The Open Radio Access Network (O-RAN) initiative, characterized by open interfaces and AI/ML-capable RAN Intelligent Controller (RIC), facilitates effective spectrum sharing among RANs. In this context, we introduce AdapShare, an ORAN-compatible solution leveraging Reinforcement Learning (RL) for intent-based spectrum management, with the primary goal of minimizing resource surpluses or deficits in RANs. By employing RL agents, AdapShare intelligently learns network demand patterns and uses them to allocate resources. We demonstrate the efficacy of AdapShare in the spectrum sharing scenario between LTE and NR networks, incorporating real-world LTE resource usage data and synthetic NR usage data to demonstrate its practical use. We use the average surplus or deficit and Jain's fairness index to measure the RL system's performance in various scenarios. AdapShare outperforms a quasi-static resource allocation scheme based on long-term network demand statistics, particularly when available resources are scarce or exceed the aggregate demand from the networks. Lastly, we present a high-level O-RAN-compatible architecture using RL agents, which demonstrates the seamless integration of AdapShare into real-world deployment scenarios.

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.

The Open Radio Access Network (O-RAN), an industry-driven initiative, utilizes intelligent Radio Access Network (RAN) controllers and open interfaces to facilitate efficient spectrum sharing between LTE and NR RANs. In this paper, we introduce the Proactive Spectrum Adaptation Scheme (ProSAS), a data-driven, O-RAN-compatible spectrum sharing solution. ProSAS is an intelligent radio resource demand prediction and management scheme for intent-driven spectrum management that minimizes surplus or deficit experienced by both RANs. We illustrate the effectiveness of this solution using real-world LTE resource usage data and synthetically generated NR data. Lastly, we discuss a high-level O-RAN-compatible architecture of the proposed solution.

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.

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.

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 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 Third Generation Partnership Project (3GPP) defines various pre-configured channel parameters for the Long-Term Evolution (LTE) Device-to-Device (D2D) communications with Physical Sidelink (SL) Channels. In this paper, we investigate the impacts of channel parameter settings on the performance of content deliveries for Public Safety Network (PSN) applications in Out-of-Coverage (OOC) scenario. We first measure the reliability of the SL channels under various sets of channel parameters using Monte Carlo simulations. Then, for a given PSN application, the acquired reliability results are utilized to help determining the amount of delay that is to be introduced to the system, such that the throughput requirement for the application is assured during the transmissions. To the best of our knowledge, this is the first LTE D2D work that focuses on OOC mission-critical communications performance in group traffic settings. Our results are valuable to both network operators, for using them as references in selecting a best set of channel parameters, and to future studies on more complex transmission patterns and network scenarios using D2D communications in PSNs.

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.

In this paper, we derive upper bounds for the number of Device-to-Device (D2D)-capable out-of-coverage (OOC) User Equipments (UEs) that can share the Physical Sidelink Discovery Channel (PSDCH) while maintaining a minimum probability of discovery message decoding. We maximize these upper bounds with respect to the UEs' transmission probability threshold by exploiting the fact that the upper bound is nearly linear with respect to the number of resources in the discovery resource pool. The resulting simple approximate bound is accurate over a large range of parameter values. We validate our results using Monte Carlo simulations in MATLAB and the ns-3 simulation tool.

In order to support the increasing demand for capacity in cellular networks, Long Term Evolution (LTE) introduced Proximity Services (ProSe) enabling Device-to-Device (D2D) communications, defining several services to support such networks. We are interested in the performance in out-of-coverage scenarios of one of these services: direct discovery. As defined in the standard, network and configuration parameters for direct discovery are predefined and do not change over time, which creates an inability to adjust to variations in topologies, number of operating devices, and/or users' mobility during the discovery process. In this paper we propose an enhanced discovery algorithm that, building on previous works, allows users to adapt to potential variations in the discovery group, using optimized transmission probabilities and transmission success probabilities. The performance of this algorithm is evaluated, and we demonstrate gains in the accuracy of the discovery information, and in the time required for discovery.

This paper examines the performance of the Long Term Evolution (LTE) Physical Sidelink Shared Channel (PSSCH) in out-of-coverage (OOC) device- to-device (D2D) communication scenarios. We develop a closed form expression for the distribution of the number of User Equipments (UEs) that successfully decode a message sent on the PSSCH, given the number of UEs that received the transmitter's Sidelink Control Information (SCI) message over the Physical Sidelink Control Channel (PSCCH). We validate our results using Monte Carlo simulations of the PSSCH and network simulations in ns-3, and discuss some of the effects of system parameters on performance.

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.

Long Term Evolution Advanced (LTE-A) introduces a new feature called Proximity Services (ProSe) that enables device-to-device (D2D) communication between User Equipment (UE), including the capability to operate out-of-coverage. In order to establish a D2D communication link the UEs need to be synchronized. In out-of-coverage scenarios, the synchronization is performed in a distributed manner by the UEs. In this paper, we studied problems associated with the simultaneous execution of the synchronization procedure by LTE-A D2D-enabled UEs operating out-of-coverage. In particular, we focused on detection and convergence problems resulting from the half-duplex constraint and periodic scheduling. We showed that if two transmitter UEs are acting as synchronization references and they perform the procedure too close in time, convergence to a synchronized state is not possible. Moreover, the periodic triggering of the procedure will make the problematic condition persistent in time. We proposed an effective algorithm that prevent these problems, or resolve them in a reasonable time. We considered the protocol and requirements specified in the LTE-A standard, and we evaluated the performance of the proposed algorithm using system level simulations.

