Evaluating cellular systems, from 5th generation (5G) New Radio (NR) and 5G-Advanced to 6th generation (6G), is challenging because the performance emerges from the tight coupling of propagation, beam management, scheduling, and higher-layer interactions. System-level simulation is therefore indispensable, yet the vast majority of studies rely on the statistical 3rd Generation Partnership Project (3GPP) channel models. These are well suited to capture average behavior across many statistical realizations, but cannot reproduce site-specific phenomena such as comer diffraction, street-canyon blockage, or deterministic line-of-sight conditions and angle-of- departure/arrival relationships that drive directional links.This paper extends 5G-LENA, an NR module for the system-level Network Simulator 3 (ns-3), with a trace-based channel model that processes the Multipath Components (MPCs) obtained from external ray-tracers (e.g., Sionna Ray Tracer (RT)) or measurement campaigns. Our module constructs frequency-domain channel matrices, and feeds them to the existing Physical (PHY)/Medium Access Control (MAC) stack without any further modifications. The result is a geometry-based channel model that remains fully compatible with the standard 3GPP implementation in 5G-LENA, while delivering site-specific geometric fidelity. This new module provides a key building block toward Digital Twin (DT) capabilities by offering realistic site-specific channel modeling, unlocking studies that require site awareness, including beam management, blockage mitigation, and environment-aware sensing. We demonstrate its capabilities for precise beam-steering validation and end-to-end metric analysis. In both cases, the trace-driven engine exposes performance inflections that the statistical model does not exhibit, confirming its value for high- fidelity system-level cellular networks research and as a step toward DT applications.

In this paper, we address the issue of Channel State Information (CSI) prediction of the Internet of Vehicles (loV) system, which is a highly dynamic network environment. We propose a deep reinforcement learning-based approach to predict CSI with historical data and video footage captured by smart cameras. Specifically, we use a Conventional Neural Network (CNN) to extract unique environmental characteristics, which will be sent to a Recurrent Neural Network (RNN)-based learning model so that the future CSI can be predicted. Our approach also considers the heterogeneous nature of IoV communication environments by adopting transfer learning to reduce the training cost when applying our approach to different IoV scenarios. We assess the efficacy of our proposed approach using our designed IoV simulation platform. The experimental results confirm that our approach can accurately predict CSI by using historically generated data.

Sensing biometric markers such as respiration rate (RR) and heart rate (HR) in non-medical contexts using the high resolution of Millimeter-Wave (mmWave) Wi-Fi networks has recently gathered considerable attention. A significant challenge in deploying a Wi-Fi system capable of performing sensing tasks is to minimize the overhead on the communication tasks associated with acquiring sensing information, both in terms radio resources and memory usage. In this paper, we explore the potential of IEEE 802.11bf passive sensing as a means to mitigate overhead, while effectively estimating both RR and HR. We showcase the potential to develop a low overhead Wi-Fi system that precisely captures vital signs, even in demanding situations, such as rapidly increasing RR interfering with HR, by integrating microdoppler processing with super-resolution eigenvector noise subspace analysis. The results shows that the proposed methodology enables RR and HR estimation without any radio-resource overhead and requiring very limited memory usage.

Integrated sensing and communication (ISAC) systems have been investigated by the research and standardization communities in the recent past. Accurately localizing the target and tracking the target’s movement are critical for numerous smart Internet of Things (IoT) systems (smart manufacturing, smart transportation, etc.). This paper aims to realize super-resolution localization and tracking in WiFi sensing by leveraging the IEEE 802.11ad beamforming training procedure. We leverage the CLEAN-Space-Alternating Generalized Expectation-maximization (CLEAN-SAGE) algorithm on a single beam sweeping cycle for target localization and investigate the targets’ delays and angle estimation. For tracking moving targets, we design mechanisms to estimate the target’s motion, including the target’s velocity and motion pattern, such as estimating the target’s spatial positions over time to obtain the Doppler shift or tracking its trajectory using a Kalman filter. In order to prove that our approach works effectively, we conduct a thorough performance evaluation study. Our evaluation results confirm that the CLEAN-SAGE algorithm can achieve estimation performance beyond the ISAC system’s inherent bandwidth and beamwidth constraints. Furthermore, we provide insights into how system configurations, including antenna size, beam overlap, and the number of iterations in the SAGE algorithm, influence its performance.

Accurately assessing the number of people in a room is essential for enhancing operational efficiency, safety, and sustainability, enabling applications such as smart building management, energy conservation, and emergency evacuation planning. Wi-Fi sensing, favored for its privacy-preserving capabilities and the ubiquity of Wi-Fi infrastructure, has become a popular method for such sensing tasks, including people counting. Within this context, our paper introduces a Convolutional Neural Network (CNN) model for millimeter wave (mmWave) Wi-Fi people counting, capitalizing on the IEEE 802.11bf amendment, and in particular its passive sensing framework. By evaluating Range-Doppler (RD), Azimuth-Doppler (AD), and AzimuthRange (AR) map representations as inputs, we establish AR maps’ superiority, achieving up to $\mathbf{9 8 . 5 7 \%}$ accuracy for counting up to four individuals. Our solution, free of communication overhead, also minimizes energy consumption and computational requirements, offering a scalable and efficient sensing solution for people counting, which is particularly well-suited for Internet of Things (IoT) devices with limited resources.

Wi-Fi sensing has been used to detect and track movements in an environment, resulting in the emergence of several innovative applications. Wi-Fi sensing can detect movement and locate objects by analyzing variations in the Wi-Fi signal due to its interaction with moving objects. Until recently, Wi-Fi sensing has been primarily available through proprietary solutions, which has limited its adoption. However, the recent initiative by the IEEE to develop the IEEE 802.11bf standard promises to make the adoption of Wi-Fi sensing widespread. Although Wi-Fi sensing procedures in communication standards can be overhead, there is currently a lack of literature exploring the sensing performance of Wi-Fi sensing procedures specified in the IEEE 802.11bf standard and its impact on data communication. Therefore, this paper presents a comprehensive evaluation of the sensing performance of the IEEE 802.11bf protocol and its impact on data communication in different configurations. Our findings expose the limitations of specific configurations and pave the way to provide guidance on efficient operating configurations of an IEEE 802.11bf network.

Sensing with communication waveforms has drawn growing interest thanks to the ubiquitous availability of wireless networks. However, the required sensing resources may not always be available in a communication system. In addition, the communication system may have limited bandwidth, beamwidth, and transmit power, which could limit the sensing accuracy. To investigate such challenges, in this article, we study the feasibility of using the sector-level sweeping (SLS) procedure of IEEE 802.11ad to provide opportunistic indoor radar sensing service, which is vital to smart Internet of Things (IoT) applications. In particular, we design a framework to estimate the target’s spatial position with respect to delay and angle by employing the multiple signal classification (MUSIC) super-resolution algorithms. We conduct an extensive performance evaluation to understand the tradeoffs between sensing accuracy and required sensing resources in terms of system configurations (e.g., antenna array size and the overlapping of neighboring beams) and the impact of signal-to-noise ratio (SNR). Furthermore, based on the human multipath reflections captured from a real-world measurement campaign, we reconstruct the sensing channel, investigate the feasibility of monitoring the gesture behavior in a smart home environment, and discuss some findings and insights.

The next generation WiFi such as IEEE 802.11ad and 802.11ay can provide stringent Quality of Service (QoS) due to its support of contention free channel access called Service Period. IEEE 802.11ad supports two types of user traffic: isochronous and asynchronous. These user traffic need guaranteed Service Period duration before their periods. Hence, admission control plays an important role in an IEEE 802.11ad system. In an earlier work we studied admission control only for isochronous requests. In this paper, we present admission control and scheduling algorithms which can handle both types of requests. We devise a proportional fair and linear run time complexity algorithm that treats asynchronous requests as periodic requests, because of which it overallocates resources to the asynchronous requests. The conditions of possible performance loss due to this overallocation are analyzed. We provide detailed simulation results which show that presence of asynchronous request degrades performance of isochronous requests in terms of number of admitted requests and channel utilization. But, the smaller number of admitted isochronous requests perform better in terms of channel allocation time and delay.

