ABSTRACT Since the opening of the 6 GHz bands for unlicensed radio access technologies (RATs), new coexistence mechanisms leveraging the currently uninhabited 6 GHz bands have been investigated, aiming for fair coexistence of Wi‐Fi 6 and 5G new radio unlicensed (NR‐U). However, our study shows that the utilization of the highly attractive 6 GHz bands can be significantly enhanced by exploring additional spectrum access opportunities, which remain unrealized in the existing channel contention mechanisms. We propose a novel two‐stage channel contention mechanism for the coexistence of Wi‐Fi 6 and 5G NR‐U in the 6 GHz bands to explore these missed spectrum access opportunities. We formulate the probability of interference to ongoing transmissions and utilize this probability to enhance the utilization of radio resources by allowing simultaneous transmissions on a channel. We incorporate cross‐technology communication (CTC) to compute this probability and formulate an optimization problem to derive the optimal CTC information required for the computation. Extensive simulation results show that the proposed framework significantly outperforms legacy channel contention mechanisms in terms of spectrum utilization while ensuring the ongoing transmissions unharmed.

Since the introduction of the 6 GHz spectrum, it has drawn widespread appeal among unlicensed radio access technologies (RATs), such as WiFi 6 and 5G New Radio-Unlicensed (NR-U). To appease this high demand and prevent the foreseeable saturation of the spectrum, efficient spectrum access strategies for competing WiFi 6 and 5G NR-U in the 6 GHz bands are essential. However, existing channel contention mechanisms of these RATs fail to capture the full availability of radio resources and consequently miss potential spectrum access opportunities. This is the first research on exploring these missed opportunities in the 6 GHz spectrum to capitalize on its full potential and prevent its imminent saturation. We propose a novel two-stage channel contention mechanism for the coexistence of WiFi 6 and 5G NR-U that facilitates the exploration of these missed spectrum access opportunities. Specifically, we formulate the probability of interference to the ongoing transmissions and enable the contending RATs to simultaneously access the channel by incorporating this probability. Extensive simulation results show that the proposed framework outperforms existing strategies in terms of spectrum utilization while maintaining interference-free coexistence of WiFi 6 and 5G NR-U.

Deep reinforcement learning (DRL) has been extensively applied to Multi-Unmanned Aerial Vehicle (UAV) network (MUN) to effectively enable real-time adaptation to complex, time-varying environments. Nevertheless, most of the existing works assume a stationary user distribution (UD) or a dynamic one with predicted patterns. Such considerations may make the UD-specific strategies insufficient when a MUN is deployed in unknown environments. To this end, this paper investigates distributed user connectivity maximization problem in a MUN with generalization to arbitrary UDs. Specifically, the problem is first formulated into a time-coupled combinatorial nonlinear non-convex optimization with arbitrary underlying UDs. To make the optimization tractable, a multi-agent CNN-enhanced deep Q learning (MA-CDQL) algorithm is proposed. The algorithm integrates a ResNet-based CNN to the policy network to analyze the input UD in real time and obtain optimal decisions based on the extracted high-level UD features. To improve the learning efficiency and avoid local optimums, a heatmap algorithm is developed to transform the raw UD to a continuous density map. The map will be part of the true input to the policy network. Simulations are conducted to demonstrate the efficacy of UD heatmaps and the proposed algorithm in maximizing user connectivity as compared to K-means methods.

The demand for radio resources, especially in the mid-band (i.e., 1-6 GHz) spectrum, is persistently increasing in wireless communications. Cognitive radio (CR) has emerged as a promising technology for addressing this increasing demand for the mid-band spectrum by enabling secondary users (SUs) to utilize the available spectrum opportunistically. This research, however, shows that more spectrum can be opportunistically accessed in the spatial domain by exploiting the frequency division duplexing (FDD) feature of cellular-based radio access technologies (RATs) in the mid-band spectrum, which remains undiscovered by existing opportunistic spectrum access techniques. This paper proposes a novel dynamic multi-channel spectrum access framework, employing actor-critic deep reinforcement learning (DRL), that leverages the FDD feature to explore these missed spectrum opportunities in CR networks. Extensive simulation results show that the proposed framework significantly outperforms FDD feature-unaware opportunistic spectrum access in terms of overall spectrum utilization.

The fifth-generation (5G) New Radio Vehicle-to-Everything (NR-V2X) technology supports unicast and groupcast with broadcast communications. These new features enable a vehicle to reliably exchange messages with another vehicle or a group of vehicles. However, these advanced features also create opportunities for malicious actors. We propose two stealthy jamming attacks that exploit the predictable physical layer and the vulnerable decentralized radio resource scheduling algorithm of NR-V2X. In these attacks, assailants leverage the unicast feature for secure coordination to attack the broadcast communications among vehicles. We conduct simulations in a highway scenario. Simulation results demonstrate that our proposed attacks can significantly decrease the packet reception ratio (PRR) by 32.65% and increase the packet inter-reception (PIR) by 30.99%. Simulation results also reveal that in specific attack windows and when vehicle density increases, the impact of our proposed attacks nearly doubles. To the best of our knowledge, this is the first work that exploits the unicast/groupcast mode of NR-V2X to carry out an attack.

Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment applications. Therefore, the freshness of information is a crucial performance metric for CAV services. However, information from roadside sensors and nearby vehicles can get delayed in transmission due to the high mobility of vehicles. Our research shows that a CAV's relative distance and speed play an essential role in determining the Age-of- Information (AoI). With an increase in AoI, incremental service aggregation issues are observed with out-of-sequence information updates, which hampers the performance of low-latency applications in CAVs. In this paper, we propose a novel AoI-based service aggregation method for CAVs, which can process the information updates according to their update cycles. First, the AoI for sensors and vehicles is modeled, and a predictive AoI system is designed. Then, to reduce the overall service aggregation time and computational load, intervals are used for periodic AoI prediction, and information sources are clustered based on the AoI value. Finally, the system aggregates services for CAV applications using the predicted AoI. We evaluate the system performance based on data sequencing success rate (DSSR), and overall system latency. Lastly, we compare the performance of our proposed system with three other state-of-the-art methods. The evaluation and comparison results show that our proposed predictive AoI-based service aggregation system maintains satisfactory latency and DSSR for CAV applications and outperforms other existing methods.

Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is required to assess the effectiveness of an XR application but is challenging to design due to the dependence of the performance metrics on several difficult-to-model parameters, such as computing resources and hardware utilization of XR and edge devices, which are controlled by both their operating systems and the application itself. Moreover, the heterogeneity in devices and wireless access networks brings additional challenges in modeling. In this paper, we propose a novel modeling framework for performance analysis of XR applications considering edge-assisted wireless networks and validate the model with experimental data collected from testbeds designed specifically for XR applications. In addition, we present the challenges associated with performance analysis modeling and present methods to overcome them in detail. Finally, the performance evaluation shows that the proposed analytical model can analyze XR applications' performance with high accuracy compared to the state-of-the-art analytical models.

Channel Rendezvous is a prerequisite and vital operation for two secondary users (SUs) to establish data communications in cognitive radio networks (CRNs). Channel hopping is a widely used method that can ensure two SUs meet on a common available channel within finite hops by following particularly designed hopping sequences. Most existing channel-hopping schemes are based on omnidirectional antennas. However, in a dense primary network, SUs with omnidirectional antennas can only use those channels that have no nearby PUs staying on. Such a limited number of available channels often leads to rendezvous failures. In this paper, we consider the utilization of directional antennas in the blind rendezvous process since more channels can be used due to the reduced interfering range. A novel joint design of sector hopping (beamforming) and channel hopping is proposed to guarantee the rendezvous. Furthermore, we identify a unique trade-off problem regarding the sector angle in these circumstances and derive an optimal solution that can adapt to different network conditions. Extensive simulation results demonstrate that the proposed framework significantly outperforms existing omnidirectional antenna-based rendezvous schemes under various dense primary networks.

The rapid integration of augmented reality (AR) and virtual reality (VR) technologies into contemporary automotive development has led to unprecedented opportunities and challenges. This work addresses the integration of edge computing and AR/VR applications within connected autonomous vehicles, focusing on the pivotal role of object detection. The edge-assisted object detection problem is formulated as a constrained optimization problem, aiming to minimize the adverse effects on the object detection process. To solve the problem, we introduce an innovative edge-assisted algorithm, transmitting live camera frames to an edge server for detailed processing. Only essential detection data is then relayed to AR/VR devices, marking a significant advancement over existing strategies. Notable outcomes include a reduction in latency (averaging between 37.06% and 44.76%), enhanced data throughput (ranging from 27.66% to 41.18%), improved freshness loss (between 36.36% and 69.57%), and a frame loss reduction to 7.5%, surpassing baseline methods by 6.5% to 36%. These findings underscore the potential of this methodology for optimizing AR/VR applications in vehicular environments.

High-definition (HD) map is an essential building block in the autonomous driving era, which enables fine-grained environmental awareness, exact localization, and route planning. However, because HD maps include rich, multidimensional information, the volume of HD map data is enormous, making it expensive and time-consuming to transmit on vehicular networks. Therefore, in this paper, we propose a data optimization scheme for effective HD map updates in vehicular named data networking (NDN) scenarios. We formulate the HD map data optimization problem as a convex optimization problem and solve it with modified convolutional neural networks (CNNs) from YOLOX's real-time object detection system. Specifically, we modify the YOLOX object detection algorithm to detect and compress redundant pixels in local map data before transmission to the MEC server. To deploy our proposed scheme, we construct a vehicular NDN environment for data collection, processing, and transmission using the CARLA simulator and robot operating system 2 (ROS2). Extensive simulations show that our proposed scheme can significantly reduce the transmission data size and time by 48.25% - 65.78% and 46.85% - 78.84% compared with state-of-the-art HD map update techniques like RLSS, Pro-RTT, and Loss-based systems.

