High-speed trains, though prevalent in Europe and Asia, are not yet a reality in the US. But interest and industry engagement are growing, especially around commercial hubs close to commuter homes for alleviating commute times. With support from the Federal Railroad Administration in the United States, the authors are exploring the design requirements, challenges, and technology capabilities for wireless communication between passenger cars, on-board systems and with trackside infrastructure, all using next-generation radio access technologies. Key aspects of this work focus on interoperability, modularity of the architecture to facilitate a future-proof design, high-performance operations for passenger services and ultra-low latency capabilities for train control operations. This paper presents the theoretical studies and computer simulations of the proposed network architectures, as well as the results of an LTE/5G field test framework using an OpenAir-Interface (OAI)-based software-defined radio (SDR) approach. Through various test scenarios the OAI LTE/5G implementation is first evaluated in a lab environment and through field tests. These tests provide ground-truth data that can be leveraged to refine the computer simulation model for evaluating large-scale environments with high fidelity and high accuracy. Of particular focus in this evaluation are performance aspects related to delay, handover, bit error rate, frequency offset and achievable uplink/downlink throughput.

Automatic Equipment Identification (AEI) tags are installed on all rail cars in North America to tag rolling stock and facilitate fault detection using wayside detectors. And yet, railcars do get lost when parked on sidings, etc., resulting in so-called Dark Cars. No effective solution has been proposed for finding such lost rail cars due to the distance limitations in the current AEI tag readers. However, recent developments in the area of AEI tags in particular multi-antenna wireless communication systems and backscatter communications, now provide the opportunity to read AEI tags from much farther distances. The adavntage of using multiple antennas is to increase read distance and directionality through beamforming while adhering to FCC power limits. The key obstacle to long-distance AEI tag reading is the limited link budget, which is the combination of the FCC-mandated power limit coupled with the minimum required EM field strength needed to power the AEI tag during a read operation. Since it is not feasible to modify the AEI tags themselves because a large number of them installed on railcars, nor boost the transmit power, our design is focused on increasing directionality of the transmitted power. In this work, the authors thus propose a novel use of a Multi-Antenna Beamforming AEI tag reader mounted on — and powered by — a locomotive. This is to detect AEI tags on dark cars by a train passing the dark cars. The authors conducted an in-depth model-driven performance evaluation of this scheme. The analysis includes different channel models, signal reflection, and impact from velocity on read success rates. The channel models considered in this evaluation include multipath models such as Rayleigh, Rician, and Two-Ray Ground. The results presented by the authors show that our proposed novel approach is not only feasible to read AEI tags in this fashion over distances exceeding multiple track separation distances, but also practical, in most cases requiring 10 or less antennas with only simple beamforming methods employed.

Automatic Modulation Classification (AMC) is becoming an essential component in receiver designs for next-generation communication systems, such as Cognitive Radios (CR). AMC enables receivers to classify an intercepted signal’s modulation scheme without any prior information about the signal. This is becoming increasingly vital due to the combination of congested frequency bands and geographically disparate frequency licensing for the railroad industry across North America. Thus, a radio technology is needed that allows train systems to adapt automatically and intelligently to changing locations and corresponding RF environment fluctuations. Three AMC approaches have been proposed in the scientific literature. The performance of these approaches depends especially on the particular environment where the classifiers are employed. In this work, the authors present a performance evaluation of the Feature-based AMC approach, as this is the most promising approach for railroads in real-time AMC operations under various different wireless channel environments. This study is done as the first one for railroads application where it considers different environments models including Non-Gaussian Class A noise, Multipath fast fading, and their combination. The evaluation is conducted for signals using a series of QAM modulation schemes. The authors selected the signal’s Cumulant statistical features for the feature extraction stage in this study, coupled with three different machine learning classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN) and Recurrent Neural Network (RNN) utilizing long-short term memory (LSTM), in order to maintain control over the classifiers’ accuracy and computational complexity, especially for the non-linear cases. Our results indicate that when the signal model noise shows higher non-linear behavior, the RNN classifier on average achieves higher classification accuracy than the other classifiers.

