5th -Generation (5G) cellular networks enable a new approach to applications that require low latency. As part of 5G infrastructure, Ultra Reliable Low Latency Communication (URLLC) was defined to support low-latency services using small payloads. However, many Operational Technology (OT) protocols designed with latency in mind require larger payloads but allow for latencies that exceed URLLC's 1ms capability for their operation. An example of such protocols is IEC-61850 GOOSE, which mandates a maximum latency of 4ms, but often transports larger payloads. In this paper, we evaluate the latency implications of incorporating the GOOSE protocol over 5G connections. We evaluate network performance by measuring two different Intelligent Electronic Devices' contact closure times and compare those to measurements obtained from a standard GOOSE network setup. This analysis shows the impact on network latency and critical application performance, from which we can derive important network parameters to improve performance for private 5G-based OT network implementations.

As battery technology advances and battery system capacity expands, its penetration in buildings enables building consumers to be energy prosumers, interact with the grid to provide services, and facilitate grid-interactive efficient buildings (GEB). As such, these Battery Energy Storage Systems (BESS) are expected to operate for a prolonged time without significant degradation, be efficient and reliable, meet the changing needs of the building and grid’s requirements, enhance grid resilience, and be fault tolerant. These novel features can be met with the application of Reconfigurable Battery Systems (RBS) technology. However, research and development of RBS integration with the built environment is limited and is the focus of this ongoing research project. This research focuses on developing versatile scalable multi-port switching topology, proposing novel sensors to improve the life span of each battery cell in the RBS system, and applying Reinforcement Learning (RL) to operate the building and its RBS as a dispatchable resource. A literature review, background information, preliminary results, and future work are presented.

Accurate assessment of the state of charge (SoC) and state of health (SoH) of batteries provides critical information for effective operation in various battery applications. Such information optimizes charging, discharging, fault isolation, and maintenance decisions. Conventional techniques have shortcomings, including accuracy, cost, speed, and invasiveness. Recent research has introduced novel techniques, many exploiting magnetic field changes that occur during battery operation. This literature review summarizes conventional and novel measurement methodologies, highlighting any limitations of each. While magnetic field and X-ray approaches can overcome many of the shortcomings of conventional methods, further research is necessary to reduce costs, enhance safety, and enable real-time monitoring. The development of accurate, quick, and cost-effective measurement techniques will accelerate the integration of batteries into a rapidly increasing range of applications, including battery energy storage systems for behind the meter in buildings, facilitating electrification goals and the expansion of renewable energy.

The IEC-61850 standard introduced multiple protocols to increase data visibility across various power grid systems. The Generic Object Oriented Substation Events (GOOSE) protocol in particular is designed for bay-level communications, distributing high-priority messages across a power grid through the use of Intelligent Electrical Devices (IEDs). However, the protocol's multicast messaging and time requirements create cybersecurity concerns that can be exploited by a malicious actor. While the literature has examined the protocol's cybersecurity vulnerabilities, there is limited research on the consequences for performance and cybersecurity of the protocol's implementation on physical IEDs. In this paper, we introduce a flexible and practical testbed for GOOSE implementation evaluations performed on different devices, and to enable the demonstration of how different implementations on physical devices aim to mitigate GOOSE's vulnerabilities. We show the results of our testbed generating GOOSE traffic at variable data rates, with varying packet sizes. These results are then compared and verified against an IED with GOOSE protocol functionality to validate protocol generation. While this paper focuses on IEC-61850 GOOSE, the testbed presented herein is not limited to a single protocol, however, and can easily be utilized to expand the evaluation scope to a variety of Operational Technology (OT) communications protocols.

Automatic modulation identification (AMI) is a technique to detect the modulation type and order of a received signal, which has the potential to enhance cognitive radio capabilities for future generations of communication devices. However, AMI classifiers traditionally have exhibited low efficiency in low signal-to-noise ratio (SNR) environments. Hence, to address this problem we present our novel Gated Graph Convolutional Neural Network (GGCNN) classifier for feature-based AMI. This architecture includes a robust feature extraction stage to extract deep correlative patterns about the received symbols. Not only does this feature extraction stage use the temporal characteristics of the received symbols, but it also takes advantage of embedded signaling features from the received signal. In the proposed classifier, the received constellations are treated as a graph, allowing it to outperform state-of-the-art classifiers due to its strong performance in graph classification. This is observed clearly in the visualization of the extracted features, even for high-order modulation schemes. In this paper, we present our systematic research conducted for maximizing the efficiency obtainable by our classifier. Extensive simulation results demonstrate a significant accuracy improvement of 18.44 percentage points, and an efficiency increase by 60.78% for our GGCNN-AMI classifier compared to state-of-the-art classifiers in low-SNR environments.

The procedure of automatically recognizing the modulation scheme of the received signal without any knowledge of the communications parameters employed by the transmitter has gained tremendous attention for developing various applications such as dynamic spectrum access in next generation communications systems and in electronic warfare (EW) applications. Amongst the proposed approaches for this procedure, the feature-based approach working on the principles of deep learning models has consistently demonstrated more favorable properties for its integration with real-world applications and realtime operations. However, this approach faces the challenges of low performance efficiency caused by low classification accuracy and high computational complexity in environments characterized by low signal-to-noise (SNR) ratio conditions. Therefore, in this paper, we present our research findings on a deep learning-based classifier for Automatic Modulation Recognition (AMR) with robust classification accuracy and lower computational complexity, thus leading to higher overall performance efficiency. The feature extraction stage of the proposed classifier operates on the translation of received signal constellations into a graph, which then follows a process of extracting graph information. Mapping the extracted information into features is implemented by a convolutional block. This design employed in our feature extraction stage is the main element for the robustness of our proposed classifier. For our performance evaluations we selected 8QAM, 64QAM and 256QAM, representing candidates for low-, medium- and high-order modulation schemes, respectively. Our simulation results exhibit higher classification accuracy compared to related literature works, by an average of 11 percentage points (p.p.), 11.31 p.p. and 9.3 p.p., respectively, for the successful classification of 8QAM, 64QAM and 256QAM across an SNR range from −10 to 30 dB. The computational complexity analysis indicates that our proposed classifier is capable of executing the AMR task with lower processing latency. Its robust classification and lower execution latency combine into a more efficient overall performance provided by our proposed classifier.

The proliferation of wireless technologies in recent years has significantly impacted the North American freight railroad industry, and enabled them to drastically expand their communications capabilities for various railroad applications. The main challenge they now face, however, is the extreme congestion of radio-frequency (RF) spectrum resources, including those licensed by the railroad industry. Hence, the railroad industry is increasingly interested in investigating underutilized RF spectrum bands, such as the 160 MHz RF band. Due to the advantages of multi-carrier communications systems over single-carrier approaches, in this paper we present our simulation results for the performance of an Orthogonal Frequency Division Multiplexing (OFDM)-based communications system designed for 160MHz and its evaluation for different railroad environments. The transceiver design follows an implementable software-defined radio operation while conforming with regulatory requirements for the 160 MHz band. Our investigation presents the performance of QAM modulation ranging from 4QAM to 256QAM utilizing the Two-Ray ground reflection, Hata, and Winner 2 models to represent different railroad environments. In this investigation we analyze the simulated throughput versus distance for each modulation scheme and channel model to determine the maximum achievable communication distance before throughput deterioration. In order to verify the performance of our developed OFDM-based transceiver we compare our simulation results with the theoretical throughput for each modulation scheme under the considered channel models. The findings in this study will assist with the performance predictions of 160 MHz-based transceivers for additional railroad applications.