Artificial intelligence (AI) has enabled a new paradigm of smart applications - changing our way of living entirely. Many of these AI-enabled applications have very stringent latency requirements, especially for applications on mobile devices (e.g., smartphones, wearable devices, and vehicles). Hence, smaller and quantized deep neural network (DNN) models are developed for mobile devices, which provide faster and more energy-efficient computation for mobile AI applications. However, how AI models consume energy in a mobile device is still unexplored. Predicting the energy consumption of these models, along with their different applications, such as vision and non-vision, requires a thorough investigation of their behavior using various processing sources. In this paper, we introduce a comprehensive study of mobile AI applications considering different DNN models and processing sources, focusing on computational resource utilization, delay, and energy consumption. We measure the latency, energy consumption, and memory usage of all the models using four processing sources through extensive experiments. We explain the challenges in such investigations and how we propose to overcome them. Our study highlights important insights, such as how mobile AI behaves in different applications (vision and non-vision) using CPU, GPU, and NNAPI. Finally, we propose a novel Gaussian process regression-based general predictive energy model based on DNN structures, computation resources, and processors, which can predict the energy for each complete application cycle irrespective of device configuration and application. This study provides crucial facts and an energy prediction mechanism to the AI research community to help bring energy efficiency to mobile AI applications.

3GPP has introduced Private 5G to support the next-generation industrial automation system (IAS) due to the versatility and flexibility of 5G architecture. Besides the 3.5GHz CBRS band, unlicensed spectrum bands, like 5GHz, are considered as an additional medium because of their free and abundant nature. However, while utilizing the unlicensed band, industrial equipment must coexist with incumbents, e.g., Wi-Fi, which could introduce new security threats and resuscitate old ones. In this paper, we propose a novel attack strategy conducted by a mobility-enabled malicious Wi-Fi access point (mmAP), namely PACMAN attack, to exploit vulnerabilities introduced by heterogeneous coexistence. A mmAP is capable of moving around the physical surface to identify mission-critical devices, hopping through the frequency domain to detect the victim's operating channel, and launching traditional MAC layer-based attacks. The multi-dimensional mobility of the attacker makes it impervious to state-of-the-art detection techniques that assume static adversaries. In addition, we propose a novel Markov Decision Process (MDP) based framework to intelligently design an attacker's multi-dimensional mobility in space and frequency. Mathematical analysis and extensive simulation results exhibit the adverse effect of the proposed mobility-powered attack.

The popularity of WiFi keeps increasing because of its capability to deliver a cost-efficient broadband Internet connection. However, a new problem emerges when many basic service sets (BSSs) are deployed in the same area, which increases inter-access point (AP) contention and co-channel interference. Inter-AP contention will decrease the overall throughput in the area and cause long application delay. The rise of bandwidth-hungry and low-latency applications like virtual and augmented reality, online gaming, and video streaming makes it urgent to solve this problem. The IEEE 802.11be Working Group (TGbe) proposes a new feature, coordinated spatial reuse (CSR). This feature allows concurrent transmissions of multiple APs through coordination between APs, reducing inter-AP contention and co-channel interference. However, existing proposed CSR algorithms use one-way coordination where the AP that initiates the CSR transmits at its maximum power and other APs optimize their transmit (TX) power accordingly, resulting in poor throughput for other APs. To solve this problem, we propose a new bidirectional CSR algorithm, Enhanced Coordinated Spatial Reuse (ECSR), which collaboratively determines the TX power for all APs by setting an appropriate interference tolerance limit. To the best of our knowledge, this is the first work that proposes bidirectional multiple AP coordination for CSR. We evaluate our proposed algorithm in dense enterprise scenarios. Simulation results show that our proposed design achieves three times higher throughput than the traditional carrier sense multiple access (CSMA) technique and four times more than the existing one-way CSR. Our experimental results also show that none of the stations participating in ECSR transmissions have low throughput.

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset in the field and the absence of a holistic energy dataset. In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices. Firstly, we present a detailed, first-of-its-kind measurement study that uncovers the energy consumption characteristics of on-device deep learning. This study results in the creation of three extensive energy datasets for edge devices, covering a wide range of kernels, state-of-the-art DNN models, and popular AI applications. Secondly, we design and implement the first kernel-level energy predictors for edge devices based on our kernel-level energy dataset. Evaluation results demonstrate the ability of our predictors to provide consistent and accurate energy estimations on unseen DNN models. Lastly, we introduce two scoring metrics, PCS and IECS, developed to convert complex power and energy consumption data of an edge device into an easily understandable manner for edge device end-users. We hope our work can help shift the mindset of both end-users and the research community towards sustainability in edge computing, a principle that drives our research. Find data, code, and more up-to-date information at https://amai-gsu.github.io/DeepEn2023.

Ultra-high-definition (UHD) videos are enjoying increased popularity in people's daily usage because of the good visual experience. However, the data size of UHD videos is 4-16 times larger of HD videos. This will bring many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. In this article, we propose a cloud computing based deep compression framework named Pearl, which utilizes the power of deep learning and cloud computing to compress UHD videos. Pearl compresses UHD videos from two respects: the frame resolution and the colorful information. In pearl, an optimal compact representation of the original UHD video is learned with two deep convolutional neural networks (DCNNs): super resolution CNN (SR-CNN) and colorization CNN (CL-CNN). SR-CNN is used to reconstruct a high resolution video from a low resolution video while CL-CNN is adopted to preserve the color information of the video. Pearl focuses on video content compression in two new directions. Thus, it can be integrated with any existing video compression system. With Pearl, the data size of UHD videos can be significantly reduced. We evaluate the performance of Pearl with a wide variety of network conditions, quality of experience (QoE) metrics, and video properties. In all considered scenarios, Pearl can further compress 84% of video size and reduce 73% of network transmission latency.

Highly-dynamic (HD) map is an indispensable building block in the future of autonomous driving, allowing for fine-grained environmental awareness, precise localization, and route planning. However, since HD maps include rich, multidimensional information, the volume of HD map data is substantial and cannot be transmitted frequently by several vehicles over vehicular networks in real-time. Therefore, in this paper, we propose a data source selection scheme for effective HD map transmissions in vehicular named data networking (NDN) scenarios. To achieve our goal, we created a vehicular NDN environment for data collection, processing, and transmission using the CARLA simulator and robot operating system 2 (ROS2). Next, due to our vehicular NDN’s dynamic and complex nature, we formulate the data source selection problem as a Markov decision process (MDP) and solve it using a reinforcement learning approach. For simplicity, we termed our proposed scheme data source optimization with reinforcement learning (DSORL), which selects suitable vehicles for HD map data transmission to MEC servers. The experiment results indicate that our suggested method outperformed existing baseline schemes, such as RLSS, Pro-RTT, and HDM-RTT, across all performance criteria in the evaluation. For instance, the system throughput increases by$65\%-72.68\%$compared to other baseline systems. Similarly, the proposed approach can minimize packet loss rate, data size, and transmission time by up to 60.6%, 77.5%, and 54.1%, respectively.

Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, and available radio resources. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as latency and detection accuracy. To thoroughly analyze the interactions among MAR configurations, user preferences, camera sampling rate, and energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR devices. Based on the proposed analytical model, we design a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. An image offloading frequency orchestrator, coordinating with the LEAF, is developed to adaptively regulate the edge-based object detection invocations and to further improve the energy efficiency of MAR devices. Extensive evaluations are conducted to validate the performance of the proposed analytical model and algorithms.

Voice Assistant Devices (VADs) such as Alexa, Google Now, and Siri have become increasingly popular because of their various voice-enabled features, including online shopping, controlling smart home appliances, accessing banking services, and more. However, it also brings unique security issues like voice replay attacks, where an attacker can generate a malicious voice command via a compromised playback device near the VAD. The usage of VADs in users' daily tasks makes such attack detection more important. To detect such attacks, we propose a defense system leveraging the impact of playback devices on the bass frequency region (0 to 500 Hz) of multi-channel audios. Currently, no prior work has exploited the playback device's impact on the bass frequency region of the multi-channel audios to prevent voice replay attacks. Specifically, our system divides the bass region of each channel into ten sub-bands and computes the total percentage of the overall signal's power presented in each sub-band. To make the system more robust against advanced audio attacks, we also extract the Modified Group Delay Function (MODGDF) cepstral coefficients of the bass area for phase features. Then, our proposed system applies a support vector machine (SVM) classifier to infer whether a human or a compromised playback device initiates the voice command. The system is then tested against a public multi-channel replay attack dataset. The system performance is checked in four different environmental conditions, achieving a maximum of 1.21 % Equal Error Rate (EER). Our experimental results also show that incorporating more audio channels improves the attack detection performance.

Edge-assisted mobile augmented reality (Edge-MAR) systems have emerged as effective ways to support computation-intensive and latency-sensitive applications for mobile devices due to the offloading capability of heavy computational burdens. However, the network- and energy-resource utilization of such systems is high. Video encoding schemes like H.264 can help Edge-MAR systems reduce latency and bandwidth utilization but at the cost of increased energy consumption. In this paper, we present a comprehensive study of Edge-MAR using H.264 video encoding with a focus on network condition, resource utilization, detection accuracy, and energy consumption of various mobile devices. We collect latency, energy, transmitted data size, and accuracy data for each segment of an object detection pipeline measured through experiments with testbeds, and analyze the non-linear behaviors of Edge-MAR. Following this, we demonstrate the challenges associated with the experiments conducted to test the system as well as the ways to overcome them. Finally, we propose regression-based models to analytically compute different Edge-MAR parameters to achieve desired outcomes. This extensive study provides essential guidelines to network- and energy-aware H.264 video encoding-based Edge-MAR system design.

