For autonomous vehicles (AVs) to navigate safely and reliably in unpredictable environments, ensuring the robustness of their perception systems is critical. This contrasts with conventional performance evaluations, which typically focus on accuracy (i.e., how ‘good’ a system is with known input). Existing robustness metrics, however, are fundamentally limited by their heavy reliance on large, fully annotated datasets, making them costly and impractical for scalable, real-time safety monitoring. This study addresses this limitation by introducing a novel robustness measure that can be applied effectively even when ground-truth annotations are very limited. The significance of our approach lies in its self-referential evaluation: instead of relying entirely on ground truth, it primarily assesses a perception model by comparing the output of a degraded input against a baseline output generated by the same model on the original clean input. This measure is calibrated using a correction factor derived from a small, available labeled subset, ensuring consistency with established metrics while removing the need for continuous, large-scale annotation. To facilitate this evaluation, we develop a novel testing framework that systematically introduces realistic input degradations—such as variations in lighting, camera artifacts, and adversarial perturbations—across six AV perception models, including two camera or LiDAR-only (i.e. uni-modal), and two multi-sensor data fusion (i.e. multi-modal) systems. Our results demonstrate that our limited-data measure highly correlates with full-data ground-truth metrics, confirming its reliability. We further discuss the insights gained from applying the robustness measure to these deep learning models against different degradation categories. To our best knowledge, the proposed measure is the first in the relevant literature to provide insights into the robustness of the AV perception systems across complex environments without a continuous reliance on the ground-truth annotations. The testing framework and robustness measure developed in this paper provide a practical tool for the continuous safety monitoring and improvement of AVs in their real-time operation.

The development of connected and autonomous vehicles opens an opportunity to manage intersections without signals. One promising approach is to use a central autonomous intersection manager to optimize the movement of the vehicles in the intersection. Existing work uses Mixed Integer Linear Programming (MILP) to find optimal solutions for this problem but is time-consuming and cannot be applied in real-time. On the other hand, the coordination of the vehicles is essentially a Multi-Agent Path Finding (MAPF) problem, for which dozens of efficient algorithms have been proposed in recent years. Inspired by these MAPF algorithms, we propose a three-level algorithm called PSL to solve the intersection coordination problem. Theoretically, PSL is complete and polynomial-time in the number of vehicles. Empirically, PSL runs significantly faster with only a slight compromise in the solution quality than the optimal MILP method. It also generates significantly better solutions with a slightly larger runtime than the traditional First-Come-First-Served strategy.

Neural network (NN)-based models have recently achieved outstanding results in short-term traffic prediction. However, most of these are based on the regression approach and trained to generate a single data point as a predicted value for future timesteps, which does not provide information on prediction uncertainty and limits its performance under different traffic conditions. To solve this problem, this study proposes a novel, high-dimensional distributional prediction (HDP) framework. This method has been validated by a series of experiments using the Caltrans Performance Measurement System dataset and four widely used NN models. The results suggest that the proposed HDP scheme can help existing NN structures to (1) generate adaptive distributional predictions for quantifying the uncertainty of multiple targets, and (2) gain better point prediction in terms of accuracy and robustness. Furthermore, we demonstrate that predicted speed distributions can be used for travel time estimation, outperforming other traditional methods in unexpected traffic conditions such as traffic incidents.

Future vehicle-to-grid (V2G) systems require more flexible scheduling to adjust and flatten the peak energy demand. For efficient scheduling and energy trading, the utility provider (UP) needs to keep track of the state of charge (SoC) of vehicle batteries (VBs). However, sharing of SoC of VBs from electric vehicles (EVs) to UP may compromise owner privacy by analyzing the electricity usage in EVs. Therefore, we propose Reinforcement learning (RL)-based demand-side energy management using a rechargeable battery (RB) for enhanced cost-friendly privacy of EVs, efficient scheduling, and accurate billing. With existing Q-Learning-based RL (using$\epsilon $-greedy exploration and exploitation), we find that the reward maximization of efficient and private scheduling is often sluggish and incurs convergence issues. Therefore, we develop a genetic algorithm (GA)-based exploration and exploitation, which solves the convergence problems. We develop theoretical analysis and implement numerical results to demonstrate that the proposed GA-based RL framework accelerates convergence and enhances cost-friendly privacy considerably.

The advancement of the Intelligent Transport Systems (ITS) and the emerging Connected and Automated Vehicles (CAVs) technology are acknowledged to hold a great potential to mitigate challenging problems in the current transportation networks. Particularly, a proper traffic control strategy with a precise vehicular movement control scheme can alleviate the congestion and improve the safety and efficiency of the traffic. This paper proposes a novel bi-level control framework that combines a design of traffic signal timings at a network level, and a detailed trajectory control policy for individual vehicles at a link-level within a network of CAVs. We develop a group-based longitudinal trajectory planning scheme to coordinate vehicular movements at the lower level of our framework while abiding by the signal operations along with end-to-end vehicle routing decisions from the upper network level optimization. This joint and mutual interaction between the two different control strategies in the urban signalized corridors is complex and can significantly affect the overall network’s performance, nevertheless has not been explored previously in the literature. The proposed framework enables such studies where we derive an efficient algorithm that iteratively solves the mixed-integer linear programming (MILP) and linear programming models in each link at the lower and over the network at the upper levels, respectively. Numerical results show the effectiveness of the proposed joint control framework in network performance regarding the average travel time, queue formation and dissipation across the network.

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods for normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. Firstly, we deploy a typical GA algorithm by considering the phase duration as the decision variable and the objective function as the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train several regression models to predict the total travel time in the studied traffic network, and select the best performing model which we further hyper-tune. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree (XGBT), which is the best performing regression model, together in a single optimization framework. Comparison and results are generated by two experiments (one synthetic and one from real urban traffic network) and show that the new BGA-ML is much faster than the original GA algorithm and can reduce the total travel time by almost half when used under incident conditions.