Cognitive radio (CR) has been introduced into the Internet of Things (IoT) domain as a potential solution to the critical spectrum resource shortage caused by a dramatic increase of IoT solutions within a limited operational spectrum. One technique of use within traditional CR processing for sensing communication activity across a spectrum of interest is automatic modulation classification (AMC). However, utilizing AMC within resource-limited cognitive radio-enabled IoT (CR-IoT) devices poses a significant challenge. In this article, we present a high-efficiency automatic modulation classifier architecture whose core is built upon the implementation of stacking quasirecurrent neural network (S-QRNN) layers acting as a feature extraction stage. This architecture utilizes the low-latency feature extraction capability of convolutional layers, and a minimalist recurrent pooling function that mimics recurrent-layer operations to aggregate the extracted features over time steps for higher classification accuracy. Additionally, implementing dense layers between two consecutive QRNN layers helps keep the network growth rate low. Therefore, the proposed S-QRNN classifier overall exhibits higher efficiency, fitting well within the limitations of CR-IoT devices. In order to demonstrate higher performance efficiency of our classifier, we conduct a comprehensive evaluation of our approach against the state-of-the-art AMC classifiers. Our analysis metrics of the classifier performance focuses on trainability, classification accuracy, execution latency, and resulting overall efficiency. Our results demonstrate that our proposed S-QRNN classifier exhibits higher trainability and, on average, a 75.83% higher efficiency, while it has, on average, a 26.54% lower execution latency. We then expand upon S-QRNN’s foundation by introducing gated recurrent units into our classifier to initially extract temporal features of the received constellations. The resulting GS-QRNN classifier demonstrates an efficiency increase by an average of 191% compared to the gated-enhanced recurrent-neural-network-based AMC classifier, with our GS-QRNN classifier average execution latency being 59.31% lower.

The process of automatic modulation classification (AMC) has gained importance in recent years. AMC enables receivers to classify an intercepted signal's modulation scheme without any prior information about the signal. Likelihood, Distribution Test- and Feature-based approaches have been proposed in the literature for AMC. All these approaches suffer from very high execution time for the AMC process. This prevents the AMC component from operating in real-time alongside other components in the receiver. Thus, in this work, we propose the use of spiking neural network (SNN) in the labeling procedure of feature-based AMC. Implementing SNN in the labeling procedure will significantly decrease the required execution time for AMC while its classification accuracy remain highly competitive with other approaches such as convolutional and recurrent neural networks (CNN and RNN). We furthermore provide a comparison in terms of the required execution time and classification accuracy between our proposed classifier and two recently published approaches that are built upon the aforementioned neural network platforms. We utilize 16QAM, 32QAM and 64QAM modulation schemes from the real-world RadioML dataset in this comparison. The analysis demonstrates that our proposed classifier compared to RNN- and CNN-based classifiers requires on average 19.52% and 14.9% less execution time while sacrificing only 4.7% in classification accuracy on average when compared to the RNN-based classifier, and achieves on average a 15.7% higher classification accuracy compared to the CNN - based classifier.

Signal compression is commonly employed in Wireless Biomedical Sensor Networks (WBSNs) as an effective energy-saving strategy to avoid the energy costs associated with wireless transmission. High compression ratios (CRs) reduce the amount of information that needs to be transmitted and therefore allow radio front-ends, such as those in Body Area Sensor Networks (BASNs) to conserve energy by operating at much lower duty cycles. Lossy compressors can readily achieve high CRs in exchange of negligible reconstruction errors. With increasing reliance on machine-learning (ML) algorithms to augment medical expertise and the performance of medical devices, one must take into account the impact that signal distortion has on the `classifiability' of the signal resulting from lossy compression in addition to the impact such distortion has on the diagnostic features deemed of clinical significance. Stated simply, it's no longer sufficient to consider distortion-rate analysis in isolation, but instead apply the notion of `accuracy-distortion-rate' analysis jointly. In this paper, we perform such an analysis and investigate the impact compression has on the reconstruction error in sample space as well as feature space using a publicly available electroencephalogram (EEG) dataset. We also evaluate the impact this distortion has on the classification accuracy of various classical ML algorithms using Python's scikit-learn ML framework. Simulation results demonstrate that the `distortion-rate latitude' can be partitioned into possibly-overlapping regions of high-fidelity, feature-preserving, perceptual-fidelity, and classifiable regions. These results furthermore reveal classifiability can be maintained even at extremely high CRs, presenting us with further energy-saving opportunities in applications where classifiability is the primary objective.

One recurring problem that the North American railroad industry faces is the tracking of lost railroad cars. Railroad cars are often parked on track sidings of train stations or train depot, and forgotten or lost over time. This results in so-called Dark Cars. All railcars in North America are equipped with automatic equipment identification (AEI) tags, which is built upon radio-frequency identification (RFID) technology to uniquely identify each railcar. When queried, the tag provides a digital version of the railcar's reporting mark, among other information. RFID is an application of radio frequency backscatter communications between a reader and the tag. Passive RFID read distances are typically fairly limited, which has hindered an effective solution to detect dark cars on track sidings. In this study, we propose a framework to significantly extend the current AEI read distance in railroad industry. To accomplish this task, we propose a novel multi-antenna beamforming AEI tag reader powered by and mounted on locomotives. As a result of the proposed multi-antenna beamforming, the directionality and steerability of the emitted beam are increased. In other words, this implies additional increase in effective read distance while staying within the FCC-power budget, and satisfying the electromagnetic field strength needed to power up the AEI tag during a read operation. We conducted an in-depth model-driven performance evaluation of different multipath channel models affecting the link budget, including Rayleigh, Rician, and Two-Ray Ground. The receiver component of the AEI tag reader can benefit from the directionality and beam steering to effectively suppress undesirable directions through null placement and thus improve noise resiliency. The simulation results of our proposed approach show a significant increase in read distance (from 3-4 meters to 35 meters) in comparison to conventional AEI tag readers that utilize a single-antenna design.

This paper presents a novel machine learning-based algorithm to estimate the frequency offset in wireless communication systems from the received signal's IQ constellations. The algorithm focuses on dividing the received signal symbols into clusters and finding the centroid of each cluster using a modified k-means algorithm. The constellation rotation angle, corresponding to the frequency offset, is then found from the angel between the obtained centroids' coordinates and the coordinates of the corresponding modulation format. The simulation results have shown 100% estimation accuracy for constellation rotation angles within the (-45, 45) degrees range. The proposed algorithm provides a low-complexity scheme that eliminates the overhead required for training preamble (TP) based techniques, thus improving the communication system's efficiency. The algorithm can be applied to wide range of communication systems especially those used in high speed train and vehicular communications.

In 5G network designs, Device-to-Device (D2D) communication is a fast and reliable approach that enables two or more devices to communicate directly with each other wirelessly, without the need to involve a base station. Considering that most commonly these devices are mobile, enabling seamless D2D support for frequent associations and disassociations between mobile devices in proximity of each other requires careful optimization. Association of two or more user devices should be guaranteed based on reliability and stability of the communication link. However, mobility tends to be disruptive for wireless communication, and thus D2D communication approaches are needed that are robust against the mobility impact and can leverage it to their advantage. In our work, we have observed mobility and corresponding device association as a biological phenomenon. In biological tissue we can observe that some cells can collectively be affine to similarly shaped cells, retain the affinity, and form their own `clique' periodically. Bringing the concept to device association - using retention of association between two devices - we present two new biologically inspired algorithms. The algorithms show how in different mobility models, i.e. Brownian, Random Walk, and Levy flight, user association can be retained 6-10% of the time while improving achievable throughput by up to 10% compared to random and social association.

Automatic modulation classification (AMC) is a signal processing approach to determine a received signal's modulation scheme without any a priori information about that signal's configuration. Three main approaches have been proposed for this task, namely Likelihood-based, Feature-based and Distribution Test-based AMC. Each of these approaches has different levels of computational complexity affecting their ability to conduct AMC in real-time and achieving different levels of modulation classification accuracy. Thus, there is a trade-off between computational complexity and classification accuracy in this domain. This paper investigates the real-world scenarios of AMC applications with the emphasis on the two most feasible low-complexity approaches: Feature-based (FB) and Distribution Test-based (DT) AMC. This study has specifically evaluated these approaches with focus on their classification accuracy and their computational complexity. For comprehensive evaluation purposes the study encompassed signals utilizing 8-ASK, 8-FSK, 32-PSK and 32-QAM modulation schemes, as representatives of the most prominent modulation schemes in current wireless systems. Our analysis shows that DT classifiers exhibit on average a 17.5% lower computational complexity than the investigated FB classifier, with about on average 12% higher classification accuracy in SNRs in the range of 0 to 10 dB. On the other hand, the DT approach classifiers show not only on average 31% higher computational complexity than the FB classifier, but it also has an approximately 10% lower classification accuracy for SNRs in the range of -10 to 0 dB.