The unprecedented growth in Internet of Things (IoT) deployment is making it difficult to safeguard IoT infrastructures against novel security threats. Recently, a new attack, hidden terminal emulation (HTE), has shed light on a vulnerability in the co-located and dense IoT networks, where the attacker emulates a hidden node from an external co-located network. HTE attack exploits the heterogeneity among different IoT networks, the shared nature of spectrum access, and the proximity to the victim IoT device in a dense IoT scenario to interrupt the victim’s communication. Prior work on HTE attack, however, considers an omniscient attack model, which has strong assumptions. In contrast, we propose a constrained attack model, which considers the sensing constraints of an attacker. Afterward, we propose a novel safeguard approach based on the Markov decision process to counteract the proposed attack model, namely Jump and Wobble. This work is among the very few to highlight the lower-layer vulnerabilities of spectrum coexistence in dense co-located IoT networks and, to the best of our knowledge, it is the first to propose a defense mechanism against HTE attacks.

Ultra-high-definition (UHD) videos are enjoying increased popularity in people’s daily usage because of the good visual experience. However, the data size of UHD videos is 416 times larger of HD videos. This will bring many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. Super resolution (SR) algorithms are widely used in video delivery applications to tackle these challenges. However, applying the super resolution model on UHD videos requires much more GPU memory, as compared with HD videos, which brings a significant challenge to existing systems.In this paper, we propose a deep compression framework named Pearl, which utilizes the power of deep learning to compress UHD videos. New channel-based super resolution models are developed to overcome the GPU memory shortage problem. In pearl, instead of applying the traditional RGB-based super resolution model, three separate super resolution models are trained based on the Y, U, and V channels of UHD videos. These super resolution models are used to reconstruct a UHD video from a low-resolution video. With Pearl, super resolution algorithms can be successfully applied to UHD videos. As a result, the data size of UHD videos can be significantly reduced during network transmission. At the same time, the efficiency of video encoding and decoding can also be improved with Pearl. To the best of our knowledge, Pearl is the first deep learning driven compression framework on UHD videos. We evaluate the performance of Pearl with extensive experiments. In all considered scenarios, Pearl can compress up to 95% of video data size during the video transmission and achieve 2.4 times faster, as compared with existing systems1.

Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme weather. To improve driving safety, we explore to wirelessly share perception information among connected vehicles within automotive edge computing networks. Sharing massive perception data in real time, however, is challenging under dynamic networking conditions and varying computation work-loads. In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the data plane of LiveMap that efficiently processes individual vehicle data with object detection, projection, feature extraction, object matching, and effectively integrates objects from multiple vehicles with object combination. We design the control plane of LiveMap that allows adaptive offloading of vehicle computations, and develop an intelligent vehicle scheduling and offloading algorithm to reduce the offloading latency of vehicles based on deep reinforcement learning (DRL) techniques. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator. We evaluate the performance of LiveMap with both experiments and simulations, and the results show LiveMap reduces 34.1% average latency than the baseline solution.

Real-time video streaming applications have become tremendously popular in recent years, such as remote control and video conferencing applications. A key characteristic that differentiates these applications from traditional live streaming applications is that these applications have a very low-latency requirement for interactivity. The stricter low-latency requirement brings many challenges: the video has to be encoded in a real-time manner; the substantial resources on the server or cloud cannot be utilized for encoding; and the adaptation strategies in live streaming applications are not adequate for real-time video streaming, such as adaptive bitrate selection (ABR). In addition, the video perceptual quality of current real-time video streaming systems is usually sacrificed to meet the very low-latency requirement.To address these challenges, in this paper, a new real-time video streaming protocol, DAVE (Dynamic Adaptive Video Encoding for real-time video streaming applications), is proposed. In the proposed real-time video streaming system, captured video frames are encoded with different configurations. Since the video encoding configuration determines the video data size, quality, and encoding time, we first conduct an experimental study on the impact of each configuration parameter. Based on our experimental findings, we then propose a super resolution based video encoding configuration selection algorithm which does not use a fixed strategy to determine the encoding configurations as in existing real-time video streaming systems but uses a reinforcement learning based model to learn the optimal video encoding configuration that includes the configuration of both regular video encoding parameters and the up-scale of super resolution models. As a result, DAVE can optimize the performance of real-time video streaming systems based on user Quality of Experience (QoE) metrics. To the best of our knowledge, this is the first work that incorporates super resolution and reinforcement learning in the protocol design for real-time video streaming systems. Extensive evaluations show that DAVE can substantially improve the video perceptual quality by 15% and can also reduce the end-to-end latency by 20%, as compared with existing systems1.

The advancement in deep learning and edge computing has enabled intelligent mobile augmented reality (MAR) on resource limited mobile devices. However, today very few deep learning based MAR applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design a user preference based energy-aware edge-based MAR system that enables MAR clients to dynamically change their configuration parameters, such as CPU frequency and computation model size, based on their user preferences, camera sampling rates, and available radio resources at the edge server. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as service latency and detection accuracy. To thoroughly analyze the interactions among MAR configuration parameters, user preferences, camera sampling rate, and per frame energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR clients. Based on the proposed analytical model, we develop a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. Extensive evaluations are conducted to validate the performance of the proposed analytical model and LEAF algorithm.

The Internet of Things (IoT) has been rapidly taking steps towards commercialization. However, the dense deployment of IoT nodes - that may follow different wireless technologies - in the shared spectrum creates a new challenge to solve: secure coordination among co-located IoT nodes from different IoT networks. In this paper, we shed light on this unique challenge, and we illustrate how this challenge has the potential to create a novel vulnerability where an attacker can pose as a hidden terminal (by manipulating its radiation patterns) and interfere with transmissions from its hidden counterparts, namely hidden terminal emulation (HTE) attack. As the dense deployment of IoT nodes will aggravate such hidden terminal interference, it facilitates the HTE attacker plausible deniability to interfere with its hidden counterparts. This paper is the first to present a theoretical analysis of the feasibility of HTE attacks (i.e., successful impersonation of hidden terminals), to illustrate how it is affected by the density of IoT nodes, and to provide insights on secure IoT deployment.

With the development of the hardware and software platforms, we can implement the deep learning model on the mobile device for mobile augmented reality (AR) applications. However, not all mobile AR tasks can be finished on mobile devices. Meanwhile, the limited computation resources on mobile devices are still the main obstacle to achieve realtime mobile AR applications. In this paper, we proposed a smart-decision framework which combines the advantages of the on-device mobile AR system and the edge-based mobile AR system to achieve real-time object recognition. High computation complexity tasks will be offloaded to the edge servers. Low complexity tasks will be executed on mobile devices or the edge server depending on the network latency. To overcome the dynamic changes of network condition and the limitations of the on-device deep learning models, we design a cache and matching algorithm on the mobile devices to enhance the performance of the recognition tasks. With our proposed system, the quality of the mobile AR application is improved. The performance of the smart-decision framework is validated through experiments with a testbed.

The main feature of Mobile Edge Computing (MEC) is to push computational resources and storage to the network edges (e.g., cellular base stations (BSs)) in order to offload computation-intensive and latency-sensitive applications from mobile devices. However, the deployment of MEC with a BS adds extra traffic to the already crowded cellular network that may lead to a radio congestion. On the other hand, user mobility triggers handoffs (HOs) not only between two BSs but also between two MECs, which creates new unique challenges of performing HOs to a target BS with an MEC which must have sufficient computational resources. Unfortunately, existing HO decision algorithms for traditional cellular networks cannot ad- dress these challenges. In addition, the radio network congestion issue is being utterly ignored. In this paper, we propose a novel HO decision algorithm for MEC systems that cooperates both radio and computation offloading. The proposed HO decision algorithm cooperates with femtocells to reduce the effect of radio congestion by offloading static traffic. It also cooperates with the remote cloud to reduce the impact of congestion at the MEC by offloading delay- tolerant computation. Simulation results show that our proposed HO decision algorithm significantly improves the offloading success and the service failure rates. To the best of our knowledge, this is the first HO decision algorithm that addresses both radio and computation offloading issues and mobility together in MEC systems.

Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computation- and energy-intensive. This paper, to the best of our knowledge, presents the first detailed experimental study of the smartphone's energy consumption and the detection latency of executing deep Convolutional Neural Networks (CNN) optimized object detec- tion, either locally on the smartphone or remotely on an edge server. We experiment with a variety of smartphones, obtaining different levels of computation capacities, in order to ensure that we are not profiling a specific device. Our detailed measurements refine the energy analysis of smartphones and reveal some interesting perspectives regarding the energy consumption of executing the deep CNN optimized object detection. We believe that these findings will guide the design of energy efficient processing pipeline of the CNN optimized object detection.

Recently, the Internet of Things (IoT) technology has been drawing increasing attention in that it has a great potential to positively impact human life in a broad range of applications. However, the dense deployment of multiple co-located IoT networks that may follow different wireless protocols would engender new vulnerabilities. In this paper, we introduce a novel attack scenario in co-located IoT networks, where a reactive jammer can emulate the transmission characteristics of a hidden terminal from another network and can interfere with its hidden counterparts, namely the hidden terminal emulation (HTE) attack. As the dense deployment of IoT nodes will naturally create such hidden terminal scenarios, it provides the HTE attacker plausible deniability to reactively interfere with its hidden counterparts; hence, the HTE attacker remains immune to conventional reactive jamming detection techniques. In this paper, we capture the behavior of a benign hidden terminal via a parsimonious Markov model and propose a detection solution using the goodness-of-fit hypothesis testing. Though there has been extensive research on jamming detection, our novelty lies in considering hidden terminals as benign interference sources and leveraging the existing carrier sensing technique as a natural and effective way to detect HTE attacks.

