This paper presents a comprehensive survey on acquisition, tracking, and pointing (ATP) mechanisms used in free-space optical (FSO) communications systems. ATP mechanisms are a critical component for a wide variety of use cases of mobile FSO communications. ATP mechanisms are used to align an FSO transmitter and receiver to attain line-of-sight, which is required for effective operation of FSO communications. Transceiver motion is not only associated to mobile stations but also to temporary displacements experienced by stationary FSO terminals, such as in building-to-building FSO communications. This survey categorizes ATP mechanisms according to their working principles, use cases, and implementation technology. This paper also discusses advantages and disadvantages of the surveyed ATP mechanisms, and presents a discussion on challenges and future research.

A Survey on Acquisition, Tracking, and Pointing Mechanisms for Mobile Free-Space Optical Communications

Free space optical (FSO) communication technology, also known as optical wireless communications, has regained a great interest over the last few years. In some cases, FSO is seen as an alternative to existing technologies, such as radio frequency. In other cases, FSO is considered as a strong candidate to complement and integrate with next-generation technologies, such as 5G wireless networks. Accordingly, FSO technology is being widely deployed in various indoor (e.g., data centers), terrestrial (e.g., mobile networks), space (e.g., inter-satellite and deep space communication), and underwater systems (e.g., underwater sensing). As the application portfolio of FSO technology grows, so does the need for a clear classification for FSO link configurations. Most existing surveys and classifications are single-level classifications, and thus not inclusive enough to accommodate recent and emerging changes and developments of different FSO link configurations and systems. In this paper, we propose a multi-level classification framework to classify existing and future indoor, terrestrial, space, underwater, and heterogenous FSO links and systems using common and simple unified notation. We use the proposed classification to review and summarize major experimental work and systems in the area until 2017. Using the proposed classification and survey, we aim to give researchers a jump-start to tap into the growing and expanding realm of the FSO technology in different environments. The proposed classification can also help organize and systematically present the progress in the research on FSO technology. This makes the identification of the market needs for standards an easier task. Moreover, different entities involved in the standardization process including academic, industry, and regulatory organizations can use the proposed classification as a unified language to communicate during the early stages of standard development which require ambiguity-free discussions and exchange of ideas between different standardization entities. We use the proposed classification to review existing standards and recommendations in the field of FSO. It is also envisioned that the proposed classification can be used as a unified framework to define different FSO channel models for simulation tools.

Classification Framework for Free Space Optical Communication Links and Systems

High reliability and low latency are crucial for urban rail transits. In this paper, we introduce communication strategies for communication-based train control (CBTC) systems using long-term evolution for metro (LTE-M) to improve the reliability and latency. To be specific, the FlashLinQ-based Train-to-Train (T2T) communication schemes are introduced considering both the transmission delay and the packet drop. The quantified resilience is also introduced as a system metric to evaluate the preservation and recovery performance of CBTC systems. First, a novel urban rail transit wireless communication model is established using FlashLinQ-based T2T communications. Then, we introduce a novel cognitive control scheme based on LTE-M with T2T communication to enhance the quality of service and the resilience of multi-train CBTC systems. In the introduced scheme, Q-learning is used to generate optimal control strategies considering both wireless communication parameters adaption and train control parameters. Extensive simulations are conducted and the results show that the resilience of CBTC systems can be enhanced using the introduced scheme. Furthermore, using the introduced scheme, not only the gaps in optimal velocity versus distance curve are smaller, but also the unplanned traction and breaking are reduced as well.

Enhancing Communication-Based Train Control Systems Through Train-to-Train Communications

Communication-based train control (CBTC) systems are widely applied in the world. The primary functions are carried out by wireless communications, which implement the communication between train and ground. However, this kind of solution involves too many subsystems and interfaces, which makes the group equipment complex and expensive to maintain. What is more, once the ground control center or the communication channel is out of service, the train must operate in degraded modes, and more human factors will be involved. As a result, the efficiency and safety of the train operation will be affected. In order to significantly increase efficiency and safety, train-to-train (T2T) communication is proposed as a new solution for railway signaling. Hence, the availability and performance analysis of the direct T2T data communication system is essential. In this paper, the system structure is described, and different parameters that can affect the performance of the communication system are discussed. In order to evaluate the system availability and performance, the stochastic Petri nets (SPNs) are applied to formalize the communication system. The numerical analysis is carried out with different parameters, which consider both bit error and transmission rates. The results show that the method applied in this paper can be used to evaluate the performance and availability of the T2T data communication system.

Availability and Performance Analysis of Train-to-Train Data Communication System

The Internet of Things (IoT) consists of sensors and actuators that facilitate many aspects of our daily life. Compared to typical computing devices such as laptops and smartphones, these devices have a very limited set of functionalities and states. Researchers have shown that it is possible to infer the device type from its network traffic. In this paper, we show that an external observer that sniffs the network traffic of an IoT device can further classify device events and hence infer user actions by employing machine learning classifiers. We evaluate and compare the performance of ten machine learning algorithms in classifying 128 device events from 39 different devices. We analyze the impact of the user interaction through LAN and WAN as well as controllers such as Alexa voice assistant on the correct classification of device actions. We also inspect whether the region from which the device is impacts the performance of classifiers as researchers have shown that differing privacy restrictions lead to different external communications.