Efficient design of integrated sensing and communication systems can minimize signaling overhead by reducing the size and/or rate of feedback in reporting channel state information (CSI). To minimize the signaling overhead when performing sensing operations at the transmitter, this paper proposes a procedure to reduce the feedback rate. We consider a threshold-based sensing measurement and reporting procedure, such that the CSI is transmitted only if the channel variation exceeds a threshold. However, quantifying the channel variation, determining the threshold, and recovering sensing information with a lower feedback rate are still open problems. In this paper, we first quantify the channel variation by considering several metrics including the Euclidean distance, time-reversal resonating strength, and frequency-reversal resonating strength. We then design an algorithm to adaptively select a threshold, minimizing the feedback rate, while guaranteeing sufficient sensing accuracy by reconstructing high-quality signatures of human movement. To improve sensing accuracy with irregular channel measurements, we further propose two reconstruction schemes, which can be easily employed at the transmitter in case there is no feedback available from the receiver. Finally, the sensing performance of our scheme is extensively evaluated through real and synthetic channel measurements, considering channel estimation and synchronization errors. Our results show that the amount of feedback can be reduced by 50% while maintaining good sensing performance in terms of range and velocity estimations. Moreover, in contrast to other schemes, we show that the Euclidean distance metric is better able to capture various human movements with high channel variation values.

In this paper, we address the issue of automating network configurations for dynamic network environments such as the Internet of Vehicles (IoV). Configuring network settings in IoV environments has proven difficult due to their dynamic and self-organizing nature. To address this issue, we propose a deep reinforcement learning-based approach to configure IoV network settings automatically. Specifically, we use a collection of neural networks to convert the observations of a communication environment (channel power gain, cross-channel power gain, etc.) into key features, which are then supplied to a deep$Q$neural network (DQN) as input for training. Afterward, the DQN will select the optimal network configuration for vehicles in the IoV environment. In addition, our approach considers both centralized and distributed training strategies. The centralized training strategy conducts the DQN training process on a roadside server, while the distributed training strategy trains the DQN on vehicles locally. Through our designed IoV simulation platform, we evaluate the efficacy of our proposed approach, demonstrating that it can improve the quality of services (QoS) in the IoV environments concerning reliability, latency, and service satisfaction.

An upsurge of low latency and bandwidth hungry applications such as virtual reality, augmented reality and availability of unlicensed spectrum in the millimeter wave band at 60 GHz have led to standardization of the new generation WiFi systems such as IEEE 802.11ad and 802.11ay. Due to the stringent Quality of Service requirement of those applications, IEEE 802.11ad/ay have introduced contention free channel access calledService Period. One type of user traffic supported by IEEE 802.11ad is isochronous traffic, which is essentially periodic traffic that requires certain channel time to be allocated before its period ends. In an earlier work, we presented three Admission Control Algorithms (ACAs) which admit isochronous requests to achieve the above goals. One of these ACAs, the proportional fair allocation admission control (PFAAC), offers the best tradeoff across different performance metrics. But it did not consider guard time (GT) overhead, which is essential in a practical system. In this paper, we present two methods to compute upper bounds on GT overhead. We evaluate performance of the modified PFAAC with the two methods and PFAAC with no GT overhead. The modified PFAAC with the method that uses a tighter upper bound on GT overhead, provides the best performance.

The interconnection and digitization of the physical world has increased dramatically with the widespread deployment of network communication and the rapid development of the Internet of Things (IoT). Application scenarios and requirements in IoT are more complex and diverse than ever before. To successfully support the design and development of complex IoT systems, a realistic evaluation platform that can accurately simulate both the physical world and network communications is necessary. Yet, most existing simulation tools are limited, simulating only specific subsets of IoT environments, such as communication network simulation or mobility simulation, rather than complete IoT scenarios. Thus, in this paper, we propose a new framework, in which several modules can work together to achieve more realistic simulation of IoT environments. Specifically, we integrate three-dimensional object motion with the OMNET++ network simulator. In our framework, we can configure and direct object movement in 3D and compute the received power of transmitted signals using ray tracing techniques. Within the framework, OMNET++ simulates the communication process based on the received power and communication protocol. As a demonstration of our framework, we conduct several experiments on two classic Internet of Vehicles (IoV) scenarios. The results indicate that our proposed framework can accurately simulate both the physical and communication aspects of IoT systems.

The ubiquitous deployment and availability of wireless communications devices, coupled with recent technical advancements, provide a unique opportunity to enable wireless sensing applications, leveraging existing communications equipment and signals. The availability of modeling tools and dataset is crucial to support the development of sensing techniques and to understand the end-to-end performance of a joint wireless communication and sensing system. However, most of the sensing performance evaluations are carried out using proprietary tools and dataset. In this paper, we present a set of open source tools and models enabling the evaluation of future WLAN sensing systems. Our framework is composed of a ray-tracing implementation specific for sensing application, an IEEE 802.11ay physical layer (PHY) digital transceiver model and a visualization application. Using these tools, we design a dataset consisting of more than 14 000 entries of millimeter wave channels and IEEE 802.11 ay signals to democratize the design of both data-driven and model driven communication and sensing algorithms. We also provide a preliminary evaluation of a CSI-based WLAN sensing system using IEEE 802.11 ay signals. The results indicate that existing communication systems can be used to enable sensing applications.

Virtual reality (VR) coupled with 360° video has been used in a variety of areas, including gaming, remote learning, and healthcare, among others. The 360° video on which VR applications are based today is mostly low resolution and, in order to improve the user experience, bandwidth requirements must increase significantly. Spatial multiplexing (SM) at millimeter wave (mmWave) is an enabling technology introduced in IEEE 802.11ay to support high throughput applications. However, since IEEE 802.11ay commercial off-the-shelf devices are not yet available and the cost for implementation of mmWave testbeds is prohibitive, the expected SM performance in a real application is still unknown. In this paper, we design a mmWave multi-user (MU)-multiple-input multiple-output (MIMO) link-level high fidelity simulation platform, based on IEEE 802.11ay, which is shared as an open source code package. Our simulation platform consists of a measurement-based mmWave channel model and a digital transceiver. To support VR applications, we design the analog-digital hybrid precoders and combiners, enabling SM for MU-MIMO transmissions. We provide an extensive evaluation of the IEEE 802.11ay PHY in terms of throughput and error rates. Our platform reveals that in a living room environment, two users can support up to four streams achieving more than 20Gbit/sec data-rate per user, enabling the transmission of uncompressed 4K videos.

Millimeter-wave technology provides the necessary improvements in capacity and performance for the next generation of wireless networks. The new IEEE 802.11ay amendment extends IEEE 802.11ad to offer 100 Gbit/s connectivity in the unlicensed 60 GHz band through technical advancements such as Multiple-Input and Multiple-Output (MIMO), channel bonding and aggregation. Additionally, it offers improvements to the Beamforming Training (BFT) process in order to increase its efficiency and accuracy. One new technique defined by IEEE 802.11ay is Group Beamforming, which allows to simultaneously train all stations, and significantly reduces training overhead, especially in very dense networks. In this paper, we provide an implementation of IEEE 802.11ay in ns-3 and perform, to the best of our knowledge, the first detailed system-level evaluation of the performance of the novel IEEE 802.11ay protocol. We specifically study the performance of Group Beamforming and compare it against the legacy 802.11ad BFT. We explore how different BFT approaches scale in large networks, identify the possible problems and evaluate at how the BFT process influences the performance of the network overall. Our analysis shows that Group Beamforming can outperform the legacy approach, resulting in lower overhead and improved network performance. However, we also found that the Access Point (AP) training is quite vulnerable to interference in dense networks, introducing severe limitations to the performance, especially in large rooms where precise BFT is crucial to maintain the communication link. Therefore, we propose several improvements to Group Beamforming that improve performance and provide robust beamforming even in very dense scenarios.

