud. It explores the bandwidth resources of edge nodes by extending the existing paradigm of edge computing in the cloud-centric IoT architecture. Specifically, the central cloud sends the replicas of IoT data to edge nodes according to designed replica distribution strategies. Then an edge node owning the replicas can respond to multiple requests from its associated local end users by using its bandwidth resources, while the central cloud supplements any shortfall to satisfy end-user demands if edge nodes cannot accommodate requests.
Exploiting Context-Aware Capabilities over the Internet of Things for Industry 4.0 Applications IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Igor Bisio; Chiara Garibotto; Aldo Grattarola; Fabio Lavagetto; Andrea Sciarrone
This article surveys the concept of Industry 4.0 (I4.0), which has become more and more pervasive in recent years thanks to the great effort that factories, researchers, and organizations are putting into its definition and development. We present the IoT as the key I4.0 technology since it enables faster and more efficient production and management processes, leveraging the flexibility of smart, ubiquitous, connected devices. In particular, we discuss the role of the I4.0 revolution in driving the diffusion of smart products and services, by focusing on ambient intelligence and context awareness in IoT and on the so-called DIKW hierarchy. Finally, to demonstrate the practical impact of this emerging framework, we show, as practical examples, three typical I4.0 applications in the smart factory, smart home, and smart health scenarios.
Analyzing and Detecting Money-Laundering Accounts in Online Social Networks IEEE Netw. (IF 7.197) 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 percen
Challenges and Solutions in Fog Computing Orchestration IEEE Netw. (IF 7.197) 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.
Drone Assisted Vehicular Networks: Architecture, Challenges and Opportunities IEEE Netw. (IF 7.197) 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.
Configurations and Diagnosis for Ultra-Dense Heterogeneous Networks: From Empirical Measurements to Technical Solutions IEEE Netw. (IF 7.197) Pub Date : 2018-06-04 Wei Wang; Lin Yang; Qian Zhang; Tao Jiang
The intense demands for higher data rates and ubiquitous network coverage have raised the stakes on developing new network topologies and architectures to meet these ever-increasing demands in a cost-effective manner. The telecommunication industry and international standardization bodies have placed considerable attention on the deployment of ultra-dense heterogeneous small-scale cells over existing cellular systems. Those small-scale cells, although they provide higher data rates and better indoor coverage by reducing the distance between BSs and end users, have raised severe configuration concerns. As the deployments are becoming irregular and flexible, inappropriate configurations occur frequently and undermine the network reliability and service quality. We envision that the fine-grained characterization of user traffic is a key pillar to diagnosing inappropriate configurations. In this article, we investigate the fine-grained traffic patterns of mobile users by analyzing the network data containing millions of subscribers and covering thousands of cells in a large metropolitan area. We characterize traffic patterns and mobility behaviors of users and geospatial properties of cells, and discuss how the heterogeneity of these characteristics affects network configurations and diagnosis in future ultra-dense small cells. Based on these observations from our measurements, we investigate possible models and corresponding challenges, and propose a heterogeneity-aware scheme that takes into account the disparity of user mobility behaviors and geospatial properties among small cells.
Mobile Social Big Data: WeChat Moments Dataset, Network Applications, and Opportunities IEEE Netw. (IF 7.197) Pub Date : 2018-03-14 Yuanxing Zhang; Zhuqi Li; Chengliang Gao; Kaigui Bian; Lingyang Song; Shaoling Dong; Xiaoming Li
In parallel with the increase of various mobile technologies, the MSN service has brought us into an era of mobile social big data, where people are creating new social data every second and everywhere. It is of vital importance for businesses, governments, and institutions to understand how peoples' behaviors in the online cyberspace can affect the underlying computer network, or their offline behaviors at large. To study this problem, we collect a dataset from WeChat Moments, called WeChatNet, which involves 25,133,330 WeChat users with 246,369,415 records of link reposting on their pages. We revisit three network applications based on the data analytics over WeChatNet, i.e., the information dissemination in mobile cellular networks, the network traffic prediction in backbone networks, and the mobile population distribution projection. We also discuss the potential research opportunities for developing new applications using the released dataset.
Scalable Spectrum Access System for Massive Machine Type Communication IEEE Netw. (IF 7.197) 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.