In this paper we consider the behavioral trust to be user-centric rather than device-centric. We propose that optimal Device-to-Device (D2D) association in mobile or vehicular networks can be attained through trust calculation after determining socially central users that are behaviorally motivated to communicate. The proposed algorithm considers user or device association as temporal and spatial human interactions to calculate trust for optimal association. Our findings show that the network performance is significantly affected by both the device-centric approaches, which have always considered social-aware models for associations, and the user-centric sporadic behavioral transition from selfishness to altruism or vice- versa. Therefore, we have considered assigning a selfishness probability to nodes based on the Shape Index metric and proposed selfish disassociation before network reconfiguration. We have shown that with this new approach it is possible to obtain a performance gain of over 300% when compared to random association or social-aware algorithms.

The continued growth and popularity of IoT-based wearables coupled with wireless body area sensor network (BASN) communication architectures continues to gain widespread adoption for e-Health applications. Challenges facing the adoption of this vision require e- Health communication architectures for wearable IoT/BASNs to make effective use of bandwidth, energy stores for extended continuous transmission of physiological data, mitigation of computational burden when analyzing the data in the cloud at a global scale and, most importantly, ensuring the features of clinical significance in the biomedical signal are not compromised. In this paper, we present a novel physiologically-aware communication architecture to address these challenges for wearable IoT/BASNs for e-Health applications. The architecture works by extracting patient health state using local in-node pre-diagnosis (or pre-screening) to help guide the operation of the communication architecture in deciding when data should be transmitted, its type and format, and given quality. This latter property on preserving signal 'quality' is central to the architecture, which employs a feature-based diagnostic distortion measure to ensure retention of features of clinical significance during source coding in order to guarantee their reconstruction; a notion which carries greater emphasis for biomedical signals as opposed to ordinary multimedia signals. Simulation work of the architecture is presented in MATLAB using Electrocardiograph (ECG) signals from PhysioNet's ECG database, where it is demonstrated energy savings are realized by a factor of 30 with a reduction in biomedical data volume as high as 86% for tested cases while preserving clinical features.

The study presented in this paper shows comprehensive simulation results of various in-train traffic loads generated by Wireless Digital Line (WiDTL)-related and passenger portable devices. It demonstrates the nature of these different traffic flows in different source, destination and aggregation points. Thus, by observing the various control- and user-application demands associated with High- Speed Train (HST) operations we can conclude that conventional networks fail to appropriately backhaul this data load. Keeping, this limitation in this mind we have proposed User Plane Wireless Adaptable Reliable Communication (UPWARC), which increases the network performance even in harsh time-variance. According to our study, it is possible for the envisioned UPWARC-based unified train communications architecture to provide high network performance and guarantee Quality of Service (QoS), in some aspects improving performance 15 fold, at the edges of cellular wayside base station coverage.

Railroad environments are generally considered to be dangerous workplace environments. The types of risks encountered there are comparable to the construction industry, ranking among the highest in workplace fatalities. However, these two industries also employ millions of people and thus safety has always been a major concern to both industries. These efforts, by industry and academia, are now starting to take an approach to collect safety related data for further analysis to comprehensively improve worker safety. Recent advances in wearable devices have greatly enhanced safety and data analytics by providing workers an advanced layered protection and also acquiring real-time incident data. In this paper we survey the challenges in these work environments to employee safety as well as wearable solutions currently designed to detect risks and alert workers of these situations. We aim to determine the current state of the art technologies in Wireless Body Area Network (WBAN), capable of addressing work environment challenges in railyards. Similarly, wearable solutions in the construction industry are evaluated for their feasibility to protect railyard workers.

There has been renewed attention given to High Speed Railways (HSR) in the US, in terms of adapting more speed, reliability, increasing passenger capacity and providing passenger centric services. Any future Train-To-Ground (T2G) communication system therefore has to embrace reliable signaling and control-specific needs of HSR, while also catering towards the high-performance networking demands of passengers. Segregation of T2G traffic into multi-frequency user/control plane separated traffic based on these respective requirements of train and passenger services may therefore provide a unique opportunity to US railroads to achieve a cost-effective yet future-proof solution fully embracing fifth-generation wireless communication paradigms. Built upon this vision we propose a logical cell and sector-based multi-railcar MIMO solution for railroad micro-/mmWave base stations to support the high performance demand imposed upon user plane traffic in HSR. The logical cells and sectors are developed solely based on railcar outdoor antennas, which can provide an optimal performance throughout the coverage area, regardless of the track-to-base station distance. The proposed UPWARC simulation results have shown great reliability and spectral efficiency compared to the conventional architectures. This is due to attaining optimality and granularity in network design, exploiting precise operation of high speed train and linear movement.

In this work, the advantages of coupling biomedical signal compressors with clinical feature-based distortion measures are demonstrated. Such a coupling allow biomedical signal compressors to self-establish hard limits with regards to choices surrounding compression ratios, or `quality settings', a compressor can safely choose from to guarantee that features of clinical significance are protected so that their reconstruction remains clinically relevant. This coupling allows biomedical signal compressors to operate in an unsupervised manner, since it is demonstrated that establishing hard limits that are applied equally to all signals does not allow one to maximize and/or strike a balance between compression ratio and signal fidelity. Such mechanisms can be employed in communication architectures in wearable body area sensor networks (BASNs) for emerging Internet of Things (IoT) applications for autonomous tasks. While feature-based distortion measures such as the Clinical Distortion Index (CDI), and the Weighted Distortion Measure (WDD) already exist, we demonstrate the viability of our work by proposing a generalizable feature-based distortion measure we call the Diagnostic Distortion Measure (DDM), which offers several benefits that address a few shortcomings present in the CDI and WDD in real-time applications for unsupervised self-guided compressors. Experimental results show successful application of our DDM with ECG signals from the PhysioNet database.

Inter-packet delays (IPD) of legitimate network traffic can be exploited for information hiding purposes and distribution of secret and sensitive data. This process is known as Covert Timing Channel (CTC), which is usually used for malicious purposes. In this paper we propose a novel approach, CTC Real-Time Detection (CTCRTD) to detect such activities based on IPD distributions of network traffic. We present and leverage three different non-parametric statistical tests that can be used to generate distinct statistical test scores for overt and covert traffic IPDs. Our new detection approach is designed around two major benefits: First, the new detection approach can detect various CTC algorithms that have similar impact on network traffic IPD distributions. Second, our detection approach reliably detects covert communication over real-time network traffic with minimal lag between the start of covert activity and the point of detection. We have evaluated and verified the reliability and effectiveness of our detection approach utilizing a large number of overt and covert traffic streams and various scenarios of the proposed detection technique. The obtained results show that the new detection approach can precisely differentiate between overt and covert network traffic and detect covert communication activities over 90 percent of time on average.

The advent of the Internet of Things (IoT) has proliferated the use of connected sensors to observe and control the world around us. Wireless Sensor Networks (WSN) are becoming ubiquitous tools that monitor their surroundings and also oftentimes provide actuation to affect the state of their environment. One of the key application domains for real-time monitoring and control is the freight railroad sector in North America. North America's freight railroad industry is responsible for transporting nearly 40% of goods, making it an inseparable aspect of national economic well-being. The current stationary wayside methodology of monitoring freight trains and the goods they transport is not effective and cannot provide the breadth of capabilities required to ensure safe and efficient operations. The use of otherwise popular existing communication technologies like ZigBee for this scenario were found to perform insufficiently due to the unique network topology imposed by the freight train. The Hybrid Technology Networking (HTN) protocol has been proposed to alleviate these issues. In this work a custom hardware platform is presented to optimally implement and deploy networks of sensors using the HTN protocol. A model for predicting network performance metrics using this platform along with performance test results are presented.