With the increasing adoption of Industrial Internet-of-Things (IIoT) devices, infrastructures, and supporting applications, it is critical to design schemes to effectively allocate resources (e.g., networking, computing, and energy) in IIoT systems, generally formalized as optimization problems. Nonetheless, because the system is highly complex, operation and networking graph-based environments are time varying, and required information may not be available, it is difficult to leverage traditional optimization techniques to solve the optimal resource allocation problem. In this article, we propose a deep$Q$-network (DQN)-based scheme to address both bandwidth utilization and energy efficiency in a networking graph-based IIoT system. In detail, we design a DQN model that consists of two deep neural networks (DNNs) and a$Q$-learning model. The DNN network abstracts the features from the highly dimensional inputs and obtains the approximate$Q$-function for the$Q$-learning model. Based on the$Q$-function, the$Q$-learning model can generate the$Q$-table and reward function. After the training process, the DQN model can select appropriate actions for the agents (i.e., robots in a smart warehouse in this study) to improve bandwidth utilization and energy efficiency. To evaluate our proposed scheme, we design a simulation environment to investigate a typical IIoT scenario: the actuation of robotics in a smart warehouse. We then implement the DQN model and conduct extensive experiments to validate the efficacy of our scheme. Our experimental results confirm that our scheme can improve both bandwidth utilization and energy efficiency, as compared to other representative schemes.

Machine learning, as a viable way of conducting data analytics, has been successfully applied to a number of areas. Nonetheless, the lack of sufficient data is one critical issue for applying machine learning in Industrial Internet of Things (IIoT) systems. Insufficient data raises could negatively affect the accuracy of machine learning models. To tackle this issue, we design a framework to systematically investigate the impacts of insufficient data on model training. This framework employs the generative adversarial network (GAN) and continuous learning to generate and engage new data in model training, enabling us to study the security risks of introducing new data in the model training process and develop countermeasures to mitigate these risks. To validate the efficacy of our framework, we consider a representative IIoT scenario, in which a variety of industrial components needs to be recognized by convolutional neural networks (CNNs), and design and implement three evaluation scenarios that are based on a real-world IIoT data set. Our experimental results confirm that insufficient data can have a significant impact on the model accuracy, but that new data generated by GAN and continuous learning can greatly improve the model accuracy. Our experimental results also show that the data poisoning threat posed by the GAN can significantly reduce the model accuracy. However, our proposed defensive mechanism is capable of securing the model learning process. We conclude this article by discussing some emerging issues that need to be addressed in future work.

The design of integrated sensing and communication (ISAC) systems has drawn recent attention for its capacity to solve a number of challenges. Indeed, ISAC can enable numerous benefits, such as the sharing of spectrum resources, hardware, and software, and improving the interoperability of sensing and communication. In this article, we seek to provide a thorough investigation of ISAC. We begin by reviewing the paradigms of sensing-centric design, communication-centric design, and co-design of sensing and communication. We then explore the enabling techniques that are viable for ISAC (i.e., transmit waveform design, environment modeling, sensing source, signal processing, and data processing). We also present some emergent smart-world applications that could benefit from ISAC. Furthermore, we describe some prominent tools used to collect sensing data and publicly available sensing data sets for research and development, as well as some standardization efforts. Finally, we highlight some challenges and new areas of research in ISAC, providing a helpful reference for ISAC researchers and practitioners, as well as the broader research and industry communities.

Virtual Reality (VR) promises immersive experiences in diverse areas such as gaming, entertainment, education, healthcare, and remote monitoring. In VR environments, users can navigate 360-degree content by moving or looking around in all directions, by rotating their heads, as in real life. A rapid head rotation can corrupt the wireless link, degrading the user experience. Due to the lack of proper head rotation models, testbeds are usually required to analyze VR systems. In this paper, we propose an open source code package that generates realistic head rotation traces. The code package is based on a simple, yet flexible, time-correlated mathematical model, which is extrapolated from a publicly available VR head rotation measurement-based dataset. We show that the probability density function of head rotation pitch and roll angles can be modeled as Gaussian distributions, while the probability density function of yaw angles can be modeled as a Gaussian mixture distribution. To introduce temporal correlation, we extrapolate the power spectral density of the angular processes, which are modeled with a bi-exponential decay. Finally, we show how the model can support and accelerate the design of future VR systems by proposing the analysis of a distributed Multiple Input Multiple Output (MIMO) system and the design of a situational awareness Machine Learning (ML) based beamforming training for millimeter wave networks.

An upsurge of low latency and bandwidth hungry applications such as virtual reality, augmented reality and availability of unlicensed spectrum in the mmWave band at 60 GHz have led to standardization of the next generation WiFi such as IEEE 802.11ad and 802.11ay. Due to the stringent Quality of Service (QoS) requirement of those applications, 802.11ad/ay have introduced contention free channel access called Service Period, which provides dedicated channel access exclusively reserved for communication between a pair of nodes. One type of user traffic supported by IEEE 802.11ad is isochronous traffic, which is essentially periodic traffic that requires certain channel time to be allocated before its period ends. So, isochronous traffic needs guaranteed channel time allocation with stringent deadlines. In this paper, we present three Admission Control Algorithms (ACAs) which admit isochronous requests to achieve the above goals while being fair. We also present an Earliest Deadline First (EDF) based scheduling algorithm for isochronous traffic. We evaluate the performance of the three ACAs in terms of different performance metrics. Our simulation results show that, out of the three ACAs, the proportional fair allocation based algorithm offers the best tradeoff across different performance metrics.

The Industrial Internet of Things (IIoT) is a critically important implementation of the Internet of Things (IoT), connecting IoT devices ubiquitously in an industrial environment. Based on the interconnection of IoT devices, IIoT applications can collect and analyze sensing data, which help operators to control and manage manufacturing systems, leading to significant performance improvements and enabling automation. IIoT systems are characterized by a variety of IIoT applications, which generate different computing tasks depending on their functionalities. Some tasks are time sensitive (TS), while others are not, and more importantly, some tasks are nonpreemptive in IIoT scenarios. Thus, processing the different IIoT applications efficiently in an IIoT environment is key to achieving automation. Since computing resources are limited in IIoT, how to rapidly process TS tasks is a critical issue. Although some existing scheduling schemes can deal with the latency requirements of TS tasks, they lack consideration for nonpreemptive tasks. To address this issue, in this article we consider a typical smart warehouse system as an example and propose a generic task scheduling scheme that reserves computing resources to wait for upcoming TS tasks in such an IIoT environment. In doing so, our proposed scheme is capable of minimizing the overall waiting time for TS tasks. To evaluate the proposed scheme, we have implemented a simulation platform for a smart warehouse and conducted extensive experiments. Our experimental results demonstrate the efficacy of our scheme, which can allocate computing resources so that the processing time for the TS tasks can be reduced. Additionally, we discuss some potential research directions toward improving performance in IIoT environments with respect to resource management, machine learning, and security and privacy.

