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作者： Kishore, Rajalekshmi Gurugopinath, Sanjeev Sofotasios, Paschalis C. Muhaidat, Sami Al-Dhahir, Naofal Department of Electrical and Computer Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Goa403726 India Department of Electronics and Communication Engineering PES University Bengaluru560085 India Department of Electrical and Computer Engineering Center for Cyber-Physical Systems Khalifa University Abu Dhabi127788 United Arab Emirates Department of Electronics and Communications Engineering Tampere University of Technology Tampere33101 Finland Department of Electrical Engineering University of Texas at Dallas DallasTX75080 United States

In the present contribution, we propose a novel opportunistic ambient backscatter communication (ABC) framework for radio frequency (RF)-powered cognitive radio (CR) networks. This framework considers opportunistic spectrum sensing (SS) integrated with ABC and harvest-then-transmit (HTT) operation strategies. Novel analytic expressions are derived for the average throughput, the average energy consumption and the energy efficiency (EE) in the considered set up. These expressions are represented in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. In addition, we formulate an optimization problem to maximize the EE of the CR system operating in mixed ABC - and HTT - modes, for a given set of constraints, including primary interference and imperfect SS constraints. Capitalizing on this, we determine the optimal set of parameters which in turn comprise the optimal detection threshold, the optimal degree of trade-off between the CR system operating in the ABC - and HTT - modes and the optimal data transmission time. Extensive results from respective computer simulations are also presented for corroborating the corresponding analytic results and to demonstrate the performance gain of the proposed model in terms of EE. © 2015 IEEE.

关键词： Energy efficiency

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Cooperative Game Model of Delegation Computing: Verifier Separated from Calculators

Cooperative Game Model of Delegation Computing: Verifier Sep...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Duo Zhang Youliang Tian Linjie Wang Sheng Gao Jianfeng Ma State Key Laboratory of Public Big Data College of Computer Science and Technology College of Mathematics and Statistics Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Cryptography and Date Security Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Information Central University of Finance and Economics Beijing China School of Cyber Engineering College of Computer Science and Technology Xi'dian University Xi'an China

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

In cloud computing, the delegation computation service is required because end users usually are resource-constrained, and at the same time the correctness of computing results needs to be verified. However, all the existing and available technologies have a high cost to verify which is the main problem faced by the delegation computing under the cloud platform. In order to address this issue, based on the game theory and a smart contract, we construct three protocols. The client and the calculator sign the Prisoner's Protocol to incentivize correct computation by asking the calculator to pay a deposit upfront; The client and the verifier sign the Long-acting Mechanism Protocol to ensure the validity of the verification results; The calculator and the verifier sign the Collusion Protocol to make collusion the most profitable strategy for all colluding parties. Through the combination of these protocols, the work is realized by separation the computing task and the verification task, and the heavy verification task is avoided. Finally, the performance analysis of the results shows that the combination of all the protocols not only solves the problem of verification complexity in the traditional delegation computing, but also guarantees the benefit of the honest player.

关键词： Calculators Contracts Protocols Task analysis Computational modeling Cloud computing security

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UAV Communication Channel Measurement, Modeling, and Application

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Journal of Communications and Information Networks 2019年第4期4卷 32-43页

作者： Xiang Cheng Yiran Li Lin Bai State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Shenzhen Research Institute of Big Data Shenzhen 518000China School of Cyber Science and Technology Beihang UniversityBeijing 100191China

As unmanned aerial vehicles(UAV)deploy-ment is shifting from military to civilian sectors,and in particular as a means of facilitating more flexible and versatile wireless communications,the UAV channels that were previously not well understood are attracting increasing research and *** the key component facilitating the UAV-aided communications,UAV channel characteristics and modeling are of critical importance to the designed UAV system to achieve satisfactory *** this article,we will provide a comprehensive overview and the future perspective of envisioned UAV communication system benefits,the existing and needed UAV channel measurements and modeling approaches,together with the new viewpoint of UAV channel applications in different communication and networking *** existing work and future directions are extensively covered.