With the rapid advancement of automobile industry, autonomous driving in connected vehicles are expected to be the key technology to satisfy the expansion of human demands on more comfortable and safer driving experience. However, only on-board computation resources are insufficient to satisfy tough computation requirements of achieving full or even high automation. Therefore, autonomous driving with cloud/edge participation is desirable. In this paper, we propose E-Auto, a novel communication scheme to enable fast, stable, and accurate edge-assisted autonomous driving service for connected vehicles within any road types (e.g., driving on highway with very high speed or local roads with slow speed due to traffic congestion). In addition, as two key components of the proposed E-Auto scheme, a service period allocation algorithm and a frame resolution selection algorithm are designed to guarantee a sufficient frame rate for connected vehicles acquiring either uplink application (offload camera captured frames to the edge server) or downlink application (download entertainment videos). Through network simulations, we evaluate the performance of the proposed E-Auto scheme. Simulation results demonstrate that E-Auto can provide a high frame rate and low energy consumption autonomous driving service for connected vehicles.

In a cognitive radio-based network (CRN), secondary users opportunistically access underutilized spectrum resources and stop utilizing these resources when licensed or primary users reappear. Recently, a new attack, off-sensing (OS), has shed light on a vulnerability in the FCC policy of CRN. OS-attack utilizes the off-sensing interval of a victim to perpetrate the attack and to manipulate the victim's spectrum availability. However, prior work on OS-attack considers a deterministic approach that is unrealistic and is futile to fortify against conventional defense techniques. In this paper, we propose a new random approach, the random-OS attack, which adapts to realistic scenarios and is difficult to detect using conventional techniques. Then, we propose a novel safeguard approach based on the Markov decision process to defend the proposed attack, namely hide and seek. We also introduce an OS-attack detection strategy, which utilizes the sensing history to detect the presence of attackers without violating any policy or design constraints and without any networking overhead. Mathematical analysis and extensive simulation results exhibit the superior performance of our proposed works and advent a direction in designing safeguard strategies without amending the current FCC policies.

Spectrum handoff is an integral part of a cognitive radio-based network (CRN). It ensures the operational integrity of opportunistic spectrum access, the avoidance of harmful interference with licensed or primary users (PUs), and the delay requirement during a handoff. However, due to the random nature of PU activity, interference between primary and secondary users (SUs) are difficult to prevent. Proactive spectrum handoff aims to control this harmful interference between PUs and SUs by predicting the future activity of PUs and initiating spectrum handoff before a PU reappears. Though a few security aspects of CRNs attracted attention of researchers, vulnerabilities in the distributed proactive spectrum handoff process remain unstudied. In this paper, we introduce a vulnerability in the proactive spectrum handoff process and demonstrate how a selfish attacker can exploit this vulnerability to achieve personal gain. We name this covert spectrum handoff. To the best of our knowledge, this is the first work to consider security aspects of spectrum handoffs and to introduce an attack in the proactive spectrum handoff process.

Computation offloading over fog and cloud is critical to improve service quality and efficiency of future networks. Mobile vehicles have also been considered as potential fog nodes by sparing their computation capability to nearby users. In this paper, we propose a multi-layer computation offloading architecture, consisting of the user layer, mobile fog layer, fixed fog layer and cloud layer. Multiple wireless roadside units (RSUs) are deployed in the network to collect computation tasks from user layer, and offload the tasks to other layers. Each layer has distinct multi-dimensional characteristics, such as different transmission rates and computation capabilities. The computation tasks may consume different communication and computation resources when they are uploaded to different layers. However, the available resources of each layer are limited. Consider that each user will pay for the offloaded computation tasks according to their sizes, we aim to maximize the total profits of computation offloading from the infrastructure perspective. Specifically, the offloading problem is formulated as a generalized multidimensional multiple knapsack problem (MMKP), in which each layer is considered as a large knapsack and the computation tasks are treated as items. We propose a modified branch-and-bound algorithm to obtain the optimal solution, and a heuristic greedy method to obtain approximate performance with much lower computational overhead. A comprehensive simulation is conducted to compare the proposed two algorithms. Simulation results demonstrate that the proposed computation offloading architecture together with the task allocation algorithms can achieve the purpose of maximizing the total profits of offloaded tasks.

Recently, Mobile Edge Computing (MEC) is proposed to deploy with cellular base stations (BSs) to reduce the offloading delay and to provide computational resources and storage to nearby mobile users. However, the deployment of MEC with a BS introduces special challenges to mobility management. First, a MEC can only be accessed within the coverage area of its BS. Therefore, a user needs to perform both a radio handoff (HO) and a service migration when moving out of the cell coverage range. Second, as MECs have limited computational resource and storage, the target MEC may not have sufficient available resources to support new users. Therefore, a user with computation task may be forced to perform a radio HO to a different target BS in order to successfully migrate the service. These cause unnecessary HOs in cellular networks, especially when MECs are deployed with small cells, e.g., femtocells. These unnecessary HOs and service migrations incur a large signaling and migration cost. In this paper, we first propose a novel architecture, SharedMEC, to support user mobility. In addition, we propose a service HO decision algorithm and an analytical model to analyze the total cost which considers the total HO signaling cost, total migration signaling cost, and total migration cost. Simulation results show that our proposed architecture and service HO decision algorithm can significantly reduce the total cost. To the best of our knowledge, this is the first work that considers HO and migration issues together and analyzes the total cost in MEC systems.

In cognitive radio (CR) networks, rendezvous is when two secondary users tune to the same frequency channel simultaneously so that they can communicate with each other. Reducing the rendezvous delay, a.k.a the time to rendezvous (TTR), has been a highly focused topic for research. Many existing papers have tried to reduce the TTR between a pair of secondary users (SUs). To the best of our knowledge, no paper has previously considered the multi-destination rendezvous issue where a SU sender has different packets in its buffer for multiple destinations. Those who approached a similar scenario relied on a common control channel or the existence of multiple radios. In this research, we consider blind rendezvous using a single radio. We propose a new rendezvous protocol to handle the multiple destination scenario to decrease the overall TTR and increase the throughput, thus enhancing the overall performance of the CR network. Extensive simulations are carried out to demonstrate the performance of the proposed multi-destination rendezvous protocol.

Mobile edge computing platforms, such as cloudlets, bring computation resources closer to mobile users, as compared to the cloud, which decreases the end-to-end network latency. This benefit enables a myriad of real-time mobile applications, especially augmented reality (AR), that require low latency and high computation power. However, when mobile users move away from the attached cloudlet, the offloaded services have to be migrated or rebuilt on a new nearby cloudlet. However, this service rebuilding process takes a lot of time and may deteriorate user experience. In this paper, we propose a smart service rebuilding scheme which seamlessly restores the offloading services on the target cloudlet while the mobile user is moving. The service rebuilding process includes the radio handoff stage and service handoff stage. A seamless service rebuilding process is achieved via predicting user's target cloudlet before being triggered a radio handoff, by leveraging extracted features from the captured frames of the mobile user's camera. Furthermore, based on the proposed service rebuilding scheme, we design a feature mapping algorithm to achieve a high prediction precision and a short prediction latency. We implement our scheme on a testbed and conduct experiments using real world AR applications. The experimental results show that our proposed scheme decreases the service rebuilding latency by around 65.8%, as compared to the conventional rebuilding process. In addition, we conduct extensive simulations to evaluate the performance of our proposed feature mapping algorithm. Simulation confirms that our algorithm is robust and can predict users' target cloudlet with high precision and low latency.

Cognitive Radio (CR) has garnered much attention in the last decade, while the security issues are not fully studied yet. Existing research on attacks and defenses in CR - based networks focuses mostly on individual network layers, whereas cross-layer attacks remain fortified against single-layer defenses. In this paper, we shed light on a new vulnerability in cross-layer routing protocols and demonstrate how a perpetrator can exploit this vulnerability to manipulate traffic flow around it. We propose this cross-layer attack in CR-based wireless mesh networks (CR-WMNs), which we call off-sensing and route manipulation (OS-RM) attack. In this cross-layer assault, off-sensing attack is launched at the lower layers as the point of attack but the final intention is to manipulate traffic flow around the perpetrator. We also introduce a learning strategy for a perpetrator, so that it can gather information from the collaboration with other network entities and capitalize this information into knowledge to accelerate its malice intentions. Simulation results show that this attack is far more detrimental than what we have experienced in the past and need to be addressed before commercialization of CR-based networks.

With the rapid popularization of large data stream mobile applications, wireless local area networks (WLANs) have been a top choice for mobile users (MUs), because of the high data rate and low monetary cost. However, the battery life of mobile devices, which is the most concerned feature of MUs, may suffer from WLAN management services, such as mobility management and load balancing services. Unfortunately, few existing WLAN systems take into account both the energy efficiency of mobile devices and the performance of management services. Even worse, to improve the performance of WLAN management services, various existing management mechanisms sacrifice mobile devices' energy. In this paper, we propose BELL, a novel WLAN system that provides two energy-efficient management services for its associated MUs by reproducing and scheduling the beacons broadcast from access points (APs). We name them BELL- handoff and BELL-2M services. We have implemented the proposed BELL-handoff using commercial Wi-Fi adapters. The experimental results reveal that BELL-handoff significantly decreases both mobile devices' energy consumption and latency during handoffs, compared with the commercial WLAN mobility management service. Furthermore, we conduct extensive simulations to evaluate APs' load and mobile devices' battery life within a large-scale deployment of BELL. Simulation results demonstrate that BELL not only balances the load among APs, but also prolongs the battery life of mobile devices.