Highway bottlenecks are responsible for the majority of traffic congestion. Although the problem of bottleneck detection is not new, contemporary methods have not solved the problem thoroughly with regards to bottleneck locations, activation time, and related congestion tracking. These elements are essential for identifying and characterizing a bottleneck. This paper proposes a comprehensive framework for detecting and extracting these features of highway bottlenecks from traffic data. We particularly focus on questions (i) whether a bottleneck is the primary source of congestion or (ii) whether it is activated due to congestion caused by another downstream bottleneck. The underlying principles of the proposed method include the detection of congestion (in spatio-temporal patterns of traffic congestion), and the detection of speed discontinuities in traffic data (since this is an important indicator of a bottleneck activation). The method is data-driven and automatic therefore can be easily applied to different highways and used to obtain meaningful statistics of existing bottlenecks. We have tested the method on simulated data and also demonstrated it on real data from a busy highway section in the Netherlands. The results suggest that the method is robust to different implementations, i.e. locations, of loop-detectors which measure traffic at discrete locations.

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.

Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs’ coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large.

Automotive infotainment systems are expected to be first deployed on highways to service drivers travelling long distances, who are more likely to utilize the infotainment applications. In order to meet the stringent requirements of the infotainment systems, road side units (RSUs) are installed along the highway to facilitate a continuous vehicle-to-infrastructure (V2I) connectivity. Due to the long travelling distance and small coverage of the individual RSU, a more cost-effective solution would be to combine V2I with the vehicle-to-vehicle (V2V) communications to maintain the continuous connectivity. In this paper, we propose a new dynamic cooperation scheme that employs a dynamic forwarder selection strategy to generate an adaptive multi-hop V2V path for connectivity maintenance and throughput enhancement at a vehicle located outside of the RSU’s coverage range. For the commonly assumed scenario that all vehicles travel in the same direction and at the same speed, we develop an analytical model and derive closed-formed expressions for the average out-of-range connection time, number of service resumptions and achieved throughput. The developed analytical model provides insights into the impacts of inter-RSU distance, vehicles’ assistance willingness and the target vehicle’s buffer size to the network performance. Simulation results with practical parameter settings show that our proposed scheme is effective in improving connectivity while offering a high throughput for the target vehicle. In particular, a high vehicle density, more assistance willingness by the forwarders and a large buffer size at the target vehicle are shown to be helpful in sparse RSU deployments.

The advancement of technologies has fast-tracked the deployment of autonomous vehicles (AVs), however, they necessitate the modernization of the existing road infrastructure to meet the technical and safety requirements of AVs. Due to the high cost and prolonged time required for upgrading infrastructure, it is expected that AVs will be introduced gradually into the existing transport network of human-driven vehicles (HVs) either jointly or separately in a special area or zone within that network. In this paper, we propose a mathematical framework for modelling and deployment of multi-lane AV zone in a mixed-user traffic network that takes into account the different route choice decisions of HVs and AVs. The model is used to study two different designs for a multi-lane AV zone, where in Design 1, both lanes are dedicated for AVs inside the AV zone; while in Design 2, one lane is dedicated for AVs, and another lane is for the HV traffic. We then derive an equivalent convex formulation and develop a novel solution method for this complex problem, as well as evaluate and compare the performance of the designs in a realistic large-scale transportation network. Numerical results show that the deployment of a multi-lane AV zone improves network performance in terms of the total system travel time (TSTT). The proposed framework can be used to assist transportation planners and decision-makers to successfully deploy AVs seamlessly with mixed traffic.

We consider a fifth-generation (5G)-empowered future Industrial IoT (IIoT) networking problem where IIoT machines are capable of communicating and sharing their data networking knowledge gained (and experiences) with other neighboring devices/tools. For such an IIoT setting, deep-learning (DL)-based communication protocols are known to be highly efficient but having a computationally complex training procedure in terms of both time/space and volume of data sets. One solution for such training is to be completed offline for each equipment and machines of IIoT before deployment. A better approach would be to replicate the model from the expert existing machine and implant it into new machines. Such training for the transfer of knowledge can be done by manufacturers using high computational power, even for large-scale DL models. After sufficient training and the desired level of accuracy, the trained machines can be deployed in the smart factory equipment to perform life-long collaborative learning. We design a novel distributed transfer learning (TL) framework to maximize multipath communication networking performance for Industry 4.0 environment. To conduct seamless sharing of knowledge gain by the multipath TCP (MPTCP) agents and tackle retraining issues of DL-based approaches, we investigate TL for MPTCP from the IIoT networking perspective. With relevant insights from transfer and collaborative learning, we develop a distributed TL-MPTCP framework to accelerate the learning efficiency and enhance the performance of newly deployed machines. Our approach is validated with numerical and emulated NS-3 experiments in comparison with the state-of-the-art schemes.

Assuring the required quality of service, despite the growing volume and variety of Internet of Things (IoT) traffic, has remained an immense challenge in the popular WiFi networks. The IoT devices using short TCP flows often attain very different levels of service due to the complicated interactions between the transport layer protocol and the shared dynamic wireless medium. We develop in this paper a novel queue management policy by using a transient model to capture the interactions of IoT (short TCP) flows over traditional traffic in WiFi networks. Based on the Markov regenerative processes coupled with fluid model, we discover that the adaptive admission control (AAC) mechanism at the WiFi access point (AP) improves the response time and fairness of IoT traffic over the lossy WiFi links. To this end, under the proposed AAC, packets are admitted into the AP in such a way that the fractions of packets in the AP buffer belonging to the different IoT devices are balanced resulting in a comparable level of service. We furthermore prove the stability of the system dynamics under AAC which provides important practical insights in designing home WiFi IoT system.

There has been an increasing level of demand for faster, safer and greener transportation systems with higher levels of capacity and convenience, though the implementation of transportation systems overall is often restricted by geographical limitations, presenting a challenge to scientists and engineers in the field. However, we have been witnessing the evolution of the transportation systems over the last few decades, and at present we are facing a new era of intelligent transportation systems (ITS) empowered by artificial intelligence (AI) technologies. There have been classification, deep learning, and reinforcement learning techniques, to name a few, which collectively have enabled almost all technical elements of the ITS. For example, autonomous vehicle technologies are now mature enough to introduce self-driving cars, taxis, buses, and trucks on the roads and streets; traffic signals are controlled by AI-based systems for far more enhanced traffic efficiency; and machine learning based on big data is improving the operational performance of transportation systems to the next level of safety, efficiency, and sustainability.

