To enhance the security of Internet of Medical Things (IoMT) systems against cyber threats, we introduce a Multi-Level Blockchain Infrastructure combined with a Differentiated Communication Strategy for real-time medical data transmission, utilizing an embedded hardware setup to provide cost-effective solution for resource-limited environments. The workflow of Multi-Level Blockchain Architecture is proposed, incorporating an embedded hardware-driven K-Nearest Neighbours (KNN) algorithm for diagnosing real-time Electrocardiogram (ECG) signals, with Rivest-Shamir-Adleman (RSA) encryption protocol to secure medical records. The simulations are performed using MIT-BIH arrhythmia and normal sinus rhythm datasets. The feature extraction algorithm, incorporated with optimized KNN model, demonstrated promising results for each emergency classification class. The evaluation metrics for Healthy, E1, and E2 classes showed an accuracy of 91.1%, 88.9%, 84.4%; precision of 78.9%, 100%, 75%; and specificity of 86.7%, 100%, 86.7%, respectively. The feasibility of executing optimized algorithms on Arduino platform is confirmed through computational complexity, execution time, and memory requirement. The proposed system architecture provides a robust solution for enhancing IoMT security, strengthening the confidentiality, integrity, and authenticity of medical data, safeguarding against cyber threats. Additionally, the simple embedded hardware setup offers a user-friendly solution, making it well-suited for resource-constrained surroundings.

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.

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.

The next-generation cyber–physical systems (CPSs) with heterogeneous applications have diverse Quality-of-Service (QoS) requirements in terms of throughput, end-to-end latency, and packet drop reliability. To meet such diverse QoS requirements, in this article, we propose a QoS-aware traffic forwarding scheme in software-defined CPS. The proposed scheme is presented as a two-stage optimization framework to minimize the associated costs in traffic forwarding. In the first stage, we aim to minimize the required number of “candidate” switches for a given network to minimize network deployment costs. In the second stage, we design a comprehensive cost function considering end-to-end delay, flow-rule utilization, and link utilization in the network. Based on the designed cost function, we formulate another optimization problem for optimal traffic forwarding (OTF). As solving OTF is NP-hard, we propose an efficient greedy-heuristic approach to solve the problem while considering application-specific QoS requirements. Further, we propose a packet-tagging method to assist the controller in mitigating rule congestion at the software-defined networking devices, and hence improve the overall network performance. Extensive results show that the proposed scheme minimizes the network delay and QoS-violated flows by up to 50% and 90%, respectively, compared to the state-of-the-art schemes.

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.

The U.S. Army is investing in research to deliver enhanced sensing, situational awareness, computing, and decision-making capabilities via IoT devices on the battlefield. One of the current shortfalls with existing capabilities involves the use of large wireless mesh networks to support disadvantaged users. Networks, like the Soldier Radio Waveform (SRW) suffer from increased latency and packet overhead that affect system performance and severely limit the operational employment of the system. In this paper, a novel hybrid topology management technique using a digital twin of the propagation environment and rapid assessment, cluster creation, and node assignment process has been proposed and investigated to show Software-Defined Networking and K-Means Clustering machine learning have potentials for increasing the number of users able to connect to wireless mesh networks. Preliminary results show that this technique can reduce the path length in large graphs at the expense of edge connectivity while delivering a level of service to all users that was previously unavailable.

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.

In this paper, we propose a novel Joint Steganography-Source-Channel Coding (JS2C2) scheme for scalable physiological signal transmission in wireless networks. Our goal is to secure patient's confidential data in medical signal by using Unequally Steganography Embedding and progressively transmit it by Joint Source-Channel Coding, limiting the extent of end to end distortion in the received signal, while abating transmission energy. The main contribution of this paper lies in the inherently joint design of the physiological signal steganography coding and transmission by taking advantage of the unequal importance among different segments of the physiological data. Higher steganography coding level and more robust source-channel coding will protect those highly important segments of the physiological data. Therefore, data integrity and transmission efficiency can be achieved in a resilient way. Simulation results demonstrate that USE attains low distortion (Wavelet-based Weighted Percent Root-mean-squared Difference, i.e. WWPRD2C2.

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.

In this paper, we propose a heterogeneous network (HetNet) system with a cloud control center to dynamically manage small base stations (SBSs) based on traffic load. The cloud can provide a user equipment (UE) association mechanism to balance both traffic load and spectrum allocation of SBSs and the macro base station (MBS) with throughput requirements of uplink and downlink. Our proposed association mechanism and SBS management mechanism can optimize the energy efficiency (EE) of the network and UE by considering EE of both uplink and downlink. Device-to-device communications are adopted under service request probability of UE and distance limitation. The EE optimization problem is solved in two steps in this paper. First, a decoupled association for UE over uplink and downlink is adopted. Least path loss criterion is used for uplink association. And priority SBS under signal-to-interference-plus-noise rate threshold and data rate requirement is applied in downlink association. After association, the SBSs management is implemented iteratively for adjusting the operation of SBSs to maximize the EE of both the network and UE. Simulation results show that our proposed method can improve the EE of the system with better performance on offloading traffic from the MBS to SBSs.

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.

Sensors are significant in electric machine applications, such as electric vehicles, to maintain the safe operation of a system. The sensor reliability must be ensured to help avoid unexpected system failure caused by faulty sensor signals. To mitigate the machine current sensor fault impact on permanent magnet synchronous machines (PMSMs), this paper proposes a novel PMSM current sensor fault detection and isolation (FDI) method based on DC link current estimation. The fault detection is achieved by comparing the residual between measured and estimated DC link currents with a threshold value; the fault isolation is achieved based on the phase signals estimation and residuals examination. The proposed method is easy to implement without complicated modeling, not influenced by system imbalance and capable of distinguishing between machine current sensor and non-sensor faults. As compared to the existing PMSM systems without the proposed FDI method, the system failure risk under a machine current sensor fault is reduced when the proposed method is integrated into the PMSM controller. The effectiveness of the proposed FDI method is validated by simulation results in MATLAB.

The cities in the world are in the process of quick transition toward more smart, automatic, responsive, and flexible societies. The Internet-of-Things (IoT) are expected to improve the intelligence of the cities, promote the interaction between the human and the environment, enhance the reliability, resilience, operational efficiency, and energy efficiency, as well as reduce costs and resource consumption. The development, adoption, and application of IoT technology into smart cities is of huge interest. Local authorities have partnered with startups, technology companies, research institutions, and universities to test and deploy IoT across all dimensions of urban life such as smart grid (SG), smart buildings, water management, connected healthcare and patient monitoring, environment/climate monitoring, connected cars, and smart transportation.

In this paper, an innovative idea of secure and energy efficient electrocardiogram steganography based on Discrete Wavelet Transform (DWT) is proposed, in which different important ECG data are steganographed with variable embedding strengths so that the reconstructed ECG signal quality is preserved. Incorporating the proposed Unequal Steganography Embedding (USE) with Unequal Error Protection (UEP), the proposed scheme further improves transmission performance in terms of ECG signal quality, ensures the confidentiality as well as reliability of a user's data, and makes the transmission more energy efficient. Simulation results demonstrate that USE attains very low distortion (WWPRD less than 0.45 %). Furthermore, high correlation between host and stego ECG and ECG data remains diagnosable after steganography. Based on our literature reviews, the reliability, robustness, and energy efficiency of this proposed method are unmatched in comparison with any other published methods.

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.