Machine learning techniques have been widely adopted to assist in data analysis in a variety of Internet of Things (IoT) systems. To enable flexible use of trained learning models, one viable solution is to leverage all categories of data from different applications to train a general model, which can be further tuned for applications through tuning process. This process incurs additional overhead at the start, but makes later revision and iteration faster and more flexible. Nonetheless, due to limited computing capabilities, IoT devices cannot handle the training process of large datasets. To address this issue, in this paper, we propose a general framework to adopt transfer learning in industrial Internet of Things (IIoT) systems. In our study, we categorize the application space of applying transfer learning to IIoT systems into four generic scenarios: centralized transfer learning with large datasets, distributed transfer learning with large datasets, centralized transfer learning with small datasets, and distributed transfer learning with small datasets. According to the characteristics of each scenario, we design workflows to apply transfer learning technique. To demonstrate the efficacy of the approach, we apply our transfer learning technique to the task of IIoT component recognition. We use the known VGG-16 model and leverage T-Less industrial datasets to evaluate the performance of our approach in different scenarios. Via performance evaluation, our experimental results confirm the efficacy of our approach, which can not only reduce training time, but also achieve higher accuracy, compared with the classical convolutional neural network (CNN) approach.

The multi-user multiple-input-multiple-output (MU-MIMO) beamforming training (BFT) enables an access point (AP) and multiple stations (STAs) to determine appropriate directional antenna patterns; to this end, the AP transmits multiple action frames to the STAs during the MU-MIMO BFT. However, if the antenna weight vectors (AWVs) are determined to transmit the action frames inefficiently, this could lead to unnecessary transmissions, which could increase the BFT time. To mitigate the signaling overhead, the schemes used in our previous work employed AWVs, which use multiple beams simultaneously to transmit the action frames. Nevertheless, these existing schemes are still adversely affected by redundant transmissions because these schemes overlook the transmit diversity gain obtained from multi-beam concurrent transmission. Therefore, in this study, we propose a novel transmit antenna configuration scheme that mitigates the signaling overhead by considering the transmit diversity of the inter-symbol interference (ISI) channel incurred when multiple beams are used simultaneously. Our proposed scheme determines each candidate AWV using multiple beams and efficiently identifies the STAs within reach of the corresponding multi-beam concurrent transmission. The numerical and simulation results demonstrate that our proposed scheme shortens the BFT time in comparison with existing schemes.

The IEEE 802.11ad standard extends WiFi operation to the millimeter wave frequencies, and introduces novel features concerning both the physical (PHY) and Medium Access Control (MAC) layers. The hybrid MAC layer provides for two different kinds of resource allocations: Contention Based Access Periods (CBAPs) and contention-free Service Periods (SPs). In this paper, we propose a Markov Chain model to represent CBAPs, taking into account operation interruptions due to scheduled SPs and the deafness and hidden node problems that directional communication exacerbates. We also propose a mathematical analysis to evaluate the interference among stations and derive analytical expressions to assess the impact of various system parameters on some key performance metrics such as throughput, delay, and packet drop rate. This information may be used to efficiently design a transmission scheduler that allocates contention-based and contention-free periods based on the application requirements.

5G and beyond networks will use, for the first time ever, the millimeter wave (mmWave) spectrum for mobile communications. Accurate performance evaluation is fundamental for the design of reliable mmWave networks, with accuracy rooted in the fidelity of the channel models. At mmWaves, the model must account for the spatial characteristics of propagation since networks will employ highly directional antennas to counter the much greater pathloss. In this regard, Quasi-Deterministic (QD) models are highly accurate channel models, which characterize the propagation in terms of clusters of multipath components, given by a reflected ray and multiple diffuse components of any given Computer Aided Design (CAD) scenario. This paper introduces a detailed mathematical formulation for QD models at mmWaves, that can be used as a reference for their implementation and development. Moreover, it compares channel instances obtained with an open source National Institute of Standards and Technology (NIST) QD model implementation against real measurements at 60 GHz, substantiating the accuracy of the model. Results show that, when comparing the proposed model and deterministic rays alone with a measurement campaign, the Kolmogorov-Smirnov (KS) test of the QD model improves by up to 0.537.

In this paper, we analyze the performance of link-level abstraction for orthogonal frequency-division multiplexing (OFDM) and single-carrier (SC) modes in IEEE 802.11ay wireless systems over the 60GHz millimeter-wave band. In particular, we evaluate the effectiveness of the three existing effective signal-to-noise ratio (SNR) metric (ESM) schemes (i.e., exponential ESM (EESM), mean mutual information per coded bit (MMIB) and post-processing ESM (PPESM)). Furthermore, to deal with the issue that EESM calibration is dominated by channel realizations with poor error performance, we introduce a classification based EESM (CEESM) scheme with a new metric named coefficient of variation, which is used to measure the severity of frequency-selective fading. Finally, we present several important insights developed through extensive experimentation. Based on our validation results, the MMIB and PPESM can be employed with minimum computational complexity for OFDM and SC modes, respectively. In contrast, EESM and CEESM can be considered for both modes with better accuracy, but at a cost of high implementation complexity.

As a typical application of the Internet of Things (IoT), the Industrial IoT (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big-data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this article, we leverage the fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed convolutional neural network (CNN) model, utilizing a real-world IIoT data set to evaluate our approach.1Our experimental results confirm the effectiveness of our approach, which cannot only reduce the network traffic overhead for IIoT but also maintain the classification accuracy in comparison with several baseline schemes.1Certain commercial equipment, instruments, or materials are identified in this article in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Industrial Internet-of-Things (IIoT), also known as Industry 4.0, is the integration of Internet of Things (IoT) technology into the industrial manufacturing system so that the connectivity, efficiency, and intelligence of factories and plants can be improved. From a cyber physical system (CPS) perspective, multiple systems (e.g., control, networking and computing systems) are synthesized into IIoT systems interactively to achieve the operator's design goals. The interactions among different systems is a non-negligible factor that affects the IIoT design and requirements, such as automation, especially under dynamic industrial operations. In this paper, we leverage reinforcement learning techniques to automatically configure the control and networking systems under a dynamic industrial environment. We design three new policies based on the characteristics of industrial systems so that the reinforcement learning can converge rapidly. We implement and integrate the reinforcement learning-based co-design approach on a realistic wireless cyber-physical simulator to conduct extensive experiments. Our experimental results demonstrate that our approach can effectively and quickly reconfigure the control and networking systems automatically in a dynamic industrial environment.

To meet the rising demand of mobile data, IEEE 802.11ac was recently developed to offer better network performance than previous IEEE 802.11 standards, representing the 5G Wi-Fi technology. One of the new technologies added to IEEE 802.11ac is a dynamic bandwidth channel access scheme that supports channel bonding (CB) with an up to 160 MHz bandwidth. However, the use of CB increases the number of stations sharing the same medium, resulting in an increased collision rate and severe interference caused by overlapping sub-channels. This problem is further aggravated in densely deployed wireless networks where the service ranges of access points (APs) are likely to be highly overlapping. To resolve these issues, we present an analytical throughput model that considers the effect of both collisions and interference. With this analytical model, we formulate the CB problem to optimize the throughput along with the traffic demand and propose a genetic algorithm for solving the problem. Further, a dynamic channel bonding algorithm (named DCB) is proposed to solve the problem with lower complexity. Extensive simulation results demonstrate that our proposed algorithm provides a higher performance than other recent dynamic CB algorithms with respect to the throughput achieved for the traffic demand.

The number of Machine-to-Machine (M2M) devices has continued to grow at an accelerated rate. Without thoughtful and efficient resource management, M2M communications will be asymmetrically handicapped by service rate scarcity as more devices are continually added. To address these issues, in this paper, we propose a dynamic rate adaptation (DRA) scheme to obtain an optimized service rate distribution among a mixture of time-driven and event-driven M2M applications. DRA introduces real time monitoring of M2M traffic arrival rate, building on which service rate distribution between M2M applications can be adjusted momentarily, by using the mean value theorem of integrals (MVTI) and generalized processor sharing (GPS). We have validated the effectiveness of our proposed DRA scheme and our experimental results demonstrate that DRA can significantly improve M2M communications performance with respect to throughput and delay.