In this paper, we develop a hierarchical approach to optimize the signal timings in an urban traffic network taking into account the different dynamics in all traffic regimes. The proposed hierarchical control framework consists of two layers. The first layer-the network coordination layer-uses a model predictive control strategy based on a simplified traffic flow model to provide reference outflow trajectories. These reference outflow trajectories represent average desired link outflows over time. These are then mapped to green-red switching signals which can be applied to traffic lights. To this end, the second layer-the individual intersection control layer-then selects at every intersection the signal timing stage that realizes an outflow which has the smallest error with respect to the reference outflow trajectory. The proposed framework is tested using both macroscopic and microscopic simulations. It is shown that the control framework can outperform a greedy control policy that maximizes the individual intersection outflows, and the control framework can distribute the queues over the network in a way that the network outflow is improved. Simulations using a macroscopic model allow the direct application of the reference outflows computed by the network coordination layer, and the results indicate that the mapping of the reference outflows to the detailed signal timings by the individual intersection control layer only introduces a small performance loss.

Recent advancements in Internet congestion control have introduced a multipath TCP (MPTCP) that aims to simultaneously utilize multiple available paths in the network. In this paper, we develop an integrated fluid and packet-level analytical model to study the coexistence of regular and MPTCP users sharing a common WiFi access point (AP). We observe a throughput unfairness of MPTCP with regular TCP in the last-mile WiFi networks. In order to fix the fairness issue, we develop a real-time Adaptive Loss Management (ALM) algorithm that continuously monitors the deviation in AP buffer occupancy and adapts its packet admission probability based on a closed form expression derived from our analytical model. We provide a proof as well as show via numerical and simulation results that the proposed ALM algorithm is TCP-friendly by design, and provably stable.

The urban road networks undergo frequent traffic congestions during the peak hours and around the city center. Capturing the spatiotemporal evolution of the congestion scenario in real-time in an urban-scale can aid in developing smart traffic management systems, and guiding commuters in making informed decision about route choice. The congestion scenario is often represented by a set of distinguishable network partitions that have a homogeneous level of congestion inside them but are heterogeneous to others. Due to the dynamic nature of traffic, these partitions evolve with time in terms of their structure and location. In this paper, we propose a comprehensive framework to capture the evolution by incrementally updating the partitions in an efficient manner using a two-layer approach. The physical layer maintains a set of small-sized road network building blocks in a fine granularity, and performs low-level computations to incrementally update them, whereas the logical layer performs high-level computations in order to serve as an interface to query the physical layer about the congested partitions in a coarse granularity. We also propose an in-memory index calledBinthat compactly stores the historical sets of building blocks in the main memory with no information loss, and facilitates their efficient retrieval. Our experimental results show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy. The proposedBinconsumes a minimum space with least redundancy at different time stamps.

Safety applications based on the dedicated short-range communication (DSRC) in vehicular networks have very strict performance requirements for safety messages (in terms of delay and packet delivery). However, there is a lack of systematic approach to achieve the performance requirements by leveraging the potential of multi-hop forwarding. This paper proposes a generic multi-hop probabilistic forwarding scheme that achieves these requirements for event-driven safety messages, is compatible with the 802.11 broadcasting protocol and inherits some of the best features of solutions proposed so far for vehicular safety applications. In addition, we develop a unified and comprehensive analytical model to evaluate the performance of the proposed scheme taking into account the effect of hidden terminals, vehicle densities, and the spatial distribution of the multiple forwarders, in a one-dimensional highway scenario. Our numerical experiments confirm the accuracy of the model and demonstrate that the proposed protocol can improve the packet delivery performance by up to 209 percent, while maintaining the delay well below the required threshold. Finally, the utility of the analytical model is demonstrated via an optimal design for the coefficients of a forwarding probability function in the proposed scheme.

We develop a comprehensive analytical model for multiple long-lived multipath Transmission Control Protocol (TCP) connections downloading content from a remote server in the Internet using parallel paths with Wi-Fi and cellular last-miles. This is the first analytical model developed in the literature that captures the coupling between the paths through heterogeneous wireless networks where the coupling arises due to the multipath TCP coupled congestion control protocol. The model also takes into account the impact of the shared nature of the wireless medium and the finite access point (AP) buffer in the Wi-Fi last-mile. The accuracy of the proposed model is demonstrated via extensive ns-2 simulations. Furthermore, we discover a new type of throughput unfairness among the competing regular and multipath TCP connections going through the same AP with a droptail buffer; the regular TCP connections essentially steal almost all the Wi-Fi bandwidth away from the multipath TCP connections. To tackle this problem, we present two simple solutions utilizing our analytical model and achieve fairness.

The congestion scenario on a road network is often represented by a set of differently congested partitions having homogeneous level of congestion inside. Due to the changing traffic, these partitions evolve with time. In this paper, we propose a two-layer method to incrementally update the differently congested partitions from those at the previous time point in an efficient manner, and thus track their evolution. The physical layer performs low-level computations to incrementally update a set of small-sized road network building blocks, and the logical layer provides an interface to query the physical layer about the congested partitions. At each time point, the unstable road segments are identified and moved to their most suitable building blocks. Our experimental results on different datasets show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy.

We propose an analytical model for a Wi-Fi network acting as a last-mile Internet access with multiple long-lived TCP connections on both the up and down links. Our model considers the joint impact of buffer losses at the access point, contention at the medium access control layer, and packet losses due to the wireless channel being erroneous. We show that the model accurately quantifies the probability of an arbitrary TCP packet being discarded, and the total throughput obtained on the up and down links. Furthermore, quantitative insights can be gained into the throughput that long-lived TCP flows achieve under the joint impact of all aforementioned types of losses. In particular, we find that the wireless channel errors and buffer overflows both lead to throughput unfairness, but that they do so in the opposite direction on the up and down links, respectively. We demonstrate that this insight can be exploited so as to significantly mitigate the throughput unfairness without compromising the total obtainable network throughput.

Accurate network-wide real time traffic forecasting is essential for next generation smart cities. In this context, we study a novel and complex traffic data set and explore the potential to apply big data and machine learning analysis. We evaluate several hypotheses and find that the availability of big data is able to facilitate more accurate predictions. Furthermore, we find that spatial aspects have more influence than temporal ones and that careful choice of thresholding parameters is crucial for high performance classification.