Cognitive Radio (CR) is a promising solution to solve the spectrum scarcity problem. It enables opportunistic access to the available licensed spectrum for secondary users (SUs). However, CR networks (CRNs) possess security vulnerabilities and are susceptible to attacks. One of the most common attacks in CRNs, under which perpetrators exploit channel availability, is Primary User Emulation (PUE) attack. Here, a perpetrator mimics the signal characteristics of a benign primary user (PU) and transmits the signal to prevent SUs to access the spectrum. Researchers have proposed many solutions based on periodic sensing of the spectrum. However, all of the existed solutions have a strong assumption that the perpetrator's transmission coincides with the sensing intervals of SUs. In this paper, we introduce a new room of vulnerability in the conventional sensing approaches, where a perpetrator attacks only when no one is sensing the channel. This attack will decrease the channel utilization by SUs and create a Denial of Service (DoS) situation for victim SUs. We name this attack as off-sensing attack. We also propose an analytical model to analyze the impact of this attack in CRNs. Numerical analysis and simulation results show that this attack possesses a serious threat to CRNs.

Femtocell technology is a promising solution for offloading high volume cellular data traffic to low-powered indoor base stations. It allows users to perform a handoff (HO) to a femto base station (FBS) and get high data rate. Despite the importance of selecting a proper target femtocell in open-access femtocell networks, effects of attacks on the target cell selection have not been paid sufficient attention. Unlike traditional cellular networks, attackers can get root access to FBSs and perform a number of attacks utilizing these FBSs. Unfortunately, existing target cell selection and HO decision algorithms are not sufficient to detect or avoid these attacks. Moreover, existing solutions to avoid these attacks in femtocell networks cannot be directly used in the HO scenario. In this paper, we first discuss possible attacks and their effects on femtocell networks during an HO process. In addition, we propose a target cell selection and HO-decision scheme which helps a user to select a trustworthy target femtocell during an HO. As the attacks are unalike in nature, it is hard to come up with a single solution which can avoid all these attacks we consider. We use both received signal strength indicator and location database of FBSs to select a target cell in a way that the probability of HOs to a malicious femtocell is reduced without increasing the HO signaling cost significantly. Additionally, simulation results exhibit significant improvement of the femtocell utilization. To the best of our knowledge, this is the first work that considers attacks on FBSs during HOs in open-access femtocell networks.

Device to device (D2D) communication is essential in Internet of things (IoT). Cognitive radio (CR)-enabled device is a promising technique to address D2D communications. Among all the functions supported in IoT, priority-based spectrum access is less investigated yet urgently desired in IoT. An important operation to achieve this capacity is channel hopping (CH) based rendezvous which allows two CR users to meet each other on a common available channel. Unfortunately, existing CH methods cannot help CR users achieve priority-based spectrum access due to various shortcomings. In this paper, we propose PCH, a priority-based spectrum access protocol for cognitive D2D, which can be integrated with any existing CH algorithm. PCH can support priority transmissions with a significantly reduced CH delay, as compared with non-priority transmissions. More importantly, PCH can work under practical scenarios such as the D2D both with priority packets, the D2D handoff, and the overhead/energy constraint IoT. The merits of PCH are proved theoretically and validated against extensive simulations. To the best of our knowledge, this is the first work that investigates priority communications in cognitive IoT.

Femtocells are deployed to provide good indoor coverage and to offload data traffic from macrocell networks. Unnecessary handoffs (HOs), ping-pong effects, and cell utilization are important performance metrics for evaluating the quality of connections and data offloading in femtocell networks. Though significant research has been conducted on HO decision algorithms to reduce unnecessary HOs and ping-pong effects, only a few of these studies consider unnecessary HOs and cell utilization together. Moreover, all of the existing HO decision algorithms add extra signaling overhead to the HO procedure. Therefore, it is important to analyze the HO signaling cost of existing HO decision algorithms. In this paper, we propose an analytical model to study the HO signaling cost of different HOs in open-access femtocell networks. In addition, we propose HO decision algorithms that can reduce unnecessary HOs without increasing HO signaling costs and sacrificing cell utilization. Simulation results show significant performance improvement as compared to the existing HO decision algorithms. To the best of our knowledge, this is the first analytical model that can be applied to all existing HOs available in open-access femtocell networks.

Wireless local area networks (WLANs) are currently among the most important technologies for wireless access. Because of its higher data rate and lower monetary cost compared with cellular networks, mobile users are likely to choose WiFi when they are using mobile applications. However, keeping continuous connectivity with access points (APs) may require frequent handoffs, which may consume much energy in the handoff process. Unfortunately, most of the existing work only focused on reducing the handoff delay of IEEE 802.11-based handoffs and many handoff approaches may even increase the energy consumption of mobile nodes (MNs) in order to reduce the handoff latency. In this paper, we introduce virtual handoff (V-handoff), an energy efficiency-based handoff protocol via generating virtual access points (VAPs) in the corresponding physical access points (PAPs). The main idea of our proposed V-handoff protocol is to create an evenly spaced periodic schedule of beacon periods for all the VAPs in one virtual AP grid. To the best of our knowledge, this is the first paper that investigates the application of the wireless virtualization technique in MN's handoff energy efficiency. Simulation results show that our proposed V-handoff protocol can significantly reduce the MN's handoff energy consumption and the average handoff delay compared with IEEE 802.11-based full scanning and selective scanning handoff protocol.

Ideally, users in cognitive radio networks (CRNs) are capable of sensing and exploiting any potential transmission opportunities in the available spectrum band ranging from 30 KHz to 300 GHz. With the multiple-diverse-band spectrum, the network can provide more radio resources and capacity to a large number of CR users. However, the multiband scenario (e.g., TV band + 2/3G band + 4/5G band) also introduces significant challenges in channel rendezvous, a fundamental operation for users in CRNs to set up their communication link on a common channel. Existing studies on channel rendezvous suffer from unacceptable long delay and high energy consumption when applied to such scenarios. In this paper, we propose a two-dimensional heterogeneous rendezvous (2D-HR) protocol which can support multi-wideband CRNs (MWB-CRNs) with a significantly reduced rendezvous delay and energy consumption for various rendezvous scenarios, such as the pair-wise rendezvous, any-wise rendezvous, and multi-wise rendezvous. The proposed design also performs better than existing efforts even when dealing with traditional single-band rendezvous. The merits of 2D-HR are proved theoretically and validated against extensive simulations. To the best of our knowledge, this is the first work that addresses heterogeneous rendezvous in MWB-CRNs.

Cognitive radio emerges as a promising technology to improve the utilization of the allocated spectrum. In a cognitive radio network, secondary users (SUs) need to sense the spectrum to obtain currently available channels and have to vacate the occupied channels when primary users (PUs) return. In cognitive radio networks, establishing a link through a common available channel is defined as the rendezvous process between two SUs. Channel hopping is a widely used method to solve the rendezvous problem that two SUs hop according to a designed channel hopping sequence until they meet on a common available channel. Past works on rendezvous focus on designing the channel hopping sequence while ignoring the necessary RTS-and-CTS exchange process to build a link when two SUs hop to a common available channel. However, during the initialization phase of a cognitive radio network (CRN), each SU may try to rendezvous with other SUs to exchange control information. There is no explicit role for an SU, since it cannot determine if other SUs are senders or receivers currently. Therefore, in this paper, we define a send-or-receive problem in a rendezvous process of SUs during the initialization phase of a CRN and propose a new rendezvous problem as the link rendezvous problem. We also propose a blind rendezvous scheme to solve the link rendezvous problem considering practical scenarios in a CRN. Simulation results show that our proposed schemes can achieve a fast successful link rendezvous considering the send-or-receive problem. To the best of our knowledge, this is the first paper that addresses the link rendezvous problem in a cognitive radio ad-hoc network.

In multi-channel cognitive radio ad hoc networks (CRAHNs), packet fragmentation is impacted by new factors besides those in traditional wireless networks due to the unique CR functions. For example, spectrum handoff is the technique for a secondary user (SU) to continue its transmission when a primary user (PU) reoccupies its current transmitting channel. Then, a short frame is less likely to be affected by PU activities, which leads to a lower probability of retransmission. However, with the same header size, a long frame can convey more data than a short frame. In addition, the optimal fragmentation in terms of maximizing the throughput is also related to the spectrum handoff delay, node mobility, and the original packet size of the SU. More importantly, all these factors may vary with time and location, which makes this issue extremely challenging. In this paper, by mathematically modeling these impacts and dynamically mining the related parameters, we propose a self-adaptive protocol guiding the SU to derive the up-to-date optimal packet fragmentation. The proposed protocol is based on practical assumptions and taking other necessary CR functions into account such as spectrum sensing, channel hopping, and spectrum handoff. Simulation results validate our probabilistic model and the optimality of the fragmentation we derived. To the best of our knowledge, this is the first practical fragmentation protocol for multi-channel CRAHNs.

Recently, cognitive radio (CR) technology is proposed to be combined with femtocell networks, which enables femtocells to access the available spectrum bands in an opportunistic manner. In practice, the available spectrums in CR femtocell networks are heterogeneous and range from hundreds of megahertz to several gigahertz. These heterogeneous spectrums have very different path- losses which may result in significantly different transmission and sensing ranges. Therefore, the change of operating frequency in CR femtocell networks introduces unique challenges. Although the effects of operating frequency are studied in pure CR networks, issues related to the femtocell network and impacts on the sensing range are never addressed. In this paper, we introduce some unique challenges resulting from frequency changes in CR femtocell networks, such as femtocell under-utilization and interference due to the change of the transmission range, and detection error and false alarm to the change of the sensing range. In addition, we propose a power control scheme to address the issue of the transmission range change. We also propose a detection sensitivity selection scheme to reduce the effect of the sensing range change. Simulation results show significant performance improvement of our propose schemes. To the best of our knowledge, this is the first paper that investigates the impact of frequency changes on both transmission range and sensing range in CR femtocell networks.