Communication networking leverages emerging network technologies such as topology management schemes to satisfy the demand of exponentially increasing devices and associated network traffic. Particularly, without efficient topology management, Machine-to-Machine (M2M) communications will likely asymmetrically congest gateways and eNodeBs in 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) and Long-Term Evolution Advanced (LTE-A) networks, especially when M2M devices are massively deployed to support diverse applications. To address this issue, in this paper, we propose a 3D Topology Optimization (3D-TO) scheme to obtain the optimal placement of gateways and eNodeBs for M2M communications. By taking advantage of the fact that most M2M devices rarely move, 3D-TO can specify optimal gateway positions for each M2M application, which consists of multiple M2M devices. This is achieved through global optimization, based on the distances between gateways and M2M devices. Utilizing the optimization process, 3D-TO likewise determines optimal eNodeB positions for each M2M application, based on the distances between eNodeBs and optimal M2M gateways. Our experimental results demonstrate the effectiveness of our proposed 3D-TO scheme towards M2M communications, with regard to throughput, delay, path loss, and packet loss ratio.

Most previous spectrum sensing techniques use omni-directional antennas. Unlike omni-directional antennas, the use of directional antennas for spectrum sensing is a promising technique that can realize fine-grained sensing for the primary user (PU) with a longer sensing range. In this paper, we propose a centralized cooperative directional sensing technique for cognitive radio networks. We assume that one secondary coordinator called the fusion center (FC), gathers sensing results from secondary nodes. Using the reported information, the FC optimizes the sensing period, sensing power, and sensing beams per secondary node. For optimization, we use a modified gradient descent method with numerical methods to solve the nonlinear optimization problem. The simulation results show that our directional spectrum sensing technique is well suited for the existing cognitive radio environment. The optimal scheme shows proposed here better performance in all simulation factors than the non-optimized scheme.

To address the issues related to the extreme densification of wireless networks, namely increased interference and lower overall throughput, the IEEE 802.11ax project was formed to revise the standard specifications for IEEE 802.11ac in order to increase the spectrum efficiency and most notably the per-user throughput by a factor of four. Improving Spatial Reuse (SR) is seen as one of the main techniques for achieving this goal. In this paper, we explore the so-called Overlap Basic Service Set Physical Detection (OBSS_PD)-based SR technique introduced in IEEE 802.11ax, which aims to increase SR by adapting both stations' Carrier Sensitivity Threshold (CST) and transmission power. To that end, OBSS_PD PD-based SR defines a proportional relationship between CST and the transmission power. In this article, we propose a novel and dynamic algorithm to implement an OBSS_PD PD-based SR. This so-called ETX To Power (ETP) algorithm uses the Expected Transmission Count (ETX) metric to dynamically compute the transmission power and the CST to use. Simulations are used to evaluate ETP in a dense scenario. We show that the overall system throughput is improved by 40 % compared to IEEE 802.11 legacy networks and at the same time the 5th percentile throughput is also increased by 22 %.

Wireless Local Area Networks (WLANs)-enabled devices are now everywhere and their rapid spread has created dense deployment environments. For such dense WLANs, the High Efficiency WLAN Study Group (HEW SG) was formed, and as an extension of their activity, effort on standardization of IEEE 802.11ax Task Group (TG) was initiated. The goal of the TG on IEEE 802.11ax is to improve per-station (STA) throughput of WLAN dense networks in the presence of interfering sources. To attain this aim, the TG is currently working on Clear Channel Assessment Threshold (CCAT) adjustment. As the CCAT is increased, more concurrent transmissions are permitted, leading to more interference. By using a small CCAT, the amount of interference can be reduced, but the transmission opportunity decays. Thus, we propose an algorithm that adjusts CCAT based on the co- channel interference and transmission opportunity for network capacity improvement in dense WLANs. In addition, traffic load may not be fairly shared by all serving APs due to the typical Received Signal Strength (RSS)-based AP selection algorithm. In this paper, therefore, we propose an Access Point (AP) selection algorithm that chooses both AP and CCAT providing the highest achievable throughput for a STA by considering the co-channel interference and the traffic load status in dense WLANs. Simulation results show that our proposed algorithm achieves better performance in terms of the average per-STA throughput and Jain's Fairness Index (JFI) in dense wireless networks with various scenarios.

Internet of Things (IoT) provides a generic infrastructure for different applications to integrate information communication techniques with physical components to achieve automatic data collection, transmission, exchange, and computation. The smart grid, as one of typical applications supported by IoT, denoted as a re-engineering and a modernization of the traditional power grid, aims to provide reliable, secure, and efficient energy transmission and distribution to consumers. How to effectively integrate distributed (renewable) energy resources and storage devices to satisfy the energy service requirements of users, while minimizing the power generation and transmission cost, remains a highly pressing challenge in the smart grid. To address this challenge and assess the effectiveness of integrating distributed energy resources and storage devices, in this paper we develop a theoretical framework to model and analyze three types of power grid systems: the power grid with only bulk energy generators, the power grid with distributed energy resources, and the power grid with both distributed energy resources and storage devices. Based on the metrics of the power cumulative cost and the service reliability to users, we formally model and analyze the impact of integrating distributed energy resources and storage devices in the power grid. We also use the concept of network calculus, which has been traditionally used for carrying out traffic engineering in computer networks, to derive the bounds of both power supply and user demand to achieve a high service reliability to users. Through an extensive performance evaluation, our data shows that integrating distributed energy resources conjointly with energy storage devices can reduce generation costs, smooth the curve of bulk power generation over time, reduce bulk power generation and power distribution losses, and provide a sustainable service reliability to users in the power grid.

Within the foreseeable future, the growing number of mobile devices, and their diversity, will challenge the current network architecture. Furthermore, users will expect greater data rates, lower latency, lower packet drop rates, etc. in future wireless networks. Ultra Dense Networks (UDN), considered to be one of the best ways to meet user expectations and support future wireless network deployment, will face multiple significant hurdles, including interference, mobility, and cost. In this paper, we review existing research efforts toward addressing those challenges and present future avenues for research. We first develop a taxonomy to review and describe existing research efforts. Next, we focus on inter-cell interference, handover performance, and energy efficiency as the key techniques to addressing the most pressing challenges. Finally, we present several future research directions, including emergent Internet-of-Things (IoT) applications, security and privacy, modeling and realistic simulations, and relevant techniques.

The Ultra Dense Network (UDN), as a key enabler for future wireless networks (such as 5G), is comprised of a massive number of small cells in the network. Nonetheless, energy consumption will be non-negligible when a large number of smallcell Base Stations (BSs) are densely deployed. One practical and effective approach to reduce the energy consumption of the UDN is through dynamically controlling the power saving mode of BSs, while the challenge is to maintain network coverage and satisfy the performance requirements of User Equipment (UEs). In this paper, we formalize the problem of minimizing the energy consumption of BSs by optimally controlling the BS's power saving mode (switching between awake mode and sleep mode). We focus on optimal BS selection with the objective of energy efficiency, while the considering the constraints of the coverage of UEs, the capacity of BSs, and the data rate UEs. To validate the effectiveness of our proposed scheme, we have conducted performance evaluations with a comprehensive scenario design, consisting of UE density, distribution, and mobility, as well as BS deployment. The evaluation results demonstrate favorable energy efficiency improvement at averages of 38.82 % and 48.05 % in scenarios where UEs are uniformly distributed and non-uniformly distributed in the network. Meanwhile, network coverage and UE's Quality of Service (QoS) requirements are provided.