With the rapidly growing population in urban areas, these days the urban road networks are expanding at a faster rate. The frequent movement of people on them leads to traffic congestions. These congestions originate from some crowded road segments, and diffuse towards other parts of the urban road networks creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this work, we propose RoadRank, an algorithm to compute the influence scores of each road segment in an urban road network, and rank them based on their overall influence. It is an incremental algorithm that keeps on updating the influence scores with time, by feeding with the latest traffic data at each time point. The method starts with constructing a directed graph called influence graph, which is then used to iteratively compute the influence scores using probabilistic diffusion theory. We show promising preliminary experimental results on real SCATS traffic data of Melbourne.

Traffic engineering is a key in effective utilisation of the road network infrastructure. Simulation assists traffic engineers making informed decisions on how to operate and direct traffic within the road networks. These simulations are complex, generate big data and require high-powered computers, which can process information faster than real time, to ensure the results can be used to affect traffic. Cloud computing, a relatively new technology paradigm, can meet the essential requirements, such as scalability, interoperability, availability and high-end performance. In this paper, a novel approach to a synchronisation strategy of large-scale complex simulations is proposed. This approach builds upon advancements achieved in distributed computing. The new synchronisation strategy is designed to allow different granularities of synchronisation accuracy. Through this strategy, synchronisation overhead is reduced, thus allowing the computing bandwidth to be applied to simulation performance increases as a result of the trade off between synchronisation accuracy and performance.

Visualizing road traffic datasets involves representing junctions, their links, and the attributes of those links. Current traffic visualization techniques are not sufficient for professional traffic engineers, as they are limited in the number of attributes that can be represented. This paper proposes a new approach to visualize multiple attributes on graph edges without compromising their visibility. In particular, we introduce a parameterized connector symbol that increases the number of attributes that can be displayed on graph edges. We demonstrate that our approach can significantly increase the number of traffic parameters that can be displayed compared to existing traffic visualizations.

Despite a significant increase in security of modern information systems, cyber attacks have become more sophisticated as attackers combine multiple vulnerabilities to penetrate networks resulting in devastating consequences. In the past, attack graphs had been important tools for analyzing and understanding how various vulnerabilities could be combined through many potential interactions and connections between network components to compromise security. Full attack graphs for a realistic network, however, can be very large and complex, making it difficult to analyze and to decide what changes should be made in the network to make it sufficiently secure. We propose in this paper a novel approach to analyze network vulnerability and to identify all the combinations of exploits that are critical to the overall security of a network. Unlike previous graph-based algorithms that generate attack trees (or graphs) to cover all possible sequences of vulnerabilities, our method directly analyzes and eliminates less critical vulnerabilities without building the actual attack graph. The proposed approach relies on a unique evaluation of a vulnerability metric defined in this paper and its effectiveness is demonstrated through an example of a network that provides voice over IP services.

Visual notations are a key aspect of visual languages. They provide a direct mapping between the intended information and set of graphical symbols. Visual notations are most often implemented using the low level syntax of programming languages which is time consuming, error prone, difficult to maintain and hardly human-centric. In this paper we describe an alternative approach to generating visual notations using by-example model transformations. In our new approach, a semantic mapping between model and view is implemented using model transformations. The notations resulting from this approach can be reused by mapping varieties of input data to their model and can be composed into different visualizations. Our approach is implemented in the CONVErT framework and has been applied to many visualization examples. Three case studies for visualizing statistical charts, visualization of traffic data, and reuse of a Minard's map visualization's components, are presented in this paper. A detailed user study of our approach for reusing notations and generating visualizations has been provided. 80% of the participants in this user study agreed that the novel approach to visualization was easy and 87% stated that they quickly learned to use the tool support.

TCP NewReno is one of the most widely deployed TCP versions in today's Internet. However, a full understanding of the complex inter-dependencies between the losses due to wireless channel errors and those due to buffer overflows, and their (joint) impact on TCP NewReno's congestion control algorithm in wireless and wired-cum-wireless networks is still lacking. In this paper, we develop a comprehensive analytical model for, and study the performance of, TCP NewReno with a cellular last-mile access, taking into account both types of losses. We assume a frame-level Markovian loss model, and build a model that features the system's basic controllable parameters (such as the number of retransmissions and the buffer size), so as to study how they (jointly) affect the TCP-level throughput. We model certain finer aspects, e.g., correlations in wireless and buffer losses and their cross-correlation. We provide a summary of numerical results highlighting several non-trivial findings. In particular, we demonstrate that there exist optimal (i.e., TCP throughput maximizing) pairs of the number of retransmissions and the buffer size.

The rapid global migration of people towards urban areas is multiplying the traffic volume on urban road networks. As a result these networks are rapidly growing in size, in which different sub-networks exhibit distinctive traffic flow patterns. In this paper, we propose a scalable framework for traffic congestion-based spatial partitioning of large urban road networks. It aims to identify different sub-networks or partitions that exhibit homogeneous traffic congestion patterns internally, but heterogenous to others externally. To this end, we develop a two-stage procedure within our framework that first transforms the large road graph into a well-structured and condensed supergraph via clustering and link aggregation based on traffic density and adjacency connectivity, respectively. We then devise a spectral theory based novel graph cut (referred as 훼-Cut) to partition the supergraph and compare its performance with that of an ex-isting method for partitioning urban networks. Our results show that the proposed method outperforms the normalized cut based existing method in all the performance evaluation metrics for small road networks and provides good results for much larger networks where other methods may face serious problems of time and space complexities.

Visual notations are a key aspect of visual languages. They provide a direct mapping between the intended information and set of graphical symbols. Visual notations are most often implemented using the low level syntax of programming languages which is time consuming, error prone, difficult to maintain and hardly human-centric. In this paper we describe an alternative approach to generating visual notations using by-example model transformations. In our new approach, a semantic mapping between model and view is implemented using model transformations. The notations resulting from this approach can be reused by mapping varieties of input data to their model and can be composed into different visualisations. Our approach is implemented in the CONVErT framework and has been applied to many visualisation examples. Two case studies for visualising statistical charts and visualisation of traffic data are presented in this paper. A detailed user study of our approach for reusing notations and generating visualisations has been provided that shows good reusability and general acceptance of the novel approach.