Data Security and Privacy in Fog Computing IEEE Netw. (IF 7.197) Pub Date : 2018-03-13 Yunguo Guan; Jun Shao; Guiyi Wei; Mande Xie
Cloud computing is now a popular computing paradigm that can provide end users access to configurable resources on any device, from anywhere, at any time. During the past years, cloud computing has been developed dramatically. However, with the development of the Internet of Things, the disadvantages (such as high latency) of cloud computing are gradually revealed due to the long distance between the cloud and end users. Fog computing is proposed to solve this problem by extending the cloud to the edge of the network. In particular, fog computing introduces an intermediate layer called fog that is designed to process the communication data between the cloud and end users. Hence, fog computing is usually considered as an extension of cloud computing. In this article, we discuss the design issues for data security and privacy in fog computing. Specially, we present the unique data security and privacy design challenges presented by the fog layer and highlight the reasons why the data protection techniques in cloud computing cannot be directly applied in fog computing.
Enabling Collaborative Edge Computing for Software Defined Vehicular Networks IEEE Netw. (IF 7.197) Pub Date : 2018-03-13 Kai Wang; Hao Yin; Wei Quan; Geyong Min
Edge computing has great potential to address the challenges in mobile vehicular networks by transferring partial storage and computing functions to network edges. However, it is still a challenge to efficiently utilize heterogeneous edge computing architectures and deploy large-scale IoV systems. In this article, we focus on the collaborations among different edge computing anchors and propose a novel collaborative vehicular edge computing framework, called CVEC. Specifically, CVEC can support more scalable vehicular services and applications by both horizontal and vertical collaborations. Furthermore, we discuss the architecture, principle, mechanisms, special cases, and potential technical enablers to support the CVEC. Finally, we present some research challenges as well as future research directions.
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.
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.
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.
Measuring Instability of Mobility Management in Cellular Networks IEEE Netw. (IF 7.197) Pub Date : 2018-05-17 Xiaohui Zhao; Hanyang Ma; Yuan Jin; Jianguo Yao
Communication in cellular networks is based on serving cells that provide the basic network service. In the real world, serving cells overlap which means the number of serving cells covering one position is usually more than one. Recently, the instability of mobility management in cellular networks has been studied to monitor and analyze the handoff process in mobile devices. However, the handoff process is actually produced by base stations instead of mobile devices. Hence, it is of great importance to measure the handoff process of mobility management from the base station side. In this article, we present a series of experiments performed using the data obtained by mobile network operators. The contributions of this study are three-fold. We reproduce a handoff process and handoff loop from both the mobile device level and the base station level, and confirm the existence of a handoff loop by measurements from the base station side. Through large-range measurements, we discover that only a small part of serving cells is involved in the handoff process, and in most cases, the number of candidate serving cells is much smaller than the number of cells that cover some position; namely, when a handoff loop occurs, the number of candidate serving cells is quite small, which is in contrast to our assumption. We confirm that the handoff loop often occurs in indoor conditions or when the mobile device has frequent communication with the base station. Finally, we present several comprehensive facts about the handoff process and handoff loop and provide suggestions that can be used to increase the quality of service of cellular networks.
Achieving Ultra-Reliable Low-Latency Communications: Challenges and Envisioned System Enhancements IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Guillermo Pocovi; Hamidreza Shariatmadari; Gilberto Berardinelli; Klaus Pedersen; Jens Steiner; Zexian Li
URLLC have the potential to enable a new range of applications and services: from wireless control and automation in industrial environments to self-driving vehicles. 5G wireless systems are faced by different challenges for supporting URLLC. Some of the challenges, particularly in the downlink direction, are related to the reliability requirements for both data and control channels, the need for accurate and flexible link adaptation, reducing the processing time of data retransmissions, and the multiplexing of URLLC with other services. This article considers these challenges and proposes state-of-the-art solutions covering different aspects of the radio interface. In addition, system-level simulation results are presented, showing how the proposed techniques can work in harmony in order to fulfill the ambitious latency and reliability requirements of upcoming URLLC applications.
Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Petar Popovski; Jimmy J. Nielsen; Cedomir Stefanovic; Elisabeth de Carvalho; Erik Strom; Kasper F. Trillingsgaard; Alexandru-Sabin Bana; Dong Min Kim; Radoslaw Kotaba; Jihong Park; Rene B. Sorensen
URLLC is an important new feature brought by 5G, with a potential to support a vast set of applications that rely on mission-critical links. In this article, we first discuss the principles for supporting URLLC from the perspective of the traditional assumptions and models applied in communication/information theory. We then discuss how these principles are applied in various elements of system design, such as use of various diversity sources, design of packets, and access protocols. The important message is that there is a need to optimize the transmission of signaling information, as well as a need for lean use of various sources of diversity.