The smart grid is a complex system that comprises components from both the power grid and communication networks. To understand the behavior of such a complex system, co-simulation is a viable tool to capture the interaction and the reciprocal effects between a communication network and a physical power grid. In this paper, we systematically review the existing efforts of co-simulation and design a framework to explore co-simulation scenarios. Using the demand response and energy price as examples of smart grid applications and operating the communication network under various conditions (e.g., normal operation, performance degrade, and security threats), we implement these scenarios and conduct a performance evaluation of smart grid applications by leveraging a co-simulation platform.

The National Public Safety Telecommunications Council (NPSTC) has defined resiliency as the ability of a network to withstand the loss of assets and to recover quickly from such losses. How to measure the resiliency of a base station deployment is an important consideration for network planners and operators. In this paper, we propose a resiliency measurement method in conjunction with a performance metric such as coverage or supported throughput, where we define the resiliency as the maximum number of sites that can fail before the metric falls below a minimum acceptable threshold. Because the number of combinations of failures increases exponentially with respect to the number of sites in a given deployment, we introduce an algorithm that generates estimates of the lowest, highest, and average values of the metric for a given failure count while examining a subset of the possible failure combinations. We use an example deployment to demonstrate how the resiliency metric can be used to identify sites that have a disproportionate impact on performance; the network planner can harden these sites or, for a future deployment, adjust the site placement to reduce the effect of the high-impact sites.

ABSTRACT The Smart Grid is a new type of power grid that will use advanced communication network technologies to support more efficient energy transmission and distribution. The grid infrastructure was designed for reliability; but security, especially against cyber threats, is also a critical need. In particular, an adversary can inject false data to disrupt system operation. In this paper, we develop a false data detection system that integrates two techniques that are tailored to the different attack types that we consider. We adoptanomaly‐based detectionto detect strong attacks that feature the injection of large amounts of spurious measurement data in a very short time. We integrate the anomaly detection mechanism with awatermarking‐based detection schemethat prevents more stealthy attacks that involve subtle manipulation of the measurement data. We conduct a theoretical analysis to derive the closed‐form formulae for the performance metrics that allow us to investigate the effectiveness of our proposed detection techniques. Our experimental data show that our integrated detection system can accurately detect both strong and stealthy attacks. Copyright © 2014 John Wiley & Sons, Ltd.

The sixteen articles in this special section explore advances in communication technologies that have the potential for improving energy efficiency and realizing the smart grid vision. The articles tackle important challenges with respect to smart grid communications and include a wide range of topics such as electric load models, electric vehicle charging, models for demand response, power management optimization and storage, pricing, and security.

The papers in this special issue explore advances in communication technologies that have the potential for improving energy efficiency and realizing the smart grid vision.

"Smart Grid" refers to the modernization of electric grid to allow for more efficient generation, transmission, distribution, and usage of energy. This is becoming necessary in order to transition to a more sustainable energy generation and consumption and to reduce any adverse effects on the environment. While more advances in areas like renewable energies and network control are necessary, advances in communication technologies, data fusion and mining, as well as scheduling and optimization are also critical in order to achieve this vision. It is anticipated that the same communication technologies that have revolutionized our way of life in the past decades, ranging from sensor networks, mobile Internet, cloud computing, smart phones and many others, will have direct applicability to Smart Grid. Our objectives for this series of IEEE JSAC are focused on identifying the numerous communication challenges posed by the various aspects and functionalities of the Smart Grid and exploring research avenues for addressing them. We have received 67 papers, and after thorough review and careful deliberations, we have accepted 10 papers in this first issue. The articles in this issue can be grouped into 4 main sub-topic areas, namely, scheduling and load balancing, energy pricing, routing, and security.

Emerging technologies for mesh networks can provide users with last-mile service to an access point by forwarding data through wireless relays instead of through expensive wireline infrastructure. While an extensive amount of literature on the subject has been amassed in the last decade, existing papers model network traffic flow solely as a function of routing topology, neglecting contention at the Media Access Control layer; as a result, the inbound flow to a relay station is independent of the transmission success rate from forwarding stations. This leads to overestimation of traffic flow, especially at network operation approaching full capacity, and in turn makes for inaccuracies in predicting throughput and delay. In our model, the inbound flow depends on the transmission success rate as well. Other novel contributions are the incorporation of a half-duplex contention model we developed in previous work, which captures both uplink and downlink traffic, and a generic framework to represent any mesh routing topology (minimum-hop, minimum-airtime, etc.).

In Proxy Mobile IPv6 (PMIPv6), any involvement by the Mobile Node (MN) is not required so that any tunneling overhead could be removed from over-the-air. However, during the PMIPv6 handover process, there still exists a service interruption period during which the MN is unable to send or receive data packets because of PMIPv6 protocol operations. To reduce the handover latency, Fast Handover for PMIPv6 (PFMIPv6) is being standardized in the IETF MIPSHOP working group. In PFMIPv6, however, handover initiation can be false, resulting in the PFMIPv6 handover process done so far becoming unnecessary. Therefore, in this paper, we provide a thorough analysis of the handover latency in PFMIPv6, considering the false handover initiation case. The analysis is very meaningful to obtain important insights on how PFMIPv6 improves the handover latency. Further, our analytical study is verified by simulation results.

Conversion from analog television in the United States combined with the appeal for broadband public safety communications is generating a lot of interest in the so-called 700 MHz band between 698-806 MHz. To our knowledge, no channel propagation model for this band exists to date. In this work, we derive such a model reduced from a measurement campaign realized through a stepped frequency system. The campaign includes 89 transmitter-receiver configurations in seven different environments relevant to residential, commercial and public safety communications, ranging from subterranean mine tunnels to an oil refinery, from mid-size to high-rise buildings. The stochastic impulse response model is complete with 17 parameters which enables reconstruction for use in simulation engines, amongst others.

Call Admission Control (CAC) between heterogeneous networks, such as an integrated 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) network and a Wireless Local Area Network (WLAN), plays an important role to utilize the system resources in a more efficient way. In this paper, we propose that the preference to the WLAN is determined based on the traffic load in the WLAN and the location of the cellular users. Our analysis relies on a previous study that divides the 3G cellular coverage area into zones based on the amount of resources that are required to support a connection to a mobile user. Using this model, we derive new call blocking and handoff failure probabilities as well as new call and handoff attempt failure probabilities. Through simulations, we investigate proper preference settings by changing the WLAN load in a 3 ring-based sector with a WLAN hotspot.

Abstract Supporting seamless handovers between different wireless networks is a challenging issue. One of the most important aspects of a seamless handover is finding a target network and point of attachment (PoA). This is achieved by performing a so‐called channel scanning. In most handovers, such as between universal mobile telecommunications system (UMTS), wireless local area network (WLAN), and worldwide interoperability for microwave access (WiMAX), channel scanning causes severe service disruptions with the current PoA and degrades the quality of service (QoS) during the handover. In this paper, a new architecture for QoS supported scanning that can be generalized to different wireless networks is proposed. It employs two techniques. The first is for determining a policy‐based order for the channel scanning sequence. With this technique, depending on the network costs and user requirements, the policy engine determines the channel scanning order for different network types and sets up a scanning sequence of PoAs for a given network type. This policy‐based scanning order provides a faster discovery of the target PoA that meets the QoS demands of the user. The second technique consists of a QoS supported dynamic scanning algorithm where the scanning frequency and duration are determined based on the user QOS requirements. Most importantly, the scanning duration is scheduled to guarantee the user QoS requirements while the scan progresses. Simulation results show that the proposed mechanism achieves relatively short service disruptions and provides the desired quality to users during the scanning period. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper we study the performance of the authentication process in media independent handovers and consider the impact of using IEEE 802.21 link triggers to achieve seamless mobility. We describe all interactions between the 802.21 services and the authentication in order to achieve timely and seamless handovers.