Covert channels exploit side channels within existing network resources to transmit secret messages. They are integrated into the elements of network resources that were not even designed for the purpose of communication. This means that traditional security features like firewalls cannot detect them. Their ability to evade detection makes covert channels a grave security concern. Hence, it is imperative to detect and disrupt them. However, a generic mechanism that can be used to detect a large variety of covert channels is missing. In this paper, we propose a support vector machine (SVM)-based framework for reliable detection of covert communications. The machine learning framework utilizes the fingerprints derived from the traffic under investigation to classify the traffic as covert or overt. We trained our classifier using the fingerprints from four popular and diverse covert timing channel algorithms and tested each of them independently. We have shown that the machine learning framework has great potential to blindly detect covert channels, even when the covert message size is reduced.

Extensive availability and development of Internet applications and services open up the opportunity for abusing network and Internet resources to distribute malicious data and leak sensitive information. One of the prevalent information-hiding approaches suitable for such activities is known as Covert Timing Channel (CTC), which utilizes the modulation of Inter-Packet Delays (IPDs) to embed secret data and transfers that to designated receivers. In this paper, we propose two different non-parametric statistical tests that can be employed to detect this type of covert communication activities over a network. The new detection metrics are evaluated and verified against four different and highly recognized CTC algorithms. The experimental results show that the proposed detection metrics can reliably and effectively distinguish between the covert and overt traffic flows, thus significantly supporting our research toward an accurate blind and comprehensive CTC detection. This is a capability vital to cyber security in today's information society.

In large-scale Wireless Sensor Networks (WSNs), one of the most important challenges is manageability of the network. With the increase in sensor nodes, data forwarding, route selection, network reliability and data accuracy are vital characteristics of WSNs that suffer from the growth in scale. In this paper, we propose a data fusion based approach to drastically improve network lifetime, reduce excessive network load, and improve overall WSN performance. Our proposed approach utilizes employment of data fusion to intelligently select a subset of nodes with information needed for the data fusion, while removing all redundant nodes without impacting the fused data quality. We also introduce two methods for reducing the number of sensor nodes in a generic estimation problem using data fusion for reliability improvement of the sensed data in the presence of noise. The first method is based on observation similarity, while the second method leverages convex optimization. Our results show that our proposed methods can greatly improve large-scale WSN operation efficiency.

Personnel safety is an integral element of any railroad operation. Rail tracks need to be regularly maintained, which often requires rail workers to be physically present on possibly live tracks along with their equipment. This results in hazardous work conditions for the workers. To ensure worker safety a reliable system for detecting oncoming trains and alerting the workers, while giving them sufficient time to disengage from the worksite, is essential. In this paper, we present a multiple sensor based system that integrates the sensor elements with a signal processing unit for this purpose. The processing unit consists of a signal conditioning unit, a data processing unit and a machine learning framework. The conditioning unit prepares the acquired signals for later operations. The data processing unit extracts fingerprints from the reported signals that are later used by the machine learning framework as training and testing samples. The machine framework is a binary tree that classifies the event under investigation as presence or absence of a train on the track under observation. We show that the system is very accurate and can alert the workers under five seconds after the arrival of the train at the test site.

In this paper, we introduce an automated framework that can model covert communication algorithms. Our proposed framework, Automated Covert Channel Modeling ACCM, can provide a reliable and valid implementation method for fully functional covert communication techniques that can be used to study various concepts, characteristics and features of covert communication. ACCM exploits the shared features of covert channel algorithms to define common blocks and automatically converts tasks and events of the common blocks to executable codes. In order to verify the accuracy of our proposed ACCM framework, we implement two different covert channel algorithms that are referenced repeatedly in literature. We demonstrate that the characteristics obtained from implementing covert communication using the ACCM framework match the theoretical concepts of network communication and the expected results. Copyright © 2014 John Wiley & Sons, Ltd.

Advances in wireless communication protocols and networking toward support of the next generation of mobile and radio broadband technologies have contributed to a strong competition among various telecommunication standards in particular Long Term Evolution LTE and Mobile Worldwide Interoperability for Microwave Access WiMAX. In this paper, we provide an in-depth comparison analysis of LTE and Mobile WiMAX at the physical PHY layer by studying the most similar PHY configuration scenarios for these two technologies. Our study includes a throughput analysis of downlink DL and uplink UL transmissions in time division duplex with the least overhead possible and different antenna schemes as well as modulation and code rates. This study also performs an overhead analysis in both protocols to provide a more in-depth understanding of the PHY layer capacity in various PHY layer configurations. Our simulation results generally show higher performance for LTE in both DL and UL transmission with 7Mbps in DL and 5Mbps in UL, when using one antenna port. However, by increasing the number of antennas for multiple-input/multiple-output configurations, the results illustrate a reduction in the performance of LTE compared to Mobile WiMAX. This arises from the increase in reference signal overhead in LTE from 4.7% in single-input/single-output SISO to 14.28% in 4×4 multiple-input/multiple-output MIMO. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we introduce a covert timing channel (CTC) algorithm and compare it to one of the most prevailing CTC algorithms, originally proposed by Cabuk et al. CTC is a form of covert channels - methods that exploit network activities to transmit secret data over packet-based networks - by modifying packet timing. This algorithm is a seminal work, one of the most widely cited CTCs, and the foundation for many CTC research activities. In order to overcome some of the disadvantages of this algorithm we introduce a covert timing channel technique that leverages timeout thresholds. The proposed algorithm is compared to the original algorithm in terms of channel capacity, impact on overt traffic, bit error rates, and latency. Based on our simulation results the proposed algorithm outperforms the work from Cabuk et al., especially in terms of its higher covert data transmission rate with lower latency and fewer bit errors. In our work we also address the desynchronization problem found in Cabuk et al.'s algorithm in our simulation results and show that even in the case of the synchronization-corrected Cabuk et al. algorithm our proposed method provides better results in terms of capacity and latency.

Covert channels are a network security risk growing both in sophistication and utilization, and thus posing an increasing threat. They leverage benign and overt network activities, such as the modulation of packet inter-arrival time, to covertly transmit information without detection by current network security approaches such as firewalls. This makes them a grave security concern. Thus, researching methods for detecting and disrupting such covert communication is of utmost importance. Understanding and developing analytical models is an essential requirement of covert channel analysis. Unfortunately, due to the enormous range of covert channel algorithms available it becomes very inefficient to analyze them on a case-by-case basis. Hence, a unified model that can represent a wide variety of covert channels is required, but is not yet available. In other publications, individual models to analyze the capacity of interrupt-related covert channels have been discussed. In our work, we present a unique model to unify these approaches. This model has been analyzed and we have presented the results and verification of our approach using MATLAB simulations.

Wireless Sensor Networks (WSN) continue their tremendous growth acceleration. WSNs have found their way into a wide range of domains, from military and transportation applications to medical and environmental monitoring. Some of these applications can include a very large number of nodes, which poses significant challenges to network lifetime, data transmission, and overall reliability. Recently, data fusion approaches are gaining traction in WSNs for improving reported data accuracy and help predict future events. They are used to improve the reliability of delivered information. While this addresses data accuracy, it does not address the inefficiencies caused by very large nodes and high data redundancy. Data aggregation is a simple way of streamlining data flow, but does not fully address the issue. The large WSN size causes congestion and increases the traffic load in the network; plus, decreasing the performance of the WSN and potentially disrupting its operation altogether. In this paper, we therefore explore Kalman Filters (KF) based data fusion as a technique to reduce the number of active sensor nodes in a very large WSN to conserve network resources while preserving the required data reliability and accuracy. Our results show that there is great potential for improving WSN operations utilizing our proposed approach.

In this paper we introduce a new approach for modeling covert channel algorithms utilizing automated code generation and evaluation in real network environments. Our proposed framework is called Automated Covert Channel Modeling over Networks (ACCM-Net), which allows the user to convert any general description of a covert channel algorithm into executable code using an automated process. The resulting code can be executed on virtual or physical hardware utilizing actual network links to evaluate the behavior of the covert channel algorithm and observe diverse characteristics and features of its covert communication. We have tested our proposed ACCM-Net framework with several well-known covert channel algorithms with positive results. Here, we present the implementation, and analysis of the widely adopted On/Off covert channel algorithm utilizing our proposed ACCM-Net. Based on our results, the implemented On/Off algorithm is functioning precisely and accurately.