With rapid development in mobile devices with high quality image and video processing capabilities, it is desirable or necessary to implement steganography technology within such devices in some applications such as source authentication. In this paper, we propose a method of implementing information hiding within a video with less processing time and memory requirement. The message to be secretly communicated is hidden in the video by modifying blocks of Discrete Wavelet Transform coefficients of feature regions. We choose corners as specific features. The localizable capability of both corner detection algorithm and the Discrete Wavelet Transform makes it possible for the entire embedding method to be localized. This makes the entire process of steganography computationally faster and memory efficient. Our simulations show that the proposed method has a processing time that is up to 2.6 times more efficient, in average, than other traditional methods without incurring memory overhead.

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.

Lost audio packet steganography (LACK) is a steganography technique established on the VoIP network. LACK provides a high-capacity covert channel over VoIP network by artificially delaying and dropping a number of packets in use to convey stegnogram. However, the increasing loss of packets will hurt the quality of the VoIP service. The quality deterioration will not only affect the legitimate VoIP service but also constrain the capacity of the covert channel. Discrete spring transform (DST) is proven to be a way to eliminate the perceptual redundancy in the multimedia signal. In this paper, the DST is applied on the LACK so that the perceptual redundancy of the voice frames is suppressed. In this way, the less redundant VoIP frames with perceptual equivalent quality can be transmitted in a channel whose capacity is squeezed by the established covert channel. As a result, the VoIP perceptual quality can be maintained with the existence of the covert channel. Meanwhile, the proposed DST-based method demonstrates the possibilities in exploiting the perceptual space of the multimedia signal. The simulation results show that the DST on LACK achieves up to 24 % more capacity over the LACK scheme.

This study formulates a novel optimisation problem for joint spectrum sensing and random access control ( JS 2 RAC ) in cognitive radio sensor networks (CRSNs). The JS 2 RAC is formulated as a network utility maximisation problem, which aims to maximise the sum of utilities over all links in the network but subject to the primary user protection constraint, the energy constraint, and the physical constraint. Due to the non‐separable and non‐convex nature of the JS 2 RAC problem, the authors propose a primal‐decomposition‐based iterative (PDI) algorithm which decomposes the JS 2 RAC problem into a spectrum sensing subproblem and a random access control subproblem, and solve the two subproblems iteratively. Then, the authors prove the convergence of the PDI algorithm and show its distributed implementation in practice. Simulations demonstrate the fast convergence and the near‐optimal nature of the PDI algorithm and show its significant improvement in the network utility of CRSNs in comparison with the method of optimising spectrum sensing and random access separately.

Digital audio signal provides a large capacity for embedding hidden messages using digital steganography techniques. How to prevent hazardous steganography embedding on the Internet becomes an important task in the field of network security. For the Internet environment, the steganography attack method is required to be generic and real time. An active warden-based attack method is potential to be a generic method for the steganography attack. A discrete spring transform (DST)-based generic active warden steganography attack framework has been proposed by us. In this paper, based on the DST, a real-time steganography attack method is proposed. The potential unauthorized hidden message is removed in a real-time manner when uploading or downloading the audio signal. The real-time signal perceptual quality control is achieved by the automatic feedback from the objective audio quality evaluation model. The attack parameters are adaptively changed to reach a balance between the attack performance and the audio signal quality. The simulation results validate the proposed method in terms of the steganography attack performance and the audio signal quality after the attack. Copyright © 2014 John Wiley & Sons, Ltd.

There have been many robust image steganography methods that are invented in recent decades. However, this technique also may be used by malicious users to transmit dangerous information through the Internet beyond the control of the security agencies. How to detect and/or block potentially dangerous information transmission on the Internet through billions of images while not affecting the normal digital images becomes a challenging problem. Existing steganalysis methods or steganography attacking methods cannot be used for analyzing a large volume of digital images in a short time. In this paper, we propose an effective steganography attacking method that is not limited by the types of the steganography method. The proposed method can process the digital images to remove the potentially dangerous hidden information while keeping the digital image in a high visual quality. Inspired by the way in which printers and scanners work, our method decouples the human visual perception and the digital image's numerical values. It causes the numerical values of the image to change dramatically, and then the hidden information are largely damaged, while at the same time, the visual image quality can be maintained. Our experimental results have demonstrated that the peak signal-to-noise ratio of images can be above 32dB while the stego data are destroyed. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Abstract Because of the practical limitations of the energy and processing capabilities, the deployment of many Wireless Sensor Networks (WSN) is facing two main challenges of increasing network lifetime and reducing End to End Delay (EED) which become critical when the nodes are mobile and use non‐rechargeable energy sources. One way to help to extend network lifetime is using fuzzy logic in a form of artificial intelligence. To this end we propose a new routing protocol for using mobile WSNs, which holds the nodes in an equal level of energy and decreases energy dissipation of the network. An optimum path is selected based on the cost of each node to increase network lifetime. In order to lessen EED, we also attempt to design a novel zoning‐scheme for the network area. In this scheme, zonation is dynamic and works based on the Data Link (DL) position. The simulation result shows a significant improvement in lifetime and EED by proposed protocol compared with existing protocols. Copyright © 2016 John Wiley & Sons, Ltd.

ABSTRACT Satisfying the different requirements of applications is important in multi‐hop cognitive radio networks, because the spectrum resources change dynamically and the requirements for various applications could be very different. In this paper, we propose new schemes of channel allocation and route selection for real‐time and non‐real‐time applications to tackle this challenge. Our scheme is flexible so that it can adapt to the different application requirements, and it can provide enough throughput while maintaining the packet loss rate and transmission rate requirements. First, we give the network model in a cognitive radio network environment, and show how to calculate the capacity of the route in multi‐hop cognitive radio networks. Second, we formulate optimization problems to fulfill the rate requirements of different applications for each unicast session. We also consider the primary user activities, channel availability, interface and interference constraint. We propose the corresponding routing and channel allocation schemes for different application scenarios. Third, we propose an admission control scheme to study the impact of application requirements for multiple sessions in cognitive radio networks. Finally, we implement simulations to show the performance of our schemes and compare them with existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract In multi‐hop cognitive radio networks, it is a challenge to improve the energy efficiency of the radio nodes. To address this challenge, in this paper, we propose a two‐level Stackelberg game model, where the primary users and the secondary users act as the leaders and the followers, respectively. Based on the game model, our proposed scheme not only considers the power allocation problem for secondary users but also takes into account the price of spectrum. First, we give the cognitive radio network model, and show how to set up the game theoretic model in multi‐hop cognitive radio networks. We then analyze this problem and show the existence and uniqueness of the Nash equilibrium point for the game. We also study the impact of the spectrum price of the primary users in the cognitive radio network and study how to select the best price for the primary users to maximize their own profit. Finally, we implement simulations to show the performance of our schemes. Our work gives an insight on how to improve the energy efficiency and allocate spectrum resources in multi‐hop cognitive radio networks. Copyright © 2016 John Wiley & Sons, Ltd.

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.

In two-tiered Wireless Sensor Networks (WSNs) relay node placement considering resource constraints and high overhead of the relay nodes plays a key role in extending the network lifetime. Therefore, approaches that support fewer relay nodes are desired to cover the WSNs. In this paper, we formulate the relay node placement problem as a Geometric Disc Covering (GDC) problem, and propose a novel local search approximation algorithm (LSAA) to solve the GDC problem. In the proposed LSAA, the sensor nodes are allocated into independent groups and then a Set Cover (SC) for each group is performed. The set of the SC for each group constitutes a SC of the GDC problem. LSAA is extensively investigated and analyzed by rigorous proof and the simulation results presented in this paper clearly demonstrate that the proposed LSAA outperform the approaches reported in literature in the reduction in deployed relay nodes.