In mobile networks, authentication is a time-consuming operation that needs to be shortened in order to provide seamless handovers. Furthermore, the time required for negotiating security parameters and obtaining security policies increases the importance of the authentication performance during handovers. Many optimizations have been proposed for different authentication mechanisms in different layers. However, they fail to provide means to learn in advance security capabilities and policies in candidate networks, and thus do not reduce the chance of connecting to potentially incompatible target networks. In this paper we use the information capabilities provided by IEEE 802.21 and propose an extension to current network selection algorithms that takes into account security parameters and policies to optimize the handover performance and reduce the negotiation delay.

We explore the use of the Media Independent Information Service (MIIS) in the IEEE 802.21 Media Independent Handover (MIH) framework to improve handover performance for Fast Mobile IPv6 by providing Authentication information. We explore the tradeoffs of Pre-Authentication before joining a new network versus authentication after connecting to a new network during a Fast Mobile IPv6 handover. We discuss our implementations of services available from the MIIS in simulation using the ns-2 simulation system and evaluate their performance.

In an integrated WLAN and cellular network, if all mobile users whose connections originate in the cellular network migrate to the WLAN whenever they enter the double coverage area, the WLAN will be severely congested and its users will suffer from performance degradation. Therefore, we propose a Call Admission Control (CAC) algorithm that allows the WLAN to limit downward Vertical Handovers (VHOs) from the cellular network to reduce unnecessary VHO processing. Numerical and simulation results demonstrate that our CAC scheme reduces the unnecessary VHO processing while keeping the DVHO blocking rate within acceptable limits and maintaining reasonable throughput in the WLAN.

Abstract Effective and timely link‐layer trigger mechanisms can significantly influence the handover performance. The handover process will not perform the correct decision and execution unless adequate and timely link‐layer trigger information is delivered. In this paper, a predictive link trigger mechanism for seamless horizontal and vertical handovers in heterogeneous wireless networks is proposed. Unlike previous link trigger algorithms based on pre‐defined signal level thresholds, the link layer triggers in this study are adaptively and timely fired in accordance with the network conditions. Firstly, the time required to perform a handover is estimated based on the neighboring network conditions. Secondly, the time to trigger a Link_Going_Down to initiate a handover is determined using a least mean square linear prediction in which the prediction interval (kh) is dynamically determined based on the estimated handover time. An upper bound for the packet loss rate during a handover is derived for a Gaussian shadowing channel. A manner in which this approach can be applied to IEEE 802.21 is shown in media independent handover scenarios. Simulation results of the proposed predictive link triggering mechanism show that it provides a timely proactive handover. The packet loss rate observed in a Gaussian shadowing channel remains low during a handover. Copyright © 2008 John Wiley & Sons, Ltd.

The media independent handover (MIH) architecture is designed to facilitate the signaling and enable seamless handovers in heterogeneous networks. In this paper, we propose a solution using the stream control transmission protocol (SCTP) to efficiently carry MIH messages. The solution uses SCTPpsilas multihoming and multistreaming capabilities along with cross-layer information available through the MIH. We analyze the performance of the proposed solution for various packet loss conditions.

Channel scanning is an important aspect of seamless handovers since it is required in order to find a target point of attachment (PoA). In the IEEE 802.11 WLAN, scanning of other channels causes service disruptions with the current AP so that the provided quality of service (QoS) will be degraded seriously during the handoff. In this paper, we propose a QoS supported dynamic channel scanning algorithm. The scanning period is scheduled to guarantee the user’s QoS requirements while the scan progresses. The simulation results show that the proposed mechanism reduces service disruptions and provides the desired quality of service to users during the scanning period.

There are currently heterogeneous wireless access networks. A large variety of applications utilizing these networks will demand features such as real-time, high-availability and even instantaneous high-bandwidth in some cases. Therefore, it is imperative for network service providers to make the best possible use of the combined resources of available heterogeneous networks for connection support. Thus, with regard to vertical handoff performance, there is a critical need for developing algorithms for connection management and optimal resource allocation for seamless mobility. In this paper, we develop a vertical handoff decision algorithm that enables a wireless access network to not only balance the overall load among all attachment points but also to maximize the collective battery lifetime of mobile nodes (MNs). Simulation results are also presented to demonstrate the efficacy of the proposed algorithms.

In heterogeneous wireless networks, there have been several efforts aimed at having mobile devices equipped with multiple interfaces connect optimally to the access network that minimizes their power consumption. However, a study of existing schemes notes that in the idle state, a device with both a WLAN and a WWAN interface need to keep both interfaces "on" in order to receive periodic beacon messages from the AP (WLAN) and downlink control information from the base station (WWAN), resulting in significant power consumption. Therefore, in this paper, we propose a Power-efficient Communication Protocol that includes turning off the WLAN interface after it enters the idle state and using the existing paging of WWAN in order to wake up the WLAN interface when there is incoming long-lived multimedia data. Further, we propose turning on the WLAN interface when the number of packets in the radio network controller's buffer reaches a certain threshold level in order to avoid repeatedly turning on and off WLAN interfaces, that consumes a significant amount of power. The tradeoffs between the power saving and the number of packets dropped at the buffer are investigated analytically. Simulation results for scenarios of interest are also provided.

Effective link layer triggering is the key in order to enable fast and reliable handovers across different access networks. In this paper, we investigate the use of signal strength as part of the trigger mechanism. We propose an analytical model to estimate the anticipation required in order to achieve a target handover packet loss performance, for both voice and video traffic patterns. Numerical results from the analytical model are presented and compared to simulation data obtained for realistic handover scenarios.

We propose the creation of a wireless storage area network (SAN) and analyze its benefits. The proposed wireless SAN (WSAN) consists of a SAN switch that is connected to multiple wireless access points (APs) that communicate with the storage devices. This network would save space and reduce overall costs by not requiring wired connections. Wireless SANS would also provide more freedom in the placement of storage devices. However, because the number of wireless access points is less than the number of storage devices, it is possible for user data requests to be blocked if all access points are busy. An important design goal is therefore to minimize the probability that a network access request will be blocked.

Agile all optical cross-connect (OXC) switches currently use an architecture in which regenerators and transceivers have pre-assigned and fixed directionality. However, technology is evolving to enable new OXC architectures in which the directionality of regenerators and transceivers can be dynamically assigned on demand for each connection that requires regeneration. We have performed detailed analytical and simulation studies to compare the two architectures. The analytical study is applicable to a single node and is very useful in providing intuitive insights into the two alternative architectures. The simulation study is based on a realistic network topology consisting of 53 nodes. The simulation study was carried out using NIST's updated GMPLS lightwave agile switching simulator (GLASS) tool. The GLASS tool was significantly enhanced over its previous version in the course of this study. We report extensive results on comparison of the two OXC architectures in a realistic network implementation in terms of connection blocking probability, efficiency of regenerator use, and carrier equipment costs. We show that fewer regenerators and transceivers need to be used with the new architecture because of sharing of resources across all directionality combinations. This translates to significant cost savings for the new architecture, especially as the traffic load in the network increases.

The introduction of new high bandwidth services such as video-on-demand by cable operators will put a strain on existing resources. It is important for cable operators to know how many resources to commit to the network to satisfy customer demands. In this paper, we develop models of voice and video traffic to determine the effect on demand growth on hybrid fiber-coax networks. We obtain a set of guidelines that network operators can use to build out their networks in response to increased demand. We begin with one type of traffic and generalize to an arbitrary number of high-bandwidth CBR-like services to obtain service blocking probabilities. These computations help us to determine how cable networks would function under various conditions (i.e., low, medium, and heavy loads). We also consider how the growth rate of the popularity of such services would change over time, and how this impacts network planning. Our findings will help cable operators estimate how much bandwidth they need to provision for a given traffic growth model and connection blocking requirement.