High Speed Trains (HST) face some of the most stringent requirements for ensuring passenger safety and comfort while operating at very high velocities. HST wireless communication systems therefore similarly require special considerations for network design, technology selection and system implementation. For infrastructure-based wireless communications, a handover occurs whenever a radio transitions from the coverage of the current base station to the next base station. The faster the train moves the shorter the time that it spends under coverage area before a handover is required. For HST this can be as short as 10–20 seconds. Furthermore, a HST moving at 220 mph experiences significant fast and small-scale wireless signal fading due to this velocity, but similarly may incur frequent connectivity losses due to the rapid transit across coverage areas. A route consisting of viaducts, bridges, tunnels or hilly regions drastically increases the need for rapid handovers since a large number of base stations are required to provide coverage and achieve acceptable Quality-of-Service (QoS) in such environments. Due to the relatively fixed amount of time needed to complete a handover, and the possibility of failed handovers, this reduces the usable time under coverage, and thus ensuring an optimal handover strategy selection is vital. Most of the routes span rural and suburban areas, which reinforces the need for a comprehensive network planning strategy, as these areas tend to be underdeveloped for cellular coverage. Given the complexity in available radio spectrum resources, detailed studies are required to aid in this technology selection process. However, with the predicted increase in the demand of railroad network data traffic generated by onboard sensors, onboard control and operation devices, passenger Internet services, etc., it becomes apparent that more resources are needed than are provided by current technologies such as LTE. Thus, including 5G New Radio for Railways (5G-NR2) in the consideration does not only provide railways a more cost-effective licensing option for frequencies, but also an opportunity to select the best possible radio access method for a given HST corridor. In this paper, we focus on the metrics related to handover and how they correlate with different radio access technologies for HST. Our results integrate the uncertainty of environmental factors to provide answers on technology selection driven by specific route, inter-site distances, and available bandwidth. Our wireless communication simulation results are driven by well recognized mathematical models that consider a variety of key parameters. The analysis of the handover performance will offer insights vital to future railroad network planning for multivariate radio access technologies, and to answer crucial questions about the potential for using frequency bands above 6 GHz in HST.

Rolling stock, particularly of freight railroads, is currently maintained using regular preventative and corrective maintenance schedules. This maintenance approach recommends sets of inspections and maintenance procedures based on the average expected wear and tear across their inventory. In practice, however, this approach to scheduling preventative maintenance is not always effective. When scheduled too soon, it results in a loss of operating revenue, whereas when it is scheduled too late, equipment failure could lead to costly and disastrous derailments. Instead, proactive maintenance scheduling based on Big Data Analytics (BDA) could be utilized to replace traditional scheduling, resulting in optimized maintenance cycles for higher train safety, availability, and reliability. BDA could also be used to discover patterns and relationships that lead to train failures, identify manufacturer reliability concerns, and help validate the effectiveness of operational improvements. In this work, we introduce a train inventory simulation platform that enables the modelling of different train components such as wheels, brakes, axles, and bearings. The simulator accounts for the wear and tear in each component and generates a comprehensive data set suitable for BDA that can be used to evaluate the effectiveness of different BDA approaches in discerning patterns and extracting knowledge from the data. It provides the basis for showing that BDA algorithms such as Random Forest [9] and Linear Regression can be utilized to create models for proactive train maintenance scheduling. We also show the capability of BDA to detect hidden patterns and to predict failure of train components with high accuracy.