关键词： UAV channel measurements modeling application future challenges

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Classification of radio signals using truncated Gaussian discriminant analysis of convolutional neural network-derived features

arXiv

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arXiv 2020年

作者： Persons, Jeffrey B. Wong, L.J. Headley, W.C. Fowler, M.C. Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Intel Corporation San JoseCA United States Hume Center for National Security and Technology Virginia Tech United States

To improve the utility and scalability of distributed radio frequency (RF) sensor and communication networks, reduce the need for convolutional neural network (CNN) retraining, and efficiently share learned information about signals, we examined a supervised bootstrapping approach for RF modulation classification. We show that CNN-bootstrapped features of new and existing modulation classes can be considered as mixtures of truncated Gaussian distributions, allowing for maximum-likelihood-based classification of new classes without retraining the network. In this work, the authors observed classification performance using maximum likelihood estimation of CNN-bootstrapped features to be comparable to that of a CNN trained on all classes, even for those classes on which the bootstrapping CNN was not trained. This performance was achieved while reducing the number of parameters needed for new class definition from over 8 million to only 200. Furthermore, some physical features of interest, not directly labeled during training, e.g. signal-to-noise ratio (SNR), can be learned or estimated from these same CNN-derived features. Finally, we show that SNR estimation accuracy is highest when classification accuracy is lowest and therefore can be used to calibrate a confidence in the classification. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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Cost-efficient and skew-aware data scheduling for incremental learning in 5G network

arXiv

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arXiv 2020年

作者： Pu, Lingjun Yuan, Xinjing Xu, Xiaohang Chen, Xu Zhou, Pan Xu, Jingdong The College of Computer Science Nankai University Tianjin300071 China The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710126 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Pazhou Lab. Guangzhou510335 China The Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recognized the power of machine learning (ML) and started to explore its potential to facilitate those network functions. Nevertheless, the current ML models for network functions are often derived in an offline manner, which is inefficient due to the excessive overhead for transmitting a huge volume of dataset to remote ML training clouds and failing to provide the incremental learning capability for the continuous model updating. As an alternative solution, we propose Cocktail, an incremental learning framework within a reference 5G network architecture. To achieve cost efficiency while increasing trained model accuracy, an efficient online data scheduling policy is essential. To this end, we formulate an online data scheduling problem to optimize the framework cost while alleviating the data skew issue caused by the capacity heterogeneity of training workers from the long-term perspective. We exploit the stochastic gradient descent to devise an online asymptotically optimal algorithm, including two optimal policies based on novel graph constructions for skew-aware data collection and data training. Small-scale testbed and large-scale simulations validate the superior performance of our proposed framework. © 2020, CC BY.

关键词： 5G mobile communication systems

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Facilitation of cybersecurity learning through real-world hands-on labs 126

Facilitation of cybersecurity learning through real-world ha...

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126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

作者： Rahouti, Mohamed Xiong, Kaiqi Department of Electrical Engineering University of South Florida Tampa33620 United States Cyber Florida University of South Florida Tampa33620 United States Department of Mathematics and Statistics University of South Florida Tampa33620 United States Intelligent Computer Networking and Security Lab University of South Florida Tampa33620 United States

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Second-Order Asymptotically Optimal Outlier Hypothesis Testing

arXiv

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arXiv 2020年

作者： Zhou, Lin Wei, Yun Hero, Alfred School of Cyber Science and Technology Beihang University Beijing100083 China Department of Statistical Science Duke University DurhamNC27708 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

We revisit the outlier hypothesis testing framework of Li et al. (TIT 2014) and derive fundamental limits for the optimal test under the generalized Neyman-Pearson criterion. In outlier hypothesis testing, one is given multiple observed sequences, where most sequences are generated i.i.d. from a nominal distribution. The task is to discern the set of outlying sequences that are generated from anomalous distributions. The nominal and anomalous distributions are unknown. We study the tradeoff among the probabilities of misclassification error, false alarm and false reject for tests that satisfy weak conditions on the rate of decrease of these error probabilities as a function of sequence length. Specifically, we propose a threshold-based test that ensures exponential decay of misclassification error and false alarm probabilities. We study two constraints on the false reject probability, with one constraint being that it is a non-vanishing constant and the other being that it has an exponential decay rate. For both cases, we characterize bounds on the false reject probability, as a function of the threshold, for each pair of nominal and anomalous distributions and demonstrate the optimality of our test under the generalized Neyman-Pearson criterion. We first consider the case of at most one outlying sequence and then generalize our results to the case of multiple outlying sequences where the number of outlying sequences is unknown and each outlying sequence can follow a different anomalous distribution. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistics

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Understanding global environment for network innovations (GENI) and software-defined networking (SDN) for computer networking and security education 126

Understanding global environment for network innovations (GE...