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.

Abstract Coverage is an importance issue in wireless sensor networks. In this work, we first propose a novel notion of information coverage, which refers to the coverage efficiency of field information covered by deployed sensor nodes. On the basis of information coverage, we consider an optimization problem of how to partition the given field into multiple parcels and to deploy sensor nodes in some selected parcels such that the field information covered by the deployed sensor nodes meets the requirement. First, we develop two effective polynomial‐time algorithms to determine the deployed locations of source nodes for information 1‐coverage andq‐coverage of the field, respectively, without consideration of communication, where informationq‐coverage implies that the field information in terms of information point is covered by at leastqsource nodes. Also, we prove the upper bound in the theoretical for the approximate solution derived by our proposed method. Second, another polynomial‐time algorithm is presented for deriving the deployed locations of relay nodes. In the theoretical, this proposed algorithm can achieve the minimized number of relay nodes. Further, the related information 1‐coverage algorithms are applied in our wireless sensor network‐based automatic irrigation project in precision agriculture. Experimental results show the major trade‐offs of impact factors in sensor deployment and significant performance improvements achieved by our proposed method. Copyright © 2013 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.

The smart grid will increase building energy efficiency and conservation via combining information and communication technologies, advanced instrumentations, system intelligence, and information on the end user. Specifically Demand Side Management (DSM) programs serve as an aid in energy conservation and management strategies as well as in the collection of real-time consumption information data. As proposed in this paper, real-time, fine-grain consumption data at the point of use in buildings can be used by building energy managers, utilities, and the end user for planning, load forecasting, and feedback for providing information that may lead to end user behavior change. This paper focuses on the development of a platform detecting every active node in the building remotely and using a combination of tools to monitor and locate these active nodes. The paper will discuss the main component of this platform, the energy node locator and how it works based on the principles of Time Domain Reflectometry (TDR). Preliminary results will be presented and used to evaluate the node locator principles. Summary and future work will be presented to discuss the framework moving forward.

In order to prevent harmful secret information sharing by steganography, a new active-warden countermeasure approach against steganography is proposed in this paper. Differently from the other countermeasure approaches presented in current literature which need to have some knowledge of the steganographic algorithms to be attacked, our proposed method is a generic method which is independent of any particular steganography methods being utilized. In other words, our approach blindly attacks the steganography without any prior knowledge of the used algorithms or their existence. In general, the hidden information is embedded in a carrier by adjusting the coefficients of the audio or image. In our method, by exploiting the large margin between the numerical value and visual perception of the images, large amounts of visually non-detectable distortions are incurred in the image. As a result, the hidden message is destroyed by this method while the perceptual quality of the image is maintained. Inspired by the print-scan process in which most of the steganographic methods cannot survive, a transform called Discrete Spring Transform (DST) is proposed in this paper as the foundation of our attack algorithm. An image geometrization method is also developed to reconstruct the image in this paper. The simulation results have demonstrated that the PSNR of the attacked image is above 30dB with a high perceptive quality while the BER of the hidden steganographic message in the attacked image is above 0.5.

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 the analysis and simulation evaluation of a cognitive radio network employing a distributed beamforming technique with imperfect phase synchronization in the presence of a primary receiver. Our system model consists of a group of cognitive transmitters, each with an ideal isotropic antenna and equal transmit power, communicating with a secondary receiver in the far-field. The objective of the network of cognitive transmitters is to optimize its beampattern in the direction of the secondary receiver while minimizing the beampattern in the direction of the primary receiver to a certain threshold. The phases of the transmitted signals determine the beampattern, and we demonstrate that an optimization problem can be formulated to determine the phases of the transmitters that satisfy the constraints. We then evaluate the beampattern under imperfect phase synchronization and present how the phase error can impact the performance of beamforming and cause protection to the primary receiver to suffer. The results bring some interesting insights to distributed beamforming with imperfect phase synchronization for cognitive radio networks.

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.

Digital steganography is often divided into two categories of research: steganographer and attacker, where a steganographer attempts to successfully hide the existence of hidden data and an attacker attempts to uncover and destroy said data. Often steganographic attacks utilize passive techniques, where the attacker monitors messages and alters or destroys the cover media only if the media is suspected to contain a message. Passive attacks are much more prevalent than active attacks for a variety of reasons. Generally passive attacks are more efficient and effective in determining the existence of hidden data in a given cover media, however, most passive attacks are not generic enough to detect the presence of a hidden message outside of a very small subset of steganographic algorithms. Conversely, current active attacks are very cost effective and often severely degrade or destroy the quality of the cover media. In this paper, we propose a novel active steganographic attack called the Discrete Spring Transform (DST). The concept of the DST attack is that of likening the cover media to a spring, in that it can be physically altered or manipulated in a manner of ways, while the basic structure and integrity of the spring is preserved despite these alterations. Since many steganographic schemes rely on the fact that the cover media remains somewhat stable the DST is effective in destroying the stego media for such types of steganographic algorithms. It follows that the DST is an effective and highly adaptable active attack that can be applied to a variety of cover media, including video. We will demonstrate that the attack is capable of defeating next-generation steganographic algorithms, including motion-vector steganography and RST-resilient algorithms by increasing the Bit Error Rate (BER) of the steganographic algorithm to approximately 0.5 while maintaining the quality of the cover media, where the Peak Signal-to-Noise Ratio (PSNR) of the image-derived media always remains above 30db.

The significant development of cell phone and mobile network in recent years leads to some security concerns caused by increasing capacity of the networks and the large margin of the multimedia data. Audio steganography is a proven way to hide information in digital audio signals, where many schemes have been effective at hiding large amounts of data. In order to prevent the malicious threat of the audio steganog-raphy, the countermeasure of audio steganography is also an important topic. In this paper, we propose a novel steganographic attack which can defeat audio steganography algorithms while maintaining an acceptable audio distortion level. The attacking method is based on a proposed transform called discrete spring transform. Similar to the time scale modification, the spring transform disables the synchronization of the hidden information. Furthermore, the proposed method has some advantages over the traditional time scale modification, therefore the steganography method which can resist to the time scale modification still can be defeated by the proposed method. The simulation results show that the proposed method successfully defeat some audio steganography methods which are resistant to the time scale modification with subjective auditory quality score no worse than -0.1.

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.

In this paper, we propose a joint channel allocation and multicast routing scheme for a multihop cognitive radio network. Our scheme can react to the dynamic change of channel availability. It can maximize the multicast throughput by jointly performing channel allocation and multicast routing. We first model the activities of the primary users, channel availability and the interference among the secondary users in a cognitive radio network, and show a basic scenario of multicast for a multihop cognitive radio network. Second, we formulate an optimization problem to maximize the multicast throughput of the network. We consider the channel availability constraints and the interference constraints jointly, and make use of the transmission of the hyper-links. Furthermore, we compare the performance of our scheme with existing schemes for different scenarios of channel availability and the number of channels through simulations. Our work brings insights on how to make route selection and channel allocation for multicast in multihop cognitive radio networks.