By 2030, the United States Federal Transit Administration (FTA) plans to have High Speed Train (HST) systems deployed that span over 12,000 miles across the US. Given the rapidly accelerating growth in consumers demand for fast on-board Internet services, there is a need for a robust and dedicated railroad wireless network architecture for their onboard and Train-to-Ground (T2G) communication systems. And while there are several potential candidates for radio access technologies (RAT), a full understanding of the benefits and drawbacks of each is still missing. We therefore have developed and studied a simulation framework that offers railroads the ability to perform an in-depth evaluation of capabilities for different RATs in terms of interoperability, throughput, handover and bit error rate for various user-driven scenarios. The framework is capable of studying and analyzing conditions such as network performance at different train velocities, base station spacing requirements, as well as analyzing US-specific geographical or track-related architectural scenarios. Our Past experiences in researching railroad wireless solutions have shown that wireless network performance varies widely in environments like tunnels, viaducts, bridges, stations, etc. The simulator offers the network designers significant flexibility in terms of defining parameters to create simulation scenarios and obtaining a detailed understanding of network performance. The work has created a novel, flexible and adaptable simulation framework for high-speed passenger train wireless network evaluation. The simulation tool supports 220MHz-100GHz systems for simulating LTE and 5G-New Radio (5G-NR), and it can support other technologies such as 220MHz PTC, in a time-variant channel. In this paper we present the architecture and the capabilities of the simulator with a sample scenario evaluation. The developed framework aims to support HST wireless communication designers to conduct more detailed analyses and to make more informed decisions in optimizing system deployments.

Train accidents can be attributed to human factors, equipment factors, track factors, signaling factors, and Miscellaneous factors. Not only have these accidents caused damages to railroad infrastructure and train equipment leading to excessive maintenance and repair costs, but some of these have also resulted in injuries and loss of lives. Big Data Analytics techniques can be utilized to provide insights into possible accident causes, thus resulting in improving railroad safety and reducing overall maintenance expenses as well as spotting trends and areas of operational improvements. We propose a comprehensive Big Data approach that provides novel insights into the causes of train accidents and find patterns that led to their occurrence. The approach utilizes a combination of Big Data algorithms to analyze a wide variety of data sources available to the railroads, and is being demonstrated using the FRA train accidents/incidents database to identify factors that highly contribute to accidents occurring over the past years. The most important contributing factors are then analyzed by means of association mining analysis to find relationships between the cause of accidents and other input variables. Applying our analysis approach to FRA accident report datasets we found that railroad accidents are correlating strongly with the track type, train type, and train area of operation. We utilize the proposed approach to identify patterns that would lead to occurrence of train accidents. The results obtained using the proposed algorithm are compatible with the ones obtained from manual descriptive analysis techniques.

In a typical railyard environment, a myriad of large and dynamic objects pose significant risks to railyard workers. Unintentional falls, trips and collisions with dynamic rolling stock due to distractions or lack of situational awareness are an unfortunate reality in modern railyards. The challenges of current technologies in detecting and tracking multiple differently-sized mobile objects in situations such as i) one-on-one, ii) many-to-one, iii) one-to-many, iv) blind spot, and v) interfering/non-interfering separation creates the possibility for reduction or loss of situational awareness in this fast-paced environment. The simultaneous tracking of assets with different size, velocity and material composition in different working and environmental conditions can only be accomplished through joint infrastructure-based asset discovery and localization sensors that cause no interference or impediment to the railyard workers, and which are capable of detecting near-misses as well. Our team is investigating the design and performance of such a solution, and is currently focusing on the innovative usage of lightweight low-cost RADAR under different conditions that are expected to be encountered in railyards across North America. We are employing Ancorteks 580-AD Software Defined RADAR (SDRadar) system, which operates at the license-free frequency of 5.8 GHz and with a variety of different configuration options that make it well-suited for generalized object tracking. The challenges, however, stem from the unique interplay between tracking large metallic objects such as railcars, locomotives, and trucks, as well as smaller objects such as railyard workers, in particular their robust discernment from each other. Our design’s higher-level system can interact with the lower-level SDRadar design to change the parameters in real-time to detect and track large objects over significant distances. The algorithm optimally adjusts waveform, sweep time and sample rate based on one or multiple detected object cross-sections and subsequently alters these parameters to be able to discern other objects from them that are in close proximity. We also use an ensemble method to determine the velocity and distance of target objects to accurately track the subject and larger objects at a distance. The methodology has been field-tested with several test cases in a multitude of weather and lighting conditions. We have also tested the proper height, azimuth and elevation angles for positioning our SDRadar to alleviate the risk of blind spots and enhancing the detection and tracking capabilities of our algorithm. The approach has outperformed our previous tests using visual and acoustic sensors for detection and tracking railroad workers in terms of accuracy and operating flexibility. In this paper, we discuss the details of our proposed approach and present our results from the field tests.