North America's railroad industry and the Federal Railroad Administration continuously strive to improve operational safety and security of their operations. In order to design a reliable and efficient real-time monitoring and control network for freight trains and railcars, we had previously proposed a novel approach called Hybrid Technology Networking (HTN). HTN deploys sensor nodes in clusters, with intra-cluster communication provided by ZigBee and inter-cluster communication accomplished via WiFi through dynamically assigned node roles called gateways. ZigBee provides HTN with low-power operations and WiFi enables long-range communication for high performance. Due to a complex working environment the network topology changes frequently, which often leads to poor resource utilization, reduced network lifetime, fluctuations in channel conditions, high latency in end-to-end data delivery and variable loading at the gateways. To resolve this problem, HTN provides for cluster adaptation. In this paper, we describe algorithms to merge and split HTN clusters for making them adaptive to changing operating scenarios. We also explore how the process of making clusters adaptive will affect latency and reliability in the network.

Currently, a rapidly increasing numbers of sensors are being used on vehicles and in surface transportation and this trend is expected to continue. In support of these sensor networks several energy management schemes, including hierarchical clustering methods, have been proposed in the literature to reduce energy usage. However, the operating conditions for wireless sensors in vehicular environments change continuously. Sensor networks must thus be able to adapt to the environment to maximize performance and reliability. In this paper, we discuss the necessity for splitting a cluster. In particular, we explore the changes in the energy usage profile when a cluster is split. Even though our algorithm is designed for specific applications relevant to automation and control of railroad operations we believe that our discussions on energy consumption is a universal issue relevant to any form of clustered networks in vehicular environments.

In wireless sensor networks, nodes periodically wake up and sleep in order to reduce their energy consumption. One challenge in this scenario is how to coordinate the sender and receiver so that they can wake up and sleep at the same pace to maintain connectivity. In this paper, we present a synchronization method that has very low energy consumption overhead yet high-precision synchronization performance. Different from existing synchronization methods, it does not need to correct the offsets and skews of the clocks of nodes. Instead, our proposed method takes advantage of the fact that the time interval between two transmitted beacons is the same as the time interval between two received beacons, since the propagation and coding delay can be assumed to be similar in these two transmissions. Thus, the receiver and the sender can be synchronized using their individual clocks. Based on this key idea, our synchronization method can overcome the offset and drift issue without modifying the clocks. This paper focuses on the theoretical analysis to evaluate this proposed method. The closed-form expression of the error limits for our method has been obtained. Also, the correctness of the theoretical analysis has been verified by our experimental results.

The growth of the Internet and online social media, especially in recent years, also gave rise to a proliferation of online multimedia content creation and distribution. The use of modern steganography methods - the art of hiding information within multimedia - presents an unprecedented opportunity for malicious uses of these materials. Therefore, developing an effective technique to avoid the distribution of secret data is a significant problem that is addressed in this paper for digital images. We introduce a novel algorithm to remove the steganography information embedded in an image without changing the quality of the image and with no prior knowledge of the utilized steganography technique. Our new algorithm called Adaptive Threshold Displacement (ATD) is applied to images in the spatial domain. ATD divides the whole image into different segments and within a given segment, some pixels are displaced according to their contents as well as their neighboring pixel values. For evaluating the effectiveness of our proposed algorithm, we apply ATD to different images that contain hidden information, embedded using two different widely used steganography techniques. The presented results show that virtually all of the hidden information is destroyed by our proposed ATD algorithm, with a retrieval BER of over 40% in average. However, the quality of the images does not change and the PSNR of the resulting images is above 32 dB.

In this paper, we present and investigate a method to multimedia steganography removal methodand remove multimedia steganography in digital audio data. Among the existing steganography methods that can be used to hide secret data in digital audio carriers, the Spread Spectrum methods are among the most effective and robust for embedding steganography data. Though there are currently a few research efforts on removing or destroying steganography in multimedia, there have been very few efforts on removing steganography from audio. In this paper, we have introduced a simple but very effective anti-steganography approach for removing hidden information from digital audio data embedded using Spread Spectrum methods. The main concept in our approach is to leverage limited pseudo-random shuffling of the samples in the audio stream so that the synchronization information necessary for decoding Spread Spectrum steganography information cannot be attained. Our simulation results in this paper have demonstrated great performance in removing audio steganography by achieving a 50% bit error rate after applying our anti-steganography approach to the digital audio data while maintaining the audio quality.

In this paper, we present a comprehensive survey of currently available train approach detection methods and discuss some of the more innovative detection techniques for addressing the safety at highway-rail intersections (HRI) as well as the safety of railway workers by providing sufficient early warning of oncoming locomotives. A comprehensive review of various sensor technologies and detection techniques is provided, all of which have been attempted in experimental settings by academia and transportation agencies. Our goal is to highlight remaining research issues to help foster innovation in this area and facilitate the research on innovative train detection methods in the pursuit of a suitable technology-based early-warning train approach detection solution.

In this paper, we have investigated Covert Channels in 3GPP LTE-A (Long Term Evolution-Advanced) for identifying headers and extension fields where covert data can be hidden in covert communications. We also discuss the covert channel capacity for LTE-A where the specific fields in the headers of the MAC (Medium Access Control), RLC (Radio Link Control) and PDCP (Packet Data Convergence Protocol) layers are analyzed. We review different scenarios of channel coding rate, modulation schemes and channel bandwidths and present our results.

The North American railroad industry, with the objective of improving safety and security of their operations, have been exploring the possibilities of real-time monitoring and control of trains and wayside equipment. Wireless Sensor Networks (WSNs) have emerged as the de-facto solution for most measurement and monitoring operations. However, widely used commercial WSN solutions, like ZigBee, exhibit significant delay and traffic congestion problems due to their limited range, throughput and overall capabilities. To overcome this problem we proposed a multi-tier multi-hop heterogeneous network technology, called Hybrid Technology Networking (HTN). HTN is comprised of sensors equipped with multiple complementary radio technologies in which the sensors form a cluster and communicate within their own cluster using low-power communication. Each cluster's gateway then utilizes WiFi or similar longer range technologies for inter-cluster communication. In this paper, we present the design of our HTN node representation in OPNET. Furthermore, we present a theoretical framework that accurately models the delay in the network and verify the model with simulation results. We also present relevant results that demonstrate its full functionality, and also explore the efficacy of such hybrid multi-tier multi-hop implementations.

3.65 GHz Mobile WiMAX spectrum is often a better commercial solution due to its attractive licensing requirements, in spite of the slightly lower coverage area. However, no significant performance data has been reported for 3.65 GHz equipment behavior. In this paper, we have presented an in-depth analysis of a 3.65 GHz Mobile WiMAX solution. Our reported data can also contribute in performing link budget analyses and benchmarking similar equipment.

In this paper, we introduce a novel algorithm to destroy the steganographic information embedded in an image without changing the quality of the image and with no prior knowledge of the used steganography scheme. We propose the new Neighbor Class Displacement (NCD) algorithm, which arranges the pixels of an image into a given number of classes. The elements of two specific classes are substituted with each other based on different conditions related to the content of the class elements. For evaluating the effectiveness of our attack, we apply NCD to different steganographically modified images to remove the embedded hidden information. Our results show that over 40% of the steganography bits are toggled in natural images by our proposed attack algorithm, which means the hidden information is removed effectively. Additionally, the visual quality of the images does not change and the PSNR of the original and attacked images is above 32 dB. We compare our attack to other signal processing and geometrical attacks and show that our NCD scheme outperforms other steganography attack algorithms while maintaining the quality of the host image.

Watermarking is one of the most effective tools to countering multimedia content tampering and digital piracy. However, due to widespread availability of watermarking tools and the ease of distribution via Internet, the misuse of watermarking algorithms adapted to distribute malicious information as well as industrial and government secrets is not only possible but prevalent. Therefore, it becomes critical that such embedded data be removed from the multimedia without degrading the quality. In this paper, we propose a general removal method to remove watermarks from images based on pseudorandom desynchronization by selective pixel removal. This method does not assume nor require any prior information about image, watermark or embedding algorithm. We have shown that our method can achieve on average a 50% BER, effectively removing all watermark information without degrading the visual quality of the images.