Cognitive radio is an efficient technique to relieve the tense of wireless spectrum scarcity by allowing unlicensed secondary users (SUs) to access the licensed band opportunistically without causing interference to primary users (PUs). Although Federal Communications Commission (FCC) recently ruled that the data of PU activity schedule is accessible to SUs 24 hours ahead, which relieves SUs from heavy sensing or interruption by sudden PU activity, however, multi-hop wireless cognitive radio networks (MWCRN) suffers a unique problem caused by the fact that the spectrum resources are not unified in different areas affected by different PUs. In other words, an SU origin-destination (OD) pair transmission would meet the bottleneck in bandwidth when crossing areas with different available spectrum resources. To solve this problem, we formulate an optimization problem to maximize the number of connection bridges to cross different areas. Moreover, we introduce channel bonding technique into the MWCRN for network performance improvement. We also propose a distributed algorithm for practical application. Simulation results verifies the better performance of our proposed scheme.

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 investigate a network coding-aware channel allocation and routing scheme for multi-hop cognitive radio networks. We consider network coding and channel availability in cognitive radio networks and maximize the throughput by allocating the channel and link rate appropriately. First, we model the activities of the primary users and the interference among the secondary users in a cognitive radio network and show how to implement network coding in the multi-hop cognitive radio network. Second, we formulate an optimization problem to maximize the throughput of the network. It takes advantage of the network coding opportunities and considers the channel availability constraint. By solving this optimization problem, we can determine how to allocate channels and rates of links in different channel availability scenarios. Furthermore, we compare the performance of our scheme with the coding oblivious routing for different scenarios of channel availability and maximum number of channels in a random wireless network. Our work brings insights on the benefits of network coding-aware routing in multi-hop cognitive radio networks.

In this paper, we propose a backbone construction scheme for multi-hop wireless cognitive radio networks (MWCRN) with a channel bonding technique. In our proposed scheme, the backbone is established purely in licensed bands using cognitive radio (CR) technology. We introduce a channel bonding technique to get higher performance of the network. To get higher reliability of MWCRN, we use backup channels in our proposed scheme. The proposed scheme includes two major steps. The first step is to formulate a backbone network; and the second step is to assign operating channels and backup channels to each backbone link. Simulation results show that the proposed scheme achieves higher network performance of multi-hop wireless cognitive radio networks.

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.

Cyber security for smart grid communication systems is one of the most critical requirements need to be assured before smart grid can be operationally ready for the market. Privacy is one of very important security consideration. The customer information privacy in smart grid need to be protected. Smart grid data privacy encompasses confidentiality and anonymity of the information extracted from smart device metering transmission in smart grid communication system. In this paper, we consider a home area network as a basic reading data aggregation and dispatch unit in smart grid systems. Then, we propose a secure in-network data aggregation and dispatch scheme to keep the confidentiality and anonymity for collecting power usage information of home smart devices to the household smart meter and the reverse control message distributing procedure. Specifically, we introduce an orthogonal chip code to spread reading-data of different home smart devices into spread code, followed by a circuit shifting operation to coupling neighboring smart devices tightly. We adopt an in-network mechanism to further mask it with its spread data with its forwarding data. Finally, we analyze the cyber security protection levels using an information theoretic quantity, residual uncertainty. Simulation studies are conducted to test the performance on different metering datasets for the proposed scheme. This paper sets the ground for further research on optimizing of home power management systems with regarding to the privacy of customer power usage behaviors.

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.

We consider various security vulnerabilities of deploying Advanced Metering Infrastructure (AMI) in smart grid, and explore the issues related to confidentiality for customer privacy and customer behavior as well as message authentication for meter reading and control messages. There are only a very few research work on AMI authentications, and no work exists on confidentiality for user privacy and user behavior, from the best of our knowledge. In this paper, we propose an in-network collaborative scheme to provide secure and reliable AMI communications in smart grid, with smart meters interconnected through a multihop wireless network. In this approach, an AMI system can provide trust services, data privacy and integrity by mutual authentications whenever a new smart meter initiates and joins the smart grid AMI network. Data integrity and confidentiality are fulfilled through message authentication and encryption services respectively using the corresponding keys established in the mutual authentications. A transmission scheme is proposed to facilitate the data collection and management message delivery between smart meters and a local collector for AMI communications. Simulation results show that the proposed method has a better end-to-end delay and packet losses comparing with a basic security method, and the proposed method can provide secure and reliable communications for AMI in smart grid systems.

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.

The Wireless Broadband (Wibro) specified by IEEE 802.16e has gained great popularity in the next generation mobile telecommunication networks. However, as mobility is supported, handover procedure especially the authentication mechanism becomes a critical part in system performance. In this paper, we present an analytical evaluation for MAC layer handover procedure with RSA-based authentication which is a security standard in IEEE 802.16e specification. Our analysis consists of differentiated considerations between mobile subscribers (MS) and base stations (BS) in signaling message transmission, queueing model and computing process for the authentication and key establishment process during handover procedure. We have observed that the major portion of the handover delay is the authentication process due to heavy computing operations. The analysis provides deep insights into the system performance of the handover procedure in a Wibro network.

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.

Media integrity, transmission quality, and energy efficiency are critical for secure wireless image streaming in a wireless multimedia sensor network (WMSN). However, conventional data authentication and resource allocation schemes cannot be applied directly to WMSN due to the constraints on limited energy and computing resources. In this paper, we propose a quality-driven scheme to optimize stream authentication and unequal error protection (UEP) jointly. This scheme can provide digital image authentication, image transmission quality optimization, and high energy efficiency for WMSN. The contribution of this research is two-fold as summarized below. First, a new resource allocation-aware greedy stream authentication approach is proposed to simplify the authentication process. Second, an authentication-aware wireless network resource allocation scheme is developed to reduce image distortion and energy consumption in transmission. The scheme is studied by unequally protected image packets with the consideration of coding and authentication dependency. Simulation results demonstrate that the proposed scheme achieves a performance gain of 3 ~ 5 dB in terms of authenticated image distortion.

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.

In this paper, a novel adaptive approach based on Alamouti scheme is proposed to estimate and compensate frequency-dependent in-phase/quadrature-phase (I/Q) imbalances in space-time coded MIMO-OFDM communication systems. The I/Q imbalances may be present at both ends of transmitter (TX) and receiver (RX). Although different methods have been proposed to utilize the Alamouti scheme in order to obtain spatial diversity in a MIMO-OFDM system, the inaccurate channel estimation caused by I/Q imbalances degrades the system performance. A new combining scheme is introduced to overcome this significant drawback by using joint estimation and compensation of the multipath fading channel and I/Q imbalances. The simulation results show that the proposed adaptive method can effectively mitigate the effect of the frequency-dependent TX and RX I/Q imbalances in the MIMO-OFDM systems.

A cross layer Unequal Error Protection (UEP) scheme based upon the multimedia position-value (P-V) partitioning has been recently proposed to only improve energy-distortion performance in Wireless Multimedia Sensor Networks (WMSN). There is also a great potential to adapt this UEP scheme into multimedia stream integrity protection. However, performance gains in multimedia transmissions with considerations of energy, distortion, and authentication in WMSN is a challenge. This difficulty results from the media codec dependency on both the content of multimedia stream authentication and protection on the integrity process itself. In this article, we propose a novel scheme which optimally matches the application layer multimedia codec dependency to both lower layer resource allocation requirements and the upper layer stream authentication. The goal of this scheme is to optimize the integrity and quality performance gain within energy constraint. The contribution of the proposed approach is two folds. Firstly, a stream authentication scheme matched with P-V codec dependency is proposed which significantly reduces additional authentication dependency overhead. Secondly, a new resource allocation scheme is proposed to improve energy-distortion-authentication performance with regard to codec dependency. Simulation studies demonstrate that the proposed scheme significantly impacts achieving multimedia transmission quality with energy efficiency and authentication assurance.