Railroad environments are generally considered to be among the most dynamic workplace environments, even with constant improvement efforts by the railroad industry. While there has been great progress in equipment safety, personnel safety is a significantly harder challenge. These challenges are primarily derived from the presence of heavy moving machinery in close proximity to personnel and the difficulty of designing reliable wearable protection devices. Additionally, variable weather conditions, challenging walking conditions (ballast, trip hazards, etc.), and difficulty to focus on environment, moving objects, and on tasks at hand place the employees in constant peril. Therefore, our survey is focused on exploring solutions for protecting employees through unified system modeling and design that makes the employee integral to the process and results in personal protective devices that work with the environment and the employee, not against them. The optimal system design integrates not only protection of the employees from falls, unsafe practices, or collisions, but also aids in resource planning, safe operation and accounting of “near-miss” situations. In recent years the railroads have made significant investments in process automation and monitoring solutions such as Wireless Sensor Networks. These technologies are becoming increasingly cloud-connected and autonomous. They provide a plethora of information about equipment positions, movement, railcar lading, and many other factors, all of which are highly useful in the design and implementation of a railyard worker protection system. They allow us to predict position and movement, and can thus be used to provide effective proximity detection and alerting in some railyard regions where these systems are installed. Additionally, we discuss several technologies addressing near-collision, fall, and proximity situations through RF and non-RF-based techniques. The railroad industry has been advancing efforts leveraging these technologies to improve the safety of their workers. However, there are also many challenges that remain largely unaddressed. For example, in railroads, a detailed and exhaustive causation analysis for worker incidents has yet to be conducted. Therefore, in an environment like a railyard there is no solution to detect or prevent Employee on Duty (EOD) fall, collision, or health issues such as dehydration, psychological issues and high blood pressure. Protective devices worn by workers is believed to be one of the most important, cost-effective, and scalable potential candidate solutions. Recent advances are making wearable wireless body area networks (WBAN) and wireless sensor networks (WSNs) that are distributed and large-scale a reality. Such distributed networks consist of wearable sensors, fixed-installation sensors and communication links between all of them. The challenges are found in selecting wearable sensors, researching reliable communication among nodes without interfering with proximity detection and suitable for high-multipath, non-line of sight channel conditions, wearable antenna designs, power supply requirements, etc. A dense, distributed, large-scale environment like a railyard requires comprehensive workspace modelling and safety analysis. Interference related to RF sensor deployment, blind spots in vision-based approaches, and wireless propagation in intra and inter-WBAN communication due to dense non-Line-of-Sight workspace environments, metallic heavy machinery and the use of RF sensors, are all individual research challenges in this domain. This paper reviews these challenges, explores potential solutions, and thus provides a comprehensive survey of a holistic system design approach for a wearable railyard worker protection system that is unobtrusive, effective, and reliable.

The North American Freight Railroad industry has been exploring ways in which on-board real time or near real-time monitoring of important railcar components and cargo can be accomplished. This approach alleviates the danger from fast occurring catastrophic events like bearing failure, which is not always possible using the traditional wayside monitoring techniques. The use of Wireless Sensor Networks is a viable candidate technology that is being explored for this application. However, popular communication protocols based on IEEE 802.15.4 have been evaluated by the railroad industry and our lab, and were found to perform unacceptably for this application domain, among other reasons, as a consequence of the long linear chain-like network topology of a sensor network deployment on a train. Hybrid Technology Networking (HTN) protocol has been designed to address these issues. HTN structures the network as a communication hierarchy using multiple different network technologies. It allows small clusters to communicate internally using IEEE 802.15.4 and utilizes IEEE 802.11 as the inter-cluster transport method for data delivery over multiple hops to the locomotive. It aims to maximize the benefits afforded by each technology. Energy is a scarce resource in such networks and hence modeling it for energy analysis and optimization is vital. A model that accurately predicts the energy consumption of a particular network deployment is therefore of utmost necessity. However, most modelling efforts concentrate on network deployments utilizing only a single type of communication protocol and the structure of such deployments are often mesh-like. Also the existing modelling approaches tend to model the entire network as a single phenomenon, which is often not the case in network deployments such as those in the freight railroad scenario. It is also expensive to commission large network deployments to evaluate energy consumption profiles. The problem is compounded when this process has to be repeated for several different communication protocols and channel conditions. The task will be made economically viable and massively scalable with the use of a modular energy model. The philosophy behind our approach is to model the important and contributing constituents of the protocol within each node and also external to the node and then utilize inter-dependencies to connect the individual models. This work is the first step towards a modular energy model for the Hybrid Technology Network, and is also applicable to many other networking approaches. In this work we propose a model design that is capable of predicting the network behavior of nodes in a linear chain-like topology utilizing the ContikiMAC duty cycling protocol for multi-hop communication with the sink node. We have used channel emulation to test hardware nodes in a chain-like topology to validate the model predictions, and present our findings in this paper.