5G Radio Network Design for Ultra-Reliable Low-Latency Communication IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Joachim Sachs; Gustav Wikstrom; Torsten Dudda; Robert Baldemair; Kittipong Kittichokechai
5G is currently being standardized and addresses, among other things, new URLLC services. These are characterized by the need to support reliable communication, where successful data transmission can be guaranteed within low latency bounds, like 1 ms, at a low failure rate. This article describes the functionality of both the NR and LTE radio interfaces to provide URLLC services. Achievable latency bounds are evaluated, and the expected spectral efficiency is demonstrated. It is shown that both NR and LTE can fulfill the ITU 5G requirements on URLLC; however, this comes at the cost of reduced spectral efficiency compared to mobile broadband services without latency or reliability constraints. Still, the impact on the overall network performance is expected to be moderate.
Packet Duplication for URLLC in 5G: Architectural Enhancements and Performance Analysis IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Jaya Rao; Sophie Vrzic
URLLC use cases demand a new paradigm in cellular networks to contend with the extreme requirements with complex trade-offs. In general, it is exceptionally challenging and, resource usage-wise, prohibitively expensive to satisfy the URLLC requirements using the existing approaches in LTE. To address these challenges 3GPP has recently agreed to adopt PD of both UP and CP packets as a fundamental technique in 5G NR. This article investigates the theoretic framework behind PD and provides a primer on the recent enhancements applied in the NR RAN architecture for supporting URLLC. It is shown that PD enables jointly satisfying the latency and reliability requirements without increasing the complexity in the RAN. With dynamic control capability, PD can be used not only for URLLC but also to increase the transmission robustness during mobility and against radio link failures. The article also provides numerical results comparing the performance of PD in various deployment scenarios. The numerical results reveal that in certain scenarios, performing PD over multiple links results in lower usage of radio resources than using a single highly reliable link. It is also found that to improve radio resource utilization while satisfying URLLC requirements, enabling PD in scenarios such as cell edge is crucial where the average SNR of the best (primary) link and the variation in SNR between all accessible links is typically low. In essence, the PD technique provides a cost-effective solution for satisfying the URLLC requirements without requiring major modifications to the RAN deployments.
Handover Mechanism in NR for Ultra-Reliable Low-Latency Communications IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Hyun-Seo Park; Yuro Lee; Tae-Joong Kim; Byung-Chul Kim; Jae-Yong Lee
For many URLLC services, mobility is a key requirement together with latency and reliability. 3GPP has defined the target of MIT as 0 ms, and a general URLLC reliability requirement as 1 - 10-5 within a latency of 1 ms for 5G. In this article, we analyzed the impact of MIT and handover failure (HOF) rate on the reliability performance. From the analysis, at 120 km/h, with MIT of 0 ms, the required HOF rate to achieve 1 - 10-5 reliability is only 0.52 percent. Therefore, to achieve the reliability for URLLC, we need to minimize not only the MIT but also the HOF rate as close to zero as possible. Hence, we propose conditional make-before- break handover to target zero MIT and zero HOF rate simultaneously. The solution can achieve zero MIT by not releasing the connection to the source cell until the first or some downlink receptions from the target cell. It can achieve the zero HOF rate, by receiving an HO Command message when the radio link to the source cell is still stable, and by executing the handover when the connection to the target cell is preferable. Simulation results show that our proposed solution can achieve almost zero HOF rate even at 120 km/h.
Zero-Zero Mobility: Intra-Frequency Handovers with Zero Interruption and Zero Failures IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Ingo Viering; Henrik Martikainen; Andreas Lobinger; Bernhard Wegmann
Today's intra-frequency hard handovers in LTE suffer from interruption even if they are successful as well as risk of failures. The next generation, New Radio, will introduce stricter requirements that cannot be fulfilled with the traditional hard handover concept. Namely, a handover with 0 ms interruption is mandated, and extreme reliability (ultra-reliable low-latency communication services) will not tolerate any mobility failures. Consequently, softer handover concepts where the UE is multi-connected to a source and one or more target cells are already under discussion. This article investigates such a method using the well-known dual connectivity principle and evaluates the performance in terms of robustness/ reliability and signaling costs.