User Equipments (UEs) that send data must advertise the upcoming transmission by broadcasting signaling messages over the Physical Sidelink Control Channel (PSCCH). Thus, it is important for the network operator to define the PSCCH resource pool to maximize the probability that each UE will be able to successfully decode all of the control messages that appear on the PSCCH. For UEs operating in Mode 2 (i.e., outside the coverage area of an eNodeB), this is especially challenging because there is no base station present that can assign PSCCH resources. UEs must choose pool resources randomly, which can lead to collisions of transmitted messages. In addition, UEs are half-duplex and a poorly designed control channel resource pool can create a significant risk that a signaling message and its duplicate will be missed by a UE that transmits its own signaling message in the same pair of subframes. In this paper, we present an analytical model that allows us to develop closed form expressions for the distribution of the number of UEs that successfully receive a transmitted message on the PSCCH. This model can support PSCCH design by network operators, and can be used to investigate other aspects of D2D communications.

Device-to-device (D2D) communications for Long Term Evolution (LTE) networks relies on a discovery process to enable User Equipment (UE) to determine which D2D applications and services are supported by neighboring UEs. This is especially important for groups of UEs that operate outside the coverage area of any base station. The amount of time required for discovery information to reach every UE in a group depends on the number of UEs in the group and the dimensions of the discovery resource pool associated with the Physical Sidelink Discovery Channel (PSDCH); an additional factor is the half-duplex property of current UEs. In this paper, we use a Markov chain to characterize the performance of Mode 2 direct discovery. The resulting analytical model gives the distribution of the time for a UE to discover all other UEs in its group. We validate the model using Monte Carlo and network simulations.

In this paper we study and improve one service used for Proximity Services and Device-to-Device (D2D) communications: D2D Direct Discovery. As defined in the Third Generation Partnership Project, for both in-coverage and out-of-coverage cases, resource pool parameters, including the transmission probability (in UE-Selected mode), are configured in advance. This means that they are independent of the network conditions and the number of users. Thus, we propose an adaptive algorithm which takes into account the available resources and the number of nearby users as they are being discovered, and adapts the transmission probability accordingly. This algorithm improves the overall performance of the discovery process. It reduces the time needed to complete the discovery within a group of UEs and makes it dynamic and adaptable to changing environments.

The introduction of Proximity services (ProSe) in Long Term Evolution Advanced (LTE-A) allows User Equipments (UEs) to communicate directly without routing the data through the LTE access network. This is a major step towards supporting mission-critical communication for first responders who need the ability to communicate ubiquitously. To properly receive data, the UEs must be synchronized. Thus, reducing the synchronization delays is important to avoid service disruption. When operating outside of the network coverage, UEs cannot rely on the synchronization information provided by the base station. In such cases, a distributed protocol is required to announce and detect the synchronization information within devices in proximity. In this paper, we present an adaptive algorithm that reduces the out-of-coverage synchronization delays while meeting the requirements specified in the LTE-A standard. The algorithm takes into account the UE traffic and synchronization conditions to achieve these goals. We evaluate the algorithm performance using our ns-3 ProSe implementation and show that fast convergence time to a synchronized state can be achieved using the proposed algorithm while satisfying the standard performance constraints.

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.

We model Mode 2 direct discovery in Device-to- Device (D2D) Long Term Evolution (LTE) networks and derive the optimal value of the discovery message transmission probability that minimizes the mean number of periods required for a successful discovery message transmission. We use Monte Carlo simulations to validate our analytical results and to show that optimizing the transmission probability produces nearly optimal performance with respect to the time required for all members of a group of User Equipments (UEs) to discover each other.

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.

Direct device-to-device (D2D) communications between mobile terminals in cellular networks allows operators to offload proximity traffic from the Radio Access Network (RAN) and permits out-of-coverage terminals to maintain peer-to-peer communications. In this study, we consider D2D communications in the context of the Long Term Evolution (LTE) RAN and, in particular, the scenario in which D2D communications share LTE uplink resources. Specifically, we evaluate the performance degradation of the cellular LTE uplink in the presence of interference from in-band (underlay) D2D communications. Through physical layer simulations, we quantify the increase in signal-to-noise ratio (SNR) needed to maintain a certain data rate or coverage criterion as a function of the induced noise rise and under various multipath channel conditions. The results can be used to develop physical layer models for network-layer analyses of D2D and cellular network performance.

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.

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.).

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.

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.

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.

High-capacity optical backbone networks protect their premium customers' information flows by routing two copies of the customer's data over disjoint paths. This scheme, known as 1+1 protection, provides extremely rapid recovery from network failures. We propose an architecture by which 1+1 protection can be extended to optical burst switched (OBS) networks. This architecture is designed by modifying the diversity routing architecture that was originally proposed for nonoptical packet networks and recently applied to networks employing the generalized multiprotocol label switched (GMPLS) architecture. We extend the architecture developed for just-in-time OBS signaling to support 1+1 protection. We also examine design issues that are raised by a difference in the propagation delays of the two disjoint paths across the OBS network. We show that a sufficiently large difference in the propagation delays can cause performance degradations that may result in an unsatisfactory quality-of-service on the protected connection. We examine the impact of this delay mismatch on restoration performance, probability of burst loss, and jitter. Through analysis and simulations, it is discussed how these negative effects can be eliminated.

Generalized multiprotocol label switching (GMPLS) is being applied to optical networks as a means of moving control functionality from the management plane to the control plane, and automating lightpath provisioning and maintenance. GMPLS enables IP networks with quality of service (QoS) to be traffic engineered efficiently. However, computation of explicitly-defined paths optimizing network performance is a difficult task. Previous versions of these optimization routines have not taken path delay, including queueing delay at layer 3, into account. In this paper we present a technique for traffic engineering in optical networks that support QoS considering the traffic flows with delay QoS constraint across lambda labeling networks. The proposed mechanism would facilitate lambda label path setup with specific delay QoS requirements.