Next Generation Wireless Lans 802 11n And 802 11ac

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and next-generation IoT security threats.

Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking

To apply cyber-physical system (CPS) technique in industrial internet, a wireless technology that is able to robustly maintain hyperconnectivity between a data center and distributed user entities and/or industrial Internet of Things devices is required. We investigate the feasibility of utilizing massive multiple-input multiple-output (MIMO) as such a wireless technology. We analyze the performance of a massive MIMO base station deployed at a data center to provide massive connectivity to a large number of devices. In addition, we discuss related research challenges for deploying massive MIMO in industrial internet applications of CPS, such as device scheduling and power control, energy efficient design and radio frequency energy transfer/harvesting, signaling techniques for drones and mobile robots, and applications of underwater industrial internet.

Massive MIMO for Industrial Internet of Things in Cyber-Physical Systems

Fetal heart rate (FHR) is very significant to evaluate the status of fetus. However, based on traditional classification criteria is not accurate. With the rapid development of computer information technology, computer technology is vital for the analysis of FHR in electronic fetal monitoring (EFM). FHR is divided into three classes as: 1) normal; 2) suspicious; and 3) abnormal. Through the cooperation with the hospital, we got 4473 records, including 3012 normal, 1024 suspicious, 437 abnormal records by our EFM system. In order to improve the accuracy of fetal status assessment, high 1-D FHR records are divided into ten d-window segments, and then use convolutional neural network (CNN) to process the data in parallel. Finally, we use the voting method to determine the class of FHR records. We also made a comparative experiment, the feature extraction method based on basic statistics is used to extract the features of FHR. And then the features were applied as the input to support vector machine (SVM) and multilayer perceptron (MLP) to classify. According to the results of the experiment, the accuracy of classification of SVM, MLP, and CNN are 79.66%, 85.98%, and 93.24%, respectively.

Automatic Classification of Fetal Heart Rate Based on Convolutional Neural Network

Cyber-physical system (CPS), regarded as the next generation of engineered system, has the capability to interact with the real physical world. Applications of CPS span various fields such as medical monitoring, traffic control, and smart grid. With such widespread applications, privacy assurance is becoming more and more important since what the CPS connects are people and the real world. Any leakage of private information will cause serious consequences. In this paper, we focus on enhancing the secrecy of wireless communications in CPS by use of physical layer security techniques. Specifically, we study an amplify and forward (AF) relay network where all devices are equipped with a single antenna. We propose a privacy-enhanced waveform design approach aided by artificial noise (AN) to enhance the communication secrecy in a wireless environment with multipath receptions. First, we consider the case with perfect eavesdropper’s channel state information (CSI). We optimize the AF coefficient for forwarding the information-bearing signal and the AN covariance to maximize the achievable secrecy rate. The optimal solution is obtained by solving a series of semidefinite programs. Then, a more practical scenario with imperfect eavesdropper’s CSI is studied. We develop a robust waveform design method and obtain the lower bound of the achievable secrecy rate. Numerical results are presented to show the effectiveness of our proposed algorithms.

Security-Aware Waveforms for Enhancing Wireless Communications Privacy in Cyber-Physical Systems via Multipath Receptions

The prosperity of e-health is boosted by fast development of medical devices with wireless communications capability such as wearable devices, tiny sensors, monitoring equipments, etc., which are randomly distributed in clinic environments. The drastically-increasing population of such devices imposes new challenges on the limited wireless resources. To relieve this problem, key knowledge needs to be extracted from massive connection attempts dispersed in the air towards efficient access control. In this paper, a hybrid periodic-random massive access (HPRMA) scheme for wireless clinical networks employing ultra-narrow band (UNB) techniques is proposed. In particular, the proposed scheme towards accommodating a large population of devices include the following new features. On one hand, it can dynamically adjust the resource allocated for coexisting periodic and random services based on the traffic load learned from signal collision status. On the other hand, the resource allocation within periodic services is thoroughly designed to simultaneously align with the timing requests of differentiated services. Abundant simulation results are also presented to demonstrate the superiority of the proposed HPRMA scheme over baseline schemes including time-division multiple access (TDMA) and random access approach, in terms of channel utilization efficiency, packet drop ratio, etc., for the support of massive devices' services.

Massive Access Control Aided by Knowledge-Extraction for Co-Existing Periodic and Random Services over Wireless Clinical Networks

We propose a robust wireless relay scheme in narrow-band communications for IoT access, which matches the typical features of IoT often carrying relatively low data rate with limited bandwidth. This framework is towards offering robustness in QoS guarantees with emphases on security and/or reliability, and we use the security-assured network as the typical scenario. In particular, we consider a dual-hop relay network including a transmitter, a receiver, an amplify-and-forward (AF) untrusted relay, and a jamming node. The jamming node is treated as a helper. Specifically, the jammer broadcasts artificial noise (AN), which in fact pollutes both the untrusted relay and the destination node’s signals. However, we show that such AN can be effectively mitigated after the destination node obtains the forwarded signal from the relay, while the untrusted relay node cannot do so. The core idea for robustness assurance is to exploit higher signal dimensions at the receiver over the untrusted relay node. Simulations and analyses are also conducted to demonstrate that our proposed scheme can make the performance at the untrusted relay an interference-limited manner while completely removing the interferences at the receiver, therefore corroborating our claim in robustness in terms of security and reliability.

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Xuelin Zhang

Papers

Massive Access Control Aided by Knowledge-Extraction for Co-Existing Periodic and Random Services over Wireless Clinical Networks

Robust Relay in Narrow-Band Communications for Ubiquitous IoT Access