Although current research focused on sensor deployment to balance the energy consumption among the sensor nodes for prolonging the lifetime of wireless sensor networks, sensor nodes with larger communication radius have to consume more energy to overcome wireless channel fading and path loss. Cooperative transmission allows nodes with single-antenna to cooperate on information transmission and/or reception to achieve a lower average error probability than its non-cooperative counterpart under the same transmit energy budge. In this work, we introduce the concept of cooperative transmission into sensor deployment to balance the energy consumption among sensor nodes. The key idea is to deploy an optimal number of cooperative nodes into a given area. In this way, batteries of all sensor nodes are discharged at the same rate. Extensive simulations have been conducted to evaluate the performance of the proposed scheme. Compared with non-cooperative cases, the proposed scheme with cooperative transmission improves the battery energy efficiency by 52.1%, and prolongs the lifetime of wireless sensor network by three times.

As a TCP-Friendly Rate Control protocol on the basis of TCP Reno's throughput equation, TFRC is designed to provide optimal service for unicast multimedia delivery over the wired Internet networks. However, when used in wireless environment, it suffers significant performance degradation. Most of the current research on this issue only focuses on the TFRC protocol itself, ignoring tightly-coupled relation between the transport layer and other network layers. In this paper, we propose a new approach to address this problem, integrating TFRC with application layer and physical layer to form a holistic design for real-time video streaming over wireless multi-hop mesh networks. The goal of the proposed approach is to achieve the best user-perceived video quality by jointly optimizing system parameters residing in different network layers, including the real-time video coding parameters at the application layer, the packet sending rate at the transport layer, and the modulation and coding scheme at the physical layer. The problem is formulated and solved as to find the optimal combination of parameters to minimize the end-to-end expected video distortion constrained by a given video playback delay. Experimental results have validated 2-4 dB PSNR gain achieved by the proposed approach in wireless multi-hop mesh networks.

One of the design challenges of wireless sensor networks is the tradeoff between network operation time and network coverage. Recent studies reveal that the useable battery capacity drops faster at a higher discharge current in a nonlinear fashion. To take advantage of this battery current effect, in this paper we explore a new sensor node deployment scheme to prolong the entire sensor network lifetime as well as each individual sensor node. The key idea of the proposed scheme is to assign a sensor node having higher traffic load to adopt lower transmission power level. In this way, batteries of all nodes in a given area are discharged at the same current, thus they are depleted at the same time. Extensive simulations have conducted to evaluate the performance of the proposed sensor node deployment scheme. Compared with peer work on heterogeneous deployment, the useable battery capacity by using the proposed scheme can be improved by 26.67%, and the operating time per sensor node can be enhanced by 20.95%. Furthermore, the proposed deployment scheme can reduce the number of sensor nodes required to cover the given area, leading to a significant reduction of deployment cost.

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.

A report by the Institute of Medicine of the National Academy of Sciences estimates that as many as 98,000 people die in U.S. hospitals each year because of medical errors. In this project, we propose an innovative IT-based approach to prevent errors in various medical processes by utilizing advance

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.

Transmissions of large sized images can be a bottleneck for a wireless sensor network (WSN) due to its limited resources. Security can be another concern. This paper proposes a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide the transmission and security sharing patterns based on the path diversities. Our proposed approach for secret image sharing on multiple node-disjoint paths for image delivery is to achieve high security without any key distribution and management, and thus the key management related problems do not exist. The energy efficiency is another major contribution made in this paper. This scheme does not only allow each image sensor to transmit optimal fractions of overlapped images through appropriate transmission paths in an energy-efficient way, but also provides unequal protection to overlapped image regions by path selections and adaptive bit error rate (BER) requirement. The simulation results show that the proposed scheme can achieve considerable gains in terms of network lifetime extension, image transmission security enhancement, image quality improvement, and energy efficiency for wireless sensor networks.

Distributed Source Coding (DSC) is an effective means in reducing data redundancy in Wireless Sensor Networks (WSNs). However, the issues on designing multirate DSC with multirate transmission in WSNs have not been well addressed in the literature. In this paper, we propose a cross-layer approach to achieve optimal DSC data quality while assuring energy efficiency and latency requirement through adjusting multirate DSC data dependencies and network multirate transmission parameters jointly. Specifically, the DSC coding dependency among multirate source coding sensors are fine-tuned to balance compression efficiency and transmission robustness. Transmission rate and retransmission limit on each wireless link are optimized accordingly to achieve Unequal Error Protection (UEP) among inter-dependent DSC streams. Simulation studies demonstrate that the proposed scheme ensures satisfactory DSC data quality and energy efficiency.

Abstract In this paper, we propose an innovative adaptive secure image delivery approach, to achieve effective and efficient image encryption based on an optimal transmission rate in terms of energy efficiency in wireless sensor networks (WSN). Our contribution is three folds—First, the proposed component based selective encryption scheme achieves high image security, while incurring little encryption overhead; furthermore, the proposed adaptive encryption scheme achieve the best security effort within delay bound; finally, the proposed transmission rate optimization improves energy efficiency, and feeds back effective path capacity parameters for adaptive encryption. Simulation results show a significant gain in both energy efficiency and security based on the proposed approach for image secure transmissions in WSN. Copyright © 2007 John Wiley & Sons, Ltd.

Multimedia delivery over Wireless Sensor Networks (WSNs) is energy critical. In this paper, we study an energy constrained cross layer optimization scheme to improve digital image quality in cross polarization discrimination (XPD) based Multiple Input Multiple Output (MIMO) WSNs. The contribution of this research can be summarized in three parts. First, link layer energy consumption and packet loss ratio in multirate XPD based MIMO WSN are modeled. Secondly, XPD based modulation optimization cross link and physical layer is proposed, achieving considerable energy efficiency. Finally, a new Position-Value (P- V) based Unequal Error Protection (UEP) scheme is studied and discussed in XPD MIMO WSNs, with more efficient resource allocation compared to traditional approaches. Simulation results show the proposed modulation optimization scheme improves energy efficiency considerably, and position based UEP achieves significant distortion-energy gain compared with traditional layer based resource allocation schemes in XPD MIMO WSNs.

A secure image transmission mechanism is necessary when malicious intruders intend to access and modify content delivery over wireless networks. To assure data integrity, authentication is required for multimedia data delivered over wireless image sensor networks. Watermarking technique is an effective vehicle to assert and assure the image data authentications. There have been recent works reported on watermarking, but few with the consideration of energy cost in terms of the data communication and processing, which is a key constraint to many embedded systems and wireless sensor networks. Most watermarking systems only target at minimizing watermarked image distortion and increasing robustness at the source coding site for lossy image processing. The watermarked image distortion caused by error-prone wireless environments during transmission has not been fully considered. In this paper, an innovative energy-aware adaptive watermarking scheme for realtime image delivery is proposed in wireless multimedia sensor networks. This new scheme allocates network resource to protect the watermarked image transmission while embedding watermark coding redundancies into the images. Dynamic watermark thresholds are applied to be adaptive to the network condition (packet loss ratio) and the inter-frame correlation is exploited to reduce processing delay. The simulation results show that the proposed adaptive watermark system can achieve considerable energy efficiency and assure the data integrity.

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.

Joint consideration of energy efficiency, multimedia quality and media authenticity has largely been overlooked in the studies of secure wireless multimedia streaming. In this paper, we propose an authentication - resource allocation framework for secure transmission of multimedia streaming over energy constrained wireless networks. The contributions of this research can be summarized in three aspects. Firstly, a hash chain based stream level authentication scheme compatible for JPEG2000 compression standard is proposed. Secondly, link layer energy- distortion is modeled, and the energy-distortion convex hull is efficiently identified, which significantly reduces optimization complexity. Finally, an authentication oriented resource allocation scheme is proposed based on the proposed authentication scheme and link layer energy-distortion convex hull, where the authenticated image quality is optimized according to energy budget constraints.