With the development of wireless broadband access, OFDMA technology is widely used for the next generation telecommunication systems. In this paper, we focus on analyzing the performance of contention-based services in IEEE 802.16 OFDMA networks with bulk services. We derive various performance measures such as queue utilization in an subscriber station, probability of unsuccessful bandwidth request, and the mean service time of a packet. The accuracy of the proposed analytical model is validated by extensive simulations.

In vehicle-to-vehicle communication, safety messages could be broadcasted over one-hop or multi-hop using different transmission ranges to warn each other of changing conditions or dangers ahead. We investigate the broadcast performance considering one-hop and multi-hop transmissions and study the effect of different transmission ranges on the multi-hop broadcasting performance. Our results show that multi-hop transmission can provide significant performance improvement when the transmission range is chosen appropriately.

Broadcasting is the most prevalent method for disseminating information in vehicular networks. At high vehicle densities, the so-called broadcast storm problem degrades the efficiency of broadcasting. A so-called Irresponsible Forwarding (IF) scheme has recently been proposed in the literature that can effectively combat the broadcast storm problem. For messages consisting of multiple packets, the coupon collector problem also degrades the broadcast efficiency at all vehicle densities. In this paper, we extend the basic IF scheme to multi-packet messages, which we call the max-min IF, and combine it with Random Linear Coding (RLC) of packets at the source to solve the coupon collector problem and improve the broadcast efficiency of IF. Through discrete event simulations, with a widely accepted realistic vehicular mobility model based on cellular automata, we demonstrate that our IF+RLC scheme can significantly improve the reachability in sparsely connected vehicular networks at low vehicle densities as well as reduce the mean delay under high probability of collisions at high vehicle densities.

The IEEE 802.16-based WiMAX technology has great potential for the fourth-generation mobile networks. Some of its service classes use the contention-based broadcast polling mechanism to request resources. In this paper, we investigate the performance experienced by these services when the network is unsaturated. In particular, we model each subscriber station as an M/G/1 queue where the service time is determined by the parameters of the network configuration and the binary exponential backoff contention resolution algorithm. We develop a fixed-point analysis to derive analytical expressions for network throughput and packet access delay. The accuracy of the analytical model is validated by comparing it with simulation over a wide range of operating conditions. The implications of various different parameter configurations on the performance are investigated using the analytical model. Moreover, we show that the model can be degenerated to the saturated condition. The utility of both the unsaturated and saturated models is further demonstrated by finding the optimal set of parameter values that maximize the network throughput.

We investigate three ways WLANs can use two channels to carry TCP traffic. Using simulation and a simple model, we show that load balancing over both channels outperforms the others while using a single double-width channel is the worst.

To achieve high system efficiency with increasing speeds, recent WiFi standards, such as IEEE 802.11e/n, allow burst transmissions with block acknowledgements, provided the initial packet is successfully received. Consequently, a user can sometimes improve its throughput by sending the initial packet at a lower rate than other users. We model such a system as a game. Our results show that the socially optimal strategy is to send the initial packet at a lower rate than the rest of the burst. Such a strategy results in a better Nash Equilibrium than using the same rate for the entire burst. Moreover, we show that using the rate that maximizes the per-packet throughput, as commonly done, can result in performance that is far from the social optimum.

Wireless LANs carry a mixture of traffic, with different delay and throughput requirements. The usual way to provide low-delay services is to give priority to such traffic. However, this creates an incentive for throughput sensitive traffic also to use this service, which degrades overall network performance. We show, analytically and by simulation, that the performance of both delay and throughput sensitive traffic can be improved by scaling IEEE 802.11's $(CW_{{\rm min}})$ and TXOP limit parameters in equal proportion. This reduces, but does not eliminate, the incentive for bulk data users to use the low-delay service. We further show that this incentive can be removed, while still giving improved performance to both classes, by reducing the $(CW_{{\rm min}})$ of the high throughput class by a constant that is independent of the traffic load.

ABSTRACT This paper investigates the maximum achievable channel throughput in a single‐channel and single‐hop wireless sensor network using IEEE 802.15.4 Medium Access Control (MAC) protocol. We introduce a simple mean‐field approach to model the Carrier Sense Multiple Access with Collision Avoidance mechanism of the 802.15.4 MAC protocol under unsaturated conditions. We derive a set of expressions such as optimal sensing rate of a sensor node, its corresponding failure probability, and the channel throughput for both saturated and unsaturated networks. With those expressions, we propose several network designs to achieve the optimal throughput by choosing the appropriate MAC parameters. We validate the proposed optimal designs using ns‐2 simulations. Furthermore, we evaluate the network lifetime expectancy of the optimal designs and compare it with the lifetime of network settings under saturated conditions that use the default MAC parameters of IEEE 802.15.4. Copyright © 2011 John Wiley & Sons, Ltd.

The Next Generation Mobile Network (NGMN) is expected to include integration of wireless technologies. In NGMN multimedia services are provided through the IP Multimedia Subsystem (IMS). Recently IMS has been enhanced to ensure session continuity when roaming among different wireless technologies. However achieving seamless mobility which is one of the goals of NGMN is still a challenge. In this paper we propose the PRIME mechanism which reduces handover delay and makes seamless mobility achievable. We evaluate the performance of the proposed method by simulating a WiMAX to LTE handover. The results show that in a typical handover the PRIME mechanism reduces the connection preparation delay by 70%.

In this paper, an approximated model is proposed to analyze the performance of the contention based services via broadcast polling in unsaturated IEEE 802.16 networks with channel errors. The main idea is that each subscriber station with buffer capacity K can be treated as a M/G/1/K queue with service time determined by the backoff process of broadcast polling. Using this model, the normalized network throughput and the distribution of the packet delay are derived. This proposed analytical model is useful for performance evaluation and optimization of best effort or contention-based non-real time polling services. Our simulator written in C++ verifies the accuracy of the proposed analytical model. Furthermore, we show that the model gives good approximations for network performance with a more realistic bursty arrival process at light load, while providing conservative performance measures at medium and high loads.

In this paper, we propose a general model for the broadcast polling protocol of unsaturated IEEE 802.16 networks in which each subscriber station has a finite buffer. A subscriber station can be modelled as a M/G/1/K queue with its service time determined by the broadcast polling protocol. The buffer overflow probability, network throughput and packet delay performances are analyzed. The proposed model is validated by simulation confirming its accuracy for various scenarios studied.