IoT Event Classification Based on Network Traffic

In this paper, we propose two different free-space-optics (FSO) coverage models for next-generation high-speed-train communications. To the best of our knowledge, these are the first coverage models proposed for FSO seamless handover. The models provide different coverage areas for performing seamless signal handover and uninterrupted ground-to-train communication. The first model uses two different wavelengths in adjacent covered areas and the second one uses a single wavelength. We find the optimal distance from the train track to a ground base station and the distance between base stations to provide seamless connectivity and handover while minimizing the number of base stations along the track. We base our estimations on a realistic model of an FSO system and provide numerical evaluations demonstrating the performance of the proposed coverage models. We show the different amounts of received power on ground-to-train communications as a function of the location of ground base stations. We also consider the effect of fog on the FSO link as the most attenuating condition for FSO communications. Our results show that communication rates of 1 Gpbs and higher may be achieved with the proposed station positioning and coverage models.

Optimal Positioning of Ground Base Stations in Free-Space Optical Communications for High-Speed Trains

In this letter, we propose a novel indirect diffused light free-space optical communications (ID-FSOC) system based on laser light. This ID-FSOC system aims to provide high-speed (i.e., $\geq 1$ Gbps) Internet access to vehicles. This approach extends the FSOC reach to locations that are out of sight from the transmitter. The proposed communications system employs diffuse reflectors (DRs) to uniformly emit diffused incident laser light in all directions, except toward the DR, which enables the receivers to receive the diffused light despite having no direct line-of-sight (LOS) to the transmitter but only to the DR. In our approach, LOS is required between the transmitter and the DR and between the receiver and the DR. We model the received power of the diffused light and estimate the power and bit error rate as perceived by a moving vehicle. Furthermore, we introduce possible operation modes to realize full duplex communications with ID-FSOC. Our results show the relationship between the transmission power and the DR-receiver distance and show that high data rates are achievable on practical DR-receiver distances.

Indirect Diffused Light Free-Space Optical Communications for Vehicular Networks

An eavesdropper may infer the computer applications a person uses by collecting and analyzing the network traffic they generate. Such inference may be performed despite applying encryption on the generated packets. In this paper, we investigate the extent of the ability of several machine learning algorithms to perform this privacy breach on the network traffic generated by a user. We measure their accuracy in identifying different applications by analyzing several statistical properties of the generated traffic rather than looking into the encrypted content. We compare the performance of these algorithms and select the one with higher precision; random forest. We also evaluate the application of packet padding to modify the packet length to avoid identification by machine learning algorithms. We test the effect of packet padding on the identification ability of the various machine-learning algorithms. We investigate the performance of the random forest algorithm in detail when applied to intact and padded traffic. We show that padding may decrease the efficacy of a machine-learning algorithm when used for application classification.

Identification of User Application by an External Eavesdropper using Machine Learning Analysis on Network Traffic

A network eavesdropper may invade the privacy of an online user by collecting the passing traffic and classifying the applications that generated the network traffic. This collection may be used to build fingerprints of the user’s Internet usage. In this paper, we investigate the feasibility of performing such breach on encrypted network traffic generated by actual users. We adopt the random forest algorithm to classify the applications in use by users of a campus network. Our classification system identifies and quantifies different statistical features of user’s network traffic to classify applications rather than looking into packet contents. In addition, application classification is performed without employing a port mapping at the transport layer. Our results show that applications can be identified with an average precision and recall of up to 99%.

Tracking User Application Activity by using Machine Learning Techniques on Network Traffic

In this paper we propose the Slot Reservation Announcement (SRA) access scheme to minimize the occurrence of channel-access collisions in infrastructure IEEE 802.11 networks under emergency and crowded scenarios. SRA is based on slot reservation in lieu of a randomized backoff approach to increase the channel-access success ratio. The proposed scheme rids IEEE 802.11-like access networks of throughput and utilization collapse under crowded scenarios. In turn, it increases channel utilization. This enabled access is critical for crowded networks under emergency scenarios where many stations suddenly contend for the channel. This scheme may be used as fallback mechanism to avoid throughput collapse in such scenarios and to enable critical communications. Our simulation results show maximum bandwidth utilization and increased throughput as compared to an IEEE 802.11 network and a recent reservation-based access scheme under crowded conditions.

Sina Fathi-Kazerooni

Papers

Optimal Positioning of Ground Base Stations in Free-Space Optical Communications for High-Speed Trains

Indirect Diffused Light Free-Space Optical Communications for Vehicular Networks

Identification of User Application by an External Eavesdropper using Machine Learning Analysis on Network Traffic

Tracking User Application Activity by using Machine Learning Techniques on Network Traffic

SRA: Slot reservation announcement scheme for medium access control of IEEE 802.11 crowded networks in emergency scenarios