Combining femtocell networks and cognitive radio (CR) technology is one of the most promising solutions to offload emerging traffic from cellular networks. Mobility management is an essential function to support traffic offloading in heterogeneous networks. In traditional femtocell networks, numerous solutions have been proposed to support mobility. However, in CR femtocell networks, the mobility management issue remains unexplored. More importantly, due to the channel heterogeneity in CR femtocell networks, some unique challenges of channel under-utilization and interference are introduced. Although channel heterogeneity is considered for spectrum handoff and spectrum sharing in pure CR networks, the impact of channel heterogeneity on mobility management is never addressed in CR femtocell networks. In this paper, we propose a mobility management scheme for both macro-to-femto and femto-to-macro handoffs while taking channel heterogeneity into account. Additionally, we propose an adaptive HO-threshold selection scheme combined with our proposed mobility management scheme by taking the interference from PUs and neighboring femtocells into account. An analytical model is proposed to calculate the interference. Simulation results show significant performance improvement of the proposed mobility management scheme. To the best of our knowledge, this is the first work that considers mobility management and channel heterogeneity jointly in CR femtocell networks.

It is challenging to obtain reliable and optimal mapping between networks for alignment algorithms when both nodal and topological structures are taken into consideration due to the underlying NP-hard problem. Here, we introduce an adaptive hybrid algorithm that combines the classical Hungarian algorithm and the Greedy algorithm (HGA) for the global alignment of biomolecular networks. With this hybrid algorithm, every pair of nodes with one in each network is first aligned based on node information (e.g., their sequence attributes) and then followed by an adaptive and convergent iteration procedure for aligning the topological connections in the networks. For four well-studied protein interaction networks, i.e., C.elegans, yeast, D.melanogaster, and human, applications of HGA lead to improved alignments in acceptable running time. The mapping between yeast and human PINs obtained by the new algorithm has the largest value of common gene ontology (GO) terms compared to those obtained by other existing algorithms, while it still has lower Mean normalized entropy (MNE) and good performances on several other measures. Overall, the adaptive HGA is effective and capable of providing good mappings between aligned networks in which the biological properties of both the nodes and the connections are important.

Cognitive radio emerges as a promising technology to improve the utilization of the allocated spectrum. In a cognitive radio network, secondary users (SUs) need to sense the spectrum to obtain currently available channels and have to vacate the occupied channels when primary users (PUs) return. In cognitive radio networks, establishing a communication link between two SUs through a common available channel is defined as the channel rendezvous problem. Channel hopping is a widely used method to solve the channel rendezvous problem under which two SUs hop according to a designed channel hopping sequence until they meet on a common available channel. Existing works on the rendezvous problem focus on designing the channel hopping schemes based on omni-directional antennas. However, omni-directional antennas can cause interference to the PUs within the entire transmission range of a SU. In this paper, we consider the rendezvous problem if SUs are equipped with directional antennas which can generate interference to less PUs. We address the sector rendezvous problem for the first time and analyze the indexing problem and the different sector number problem which make the sector rendezvous problem different from the channel rendezvous problem. We propose fully distributed sector rendezvous schemes for each SU that can guarantee a successful sector rendezvous and channel rendezvous simultaneously within a bounded time. Our proposed schemes can execute in a distributed way without information exchange between SUs, which is very practical. To the best of our knowledge, this is the first paper on designing fully distributed rendezvous schemes for SUs equipped with directional antennas.

Cognitive radio (CR) technology is a promising solution to the spectrum scarcity problem. Due to the spectrum varying nature of CR networks, unlicensed users are required to perform channel hopping to realize blind rendezvous. However, blind rendezvous can easily cause deadlock in the network. Moreover, due to the nature of blind rendezvous, a deadlock cannot be detected by either the suffering users or other users. Consequently, a deadlock will eventually cause the whole network stuck and prohibit the network throughput. In this paper, the challenge of deadlock-free blind rendezvous in CR networks is addressed for the first time. By analyzing the deadlock issues in both two-user and multi-user scenarios, we propose a novel MAC protocol with an optimal contention window size which can avoid deadlock and provide high network throughput. In addition, we also propose a probabilistic model for analyzing the network performance with our MAC. Simulation results validate our analytical model and demonstrate that our proposed protocol outperforms other possible attempts.

Directional antennas have been intensively studied in wireless local area networks (WLANs) in order to increase space reuse rate, reduce interference, and extend transmission range, etc. However, the handoff techniques of directional antennas still need improvement. One critical problem is to reduce the extremely long handoff latency of directional antennas caused by the sequential search of sectors in the channel scanning phase. In this paper, we propose a moving-direction-oriented handoff scheme which prioritizes the scanning of sectors by the moving direction of a mobile station. The moving direction is calculated by an analytical method utilizing the direction-of-arrival information of the mobile station. Simulation results show that the calculated moving direction can achieve an accuracy of over 98%. The proposed scheme can significantly reduce the channel scanning latency without degrading the searching range. Moreover, the handoff frequency of stations is also reduced, which is beneficial in saving network resources.

In cognitive radio (CR) networks, channel rendezvous is a significant operation for two secondary users (SUs) to find a common available channel and establish a link between them. Currently, all previous channel rendezvous designs make the same assumption to ensure that the channel rendezvous process is successful: there must exist at least one common available channel between the SU transmitting pair. However, this assumption is not always feasible. When the number of primary users (PUs) is large, there could be no common available channel between two SUs. This issue results in serious problems in successfully implementing channel rendezvous in CR networks. In this paper, a framework is proposed to tackle this issue by using directional antennas instead of traditionally used omni-directional antennas. Our proposed framework aims to ensure that the probability that the SU pair have at least one common available channel is larger than a pre-defined threshold. Meanwhile, given a channel rendezvous scheme, the average channel rendezvous delay is minimized. Simulation results show that the proposed framework significantly outperforms the scenario with omni-directional antennas in terms of the probability of successful channel rendezvous when the number of PUs is large. To the best of our knowledge, this is the first paper that utilizes directional antennas to enhance the channel rendezvous performance in CR networks.

Without a common control channel in cognitive radio ad hoc networks (CRAHNs), two secondary users have to first hop on a common available channel before setting up their communication link. Existing papers on this blind rendezvous process mainly focus on the sequence design of channel hopping but do not consider the selection of available channels. Their time to rendezvous (TTR) and operation complexity increase with the number of available channels, which is against the concept that cognitive radios should perform better when there are more unused channels in primary networks. Thus, a new blind rendezvous design that can address this paradoxical issue is desirable. In this paper, we propose a joint design of channel selection and channel hopping for guaranteed blind rendezvous. For the first time, the TTR is significantly reduced to O(1) with a low operation requirement. An analytical model of TTR is also proposed and validated against the simulation. More importantly, under our proposed protocol, TTR decreases with the increasing number of available channels in the network. This is a very attractive feature in spectrum-under-utilized scenarios which has not been achieved by any existing CRAHN rendezvous work.

Due to the high traffic demand in cellular networks, femtocells are considered as one promising solution for providing cellular traffic offloading and better indoor coverage. However, coexistence of femtocells with macrocell networks introduces special challenges to mobility management. In particular, since indoor and unplanned deployment of femtocells usually suffers abrupt signal drop due to mutipath propagation, wall penetration loss, and shadowing, unnecessary handoffs and ping-pong effects may happen frequently, which severely degrades the quality of connections and user experience. On the other hand, offloading in femtocells requires a high cell utilization. Therefore, handoff decision algorithms should be carefully designed to trigger proper handoffs and fulfill the different requirements of macro-to-femto and femto-to-macro handoffs. In this paper, we propose a location history based adaptive handoff decision algorithm to address the special challenges of indoor and unplanned deployment of femtocells. Our proposed algorithm uses the neighboring cell list in dense femtocell networks to obtain the location of users. Based on the user location history, a new concept, handoff frequency of occurrence, is introduced to assist intelligent handoff decision-making. The hysteresis margin in our proposed handoff decision criteria can be adaptively adjusted to meet various handoff requirements. Simulation results show that our proposed location history based adaptive handoff decision algorithm can significantly improve the femtocell utilization and handoff failure rate. To the best of our knowledge, this is the first adaptive handoff decision algorithm that considers specific challenges of indoor deployment of femtocells.

Broadcast is an important operation in wireless ad hoc networks where control information is usually propagated as broadcasts for the realization of most networking protocols. In traditional ad hoc networks, since the spectrum availability is uniform, broadcasts are delivered via a common channel which can be heard by all users in a network. However, in cognitive radio (CR) ad hoc networks, different unlicensed users may acquire different available channel sets. This non-uniform spectrum availability imposes special design challenges for broadcasting in CR ad hoc networks. In this paper, a fully-distributed Broadcast protocol in multi-hop Cognitive Radio ad hoc networks with collision avoidance, BRACER, is proposed. In our design, we consider practical scenarios that each unlicensed user is not assumed to be aware of the global network topology, the spectrum availability information of other users, and time synchronization information. By intelligently downsizing the original available channel set and designing the broadcasting sequences and scheduling schemes, our proposed broadcast protocol can provide very high successful broadcast ratio while achieving very short average broadcast delay. It can also avoid broadcast collisions. To the best of our knowledge, this is the first work that addresses the unique broadcasting challenges in multi-hop CR ad hoc networks with collision avoidance.