Wireless LANs carry a mixture of traffic, with different delay and throughput requirements. The usual way to provide low-delay services is to give priority to such traffic. However this creates an incentive for throughput sensitive traffic also to use this service, which degrades overall network performance. We propose to allow applications to trade off delay for throughput, without giving preference to one class over another, by simultaneously scaling IEEE 802.11's CWminand TXOP limit parameters. We provide a model of this scheme with two traffic classes, and show that increasing CWminand TXOP limit in equal proportion reduces, but does not eliminate, the incentive for bulk data users to use the low-delay service. We show that subtracting a small constant from CWmineliminates this incentive, while still giving improved performance to both classes.

In this paper, we propose an analytical model for evaluating the MAC throughput in an unsaturated IEEE 802.11 wireless local area network (WLAN) where multiple packets reception (MPR) is possible using multiuser detection techniques. In particular, a recently proposed successive interference cancellation (SIC) scheme for MPR is considered where users can randomly choose the transmission power from a set of discrete power levels. We derive an explicit expression for throughput of the WLAN based on such an SIC scheme and validate the accuracy of the model via ns-2 simulation results. We show that the throughput is significantly improved compared to the conventional 802.11 MAC protocol just by resolving collisions between two packets with different transmission power levels. In addition, we provide the optimal power distribution to maximize the throughput achievable in an SIC-enabled WLAN.

Research on Voice over IP (VoIP) in infrastructure wireless local area networks (WLANs) has shown that, because of the access mechanism, the access point severely limits the number of voice calls a WLAN can support. To address this problem we propose a new scheme based on the IEEE 802.11e quality of service (QoS) mechanism where a tradeoff between codec quality and priority is exploited to improve the number of calls that can be supported. In particular, we propose certain priority settings at the access point (AP) to encourage users to switch to a lower quality codec during periods of high contention and thus enable them to maintain the call. We develop a detailed analytical model to show the benefit of this scheme. Our analytical results are validated by extensive simulation and show that the voice capacity in the network can be significantly improved. Furthermore, by using the ITU-T E-model to assess the voice quality, we show that users with a lower quality codec can still maintain an acceptable level of quality using the proposed scheme.

This letter establishes the similarity between the sensor scheduling problem and the handoff (i.e., base station assignment) problem in cellular networks. A mobile user behavior is then modelled by a Hidden Markov Model (HMM). The handoff problem is formulated as an optimization problem of base station scheduling that minimizes a cost function that involves the HMM state estimation error and base station measurement costs. The optimization problem can be solved using algorithms known as partially observed Markov decision processes.

Seamless handoff between different wireless technologies is a vital requirement for the next generation mobile networks (NGMN). The soft handoff (SHO) method provides seamless roaming, but makes heavy use of battery and radio resources. In this paper, we propose a conservative soft handoff (cSHO) which maintains the benefits of SHO and reduces the consumption of resources. Instead of using both radio interfaces during the handoff period, the mobile station (MS) activates and deactivates its radio interfaces and switches between them to maintain the best possible radio connection. Since multimedia packets are already duplicated in the network and buffered in the base stations, fast switching between interfaces is possible. We investigate the performance of our proposed scheme by simulation and compare the performance of the cSHO scheme with the SHO and hard handoff (HHO) mechanisms.

Wireless local area networks (WLANs) support a wide range of applications, with various packet sizes. This diversity is set to increase in 802.11e WLANs which effectively allow very large packets controlled by a transmission opportunity (TxOP) parameter. This paper demonstrates a new phenomenon which occurs as a result of this diversity: When a network carries some large packets and many small packets, the collision probability after a large packet is much larger than predicted by previous models. This can be important because collision probability determines the number of packet transmissions, and hence the energy consumption. We propose a candidate model which captures this effect.

We develop an accurate analytical model for a dedicated short range communication (DSRC) network that uses the IEEE 802.11 distributed coordination function (DCF) MAC protocol, as adopted by the forthcoming IEEE 802.11p specification for DSRC. The specific focus is on broadcast vehicle-to-vehicle safety messages. We derive explicit expressions for the mean of the total packet delay and the packet delivery ratio (PDR) in an unsaturated network formed by moving vehicles on a highway. Our model is validated using extensive simulations and we show that our model yields better predictive accuracy than other existing models. The model is then used to investigate the performance of a modified DCF that uses a fixed number of sequential retransmissions to improve the reliability of packet delivery. We find that with sequential retransmissions, the PDR improves at low vehicle density (i.e. low traffic load), but degrades at heavy loads where higher collisions induced by the retransmissions outweighs the benefit of repeated attempts.

To compute link blocking rates in an optical burst/packet switched network, it has been shown that the Engset formula should be used with a reduced packet arrival rate to account for packet dumping. This reduction is realized by lengthening the off-time during which a source remains idle, which is referred to as the lengthened off-time approximation (LOA). It is known that if the blocking probability is small or the number of sources (input links) is large, the LOA is redundant. We show here that the LOA is also redundant for certain state independent approximations and prove that they are equivalent to the Engset formula if the blocking probability is evaluated as a function of utilization. We consider a new LOA whereby the interval by which the off-time is lengthened depends on the number of busy channels. We demonstrate that this new state-dependent LOA (sdLOA) yields higher accuracy irrespective of whether blocking probability is evaluated as a function of either utilization which is a system-dependent measure, or system independent measures such as the intended arrival rate.

Wireless voice over IP (VoIP) is an important emerging service in telecommunications due to its potential for replacing cell phone communication wherever wireless local area network (WLAN) is installed. Recent studies, however, suggest that the number of voice calls that can be supported in the widely deployed IEEE 802.11 WLAN is limited. In this paper, we utilize a so-called transmission opportunity (TXOP) parameter of a medium access control protocol as a simple solution to improve the VoIP capacity. We provide a detailed analytical model to show that the capacity can be improved significantly, and discuss the implications of the TXOP parameter in terms of the maximum number of calls the 802.11 network can support. The analytical results are validated by simulations for a wide range of parameters. Furthermore we investigate the impact of the buffer at the access point (AP) on the number of obtainable voice calls. We show that there exists an optimal buffer size where the maximum voice capacity is achieved, but further increasing the buffer beyond this value will not result in an increased voice capacity. Based on this finding a closed form expression for the maximum number of voice calls is developed as a function of TXOP value. Finally, we propose a simple yet accurate voice capacity approximation formula for voice capacity estimation in WLAN and provide some insights that an be gained from this formula.