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.

Abstract Security is of ultimate importance to the global communication and information networks. Both academia and industry have seen the importance of the R&D activities in security and communication networks, and a great deal of resources has been invested by both governments and private sectors around the world to enhance the security of global information and communication networks, which virtually cover all regions of the world. Therefore, the timely launch of Security and Communications Networks is of great interest to the world community. In this inaugural issue, worldwide experts contribute their papers, covering various important research areas in security. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract The ever‐increasing demand for the services of wired and wireless data networks has led to network security being a prerequisite for all successful data transactions. This has accelerated research activities in academia, private sectors, and governments to respond to the challenges of providing complete solutions for secure communications and networks. Security and Communication Networks provides a distinctive global platform to explore the research advances in the communications network security from academia, industry, and governments. In this second issue, six thought provoking papers discussing various element of security research in communications and networks are presented. Copyright © 2008 John Wiley & Sons, Ltd.

Image transmissions in Wireless Multimedia Sensor Networks (WMSNs) are often energy constrained. They also have requirement on distortion minimization, which may be achieved through Unequal Error Protection (UEP) based communication approaches. In related literature with regard to wireless multimedia transmissions, significantly different importance levels between image-pixel-position information and image-pixel-value information have not been fully exploited by existing UEP schemes. In this paper, we propose an innovative image-pixel-position information based resource allocation scheme to optimize image transmission quality with strict energy budget constraint for image applications in WMSNs, and it works by exploring these uniquely different importance levels among image data streams. Network resources are optimally allocated cross PHY, MAC and APP layers regarding inter-segment dependency, and energy efficiency is assured while the image transmission quality is optimized. Simulation results have demonstrated the effectiveness of the proposed approach in achieving the optimal image quality and energy efficiency. The performance gain in terms of distortion reduction is especially prominent with strict energy budget constraints and lower image compression ratios.

The advancement in Distributed Source Coding (DSC) for mission-driven wireless sensor networks (WSNs) has opened a new vista for its wide applications in multiple correlated sensor networks such as real time target tracking and environment monitoring. The features of the DSC applications offer many potential opportunities for sensor networks to utilize multirate transmissions for network performance enhancement. In this paper, we propose a methodology for cross-layer optimization between routing and DSC in WSNs, and we introduce a multirate based routing scheme for mission-driven DSC applications that can considerably extend network lifetime. The proposed scheme adopts the rate assignment based on residual energy and employs a joint rate and energy scheduling mechanism to meet the end-to-end transmission rate demand, information precision requirement, and energy constraints in the networks adopting DSC. This approach is different from the traditional multirate research in link adaptation, where the focus was to increase channel throughput based on rate adaptation from variable channel conditions. The objective of this work is not to utilize multirate for increasing channel throughput, but to exploit multirate capability for routing optimization in DSC based applications with consideration for energy consumption. We also introduce the concept of "Energy Usage Scheduling (EUS)" to optimize the energy usage based on the rate constraints and DSC application needs. Simulation results show that the proposed scheme can achieve a significantly longer network lifetime than that reported in the literature.

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.

Many Sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in Wireless Sensor Network (WSN). The large size image transmission in WSN is a bottleneck due to the limited energy resources. In this paper, we propose an optimal scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on a Multi-Rate Least Cost Path (MRLCP) routing scheme, which achieves high energy efficiencies and longer network lifetime. This optimization scheme allows each image sensor to transmit optimal fractions of the overlapped images through appropriate ratebased routing paths. A specific genetic algorithm is designed to solve such discrete optimization problem. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the WSN energy efficiency and network lifetime extension.

In this paper, we propose a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification power by capturing all possible interactions among two or more attributes. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Instead of using a discrete misclassification rate, the objective function to be optimized in this research is a continuous Choquet distance with a penalty coefficient for misclassified points. The numerical experiment shows that the special genetic algorithm effectively solves the nonlinear classification problem and this nonlinear classifier accurately identifies classes.

Battery-powered embedded networked sensing systems become more and more pervasive with the fast-paced deployment of various remote sensing applications. How to prolong the battery operating time is one of the most challenging areas in the design and development of these sensing systems due to the fact that the battery operation is much more dynamic and complex than considered, which is derived from its application and its internal structure (multiple hardwired series/parallel cells) to produce a specific voltage and capacity. Current research on prolonging the battery operating time is mainly focusing on the low-power hardware and energy-efficient network protocol designs, and simply treats the battery as a passive two-terminal energy source. In this paper, we will propose a novel adaptive, proactive, and reconfigurable multicell battery design for supporting power-aware hardware and energy-efficient network protocols for embedded networked systems, which provides a whole new perspective to look at the energy problems of battery- powered embedded networked sensing systems. A theoretical modeling of the proposed design is provided, and simulation results show that the proposed design can significantly enhance the energy performance, especially for low voltage and low discharge current scenarios.

Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in wireless sensor networks (WSN). The large size image transmissions cause bottlenecks in WSN due to the limited energy resources and network capacity. In this paper, we propose a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on transmission path diversities, which achieves minimal energy consumption, balanced sensor lifetime and required image quality. This optimization scheme not only allows each image sensor to transmit optimal fractions of the overlapped images through appropriate transmission paths in energy-efficient way, but also provides unequal protection on the overlap image regions through path selections and resource allocations to achieve good transmission image quality. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the network lifetime extension, image distortion reduction, and energy efficiency.

In image transmission applications over wireless sensor networks (WSN), energy efficiency and image quality are both important factors for joint optimization. In this paper, we propose a new position-value (P-V) oriented cross layer optimization approach to minimize energy consumption with distortion bounds for image transmission applications in WSN, by exploring importance level diversity for position-magnitude information. To put more effort on transmitting important position information and relatively less effort on unimportant magnitude information, desirable BER, ARQ retry limit, transmission rate and distortion reduction of image are jointly optimized cross PHY, MAC and APP layers, and image quality is assured while energy consumption is minimized. Simulation results demonstrate the effectiveness of the proposed approach in achieving energy efficiency while maintaining image quality.

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.

Recent advances in distributed source coding (DSC) for mission-driven wireless sensor networks (WSN) are related to the coding for multiple correlated sensors in applications such as real time target tracking and environment monitoring. The characteristic of these DSC applications provides significant potential opportunities in the associated sensor network to utilize multirate transmissions for enhancing network performance. In this paper, we study a methodology for interplay optimization between routing and DSC in WSN, and propose a novel multirate based routing scheme for mission-driven DSC applications that considerably extends network lifetime. The proposed scheme adopts the rate assignment based on the residual energy, and employs a joint rate and energy scheduling mechanism to meet the end-to-end transmission rate constraint, information precision requirement, and the energy constraints in the network for DSC. Simulation results show that this multirate based routing scheme achieves significantly longer network lifetime compared to other existing research in WSN for DSC applications.

Many Sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in Wireless Sensor Network (WSN). The large size image transmission in WSN is a bottleneck due to the limited energy resources. In this paper, we propose an optimal scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on a Multi-Level Rate-Oriented routing (MLRR) routing scheme, which achieves high energy efficiencies and longer network lifetime. This optimization scheme allows each image sensor to transmit optimal fractions of the overlapped images through appropriate multiple Rate-Oriented routing paths. The simulation results by using GA algorithm show that the proposed image transmission scheme can achieve considerable gains in terms of the WSN energy efficiency and network lifetime extension.