In cognitive radio networks (CRNs), two users have to rendezvous on a common available channel before communications. Most existing rendezvous papers focus on the channel-hopping (CH) sequence design. However, rendezvous may suffer from the handshake failure on the rendezvous channel, especially in unsynchronized-slot scenarios. In this paper, the challenge of slot-asynchronous rendezvous in CRNs is addressed for the first time. A protocol aiming to improve the handshake performance during the CH process is proposed. By analyzing the potential factors leading to the handshake failure, we design a novel MAC protocol with an optimal size of a time slot which can mitigate the effects of these factors and provide the shortest time for rendezvous. In addition, we also propose a probabilistic model for estimating the average rendezvous time under different CRNs. Simulation results validate our analytical model and demonstrate that our proposed protocol can achieve the rendezvous time close to the theoretical value under slot-asynchronous scenarios.

In cognitive radio ad-hoc networks, two users rendezvous on a common available channel to realize communications. Most existing rendezvous papers focus on success-guaranteed channel-hopping sequence design. However, the theoretical rendezvous successful rate may suffer from the available channel status changing, collisions on channels, congestion at users, and target users unavailability in practical scenarios. Thus, a practical rendezvous framework that can address these issues is highly demanded. In this paper, we develop analytical models for each possible factor which may influence the performance of rendezvous. Then, based on the analysis of each factor, we propose corresponding schemes and integrate them into a self-adaptive protocol which can adjust its reaction and optimize system parameters to adapt to the dynamic network. Simulation results demonstrate that our proposed protocol gains better performance in terms of true rendezvous successful rate, short rendezvous delay, and low congestion. To the best of our knowledge, this is the first rendezvous protocol that addresses practical issues in realistic communication scenarios in cognitive radio networks.

Cognitive radio is a promising technology to solve the spectrum scarcity problem. In a cognitive radio network, a secondary user (SU) first senses a specific range of the spectrum to get its available channels and two SUs need to rendezvous on a common available channel to establish a physical link for communications. The spectrum band allocated for existing wireless services is very wide. However, when a SU works in wideband spectrum, the increasing delay and energy consumption in spectrum sensing and the time for two SUs to rendezvous will be a serious problem. All the existing rendezvous papers only focus on designing efficient algorithms for guaranteed rendezvous without considering practical communication issues caused by the wide-band spectrum. In this paper, we propose a novel communication framework for wide-band cognitive radio networks without a common control channel. Our proposed framework can efficiently solve the problems coming from the wide-band spectrum and newly joined SUs. Simulation results show that our proposed scheme can achieve fast rendezvous and a high network throughput. To the best of our knowledge, this is the first work with an effective solution to improve the communications in a wide-band cognitive radio network.

Broadcast is an important operation in wireless ad hoc networks where control information is usually propagated as broadcasts for the realization of most networking protocols. In traditional ad hoc networks, since the spectrum availability is uniform, broadcasts are delivered via a common channel which can be heard by all users in a network. However, in cognitive radio (CR) ad hoc networks, different unlicensed users may acquire different available channels depending on the locations and traffic of licensed users. This non-uniform channel availability leads to several significant differences and causes unique challenges when analyzing the performance of broadcast protocols in CR ad hoc networks. In this paper, a novel unified analytical model is proposed to address these challenges. Our proposed analytical model can be applied to any broadcast protocol with any CR network topology. We propose to decompose an intricate network into several simple networks which are tractable for analysis. We also propose systematic methodologies for such decomposition. Results from both the hardware implementation and software simulation validate the analysis well. To the best of our knowledge, this is the first analytical work on the performance analysis of broadcast protocols for multi-hop CR ad hoc networks.

Resource management is a key challenge in multiagent systems. It is especially important in dynamic environments where decisions need to be made quickly and when decisions can get obsolete quickly. In wireless local area networks (WLANs), resource ma

Fast handoff support is a basic requirement for an Internet-based wireless mesh network (WMN), aiming to guarantee mobile users to be continuously connected to the Internet, regardless of their physical locations or moving trajectory. Due to the multi-hop transmission of network-layer handoff signaling packets, handoff performance in WMNs can be largely degraded by the increasing number of wireless hops as well as the channel access contentions between data and signaling packets in the mesh backbone. However, these issues are ignored in existing handoff solutions and multi-channel medium access control schemes. In this paper, we address the seamless handoff support from a different perspective and propose a gateway scheduling-based handoff scheme in single-radio multi-hop WMNs. Our proposed handoff scheme can realize single-hop handoff signaling packet transmissions and eliminate the channel contentions between data and signaling packets. Simulation results show that the total handoff delay is improved significantly using our proposed gateway scheduling-based handoff scheme under various scenarios, as compared to existing handoff solutions in multi-hop WMNs. In addition, due to the single-hop transmission of signaling packets, the signaling overhead in the wireless mesh backbone can be substantially reduced.

Wireless mesh networks (WMNs) have been deployed in many areas. There is an increasing demand for supporting a large number of mobile users in WMNs. As one of the key components in mobility management support, location management serves the purpose of tracking mobile users and locating them prior to establishing new communications. Previous dynamic location management schemes proposed for cellular and wireless local area networks (WLANs) cannot be directly applied to WMNs due to the existence of multihop wireless links in WMNs. Moreover, new design challenges arise when applying location management for silently roaming mobile users in the mesh backbone. Considering the number of wireless hops, an important factor affecting the performance of WMNs, we propose a DoMaIN framework that can help mobile users to decide whether an intra- or intergateway location update (LU) is needed to ensure the best location management performance (i.e., packet delivery) among dynamic location management solutions. In addition, by dynamically guiding mobile users to perform LU to a desirable location entity, the proposed DoMaIN framework can minimize the location management protocol overhead in terms of LU overhead in the mesh backbone. Furthermore, DoMaIN brings extra benefits for supporting a dynamic hop-based LU triggering method that is different from previous dynamic LU triggering schemes proposed for cellular networks and WLANs. We evaluate the performance of DoMaIN in different case studies using OPNET simulations. Comprehensive simulation results demonstrate that DoMaIN outperforms other location management schemes and is a satisfactory location management solution for a large number of mobile users silently and arbitrarily roaming under the wireless mesh backbone.

Seamless handoff support is an essential issue to ensure continuous communications in multi-hop wireless mesh networks (WMNs). Due to the multi-hop transmission of network-layer handoff signaling packets, the handoff performance in WMNs can be largely degraded by the long queueing delay and medium access delay at each mesh router, especially when the backbone traffic volume is high. However, this issue is ignored in existing handoff solutions and multi-channel allocation schemes. In this paper, we address the seamless handoff support from a different perspective and propose a novel channel allocation design in hierarchical WMNs to reduce the queueing delay and medium access delay of handoff signaling packets over multi-hop wireless links and to improve the average channel utilization simultaneously. Both analytical and OPNET simulation results show that the performance of the average channel utilization and total handoff delay can be improved significantly using our proposed channel allocation scheme under various scenarios, as compared to other existing channel allocation and handoff solutions in multi-hop WMNs.

Handoff delay is a key metric to evaluate the performance of wireless mesh networks (WMNs). Since data packets and handoff signaling packets are transmitted on the same backbone channel in single-channel single-radio based WMNs, the channel contentions between data and signaling packets often lead to long signaling packet queueing delay and channel access delay. Therefore, the handoff performance is largely determined by the backbone data traffic volume. However, this important issue is ignored by existing handoff solutions in multi-hop WMNs. In this paper, we consider to reduce the handoff delay in single-backbone-channel-based WMNs from a different perspective and propose a contention-based time division (ConT) scheme to shorten the channel access delay and queueing delay of handoff signaling packets over multi-hop wireless links. Simulation results show that the multi-hop handoff performance can be improved significantly without undermining the data throughput by our proposed ConT scheme.

Cognitive radio (CR) technology has emerged as a promising solution to alleviate the spectrum scarcity problem. With the capability of sensing the frequency bands in a time and location-varying spectrum environment, CR technology allows an unlicensed user to exploit those frequency bands unused by licensed users in an opportunistic manner. Since different unlicensed users may acquire different channel availability information, they often need to exchange their channel information with each other for the realization of many networking protocols. However, if malicious unlicensed users exchange false channel information among other nodes, the networking protocols may fail, which leads to significant performance degradation. We name this type of attack as the false channel information exchange attack. In this paper, a distributed algorithm is proposed to identify the malicious nodes and fight against the false channel information exchange attack in CR ad hoc networks. The spatial correlation of the channel information is used to analyze the authenticity of the received channel information. Simulation results show that our proposed algorithm achieves very high detection rate to find out the malicious nodes, while the false alarm rate is relatively low. To the best of our knowledge, this is the first paper that investigates the false channel information exchange attacks in CR ad hoc networks.

Seamless handoff support is an essential issue to ensure continuous communications in multi-hop wireless mesh networks (WMNs). Due to the multi-hop transmission of network-layer handoff signaling packets, the handoff performance in WMNs can be largely degraded by the long queueing delay and medium access delay at each mesh router, especially when the backbone traffic volume is high. However, this issue is ignored in existing handoff solutions and multi-channel allocation schemes. In this paper, we address the seamless handoff support from a different perspective and propose a novel channel allocation design in hierarchical WMNs to reduce the queueing delay and medium access delay of handoff signaling packets over multihop wireless links and to improve the average channel utilization simultaneously. Both analytical and OPNET simulation results show that the performance of the average channel utilization and total handoff delay can be improved significantly using our proposed channel allocation scheme under various scenarios, as compared to other existing channel allocation and handoff solutions in multi-hop WMNs.