We analyze the MAC access delay of the IEEE 802.11e EDCA mechanism under saturation. We develop a detailed analytical model to evaluate the influence of all EDCA differentiation parameters, namely AIFS, CWmin, CWmax and TXOP limit, as well as the backoff multiplier $\beta$. Explicit expressions for the mean, standard deviation and generating function of the access delay distribution are derived. By applying numerical inversion on the generating function, we are able to efficiently compute values of the distribution. Comparison with simulation confirms the accuracy of our analytical model over a wide range of operating conditions. We derive simple asymptotics and approximations for the mean and standard deviation of the access delay, which reveal the salient model parameters for performance under different differentiation mechanisms. We also use the model to numerically study the differentiation performance and find that $\beta$ differentiation, though rejected during the standardization process, is an effective differentiation mechanism that has some advantages over the other mechanisms.

In this paper we develop a simple yet effective heuristic algorithm to estimate the location of a sensor node in wireless sensor networks. Our method utilizes a simple and inexpensive received signal strength (RSS) measurement at a node to maximize the maximum likelihood (ML) function in a localization problem. We show via numerical results that the location estimations obtained from the new method are more accurate compared to those of traditional techniques. The algorithm can also be easily used to track the movement of a node in wireless sensor networks.

We propose in this paper a novel approach to analyze network vulnerability and to obtain a quantitative value representing the level of security achieved in an arbitrary network. Unlike previous graph-based algorithms that generate attack trees (or graphs) to cover all possible sequences of vulnerabilities and therefore are not scalable, our method utilizes the attack graph’s principles, but directly analyzes and produces the desired security measure for a network without building the actual attack graph. The proposed approach relies on a unique evaluation of vulnerability metric defined in this paper and is demonstrated through an example of a network that provides voice over IP services.

We compare the performance of different backoff functions for the multiple access protocol in an IEEE 802.11 wireless LAN (WLAN). We provide a unified analytical model with explicit expressions for the mean and standard deviation of the access delay for generalized exponential, polynomial and linear backoff functions. Using our model, we show that linear and polynomial backoffs with appropriate parameter settings can improve upon binary exponential backoff specified in the 802.11 WLAN standards, in terms of throughput, access delay statistics and packet drop rate.

This paper studies the blocking performance of optical burst switching (OBS) networks using a sequential office control (SOC) state-independent deflection routing policy. We show that unprotected deflection routing may destabilize OBS resulting in higher blocking probabilities than if bursts were not deflected but simply blocked. This study was motivated by the well-known destabilizing effect that alternative routing has on circuit switching in classical telephony networks. We propose two forms of protection to guard against destabilization: 1) wavelength reservation, which is analogous to trunk reservation in circuit switching; and, 2) preemptive priority, which is a new form of protection where bursts that have not been deflected are given preemptive priority over bursts that have been deflected. Our main contribution is a one-moment reduced-load approximation to evaluate the blocking performance of OBS networks using deflection routing protected by either wavelength reservation or preemptive priority. Our reduced-load approximation relies on the usual assumptions of link independence and Poisson distributed link arrivals. We quantify the error admitted in making these two assumptions via simulation. Using our reduced-load approximation, we evaluate the blocking performance of protected and unprotected deflection routing in several randomly generated networks. The chief conclusion of our study is that deflection routing in OBS should be given some form of protection to avoid destabilization resulting from upward load variations, and in terms of blocking performance, preemptive priority is the best form of protection for OBS. We use simulation to verify that our conclusions remain valid for a realistic traffic scenario.

The MAC access delay in a saturated IEEE 802.11 DCF wireless LAN is analyzed. We develop a unified analytical model and obtain explicit expressions for the first two moments as well as the generating function. We show via comparison with simulation that our model accurately predicts the mean, standard deviation, and distribution of the access delay for a wide range of operating conditions. In addition, we show that the obtained generating function is much more accurate than others that have appeared in the literature. Using our model, we prove that the binary exponential backoff mechanism induces a heavy-tailed delay distribution for the case of unlimited retransmissions. We show using numerical examples that the distribution has a truncated power-law tail when a retransmission limit exists. This finding suggests that DCF is prone to long delays and not suited to carrying delay-sensitive applications

This paper presents a framework to minimize energy consumption in the medium access control (MAC) layer for wireless sensor networks. While satisfying a range of quality of service (QoS) requirements, such as the packet transmission success rate and maximum delay constraint, we optimally choose the lengths of periods in which sensors are active and inactive, such that the energy consumption per unit time in the entire network is minimized. We first use our framework to optimize the values of the MAC attributes macBeaconOrder and macSuperframeOrder in an IEEE 802.15.4 beacon-enabled star network. Then we consider a much simpler protocol, which we call "select-and-transmit" (S&T), and the same framework is applied to find the optimal lengths of the active and inactive portions. Finally, we compare the minimal energy consumption of the IEEE 802.15.4 MAC and S&T under the same QoS requirements and show that the IEEE 802.15.4 MAC outperforms S&T in most cases. However, the S&T MAC performs better than the standard under our framework in certain scenarios, e.g., event-driven sensor networks where the packet transmission success rate is usually low.

In this paper, we analyze the MAC access delay of the EDCA mechanism in the IEEE 802.11e standard under saturation. We model the effect of differentiation based on both contention window size and inter-frame spacing. Explicit expressions for the mean, the standard deviation and the generating function of the distribution of the access delay are obtained. By applying numerical inversion on the generating function, we are able to compute values of the distribution. We show that our analytical model is accurate through comparison with simulation.