In any Wireless Sensor Network, the principal operation is the data acquisition by remote wireless sensor nodes, and data reporting through multi-hop communication to a central hub. In this paper, we designed a communication protocol, Ultra-Low Energy Consumption Communication Protocol (ULECCP), especially for data acquisition applications exhibiting very stringent power constraints and relatively large observation intervals. Our new communication protocol has two significant contributions compared to existing protocols: 1) to provide full functionality at a 1/1000 duty cycle in order to maintain ultra-low energy consumption. 2) to provide routing functionality for the data reporting with virtually no additional energy consumption. For evaluation of its performance, we implemented ULECCP on a Texas Instruments eZ430-RF2500 platform. The experimental result showed a packet success rate of 97% in an 8-hop chain topology, and over 99% packet success rate in a 1-hop star topology.

LTE (3GPP Release8) and Mobile WiMAX (IEEE 802.16e) are discussed extensively as choices for the next generation of mobile broadband technologies. In this paper, LTE and Mobile WiMAX are analyzed comprehensively from a physical layer performance point of view for different antenna diversity modes in downlink and uplink transmissions. The objective of this study is to fill the current gaps in published literature for comparative in-depth analysis of the performance of LTE and Mobile WiMAX. Our study considers maximum channel bandwidth and the least control information overhead for both protocols. Performance comparison results for different scenarios of TDD (Time Division Duplex) operation in similar configurations suggest that LTE in general outperforms Mobile WiMAX. The throughput comparison of FDD (Frequency Division Duplex) and TDD operations is also presented in this paper to provide a complete discussion of physical layer maximum throughput in LTE.

Smart antennas have emerged as one of the most promising directions in supporting maximum communication link throughput. In this paper, we have investigated the impact of smart antennas on a complex mobile network such as a railroad wireless communications system. The objective is to analyze the selection of a Direction-Of-Arrival (DOA) estimation algorithm which provides the maximum efficiency when deployed in our railroad testbeds for wireless vehicular communication. Our findings are discussed to provide an in-depth understanding of how different algorithms should be selected to support efficient network operations.

Mobile WiMAX is a popular broadband solution with diverse applications. In the United States, the Federal Communications Commission (FCC) currently issues licenses for Mobile WiMAX in several frequency bands, of which 2.5 GHz and 3.65 GHz are the most prevalent. A significant amount of research has been conducted in the domain of 2.5 GHz due to its widespread commercial use. However, no such work - academic or industrial - has been reported for 3.65 GHz, in spite of it being a more favorable option for many applications, particularly because of its licensing requirements. In this paper, we present a comprehensive comparison of these two frequency bands in order to provide benchmark results for use by network planners, engineers and researchers. Our analysis indicates that, while 2.5 GHz Mobile WiMAX generally offers a larger coverage area, the attractive licensing options for 3.65 GHz may present an interesting alternative for many deployment scenarios and applications.

ABSTRACT Information transmission in wireless communication systems is traditionally protected by cryptographic techniques at the higher layers. Recently, a method of physical layer security has attracted much attention because it can potentially provide lower probability of interception of the signals at the eavesdroppers and hence augment the link security in addition to the conventional encryptions. In this paper, we propose a novel approach to securing transmit‐beamforming systems by using uniquely designed jamming noise signal, which can significantly degrade the signal quality at the eavesdropper but not at the intended receiver. The jamming noise signal is generated from the null space of the channel matrix. An eigenvector‐based implementation of this theoretical method is also provided. Unlike previous physical layer security methods, the proposed approach can provide secure communications over systems with arbitrary antenna configurations. Our proposed method offers more degrees of freedom to generate the jamming noise signal, resulting in the eavesdropper being unable to decode the information signals. Moreover, the eavesdropper cannot influence the system secrecy capacity by employing more antennas or by moving close to the transmitter. Simulation results show that the secrecy capacity increases significantly, by about 7 bits/s/Hz for a 4 × 4 antenna configuration, under typical transmit power constraints. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we propose a fast, simple and efficient image codec applicable for embedded processing systems. Among the existing image coding methods, wavelet quad-tree is a foundation leading to an efficient structure to encode images. By searching significant coefficients along quadtrees, an embedded efficient code can be obtained. In this work, we exploit hierarchical relations of the quad-tree structure in terms of searching entropy and present a quadtree searching model that is very close to the searching entropy. By applying this model, our codec surpasses SPIHT [1] by 0.2-0.4 db over wide code rates, and its performance is comparable to SPIHT with arithmetic coding and JPEG2000 [2]. With no additional overhead of arithmetic coding, our code is much faster and simpler than SPIHT with adaptive arithmetic coding and the more complicated JPEG2000 algorithms. This is a critical factor sought in embedded processing in communication systems where energy consumption and speed are priority concerns. Our simulation results demonstrate that the proposed codec is about twice as fast with very low computational overheads and comparable coding performances than existing algorithms.

The North American freight railroad industry is trying to leverage wireless sensor networks (WSN) onboard railcars for advanced monitoring and alerting. In railroad environments, freight train WSNs exhibit a linear chain-like topology of significant length. Thus, existing wireless technologies such as the IEEE 802.15.4 communication protocol, based on a star topology, are unable to provide reliable service. The end-to-end communication between nodes generally relies on individual nodes communicating with their respective neighbors to carry the information over multiple hops and deliver it to the preferred destination. The routing performance and reliability significantly degrades with increasing number of hops. We proposed a multitier multi-hop network which is designed to overcome these issues in large-scale multi-hop WSNs in railroad environments. This approach has significant advantages, such as more data bandwidth, higher reliability, and lower energy consumption. Our analytical results show that the proposed multi-tier communication approach spends energy more efficiently and utilizes less resource than the traditional chain topology onboard freight trains.

Inter-vehicle and roadside-to-vehicle communications can contribute to a safer and more efficient driving experience by providing time-sensitive and location-aware information. However, its performance suffers from vehicle mobility, intermittent user connectivity, and wireless channel unreliability. In this paper, we propose a novel cross-layer optimization approach based on our Adaptive Distributed Cooperative Medium Access Control (ADC-MAC) protocol to guarantee the quality-of-service (QoS) of real-time applications. Markov chain based theoretical analysis show that our proposed priority preemption approach can improve the quality of a real-time application by guaranteeing its bandwidth and reducing its transmission latency.

Explosive growth in Information Technology has enabled many innovative application areas such as large-scale outdoor vehicular networks for vehicle-to-vehicle communications. By providing time-sensitive and location-aware information, vehicular networks can contribute to a safer and more efficient driving experience. However, the performance of vehicular networks requires robust and real-time data communications and is impacted by high mobility, intermittent connectivity, and unreliability of the wireless channel. In this paper, a novel adaptive distributed cooperative medium access control (ADC-MAC) protocol is proposed in order to address the inherent problems in the IEEE 802.11 standard when employed in vehicular networks. ADC-MAC exploits spatial diversities to maximize the system throughput as well as the service range of vehicular networks. This is accomplished through adaptively selecting the most suitable helper and transmission mode for transmit/receive pairs among direct transmission (DT), cooperative relay (CR) transmission and two-hop relay (TR) transmission, in accordance with the channel quality and the positioning of relay nodes. Both our Markov Chain modeling based theoretical analysis and ns-2 simulation experiments show that our ADC-MAC protocol outperforms existing schemes under the same network scenarios and maximizes the achieved system throughput and service distance.

Joint exploration of intra/inter-video coding versatility in signal processing domain and signature/hash diversity in the information security domain has not been well investigated in the literature. In this paper, we show multimedia stream authentication quality can be improved significantly by exploring these diversities. Specifically, we propose a new rate distortion framework by considering intra/inter-signature/hash diversity to simultaneously provide video service quality, communication rate efficiency and video content integrity for wireless video streaming. This research aims to provide a unique fusion of inter/intra video coding versatility and signature/hash authentication diversity to provide video authentication and traffic control. The results based on simulations demonstrate the effectiveness of the proposed scheme in achieving video robust authentication quality with limited rate constraint.