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126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

Hands-on modules and experiments are considered essential and fundamental components in cyber security curriculums. However, because of the significant increase in student enrollments in both online and face-to-face courses, universities face various challenges mainly related to financial support and computing resources that could be limited. To overcome such key challenges, universities have been considering alternative solutions to solve resource issues and allow students to practice real-world lab experiments through a virtual environment such as the Global Environment for Network Innovations (GENI). GENI is a real-world, repeatable, programmable, at-scale, virtual infrastructure for experiments in a variety of computer science areas such as networking, security, and distributed computing sponsored by National Science Foundation (NSF). Furthermore, Software-Defined Networking (SDN) has been a core technology in cloud computing and other cyber-physical systems where SDN facilitates network management and enables network programmability and efficient network configuration to improve network performance, monitoring, and security. In this paper, we will demonstrate our great efforts in the development of GENI and SDN learning and experimental modules for computer networking and security courses in order to achieve the goal of our funded NSF project. Specifically, we will first present our methodology for the design of our modules and then give the detail of GENI and SDN modules including GENI account setup and resource reservation, measurement tool labs, as well as SDN labs for network traffic management and the detection and mitigation of several well-known security attacks, such as Denial of Service Attacks (DoS), Distributed Denial of Service Attacks (DDoS), phishing attacks, and Domain Generation Algorithm (DGA) malware detection. Those learning and experimental modules have been developed at different levels to meet the need of different types of students. Final

关键词： Software defined networking

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An IoT and blockchain-based multi-sensory in-home quality of life framework for cancer patients 15

An IoT and blockchain-based multi-sensory in-home quality of...

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15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019

作者： Rahman, Md. Abdur Rashid, Mamunur Barnes, Stuart Shamim Hossain, M. Hassanain, Elham Guizani, Mohsen Cyber Security and Forensic Computing Department University of Prince Mugrin Madinah Saudi Arabia Research Centre King's College London United Kingdom Department of Software Engineering King Saud University Riyadh11543 Saudi Arabia Dept. of Electrical and Computer Engineering University of Idaho MoscowID83844-1023 United States Dept. of Computer Science Umm Al-Qura University Makkah Saudi Arabia

ISBN: (纸本)9781538677476

Once a subject is diagnosed with cancer, a patient goes through a series of diagnosis and tests, referred to as after cancer treatment. Due to the nature of the treatment and side effects on regular lifestyles, maintaining quality of life in the home environment is a challenging task. Sometimes within a home environment, a cancer patient's situation changes abruptly, as the functionality of certain organs deteriorate, which affects their quality of life. In this paper, we propose a Blockchain and off-chain based framework which will allow multiple medical and ambient intelligent IoT sensors to capture quality of life information from one's home environment and securely share it with one's community of interest. Using our proposed framework, both transactional records and multimedia big data - consisting of a user's physiological as well as mental states - can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed Blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. We further designed a generic oncologist smart contract and digital wallet for different stakeholders to automate the treatment plan of a particular patient. Finally, we will present our current implementation status, which provides significant encouragement for further development. © 2019 IEEE.

关键词： Blockchain

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LOFFS: A Low-Overhead File System for Large Flash Memory on Embedded Devices

LOFFS: A Low-Overhead File System for Large Flash Memory on ...

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Design Automation Conference

作者： Runyu Zhang Duo Liu Xianzhang Chen Xiongxiong She Chaoshu Yang Yujuan Tan Zhaoyan Shen Zili Shao College of Computer Science Chongqing University Chongqing China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University College of Computer Science Shandong University Qingdao China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Emerging applications like machine learning in embedded devices (e.g., satellite and vehicles) require huge storage space, which recently stimulates the widespread deployment of large-capacity flash memory in IoT devices. However, existing embedded file systems fall short in managing large-capacity storage efficiently for excessive memory consumption and poor booting performance. In this paper, we propose a novel embedded file system, LOFFS, to tackle the above issues and manage large-capacity NAND flash on resource-limited embedded devices. We redesign the space management mechanisms and construct hybrid file structures to achieve high performance with minimum resource occupation. We have implemented LOFFS in Linux, and the experimental results show that LOFFS outperforms YAFFS by 55.8% on average with orders of magnitude reductions on memory footprint.

关键词： Metadata Indexes Performance evaluation Hardware Booting File systems Flash memories

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