Physical layer security (PLS) has emerged as a new concept and powerful alternative that can complement and may even replace encryption-based approaches, which entail many hurdles and practical problems for future wireless systems. The basic idea of PLS is to exploit the characteristics of the wireless channel and its impairments including noise, fading, interference, dispersion, diversity, etc. in order to ensure the ability of the intended user to successfully perform data decoding while preventing eavesdroppers from doing so. Thus, the main design goal of PLS is to increase the performance difference between the link of the legitimate receiver and that of the eavesdropper by using well-designed transmission schemes. In this survey, we propose a conceptual, generic, and expandable framework for classifying the existing PLS techniques against wireless passive eavesdropping. In this flexible framework, the security techniques that we comprehensively review in this treatise are divided into two primary approaches: signal-to-interference-plus-noise ratio-based approach and complexity-based approach. The first approach is classified into three major categories: first, secrecy channel codes-based schemes; second, security techniques based on channel adaptation; third, schemes based on injecting interfering artificial (noise/jamming) signals along with the transmitted information signals. The second approach (complexity-based), which is associated with the mechanisms of extracting secret sequences from the shared channel, is classified into two main categories based on which layer the secret sequence obtained by channel quantization is applied on. The techniques belonging to each one of these categories are divided and classified into three main signal domains: time, frequency and space. For each one of these domains, several examples are given and illustrated along with the review of the state-of-the-art security advances in each domain. Moreover, the advantages and disadvantages of each approach alongside the lessons learned from existing research works are stated and discussed. The recent applications of PLS techniques to different emerging communication systems such as visible light communication, body area network, power line communication, Internet of Things, smart grid, mm-Wave, cognitive radio, vehicular ad-hoc network, unmanned aerial vehicle, ultra-wideband, device-to-device, radio-frequency identification, index modulation, and 5G non-orthogonal multiple access based-systems, are also reviewed and discussed. The paper is concluded with recommendations and future research directions for designing robust, efficient and strong security methods for current and future wireless systems.

Classifications and Applications of Physical Layer Security Techniques for Confidentiality: A Comprehensive Survey

The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey toward the fifth generation of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of multi-access edge computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC’s ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultralow latency and high quality of service due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

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Survey on Multi-Access Edge Computing for Internet of Things Realization

Key generation from the randomness of wireless channels is a promising alternative to public key cryptography for the establishment of cryptographic keys between any two users. This paper reviews the current techniques for wireless key generation. The principles, performance metrics and key generation procedure are comprehensively surveyed. Methods for optimizing the performance of key generation are also discussed. Key generation applications in various environments are then introduced along with the challenges of applying the approach in each scenario. The paper concludes with some suggestions for future studies.

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Key Generation From Wireless Channels: A Review

Physical layer key generation that exploits reciprocity and randomness of wireless fading channels has attracted considerable research attention in recent years. Although theoretical study has shown its potential to generate information- theoretic secure keys, great challenges remain when transforming the theory into practice. This article provides an overview of the physical layer key generation process and discusses its practical challenges. Different passive and active attacks are analyzed and evaluated through numerical study. A new key generation scheme using random probing signals, and combining user generated randomness and channel randomness, is introduced as a countermeasure against active attacks. The numerical results show that the proposed scheme achieves higher security strength than existing schemes using constant probing signals under active attacks. Future research topics on physical layer key generation are discussed.

Physical layer key generation in wireless networks: challenges and opportunities

Driver behavior monitoring system as Intelligent Transportation Systems (ITS) have been widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring the driver behavior are rely on computer vision techniques. Such methods suffer from violation of privacy and the possibility of spoofing. This paper presents a novel yet efficient deep learning method for analyzing the driver behavior. We have used the driving signals, including acceleration, gravity, throttle, speed, and Revolutions Per Minute (RPM) to recognize five types of driving styles, including normal, aggressive, distracted, drowsy, and drunk driving. To take the advantages of successful deep neural networks on images, we learn a 2D Convolutional Neural Network (CNN) on images constructed from driving signals based on recurrence plot technique. Experimental results confirm that the proposed method can efficiently detect the driver behavior.