Selective encryption for secure image transmission is an important ongoing research area which aims to scramble the critical information correlation and inter-dependency within final image compression bit streams. However, how to take Unequal Error Protection (UEP) method for transmitting partially encrypted image data in error-prone communication channels is still an open research problem in Wireless Sensor Networks (WSN). In this paper, we propose a new cross layer optimization approach for energy efficient delivery of selectively encrypted images in WSN while assuring image quality. In the proposed approach, identified importance levels of diversity for encrypted procession blocks as well as position-value (P-V) information are extensively explored within image compression and encryption bit streams. Desirable BER, ARQ retry limit, transmission rate are jointly optimized across PHY, MAC and APP layers regarding the image distortion reduction bound and communication energy efficiency. Both image quality and security are assured while energy consumption is minimized. Simulations results have shown significant gain in image security assurance and transmission quality achievement with energy efficient communications in WSN.

This paper proposes a new tiered heterogeneous wireless image sensor network for real-time unobtrusive species detection and video cataloguing based on a new hierarchically scalarized-character oriented detection (HIS-CODE) algorithm and architecture. An experimented test bed has been set up at the Omaha Henry Doorly Zoo, and data collected over wireless network from this test bed has been processed and classified based on the proposed HIS-CODE. This work contributes to a novel integration in wireless sensor networks with the in-network distributed image recognition functionality, especially with the consideration of wildlife species image recognitions.

Image sensors' digital transmissions are significant challenge to the Wireless Sensor Networks (WSN) due to resource constraints. In this paper, we propose a novel position-value (P-V) oriented multirate image transmissions approach called Image Component Multirate Transmissions (ICMT) over WSN. This method adapts different reliable levels of transmissions to various P-V segmented components within the compressed image bit stream. Simulation results show that the proposed scheme achieves high energy efficiency in WSN while enhancing the image transmission quality.

Signal processing applications such as Distributed Source Coding (DSC) in Wireless Sensor Network (WSN) are often multirate in nature, which provide additional significant opportunities in WSN design for achieving energy efficiency. In this paper, we analyze the DSC traffic for signal processing applications in multirate WSN and propose a novel rate oriented approach with MAC and PHY cross layer utilization to achieve high energy efficiency. Simulation results show that the proposed approach achieves significant energy efficiency by a factor of three while maintaining the transmission quality in WSN. This approach can be easily extended to other WSN MAC and PHY designs, providing multirate service to application layer to allow achieving higher energy efficiency

This letter proposes a simple but highly efficient technique to jointly utilize space, time, and polarization diversities. The technique is based on extending orthogonal space-time block codes into the quaternion domain and utilizing a description of the dual-polarized signal by means of quaternions, which are a generalization of complex numbers. In the given example, the achievable performance gain for two transmit and one receive antennas is approximately 6 dB at a bit error rate of 10-4when compared with the Alamouti code.

Distributed source coding (DSC) based signal processing applications involve data transmissions at multirates in wireless sensor network (WSN). In this paper, we propose a novel cross layer approach in WSN to achieve energy efficiency using multirates desirable for the DSC based signal processing applications and current network conditions. Our approach can be summarized into two parts: first, energy efficient multirate MAC-PHY supporting DSC applications; and second, rate oriented interaction of MAC-PHY and DSC applications. Multirate MAC-PHY utilizes desirable bit error rate (BER), channel attenuation, corresponding modulation schemes, and multirate requirements, to determine the minimum desirable transmission power. Rate oriented interaction provides optimal transmission rates feedback to interact with DSC applications, making DSC applications adaptively adjust information coding rate according to network conditions. This interactive approach achieves further energy saving significantly by making the DSC application aware of WSN conditions. Besides DSC, this work can be easily extended to other multirate signal processing applications in WSN.

In this paper, we propose a link adaptation scheme to adapt the packet size as a crucial system parameter for quality of service (QoS) provisioning in multi-rate wireless networks. The link adaptation scheme combines adaptive modulation and coding (AMC) at the physical layer with type-II hybrid automatic repeat request (HARQ) at the data link layer in a cross-layer fashion. We then study the goodput performance of the proposed link adaptation scheme at the medium access control (MAC) layer. We show that there exists an optimal packet size to achieve the maximum goodput in transmitting messages over transport layer sessions. Based on this observation, we provide an efficient and effective algorithm to search the optimal packet size. Finally, we propose a link adaptation architecture, through which the proposed link adaptation scheme can adapt packet sizes to instantaneous channel conditions to fulfill committed QoS in higher layers. Numerical results show that the system adopting the proposed optimal packet sizes has superior goodput performance over the systems using fixed packet sizes.

This paper proposes an approach to analyze pulse waveform dependent bit error rate (BER) performance of a DS-CDMA ultra wideband (UWB) radio, which operates in a frequency selective fading channel. The analysis takes into account almost all real operational conditions, such as asynchronous transmissions, RAKE receiver, multiple access interference (MAI), multipath interference (MI), log-normal shadowing, and noise. The main objective of the paper is to reveal the relationship between time domain characteristics of pulse waveforms and BER of a UWB radio. It is shown through analysis (also validated by simulation) that the normalized mean squared auto-correlation function (ACF) of the pulse waveforms can be used as an effective merit figure to judge the suitability for their applications in a DS-CDMA UWB radio. In fact, the normalized mean squared auto-correlation function (ACF) governs the average inter-chip interference caused by imperfect auto-correlation function of the pulse waveforms. The paper concludes that, as long as the power spectral density (PSD) functions of the pulse waveforms fit the FCC spectral mask, the pulse waveforms' normalized mean squared ACF should be minimized to ensure an acceptable BER.

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.

This paper introduces an approach to generate generalized pairwise complementary (GPC) codes, which offer a uniform interference free windows (IFWs) across the entire code set. The GPC codes work in pairs and can fit extremely power efficient quadrature carrier modems. The characteristic features of the GPC codes include: the set size is 2K, the processing gain is 4NK, and the IFW's width is 8N identically for all codes in a set, where K is the times to perform Walsh-Hadamard expansions and N is element code length of seed complementary codes. Therefore, by using different N, the IFW width of a GPC code set can be adjusted with its set size unchanged. Each GPC code set consists of two code groups, with each having K codes, and they have sparsely and uniformly distributed autocorrelation side lobes and cross-correlation levels outside the IFWs.

Summary form only given. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In this paper, we study a performance comparison of two recently proposed link adaptation techniques based on Kalman filter at MAC layer to enhance energy efficiency of the sensor nodes in mobile wireless sensor networks. These two new approaches use extended Kalman filter (EKF) and unscented Kalman filter (UKF) to predict the optimal frame size for improving energy efficiency and goodput, while minimizing the sensor memory requirement. We designed and verified different network models to evaluate and analyze the proposed link adaptation schemes. The simulation results show that the UKF-based link adaptation algorithm offers a better performance than the EKF-based algorithm due to less errors on estimation and prediction in a nonGaussian nonlinear scenario.