Some theoretical and experimental models have been considered for the prediction of the path loss in mobile communication systems. This paper presents an alternative method to model the path loss characteristics, taking into account data analytical methods to discover dependency pattern among propagation model parameters. We applied the concept of nonlinear multi-regression to predict path losses from the collected data, such as considering carrier frequency, path loss exponent, and distance between transmitter and receiver. Based on this information, the pseudo gradient search approach is adopted to determine the optimal search direction, build propagation knowledge, and accurately estimate the path loss. Finally, the free space path loss is adopted in order to validate our approach. The results are found to be well in agreement with theoretical results.

IEEE 802.16e-based Mobile WiMAX has gained considerable popularity recently. However, there is no current simulation model that provides feature completeness and performance accuracy. In this paper we propose the design and implementation methodology of a complete, accurate, realistic and versatile simulation model to support Mobile WiMAX for the NS-2 simulation platform. In the proposed simulation model design, all major mandatory as well as many optional features in the IEEE 802.16e standard are implemented based on the enhancement of an open-source WiMAX model published by NIST. The major contributions of this work are: 1) the additional features integrated to the NIST model including OFDMA physical layer, 2) periodical ranging and Adaptive Modulation and Coding (AMC) for robust link adaptation, 3) multi-hop support for large-scale test-bed simulation, as well as 4) the simulation model validation using WiMAX Forum certified devices. Versatile simulation scenarios are also designed to validate the implemented features.

In this paper, we propose a novel approach for providing secure wireless communications in transmit-receive diversity systems. In this approach, we precode the information-bearing signal with a uniquely generated randomized eigenvector-based jamming signal to impair the eavesdropper's received signal, while the main channel, the link between the transmitter and the desired receiver, remains unaffected. Unlike existing methods, our approach can be applied to any antenna array configuration, even when the receiver and eavesdropper utilize more antennas than the transmitter, with no benefits to the eavesdropper's information detection capability from employing more antenna elements. Moreover, our proposed approach can provide more degrees of freedom for the jamming signal, significantly increasing the provided level of security. Additionally, our scheme does not assume any knowledge about the eavesdropper or even the number of collaborating eavesdroppers. Our simulation results show a secrecy capacity increase of about 7 bits/s/Hz for a 4 x 4 antenna configuration under typical transmit power constraints, which results in significant improvement in security performance and enables physically secure wireless communications.

Reducing transmission energy consumption is one of the most critical research issues in the area of Wireless Sensor Networks (WSNs). In this paper, we present our scheme for employing collaborative signal enhancement to achieve energy efficient image transmissions in WSNs. A collaborative signal enhancement approach is compelling due to its capability of saving individual energy consumption by spreading total transmission consumption over multiple sensors. Individual packets describing an embedded wavelet-encoded image exhibit a significantly unequal contribution towards image quality. By leveraging this fact we develop a strategy of appropriately selecting the number of collaborative sensors for each packet transmission in order to achieve the highest possible image quality given a restricted transmission energy consumption budget. Experimental results show that our proposed approach can provide about 2dB higher image quality under the same energy budget compared with the state-of-the-art collaborative transmission scheme in WSN literature.

In this paper, we propose an innovative low-cost biometric authentication scheme based on uniformed Gaussian mixture model (GMM) for secure communications within wireless body area sensor networks (WBASNs), to obviate the cost-prohibitive key exchange problem. In this approach, the sender Inter Pulse Interval (IPI) information is selected as the biometric key for authentication, and a statistical based signing scheme is developed using the uniformed GMM. Experimental analysis shows that the proposed approach effectively protects the communication channels in WBASN with a low computational complexity, significantly removing the need for secret key exchange and critical time synchronization overheads.

In this paper we present a novel approach that leverages overlap regions of images from different wireless sensors for mutual authentication of the transmitted images. We present our theoretical analysis for this approach and derive the authenticity ratio for a binary symmetric channel. Our results show that by selecting appropriate authenticity constraints and appropriate numbers of overlap region packets, a significant improvement of the authentication success probability can be achieved compared to traditional image authentication approaches.

Wireless multimedia sensor networks (WMSNs) support many acoustic applications for audio surveillance, animal tracking/vocalization, human health monitoring, etc. However, resource constraints in sensor networks (such as limited battery power, bandwidth/computation capability, etc.) pose challenges for the quality and security of audio data transmission and processing. The security is a critical issue since audio information can be accessed or even manipulated in WMSNs. In order to ensure security, audio quality and energy efficiency, we propose an index-based selective audio encryption scheme for WMSNs. The scheme protects data transmissions by incorporating both resource allocation and selective encryption based on modified discrete cosine transform (MDCT). In this proposed scheme, the audio data importance is leveraged using the MDCT audio index, and wireless audio data transmission proceeds with energy efficient selective encryption. The simulation results show that the proposed approach offers a significant gain in terms of energy efficiency, encryption performance and audio transmission quality.

Selective encryption for video streaming was proposed for efficient multimedia content protection. However, the issues on joint optimization of video quality, content protection, and communication energy efficiency in a wireless sensor network (WSN) have not been fully addressed in the literature. In this paper, we propose a scheme to optimize the energy, distortion, and encryption performance of video streaming in WSNs. The contribution of this work is twofold. First, a channel-aware selective encryption approach is proposed to minimize the extra encryption dependency overhead at the application layer. Second, an unequal error protection (UEP)-based network resource allocation scheme is proposed to improve the communication efficiency at the lower layers. Simulation experiments demonstrate that the proposed joint selective encryption and resource allocation scheme can improve the video transmission quality significantly with guaranteed content protection and energy efficiency.

This paper presents a novel passive measurement approach to accurately evaluate the performance of outdoor multi- hop 802.11 networks from the wireless side directly. Our approach employs five processing steps to evaluate per-hop system performances. Firstly, reference packets are identified from multiple independent packet traces. Secondly, multiple packet traces in one channel are merged together using the identified reference packets. Thirdly, redundant packets are filtered from the merging packet traces. To adjust for propagation differences between access points and monitors, the status of each packet is then corrected according to its context packets. Finally, an inference approach based on finite state machines (FSM) is designed to infer missing packets which are not recorded in any of the monitor traces but known to have existed in the real traffic. The unique features in our approach include propagation delay evaluation and FSM designs for existing packet status correction and missing packet inference. The measurement results collected from our outdoor multi-hop 802.11 testbed are used to validate the accuracy of our approach.

Current propagation models used in network simulations neglect obstacles of a propagation environment and employ over-simplified assumptions that the received signal strength is a simple function of distance and the transmission area of a node is circular. These assumptions are in contradiction with the actual tests and the measurement results. To run reasonable simulations for evaluating the performance of a specific outdoor wireless testbed, a novel multipath propagation framework is proposed in this paper, which is based on the ray tracing technique and uses site-specific geographic data of the analyzed area. It provides accurate propagation predictions, and it does not significantly prolong the simulation duration.

While the development of Mobile WiMAX devices is still an ongoing process, complete and accurate simulations become more important in order to study the performance of Mobile WiMAX based broadband wireless access networks. To further improve network simulation models for Mobile WiMAX, we have theoretically modeled the Hybrid Automatic Retransmission reQuest (HARQ) mechanism and evaluated its performance and accuracy. In this paper, we present the design and implementation methodology of the Mobile WiMAX HARQ simulation model in system-level network simulators in order to gain the benefit of link quality improved by enabling HARQ. Our results show that HARQ achieves higher throughput even at low signal strength which can be considered as non-line-of-sight (NLOS) environments. We believe the implementation of HARQ in the network simulation model and its evaluation studies will provide profound understanding to Mobile WiMAX field test studies.

Accurate network planning is critical with deployments of large outdoor wireless networks. Propagation and path loss considerations are key factors in predicting performance of wireless networks. We are currently studying large-scale wireless networks for US railway environments. In this study, both theoretical and measurement based propagation models indicate that the average received signal power decreased logarithmically with distance. The average large-scale path loss is expressed as a function of distance between the transmitter and the receiver by using a path loss exponent n and the close-in reference distance d0. However it is very important to select a suitable space reference distance that is appropriate for a specific propagation environment. In this paper, a deterministic approach is proposed to select an appropriate reference distance for a given propagation environment. Also a Minimum Mean Square Error (MMSE) based method is introduced to compute path loss exponents. Furthermore the comparison between the site specific measurement data collected from our testbed and the estimated values verifies the accuracy of our proposed method.