Wireless Sensor Networks have been a focus of research in the North American freight railroad industry to enable on-board real-time sensing of critical railcar parameters. Important railcar aspects like wheel bearing temperature, air pressure, brake failure, and the integrity of transported goods can then be monitored closely and reliably. This enables immediate preventive actions in case of impending failures and also enables trend analysis that can be used to fine-tune maintenance efforts on railcars. These measures increase the safety, efficiency, and dependability of freight railroad operations. In our previous work [1–3] we have presented our Hybrid Technology Networking (HTN) protocol. This protocol provides optimal network performance for railcar monitoring applications. We have also presented HTNMote, a hardware prototyping platform that implements HTN. A deployment of HTNMotes was conducted and evaluated at the TTCI facility in Pueblo, Colorado in the US. The results from our field tests confirm that this approach is an order of magnitude better in performance compared to solutions based on ZigBee alone. In such an application, energy considerations represent a key challenge. These sensors have no readily available continuous energy source, but are expected to operate for years in harsh conditions. Energy harvesting — from vibrations, temperature differences, or solar radiation — may provide a potential solution to the energy scarcity. This also mandates that the HTNMote hardware and HTN protocol both be as energy efficient as possible. In this paper we present detailed measurements of the energy consumed by the HTNMote in various operational situations that are encountered during their operation onboard freight railcars. We introduce an energy consumption model based on our analysis of the measurements. This model demonstrates the energy-efficiency of the HTNMote implementation.

Real-time monitoring of various components of a railcar such as wheel bearing temperature, brake line status, integrity of transported goods and many more has become a key focus area of research for the North American freight railroad industry. The ability for timely detection of abnormalities and impending failures prevents costly accidents, the potential loss of life and damage to the environment. Monitoring also increases overall operational efficiency, reliability and safety of freight railroads. Wireless Sensor Networks (WSN) are an obvious choice for implementing such a monitoring scheme. The accumulated data from various sensors distributed throughout each railcar along the length of the train is transmitted wirelessly using multi-hop transmissions to the locomotive for alerting and monitoring. From there, this information is also transmitted to dispatch centers for further analysis and recording. ZigBee technology based on the IEEE 802.15.4 standard is a popular choice among WSN communication protocols, owing to its low cost and low power requirements. ZigBee performance degrades severely in the long chain-like topology characteristic of the railroad application environment. This effectively disqualifies ZigBee as a candidate technology for such railcar monitoring deployments. To overcome these issues with ZigBee deployments for freight train monitoring we developed our Hybrid Technology Networking (HTN) approach [5–7]. HTN leverages both ZigBee and Wi-Fi communication to achieve reliable communication along an entire freight train. Railcar monitoring nodes are grouped into smaller clusters, within which we utilize ZigBee for its low-power operation and report to each cluster’s gateway node. The gateway nodes of all the clusters on a train communicate using Wi-Fi, to benefit from the high throughput and long communication range. This tiered architecture also results in a drastic reduction in overall hop count for end-to-end communication. In this paper we present HTNMote, a hardware platform that we are developing and employing for real-world evaluation of the HTN protocol. We also present results from our field tests of the HTNMotes at the Transportation Technology Center (TTCI) facility in Pueblo, Colorado, operated by the US Association of American Railroads (AAR). The results show that the use of HTN improves performance of the network by at least an order of magnitude compared to a ZigBee-only network. This paper details the design of our HTNMote platform, present the test setup and results, as well as conduct an in-depth analysis of the obtained results as they relate to railroad operations.