This paper proposes a new call quality measure based on mobile signal strength measurements to evaluate performance of handoff algorithms in wireless cellular networks. The proposed measure allows the quantification of the impact of the handoff algorithms of performance. Using the proposed measure we compare existing handoff algorithms to identify the trade-off between signal quality and required number of handoffs. Our results indicate that a handoff method based on a threshold with 2 dB hysteresis provides better performance compared to the conventional wisdom of 3 dB hysteresis. We provide a benchmark value for handoff algorithms based on an off-line heuristic method using the new measure. Our benchmark shows that there is substantial room for improvement of the existing handoff algorithm

In this paper, we study the MAC access delay of EDCA in the draft IEEE 802.11e standard under saturated conditions. We obtain explicit expressions for the mean, the standard deviation and the generating function of the distribution of the access delay. By using numerical inversion on the generating function, we are able to compute values of the distribution. We show that our analytical model has high accuracy via comparison with simulation

This paper proposes a new optical burst switching (OBS) paradigm known as dynamic two-way reservation OBS (DTWR/OBS), in which the burst length is dynamically determined at an ingress router, according to the minimum length reservation period available on each wavelength channel within a lightpath, from the ingress router to the egress router. The dynamic nature of DTWR/OBS ensures the packet transmission rate is controlled at the ingress routers, based on the level of network congestion. This increases network utilization and eliminates burst blocking due to wavelength contention. An analytical model for a single isolated optical cross-connect (OXC) is derived to determine the waiting time distribution of an arbitrary IP packet at the ingress router using the proposed DTWR/OBS. The accuracy of the analytical model is confirmed by simulations.

We obtain detailed analytic formulas for the density and probability distribution of the waiting time in a time-division multiple-access (TDMA) model with a finite buffer and state-dependent service. On successive intervals of length equal to the duration of a slot, the density is expressed as a linear combination of beta densities with positive coefficients. A recursive scheme, obtained by a matrix-analytic derivation, allows for the highly efficient computations of the coefficient sequences. An expression for the mean waiting time is derived using the classical queueing formula L=/spl lambda/W. We also demonstrate that our methodology provides a concise treatment of various special cases that have been studied over the past half century.

Limited wavelength conversion and deflection routing are considered as viable methods to reduce the burst blocking probability in optical burst switched (OBS) networks. The question of which of the two methods is most effective has yet to be resolved. In this paper, an analytical model is derived that provides an accurate and quick means to evaluate and compare the effectiveness of limited wavelength conversion and deflection routing. First, an overflow load fixed point approximation model is derived for a single link in isolation. Then the single link model is integrated into a reduced load fixed point approximation model for a general network topology with deflection routing. The accuracy of the analytical model is verified through simulation.

Methods to resolve wavelength contention are needed to improve the performance of optical burst switched (OBS) networks. Network simulations and Markovian queuing models for nodes in isolation have suggested that deflection routing (alternate routing) may be a viable method to resolve wavelength contention. However, we show that deflection routing may destabilise OBS networks operating at high loads. To prevent the destabilising effect of deflection routing, we propose and analyse a technique called wavelength reservation to intentionally limit the amount of deflection at high loads. Wavelength reservation is analogous to trunk reservation in circuit switched networks. This paper is the first to present a new reduced load Erlang fixed point analysis of OBS networks with deflection routing and wavelength reservation. We apply the new analysis to evaluate the benefit of deploying deflection routing and wavelength reservation in a sample OBS network.

This paper provides and efficient for the derivation of the delay distribution for a TDMA system with a finite buffer. Assuming successive intervals of length equal to the duration of a slot, the density is expressed as (finite) linear combinations of gamma densities and beta densities with positive coefficients. The results are verified by simulations. Comparison with a previously proposed solution demonstrates that the solution presented here is more efficient.

In this paper we provide an analytical framework for studying the performance of optical burst switching (OBS) networks using burst segmentation. We first consider a single link model to evaluate the blocking probability of OBS using burst segmentation and confirm it by simulation. We use this analysis to demonstrate the benefit of burst segmentation over the well-known just-enough-time (JET) policy for the single link case. We then extend these models to a network scenario using a reduced load fixed point approximation to evaluate blocking probabilities and to show the advantages provided by burst segmentation for OBS networks.

Detailed analytic formulas for the density and probability distribution of the waiting time in a TDMA model with a finite buffer is obtained. On successive intervals of length equal to the duration of a slot, the density is expressed as (infinite) linear combinations of beta densities with positive coefficients. A recursive scheme, obtained by a matrix-analytic derivation, allows for the highly efficient computations of the coefficient sequences.

This paper provides a framework for analysis and performance evaluation of Optical Burst Switching (OBS) networks. In particular, a new reduced load fixed point approximation model to evaluate blocking probabilities in OBS networks is introduced. The model is versatile enough to cover known OBS reservation policies such as Just-Enough-Time (JET), Just-In-Time (JIT), Burst Segmentation and Route-dependent Priorities. The accuracy of the model is confirmed by simulation and the various policies are compared.

This paper provides a scalable framework for analysis and performance evaluation of optical burst-switching (OBS) networks. In particular, a new reduced load fixed point approximation model to evaluate blocking probabilities in OBS networks is introduced. The model is versatile enough to cover known OBS reservation policies such as just-enough-time, just-in-time, burst segmentation, and route-dependent priorities. The accuracy of the model is confirmed by simulation and the various policies are compared.

This paper presents a new distance measure that is based on the spectral sensitivity of the line spectrum frequency parameters (LSFs) and its Karhunen-Loeve (KL) transformed coefficients. It is shown that the proposed distance measure achieves better performance of vector quantization (VQ) compared to other methods in the field of LSF coding. In most cases, the percentage of outliers is reduced when using the new one, compared to the best results of using other conventional weighting functions, The use of this distance as the weighting function of the LSF transformed parameters is also suggested.

We have developed a new general distance measure that not only can be used in a vector quantization (VQ) of the line spectrum frequency (LSF) parameters but performs well in the LSF transformed domain. The new distance is based on the spectral sensitivity of the LSF and their transformed coefficients. In addition, the fixed scaling factor is used to decrease the sensitivity of the spectral error at higher frequencies. Experimental results have shown that the proposed distance measure leads to as good as or better performance of VQ compared to other methods in the field of LSF coding. The use of this distance as the weighting function of the LSFs' transformed parameters is also suggested.

The intraframe correlation properties of line spectrum pair (LSP) are used to develop an efficient encoding algorithm using the Karhunen-Loeve (KL) transformation. The important nonuniform statistical characteristics of LSP frequencies are investigated. Based upon this nonuniform property the neural network based techniques for generating the transform vectors via system training are studied. Using the principal component analysis (PCA) network to decorrelate LSP coefficients, we show that these new approaches lead to as good or better distortion as compared to other methods for speech analysis-synthesis.