It is widely believed that IEEE 802.11 standard is aimed mainly for fixed indoor wireless local area networks and is not suited for mobile applications, even though the IEEE 802.11b systems may work in either infrastructure mode or ad hoc mode. The impact of node mobility on ad hoc network performance has already been studied intensively, but these studies mostly do not consider temporal fluctuations of the mobile wireless channel due to the Doppler shift. An investigation of the mobility impact on the performance of IEEE 802.11b ad hoc systems with Rician/Rayleigh fading under different node velocities is presented. A comprehensive and in-depth analysis of the impacts of a multitude of different signal distortions on an IEEE 802.11b system performance is also presented. Specifically, the authors study the bit-error rate performances with respect to node velocities for different modulation schemes. The simulation results show that, owing to its extremely low implementation and deployment cost, the current IEEE 802.11b standard has its potential to be deployed in a mobile ad hoc environment if the line-of-sight path between transmitter and receiver exists.

With the steady development of WiMAX based broadband wireless access networks, complete and accurate simulation studies become crucial. In an effort to further improve our ns-2 simulation model for mobile WiMAX, we have implemented a Selective Repeat Automatic Retransmission reQuest (SR-ARQ) mechanism into our model and evaluated its performance and accuracy. In this paper, we present the design and implementation methodology of the WiMAX SR-ARQ simulation model in ns-2 and discuss its accuracy via extensive simulation studies. Our evaluation clearly shows that SR-ARQ achieves increased successful packet delivery ratio and goodput, which is true for both non-saturated channels and saturated channels. In each case a cost of increased bandwidth consumption and latency overhead for received packets is also quantitatively analyzed. We believe the implementation of SRARQ in the ns-2 simulation model and its evaluation studies will provide profound empirical values to WiMAX field test studies.

Throughput vs. distance analysis and coverage requirements are critical issues in planning wireless network deployments. With the recent availability of IEEE 802.16e and its promise of larger coverage areas for high-speed data communication, evaluating it in a real-world environment is very important to us in our ongoing study of standards based wireless technologies for the Federal Railroad Administration (FRA). In this paper, we present results obtained with our IEEE 802.16e module for NS-2, particularly throughput vs. distance performance, and its consideration for designing a real-world multi-hop IEEE 802.16e testbed. Firstly, a quantitative analysis of throughput vs. distance performance is conducted via extensive simulation, and the optimal modulation and coding scheme as well as channel bandwidth profiles are identified for specific distances. Secondly, we present a real-world multi-hop IEEE 802.16e testbed design intended for installation along BNSF Railway track in Nebraska. We apply our findings towards selecting various base station deployment locations and their appropriate communications parameters in order achieve best possible multi-hop throughput performance. We also provide a client access coverage analysis for the various locations.

In this paper, we analyze the performance of mobile real time services in a large-scale IEEE 802.11 multi-hop network. We present our field measurements with discussion of performance bottlenecks for VoIP services under mobile scenarios. We utilized our test bed which is located in Nebraska and supported by the University of Nebraska-Lincoln (UNL), US Federal Railroads Administrations (FRA), and major US railroad companies. Our UNL-FRA test bed consists of 8 outdoor access point towers, which are deployed along 3.5 mile of BNSF railroad. Passive measurement approaches are taken to ensure the integrity of collected data and multi-layer stream based packet analyzer has been implemented to provide a global view of the entire performance of the monitored network from the physical layer to the application layer. Based on our analysis of collected data, we conclude that in a typical outdoor 802.11 environment similar to our UNL-FRA test bed, uncertain handoff latencies and lack of Quality of Service guarantee are main performance bottlenecks for real-time applications. Furthermore, we discuss the enhancement strategies to support mobility in high-speed railway networks. We believe our work is one of the initial efforts in wireless mobile network field measurement in terms of scale, methodology, and analysis.

Because of the wide deployment of 802.11 equipments in the past decade, current applications are not limited anymore to only single Access Points (AP) deployments for indoor usage, but have been extended to multi-hop networks to fulfill the need of high speed connectivity in mobile environments, where the analysis of multi-hop networks is extremely complicated. The behavior of an AP is dependent not only on its neighbors’ behavior, but also on the behavior of other hidden nodes. In this paper, we provide an accurate and verified multi-hop wireless backhaul analysis for performance of IEEE 802.11 DCF in terms of the channel throughput using a static channel error rate. The model is based on analysis of a single hop communications for evaluating the multi-hop wireless backhaul networks. We utilized our existing 3.5-mile Federal Railroad Administration (FRA) test bed on the BNSF railroad track in Nebraska to validate the model. Our field measurements and simulation results show that our proposed model is accurate.

The 802. llg offers backward compatibility to 802.11b operating in 2.4 GHz with the same set of MAC parameters. However, this compatibility causes 802.1 lg to perform differently as 802.11a in terms of radio throughput performance, although both standards employ orthogonal frequency division multiplexing (OFDM) technology to achieve high data rate. In this paper, we compare and present an in- depth analysis of the throughput performance of both 802.11a and 802.1 lg standards. In order to compare the throughput performance of 802.11a and 802.1 lg, a Markov chain model of IEEE 802.11 MAC is adopted to evaluate the saturation throughput. Our analytical and experimental results show an inferior throughput performance for 802.1 lg even with the same OFDM technology as employed for 802.11a.

Most of the current 802.11b research activities are conducted under stationary environment. As the need for high-speed connectivity in mobile environments increases, it becomes necessary to understand the impact of mobility on performance characteristics of 802.11b. In this paper, we present an investigation of the mobility impact on performance of the 802.11b system with Rician/Rayleigh fading under different client velocities. This includes analysis of the Doppler shift caused by the velocity of transmitter and receiver, the multipath interference due to reflections and diffractions from terrains in the radio service coverage area, and other serious impairing factors. We have also studied the bit error rate performances for various velocities with different data rates. Our analysis includes our analytical and simulation data verified with the results from our 3.5 mile Federal Railroad Administration test bed on the BNSF railroad track in Nebraska. Our results show that the current implementation of 802.11b standard has promising potential to be used in a mobile environment if the line-of-sight path between transmitter and receiver exists.

Several MAC protocols have been proposed for Wireless Sensor Networks (WSNs). These include T-MAC , D- MAC and the more commonly utilized SMAC. In this paper, we propose a new MAC layer approach to support mobility in WSNs. The proposed technique utilizes an adaptive frame size approach to overcome the effect of frame losses caused by the Doppler shifts under mobile scenarios. An Extended Kalman Filter is used to predict the frame size for each transmission, which also directly enhances the energy efficiency of the system. Our results show that based on the adaptive frame size predictor and its comparison with the SMAC protocol, the proposed technique can improve overall system performance and deliver enhanced energy efficiency of 24% under mobility. The current implementation of ns-2 does not take into consideration the packet error rate. As another contribution of our work, we have developed a physical layer model for ns-2, which processes the received frame based not only on the fading characteristics of the signal but also the SNR and relative velocity between the nodes. To characterize a more accurate wireless sensor networks' physical layer, we have modeled the Mica-2 sensors in MATLAB and implemented the model in ns-2 for simulations.

In this paper, we present our approach of establishing a wireless test bed as a part of our collaborations with the Federal Railroad Administration (FRA) for studying the performance of current and upcoming wireless technologies in a mobile railroad environment. The focus is on studying the impact of mobility on the wireless system throughput for moving trains with different velocities. We describe details of our test bed design including selection of the location, equipment as well as system topology and performance evaluations approach. We also present and discuss test results obtained from our test bed.

In this paper, we compare the asymptotic performance of the decorrelator approaches to multiuser detection in code-division multiple-access (CDMA). Precoding is the decorrelation which is performed at a common transmitter such as the base station or satellite that allows single-user detection at the handset receiver. Receiver-based decorrelator decorrelates multiple user signals at the receiver side. We analyze the performance of the aforementioned decorrelation approaches with time-varying random spreading sequences in additive white Gaussian noise channels. The results demonstrate that the performance of precoding and that of receiver-based decorrelator are asymptotically equivalent.