Energy Efficiency and Delay in 5G Ultra-Reliable Low-Latency Communications System Architectures IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Amitav Mukherjee
Emerging 5G URLLC wireless systems are characterized by minimal over-the-air latency and stringent decoding error requirements. The low latency requirements can cause conflicts with 5G EE design targets. Therefore, this work provides a perspective on various trade-offs between energy efficiency and user plane delay for upcoming URLLC systems. For network infrastructure EE, we propose solutions that optimize base station on-off switching and distributed access network architectures. For URLLC devices, we advocate solutions that optimize EE of discontinuous reception (DRX), mobility measurements, and the handover process, respectively, without compromising on delay.
Relaying-Enabled Ultra-Reliable Low-Latency Communications in 5G IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Yulin Hu; M. Cenk Gursoy; Anke Schmeink
Supporting URLLC has become one of the major considerations in the design of 5G systems. In the literature, it has been shown that cooperative relaying is an efficient strategy to improve the reliability of transmissions, support higher rates, and lower latency. However, prior studies have demonstrated the performance advantages of relaying generally under the ideal assumption of communicating arbitrarily reliably at Shannon's channel capacity, which is not an accurate performance indicator for relaying in URLLC networks in which transmission is required to be completed within a strict time span and coding schemes with relatively short blocklengths need to be employed. In this article, we address the performance modeling and optimization of relaying-enabled URLLC networks. We first discuss the accurate performance modeling of relay-enabled 5G networks. In particular, we provide a comprehensive summary of the performance advantage of applying relaying in 5G URLLC transmissions in comparison to the case of direct transmission (without relaying). Both a noise-limited scenario and an interference- limited scenario are discussed. Then we present tools for performance optimization utilizing the knowledge of either perfect or average channel side information. Finally, we summarize the proposed optimization schemes and discuss potential future research directions.
Enabling Ultra-Reliable and Low-Latency Communications through Unlicensed Spectrum IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Gordon J. Sutton; Jie Zeng; Ren Ping Liu; Wei Ni; Diep N. Nguyen; Beeshanga A. Jayawickrama; Xiaojing Huang; Mehran Abolhasan; Zhang Zhang
In this article, we aim to address the question of how to exploit the unlicensed spectrum to achieve URLLC. Potential URLLC PHY mechanisms are reviewed and then compared via simulations to demonstrate their potential benefits to URLLC. Although a number of important PHY techniques help with URLLC, the PHY layer exhibits an intrinsic trade-off between latency and reliability, posed by limited and unstable wireless channels. We then explore MAC mechanisms and discuss multi-channel strategies for achieving low-latency LTE unlicensed band access. We demonstrate, via simulations, that the periods without access to the unlicensed band can be substantially reduced by maintaining channel access processes on multiple unlicensed channels, choosing the channels intelligently, and implementing RTS/CTS.
Toward Low-Latency and Ultra-Reliable Virtual Reality IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Mohammed S. Elbamby; Cristina Perfecto; Mehdi Bennis; Klaus Doppler
VR is expected to be one of the killer applications in 5G networks. However, many technical bottlenecks and challenges need to be overcome to facilitate its wide adoption. In particular, VR requirements in terms of high throughput, low latency, and reliable communication call for innovative solutions and fundamental research cutting across several disciplines. In view of the above, this article discusses the challenges and enablers for ultra-reliable and low-latency VR. Furthermore, in an interactive VR gaming arcade case study, we show that a smart network design that leverages the use of mmWave communication, edge computing, and proactive caching can achieve the future vision of VR over wireless.
Professional Live Audio Production: A Highly Synchronized Use Case for 5G URLLC Systems IEEE Netw. (IF 7.197) Pub Date : 2018-04-02 Jens Pilz; Bernd Holfeld; Axel Schmidt; Konstantin Septinus
The fifth generation of cellular mobile communication networks is on the horizon and aims to integrate new vertical markets. In this article, we discuss professional wireless audio systems used for live productions as a future use case for 5G. Wireless live audio productions require high communication reliability as well as ultra-low signal delay. Furthermore, these services demand strict synchronization of devices to function properly. The need for low latency and for precise time and phase synchronization goes beyond what is currently under discussion in the context of URLLC. We seize on this aspect, discuss how isochronous data transmission can be implemented and integrated into 5G networks, and show similarities with other 5G verticals such as industrial automation.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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