Personnel safety is a cornerstone of railroad operations. However, various activities such as track maintenance and repair require workers to be physically present on or near railroad tracks that are potentially active. This creates a significant risk to the workers, especially at very high train speeds and in places where the geographical topography reduces direct line of sight. In fact, fatalities and injuries continue to be reported. A common approach to lessen this problem is to employ workers specifically to act as lookouts — to observe approaching trains and report them back to the work site. However, maintenance operations are often lengthy and it is common for the lookouts to lose concentration and thus the possibility arises that they fail to report incoming trains. Another possibility is the failure of the communication link between the observer and the workers. In an attempt to remove the human element and the associated risks, commercial vendors have developed automated systems to detect approaching trains and trigger alarms. However, the current commercial solutions available have several drawbacks. Firstly, deploying the available solutions requires procedures that are invasive and possibly destructive for the railroad tracks. They often involve digging underneath the tracks to install devices. This also requires significant installation effort and makes the solution often nonportable. This has serious operating concerns for the railroads since maintenance operations are temporary and installing permanent devices becomes inefficient. Also, since the solutions are not portable, deploying them on a large scale is not cost effective. Secondly, the solutions have limited sensing capabilities, employing only a single detection approach such as mechanical treadles, and do not provide comprehensive information about the trains being detected. For example, if the maintenance is occurring on parallel tracks, the sensors may fail to detect which of the tracks the train is currently operating on. Also, they cannot accurately capture all the characteristics of the approaching train, such as length, velocity, identifying signature, etc. And thirdly, they often have high false detection rates as they lack methods to differentiate the signature of a train from other events, such as drilling and heavy machinery operation. With the objective of addressing all these issues, we have proposed a multi-sensor based system that can integrate the abilities of the different sensors so as to reliably predict all the essential characteristics of a train. In this paper we present the architecture of our multi-sensory system. We also report our findings and analysis of evaluating our sensor systems on tracks at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO.

Traditional Wireless Sensor Network (WSN) solutions have been deemed insufficient to address the requirements of freight railroad companies to implement real-time monitoring and control of their trains, tracks and wayside equipment. With only ZigBee-based elements, the transmission capabilities of WSN devices are limited in terms of coverage range and throughput. This leads to severe delay and congestion in the network, particularly in railroad scenarios that usually require the nodes to be arranged in linear chain-like topology. In such a multi-hop topology to communicate from one end of a train to the locomotive — and due to ZigBee’s limited communication range — data needs to be transmitted using a very high number of hops and thus generates long delays and congestion problems. To overcome this drawback, we have proposed a heterogeneous multi-hop networking approach called “Hybrid Technology Networking” (HTN). In HTN we combined Wireless Local Area Network (WLAN) technologies like WiFi, which provide improved communication range and higher data rates, with low-power communication technologies like ZigBee. This significantly reduces the number of hops required to deliver data across the network and hence solves the issues of delay and congestion, while also achieving superior enery efficiency and network lifetime. The sensor nodes are logically divided into clusters and each cluster has a WiFi “gateway”. All intra-cluster communication is achieved via IEEE 802.15.4 and ZigBee protocols, while all inter-cluster communication utilizes WiFi protocol standards. To implement our proposed technology in railroad networks, we are designing hardware prototypes and simulation models to evaluate the functionality and performance of our HTN solution, which is designed around a dual network stack design governed by the HTN protocol. This ensures full compliance with IEEE and industry communication protocols for interoperability. Since no simulation tools that seamlessly combine both WSN and WLAN technologies in a single module exist, we wrote our own simulation environment using OPNET. In this paper, we have provided information of implementing the HTN protocol in OPNET and the simulation results for different scenarios relevant to railroad operations. These results will demonstrate the efficacy of our proposed system as well as provide the baseline data for testing the hardware devices in live networks. Under simulated traffic and channel conditions and device configurations, we observed a decrease of 77.27% in end-to-end delay and an increase of 69.70% in received data volume when using HTN compared to ZigBee-only multi-hop networks, simulated over 14 railcars in railroad-relevant scenarios.