As network utilization continues to grow in the coming years, there will be increased pressure on network operators to use traffic engineering to provision resources more efficiently. One way to do this is to allow backup paths associated with disjoint working paths to share bandwidth. Increasing the amount of sharing will naturally increase the risk that a failed working path will either be unrecovered or forced to use dynamic recovery mechanisms. To examine the tradeoffs between robustness and efficiency and to develop useful performance bounds, we develop theoretical models for (1:1)nrecovery schemes that are independent of the network's topology and management plane. We confirm our results using simulations of uncorrelated failures in a wide-area optical network with various degrees of resource sharing.

In this paper, we investigate the use of adaptive techniques to mitigate interference for Bluetooth systems in the presence of WLAN direct sequence spread spectrum devices. We consider two different techniques that attempt to avoid time and frequency collisions of WLAN and Bluetooth transmissions. We conduct a comparative analysis of their performance for several dynamic scenarios where the WLAN interference varies over time due to either change in user activity or number of non-overlapping WLAN systems. We discuss the trade-offs involved in terms of delay, packet loss performance, and synchronization.

A major challenge for the WLAN technology stems from having to share the 2.4 G Hz ISM band with other wireless devices such as Bluetooth radios. The main goal of this paper is to investigate the use of techniques to mitigate the effects of interference for Bluetooth and WLAN and discuss the resulting performance trade-offs. We compare the performance of the Bluetooth and WLAN systems and evaluate how each technique improves or degrades TCP performance. Simulation results for selected scenarios and configurations of interest are obtained and the performance of Bluetooth and WLAN is measured in terms of packet loss, TCP throughput and delay.

Since Bluetooth devices have to share the already crowded unlicensed ISM band with WLAN spread spectrum devices that may be operating in close proximity, a mechanism that detects interference and avoids transmission in the so-called 'bad' channels is key in order to prevent mutual interference and the resulting performance degradation. We describe a dynamic channel estimation algorithm and evaluate its performance in conjunction with a Bluetooth scheduling algorithm that avoids transmission in frequencies used by other devices. Simulation results for several scenarios of interest including different traffic types are presented and analyzed.

Bluetooth is a radio technology for wireless personal area networks operating in the 2.4 GHz ISM band. Since both Bluetooth and IEEE 802.11 devices use the same frequency band and may likely come together in a laptop or may be close together at a desktop, interference may lead to significant performance degradation. The main goal of this paper is to propose a scheduling algorithm aimed at reducing the impact of interference. This algorithm takes advantage of the fact that devices in the same piconet will not be subject to the same levels of interference on all channels of the band. The basic idea is to utilize the Bluetooth frequency hopping pattern and distribute channels to devices in order to maximize their throughput while ensuring fairness of access among users. Simulation results are given for selected scenarios and configurations of interest.

Bluetooth is a radio technology operating in the 2.4 GHz ISM frequency band, that is emerging as a low-level and low-power wireless communication protocol used for wireless personal area networks (WPANs) where proximal devices can share information and resources. We quantify the performance of the Bluetooth access control layer when the radio is operating in close proximity to a WLAN system. We use a probability analysis approach to derive the packet error for Bluetooth. The analytical results are validated using detailed simulation models for an interference scenario consisting of Bluetooth and WLAN devices. The packet loss is obtained for voice and data traffic for different interference conditions.

The emergence of several radio technologies such as Bluetooth, and IEEE 802.11 operating in the 2.4 GHz unlicensed ISM frequency band may lead to signal interference and result in significant performance degradation when devices are co-located in the same environment. The main goal of this paper is to present a simulation environment for modeling interference based on detailed MAC and PHY models. This framework is then used to evaluate the impact of interference on the performance of Bluetooth and IEEE 802.11. We use several simulation scenarios and measure performance in terms of packet loss, residual number of errors, and access delay.

In this paper, we propose a bandwidth guaranteed multi-access protocol for broadcast-and-select WDM local networks with a star topology. The proposed protocol is based on a combination of contention and reservation mechanisms for time slotted WDM networks. Every node accesses the data channel by transmitting request packets in minislots on a separate control channel. There are two types of minislots; reservation minislots and contention minislots. Nodes repairing bandwidth guarantees, called guaranteed nodes, use reservation minislots that are assigned by the control node. The remaining nodes share contention minislots using a random access mechanism. Each node dynamically assigns data channels for the minislots successfully returned on a first-come-first-served (FCFS) basis. Then, the number of reservation minislots is allocated by the control node. The remaining minislots are used for contention minislots. The reservation minislots can guarantee a minimum bandwidth for the guaranteed nodes. The contention minislots enable on-demand services at the optical layer and achieve good fairness for the remaining bandwidth. This protocol can be implemented with a simple distributed algorithm that efficiently utilizes the data channel.

In order to support quality-of-service (QoS) for real-time data communications such as voice, video and interactive services, multiaccess networks must provide an effective priority mechanism. The context of this work is the IEEE 802.14 standard for hybrid fiber coaxial (HFC) networks which has a shared upstream channel for transmissions from stations to the headend. This work presents a multilevel priority collision resolution scheme, which separates and resolves collisions between stations in a priority order, thereby, achieving the capability for preemptive priorities. We present a set of simulation scenarios which show the robustness and efficiency of the scheme, such as its ability to isolate higher priority traffic from lower priorities and to provide quick access to high-priority requests. In March 1998, a framework for handling priorities in the collision resolution process, which adopts a semantics similar to the semantics of our scheme, was included in the 802.14 standard.

Both the IEEE and MCNS medium access control (MAC) interface specifications for hybrid fiber/coax networks share a common goal to provide a standard interface to the same anticipated services (such as cable telephony, Internet and on-line access for Web browsing, chat rooms, interactive games, local area network emulation for work-at-home applications and desktop conferencing). However the solutions offered by the two groups have little in common at the MAC layer. IEEE 802.14 chose ATM transfer as its default solution while MCNS uses a scheme that favors the delivery of variable length IP packets. Our goal in this study is to conduct a comparative performance evaluation of both IEEE 802.14 and MCNS MAC specifications. We focus on the efficiency of the two solutions in terms of contention access, ATM vs IP transfer, and adequate quality of service provision.

In order to provide quality of service (QoS) to users with real-time data such as voice, video and interactive services, the evolving IEEE 802.14 standard for hybrid fiber coaxial (HFC) networks must include an effective priority scheme. In this paper we investigate the ability of the current IEEE 802.14 specification to provide priority service and show that a preemptive scheduler is not a sufficient solution. We propose to augment the scheduler with a novel scheme for implementing priority access in an HFC random access environment. The proposed mechanism integrates a multilevel priority collision resolution system into the proposed IEEE 802.14 MAC. The scheme separates and resolves collisions between stations in a priority order. A set of simulation scenarios is presented that shows the robustness and efficiency of the protocol, such as its ability to isolate higher priorities from lower ones and provide quick access to high priority requests.

The rate-based flow control mechanism in asynchronous transfer mode (ATM) networks controls the transmission rate of available bit rate (ABR) traffic sources, based on feedback information contained in the resource management (RM) cells coming from the destination and/or the network switching nodes. While the source and destination end systems behavior are fully described in the ATM Forum Traffic Management Specification, the network switch mechanism is implementation dependent. Since there is a variety of algorithms an ATM switch vendor can choose from, different performance characteristics are expected. This paper investigates the interoperability issues in terms of network performance and fairness of bandwidth allocation when an ATM network consists of switches using different rate control mechanisms, namely, the explicit forward congestion indication (EFCI) mode and the explicit rate (ER) mechanism. Simulation results show that these algorithms will interoperate as long as the switch implementation conforms to the end system behavior for congestion notification and usage of RM cells. Moreover, the location of an ER switch in a given topology might be critical to solving the "beat-down" phenomenon, when traffic passing through a larger number of switches might get less bandwidth than single hop traffic.