Accurately modeling the IEEE 802.11 MAC is critical to design resource allocation, admission control and scheduling schemes for QoS provisioning in wireless LANs. However, it is difficult to model the saturation throughput performance of the IEEE 802.11 MAC due to the fact that the memory effect existing among different stages in the random backoff procedure. Therefore, Markov chain has been used to characterize the saturation throughput performance of the IEEE802.11 CSMA/CA protocol in many research efforts. However, one of the problems existing in the current research failed to consider all key practical issues altogether such as retry limit, backoff suspensions and the fading channel errors. Therefore, an accurate and realistic model of the IEEE 802.11 MAC protocol with fading channel errors is needed. In this work, we propose a new model based on Markov chain and validate its correctness by theoretical analyses and Ns-2 simulations. We also compared our results with other current major models. Our contributions in this work are: 1) taking the backoff suspension into considerations and derive a more accurate model than other current models; 2) introducing the fading channel errors into the modeling process and derive a close form equation of the saturation throughput performance of the 802.11 DCE simulation results show that the proposed model is same as accurate as other current models, and becomes much more accurate when considering fading channel errors.

Self-regulating network utilization is very important to the wireless network service providers to carry out new services such as wireless network gaming, which means that more users with a prepaid level of quality of service can be accommodated in a bandwidth limited network. However, in mobile ad-hoc wireless LANs, it is very difficult to regulate network utilization due to the time-varying nature of many important system parameters such as network load and channel quality. Moreover, the contention-based MAC protocols such as CSMA/CA adopted by IEEE 802.11 standard have been widely deployed in a wireless LAN environment for its flexibility, making self-regulating network utilization even more difficult. In this paper, we have proposed a fully distributed scheme running on each wireless station for self-regulating its network utilization to meet its prepaid service level. Simulation results show that the proposed scheme allows a wireless station to self-regulate the network utilization to its prepaid service level as close as possible by adapting the system parameters of MAC and PHY layers with changes of network environment such as the number of users and channel quality.

Technological advances in low-power digital signal processors, radio frequency (RF) circuits, and micromechanical systems (MEMS) have led to the emergence of wirelessly interconnected sensor nodes. The new technological possibilities emerge when a large number of tiny intelligent wireless sensor nodes are combined. The sensor nodes are typically battery operated and, therefore, energy constrained. Hence, energy conservation is one of the foremost priorities in design of wireless sensor networks (WSNs) protocols. Limited power resources and bursty nature of the wireless channel are the biggest challenges in WSNs. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In This work, our study emphasizes optimizing WSNs by building a reliable and adaptive MAC without compromising fairness and performance. Here, we present link adaptation techniques at MAC layer to enhance energy efficiency of the sensor nodes. The proposed MAC uses a variable frame size instead of a fixed frame size for transmitting data. In order to get accurate estimations, as well as reducing the computation complexity, we utilize the extended Kalman filter to predict the optimal frame size for improving energy efficiency and goodput, while minimizing the sensor memory requirement. Next, we designed and verified different network models to evaluate and analyze the proposed link adaptation schemes. The correctness of the proposed theoretical models have been verified by conducting extensive simulations. We also prototype the proposed scheme with the MAC protocol on Berkeley Motes. Both prototype and simulation results show that the proposed algorithms improve the energy efficiency by up to 15%.

In IEEE 802.11 wireless LANs, channel quality, network load, as well as the protocol itself are time-varying, limiting the goodput performance improvement in wireless LANs. Therefore, it becomes critical to dynamically adjust parameters of MAC and PHY layers according to variations of channel quality. In this paper, we propose variable frame size and variable data rate schemes for goodput enhancement. We first propose two optimal frame size predictors: a goodput regulator to maintain the committed goodput for non-greedy applications and an optimal frame size predictor for maximizing the goodput for greedy applications. Then, we propose a data rate drafting scheme and develop a variable size and variable rate (VSVR) scheme for further goodput improvement. Our extensive simulation results show that the proposed VSVR algorithm can double the channel goodput of current implementations. Moreover, the proposed scheme can be easily integrated with the current implementations of the wireless LAN MAC protocol. Copyright © 2004 John Wiley & Sons, Ltd.

In wireless sensor networks, each sensor node is normally powered by batteries without replenish. Radio operations of a sensor node is a big user of the limited energy. This situation is further exaggerated by retransmissions caused by a time-varying channel. Therefore, how to design an energy efficient sensor MAC is one of the most important issues in sensor networks. Unlike other reported work utilizing a fixed frame size, we propose a new approach to enhance the energy efficiency by introducing an optimal frame size predictor into the sensor MAC, combating the unfavorable time-varying channel quality. We also propose a new sensor MAC to balance goodput performance and energy efficiency, as well as keep fairness among different nodes. Simulation results show that the proposed sensor MAC can largely reduce the average number of transmissions. Therefore, the energy efficiency can be improved by at least 10%, still keeping the same good throughput performance. The proposed sensor MAC can be easily implemented in practical.

Network-on-a-chip (NoC) is becoming a promising paradigm of core-based system. We propose a new method for test scheduling in NoC. The method is based on the use of a dedicated routing path for the test of each core. We show that test scheduling under this approach is NP-complete and present an ILP model for solving small NoC instances. For NoCs with larger number of cores, we present an efficient heuristic. We then improve the heuristic by including BISTs and precedence constraints. Experimental results for the ITC'02 SoC benchmarks show that the new method leads to substantial reduction on test application time compared to previous work. The inclusion of BIST tests and precedence constraints provides a comprehensive solution for test scheduling in NoC.

Sensor nodes are usually battery operated and therefore energy constrained. Thus, energy conservation becomes one of the foremost priorities in designing the wireless sensor networks (WSNs). We present the enhancements at the medium access control (MAC) layer in order to improve the energy efficiency and increase the longevity of wireless sensor networks. The proposed MAC uses an optimal frame size instead of using a fixed frame size for every transmission. We present our study of the unscented Kalman filter (UKF) approach to predict the optimal frame size. The proposed variable frame size conserves energy as well as enhance the system performance by minimizing the throughput degradation caused by a time varying wireless channel. We designed and verified a different network model to evaluate and analyze the proposed approach. The simulation results show an improvement in energy efficiency of up to 15 %.

Energy efficiency is critical to wireless network terminal devices, since they are powered by battery in many cases. Our approach to improving energy efficiency is to reduce the idle energy consumption by increasing the amount of information data transmitted by the wireless network interface card for a given amount of energy. We propose a simple TCP quick timeout mechanism (TCP-QT) for the improvement of energy efficiency. Using feedback from the MAC layer, the TCP layer can be made aware of the transmission history of a packet in the MAC layer If a packet has been dropped by the MAC layer, a quick timeout in the TCP layer is triggered, resulting in a fast retransmission of the packet. Thus, the average energy efficiency observed in the application layer is improved, while the current TCP semantics are largely unchanged. Simulation results show that the TCP-QT mechanism can improve the energy efficiency by up to 5 times.

Throughput performance is very important to the QoS provisioning in wireless data networks. We propose a link adaptation algorithm for the throughput performance improvement of the IEEE 802.11 wireless LAN. In this algorithm, an optimal frame size predictor with considerations of collisions and frame errors is proposed. Simulations results show that the throughput performance of the wireless LAN can be enhanced greatly than that achieved by traditional fixed frame size scheme.

We propose a new link adaptation approach for QoS enhancement in wireless networks. We utilize the Kalman filter, which predicts the local optimal frame size based on the channel quality varying based on the derived channel model. We have also presented the simulations and experimental results. The results show that the performance of proposed predictor is much better than other prediction methods like the moving average.

Two simple back-off based fragment adaptive algorithms for improving the throughput of IEEE 802.11 wireless LAN are proposed and studied. The simulation results show major improvement in channel throughput and reduction in system end-to-end delay. The results also indicate that proposed adaptive fragmentation algorithms perform the best when the channel is very noisy.