The North American freight railroad industry continuously strives towards improvements in the safety and security of freight transportation. One key effort focuses on the use of Wireless Sensor Networks (WSN) technologies to monitor and report mechanical and electrical component status for each railcar in real-time, as well as the status of the transported goods themselves. This allows real-time monitoring of railcar components such as air pressure, wheel bearing temperature, brake failure, wiring integrity, refrigeration unit failure, boxcar door opening, the detection of radioactive materials, dangerous substance leaks, and much more. The aggregated sensor data is transmitted to the locomotive, dispatch centers or regional offices for early fault detection and accident prevention. Our previous work [1] has shown that ZigBee technology based on the IEEE 802.15.4 faces numerous obstacles when applied to freight railcar monitoring. To address these problems our team proposed an alternate approach called Hybrid Technology Networking (HTN), which combines the benefits of ZigBee for low-power short-range communication and WiFi for high-performance long-distance communication between HTN sensor clusters. In this paper, we present our simulation results using our HTN protocol. We compare and discuss the performance of the ZigBee-only network environment with the proposed HTN and demonstrate the advantages offered by HTN. We also discuss our prototype sensor hardware platform using the HTN protocol and provide an outlook of the future work planned for HTN.

With the anticipation of Positive Train Control (PTC), as well as many other advanced rail applications, a modern end-to-end communication network infrastructure has become an essential requirement for the North American railroad industry. One focus area is on wireless technologies for track-side and yard deployments. Among the available broadband technologies, Mobile WiMAX has emerged as the most favorable solution for deployment in such an environment. It provides high throughput, large coverage and the support for many diverse applications, with attractive features like seamless handover, Quality-of-Service (QoS), the recovery from packet loss through mechanisms like Hybrid-Automatic Repeat reQuest (H-ARQ) and vehicular mobility support. In this paper, we provide an overview of our research endeavors in evaluating Mobile WiMAX. We discuss our design and implementation of Mobile WiMAX computer model based on Network Simulator 2 (NS-2), which can simulate sophisticated scenarios frequently encountered by the railcars. We describe our experience in setting up our WiMAX testbeds in Ashland, NE and Logan, IA, utilizing the infrastructure from BNSF and UP in those locations, respectively. Since our test beds serve as prototypes, similar design principles can be applied for the eventual large-scale nationwide network deployment by the railroad industry. Furthermore, we present laboratory and field test results that clearly validate our design principles and demonstrates the advantages of Mobile WiMAX deployments for railroad operations.

Ensuring rail safety is a priority for the Federal Railroad Administration (FRA) and the railroad industry in North America. One such endeavor is to leverage Wireless Sensor Networks (WSN) to monitor and report in real-time the status of mechanical and electrical components for each railcar, and in conjunction with other railroad subsystems, ensure the safety, security and integrity of transported goods. The envisioned solution utilizes sensors installed on each railcar to form a train-based wireless network and collect real-time (or near real-time) information on different elements of a train and transmit aggregated information to the locomotive, dispatch centers or regional offices for early fault detection and accident prevention. The railroads have been interested in using a standards-based low-cost communication protocol for this purpose, such as IEEE 802.15.4, often referred to as ZigBee. Our results show, however, that ZigBee was designed for smaller wireless networks, such as a single railcar. It exhibits several critical problems associated with the unique network topology found on a freight train and the size of such a network. In essence, the network would take the shape of a very long chain of nodes. Some of the problems stemming from this topology are excessively long synchronization delays for establishing the network along the entire train, severe problems with route discovery and maintenance necessary for selecting the next relay node along the chain, aggregation of data errors and a resulting unacceptable packet loss rate, the lack of a traffic prioritization mechanism to protect important packets such as those containing critical alarms of equipment failure, and many more. In this paper, we describe our findings and experiences in our evaluation of ZigBee for railcar monitoring onboard freight trains, a detailed analysis of the identified problems and their impact on the envisioned railcar monitoring as well as discuss potential solutions to these problems.