Driver behavior detection and classification using deep convolutional neural networks

Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result, i.e., distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a fine-grained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e., Weaving , Swerving , Sideslipping , Fast U-turn , Turning with a wide radius , and Sudden braking . Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a fine-grained abnormal D riving behavior D etection and i D entification system, $D^{3}$ , to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. We extract effective features to capture the patterns of abnormal driving behaviors. After that, two machine learning methods, Support Vector Machine (SVM) and Neuron Networks (NN), are employed, respectively, to train the features and output a classifier model which conducts fine-grained abnormal driving behaviors detection and identification. From results of extensive experiments with 20 volunteers driving for another four months in real driving environments, we show that $D^{3}$ achieves an average total accuracy of 95.36 percent with SVM classifier model, and 96.88 percent with NN classifier model.

Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones

Secret key generation among wireless devices using physical layer information of radio channel has been an attractive alternative for ensuring security in mobile environments. Received signal strength (RSS) based secret key extraction gains much attention due to its easy accessibility in wireless infrastructure. However, the problem of using RSS to generate keys among multiple devices to ensure secure group communication in practice remains open. In this work, we propose a framework for collaborative key generation among multiple wireless devices leveraging RSS. To deal with mobile devices not within each other’s communication range, we employ relay nodes to achieve reliable key extraction. To enable secure group communication, two protocols are developed to perform collaborative group key generation via star and chain topologies respectively. We further provide the theoretic analysis on the achievable secrecy rate for both star and chain topologies in the presence of an eavesdropper. Our prototype development using MICAz motes and extensive experiments using fading trend based key extraction demonstrate the feasibility of using RSS for group key generation in both indoor and outdoor environments, and concurrently achieving a lower bit mismatch rate compared to existing studies.

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Group Secret Key Generation via Received Signal Strength: Protocols, Achievable Rates, and Implementation

Acquiring instant vehicle speed is desirable and a corner stone to many important vehicular applications. This paper utilizes smartphone sensors to estimate the vehicle speed, especially when GPS is unavailable or inaccurate in urban environments. In particular, we estimate the vehicle speed by integrating the accelerometer’s readings over time and find the acceleration errors can lead to large deviations between the estimated speed and the real one. Further analysis shows that the changes of acceleration errors are very small over time which can be corrected at some points, called reference points , where the true vehicle speed can be estimated. Recognizing this observation, we propose an accurate vehicle speed estimation system, SenSpeed, which senses natural driving conditions in urban environments including making turns , stopping , and passing through uneven road surfaces , to derive reference points and further eliminates the speed estimation deviations caused by acceleration errors. Extensive experiments demonstrate that SenSpeed is accurate and robust in real driving environments. On average, the real-time speed estimation error on local road is $2.1\,\mathrm {km/h}$ , and the offline speed estimation error is as low as $1.21$ km/h. Whereas the average error of GPS is $5.0$ and $4.5$ km/h, respectively.

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SenSpeed: Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments

Rogue access point (AP) has emerged as an important security problem in WLANs. However, it is a challenge task to localize the rogue AP with both high accuracy and minimal infrastructure cost. Either expensive professional infrastructure (e.g., multiple wireless sniffers) or additional hardware (e.g., directional antenna) need to be pre-deployed for rogue AP localization with high cost. Moreover, existing methods using Received Signal Strength (RSS) result in a large error as RSS is suffered from the multipath and shadowing effects in complex wireless environment. In this work, we exploit the channel state information (CSI), which is readily available from commercial Wi-Fi devices, to locate the rogue AP with high accuracy. We use only a single off-the-shelf Wi-Fi device for rogue AP localization which involves minimal infrastructure requirement. Our proposed rogue AP localization framework consists of two components: direction determination and position estimation. The direction determination can be carried out by using the human blocking effect on the CSI amplitude or phase. The multiple antennas on the Wi-Fi devices can be further utilized to enhance the rogue AP direction estimation. Given the estimated direction, two schemes are proposed to pinpoint the position of the rogue AP: determining directions at multiple locations grounded on triangulation and walking towards the rogue AP with direction adjustment. Results from extensive experiments in both indoor and outdoor environments show that our framework can achieve more practical and accurate rogue AP localization when comparing with the existing RSS-based approach.

Yingying Jennifer Chen

Papers

Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones

Group Secret Key Generation via Received Signal Strength: Protocols, Achievable Rates, and Implementation

SenSpeed: Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments

Locating Rogue Access Point Using Fine-Grained Channel Information