Broadcast is an important operation in wireless ad hoc networks where control information is usually propagated as broadcasts for the realization of most networking protocols. In traditional ad hoc networks, since the spectrum availability is uniform, broadcasts are delivered via a common channel which can be heard by all users in a network. However, in cognitive radio (CR) ad hoc networks, different unlicensed users may acquire different available channel sets. This non-uniform spectrum availability imposes special design challenges for broadcasting in CR ad hoc networks. In this paper, a fully-distributed broadcast protocol in multi-hop CR ad hoc networks without a common control channel is proposed. In our design, we consider practical scenarios that each unlicensed user is not assumed to be aware of the global network topology, the spectrum availability information of other users, and time synchronization information. By intelligently downsizing the original available channel set and designing the broadcasting sequences and scheduling schemes, our proposed broadcast protocol can provide very high successful broadcast ratio while achieving the shortest average broadcast delay. It can also eliminate broadcast collisions. To the best of our knowledge, this is the first work that addresses the broadcasting challenges specifically in multi-hop CR ad hoc networks under practical scenarios.

Cognitive Radio (CR) technology is a promising solution to enhance the spectrum utilization by enabling unlicensed users to exploit the spectrum in an opportunistic manner. Since unlicensed users are temporary visitors to the licensed spectrum, they are required to vacate the spectrum when a licensed user reclaims it. Due to the randomness of the appearance of licensed users, disruptions to both licensed and unlicensed communications are often difficult to prevent, which may lead to low throughput of both licensed and unlicensed communications. In this paper, a proactive spectrum handoff framework for CR ad hoc networks, ProSpect, is proposed to address these concerns. In the proposed framework, Channel-Switching (CW) policies and a proactive spectrum handoff protocol are proposed to let unlicensed users vacate a channel before a licensed user utilizes it to avoid unwanted interference. Network coordination schemes for unlicensed users are also incorporated into the spectrum handoff protocol design. Moreover, a distributed channel selection scheme to eliminate collisions among unlicensed users in a multiuser spectrum handoff scenario is proposed. In our proposed framework, unlicensed users coordinate with each other without using a Common Control Channel (CCC), which is highly adaptable in a spectrum-varying environment. We compare our proposed proactive spectrum handoff protocol with a reactive spectrum handoff protocol, under which unlicensed users switch channels after collisions with licensed transmissions occur. Simulation results show that our proactive spectrum handoff outperforms the reactive spectrum handoff approach in terms of higher throughput and fewer collisions to licensed users. Furthermore, our distributed channel selection can achieve higher packet delivery rate in a multiuser spectrum handoff scenario, compared with existing channel selection schemes.

Wireless mesh networks (WMNs) have recently emerged to be a cost-effective solution to support large-scale wireless Internet access. Handoff management plays an important role in WMNs in delivering Quality of Service to mobile users. In this paper, the handoff support in Internet-based infrastructure WMNs is investigated. Previous solutions on handoff management in infrastructure WMNs mainly focus on intragateway mobility (e.g., single gateway is assumed in IEEE 802.11s WMNs). Furthermore, new handoff issues involved in intergateway mobility in WMNs (e.g., the network-layer handoff detection issue) have not been properly addressed. In this paper, a new architectural design, IMeX, is proposed to facilitate intergateway handoff management in Internet-based infrastructure WMNs. The proposed IMeX architecture is designed to specifically address the special handoff challenge in Internet-based WMNs. It can facilitate parallel executions of handoffs from multilayers, in conjunction with a data caching mechanism which guarantees minimum packet loss during handoffs. The required number and optimal placement of special mesh routers that form the IMeX architecture are modeled as a set covering problem which is solved based on a greedy algorithm. A comprehensive simulation study shows that the IMeX architecture enables fast handoffs and reestablishment of session communications in the intergateway mobility environment. Performance results demonstrate that the proposed IMeX architecture, with both the parallel handoff execution and data caching mechanism, outperforms the conventional WMN handoff schemes in terms of reducing handoff delays caused in multilayers and improving end-to-end performance of applications in WMNs.

Seamless handoff support is an essential issue to ensure continuous communications in wireless mesh networks (WMNs). Due to the existence of multi-hop wireless links, traditional handoff schemes designed for single-hop wireless access networks can hardly guarantee the low handoff latency requirement in WMNs. Existing solutions on reducing the handoff delay in WMNs ignore one important factor for the long handoff delay: the channel access delay of handoff signaling packets over the multi-hop wireless mesh backbone network. Various WMN channel assignment algorithms are designed to optimize the performance of data packets, but they do not address how to resolve the contentions between data packets and handoff signaling packets. In this paper, we propose a novel channel assignment algorithm in WMNs to reduce the channel access delay of handoff signaling packets over multihop wireless links. In addition, handoff solutions under the new channel assignment scheme are proposed. OPNET simulation results show that handoff performance can be improved by our proposed handoff schemes, as compared to existing solutions.

Broadcast is an important operation in wireless networks where control information is usually propagated as broadcasts for the realization of most networking protocols. In traditional ad hoc networks, broadcasts are conducted on a common channel which is shared by all nodes in the network. However, in cognitive radio (CR) networks, unlicensed users may observe heterogeneous spectrum availability which is unknown to other users before broadcasts are executed. Thus, it is extremely challenging that broadcasts can be successfully conducted without the spectrum availability information. In addition, since broadcast collisions (i.e., simultaneous receipt of broadcast messages at the same node) often lead to the waste of network resources, they should be efficiently mitigated in multi-hop scenarios. In this paper, a Quality-of-Service (QoS)-based broadcast protocol for multi-hop CR ad hoc networks under blind information is proposed with the aim of high success rate and short broadcast delay. In our design, we do not assume that unlicensed users are aware of the network topology, the spectrum availability information, and time synchronization information. To the best of our knowledge, this is the first paper that investigates the broadcast issue in multi-hop CR ad hoc networks under realistic scenarios. Simulation results show that our proposed broadcast protocol outperforms the random broadcast scheme in terms of higher success rate and shorter average broadcast delay.

Location management in wireless networks serves the purpose of tracking mobile nodes and locating them prior to establishing new communications. However, existing location management schemes in cellular and wireless local area networks cannot be directly applied to wireless mesh networks (WMNs) without non-trivial modifications due to the special properties of WMNs. In this paper, we propose a hybrid location tracking chain (Hatch) framework which includes a hierarchical architecture design and a hybrid location tracking scheme for location management in WMNs. Under the Hatch framework, a location update scheme is developed that takes the number of hops into consideration. Moreover, the proposed location tracking chain is formed by the routing protocol adopted by WMNs and the length of location tracking can be dynamically changed to improve the performance of packet delivery. We evaluate the proposed location management schemes in different case studies using OPNET simulations, the results of which show the efficiency of our proposed location management scheme in WMNs.

Location management in wireless networks serves the purpose of locating silently roaming mobile nodes (MNs) prior to establishing new communications. However, existing fixed location area (LA) designs and dynamic location management schemes proposed for cellular and wireless local area networks are not applicable to Internet-based infrastructure wireless mesh networks (IiWMNs) because they do not consider the special properties of IiWMNs. In this paper, we propose a resilient location area design (ReLoAD) for IiWMNs. Under ReLoAD, the formation of LAs can adapt to the changes of both paging load and service load of existing active data sessions in the mesh backbone network. Hence, the proposed ReLoAD can balance the tradeoff between signaling overhead caused by the paging procedure and MN power consumption caused by the location update procedure while maintaining the required Quality of Service (QoS) performance of existing traffic. We evaluate the proposed ReLoAD using OPNET simulations. Simulation results reveal that the proposed ReLoAD is a scalable solution for location management in IiWMNs.

Cognitive radio (CR) technology is regarded as a promising solution to the spectrum scarcity problem. Due to the spectrum varying nature of CR networks, unlicensed users are required to perform spectrum handoffs when licensed users reuse the spectrum. In this paper, we study the performance of the spectrum handoff process in a CR ad hoc network under homogeneous primary traffic. We propose a novel three dimensional discrete-time Markov chain to characterize the process of spectrum handoffs and analyze the performance of unlicensed users. Since in real CR networks, a dedicated common control channel is not practical, in our model, we implement a network coordination scheme where no dedicated common control channel is needed. Moreover, in wireless communications, collisions among simultaneous transmissions cannot be immediately detected and the whole collided packets need to be retransmitted, which greatly affects the network performance. With this observation, we also consider the retransmissions of the collided packets in our proposed discrete-time Markov chain. In addition, besides the random channel selection scheme, we study the impact of different channel selection schemes on the performance of the spectrum handoff process. Furthermore, we also consider the spectrum sensing delay in our proposed Markov model and investigate its effect on the network performance. We validate the numerical results obtained from our proposed Markov model against simulation and investigate other parameters of interest in the spectrum handoff scenario. Our proposed analytical model can be applied to various practical network scenarios. It also provides new insights on the process of spectrum handoffs. Currently, no existing analysis has considered the comprehensive aspects of spectrum handoff as what we consider in this paper.

Seamless handoff support is an essential issue to ensure continuous communications in wireless mesh networks (WMNs). Due to the existence of multi-hop wireless links, traditional handoff schemes designed for single-hop wireless access networks can hardly guarantee the low handoff latency requirement in WMNs. Existing solutions on reducing the handoff delay in WMNs ignore one important factor for the long handoff delay: the channel access delay of handoff signaling packets over the multi-hop wireless mesh backbone network. In this paper, we address the seamless handoff issue in WMNs from a different perspective and propose a channel splitting strategy to reduce the channel access delay of handoff signaling packets over multi-hop wireless links. Based on the proposed channel splitting strategy, the handoff procedures and two transmission strategies for scheduling the delivery of handoff signaling packets are designed. Simulation results show that using the proposed channel splitting strategy, the handoff delay requirement in WMNs can be guaranteed regardless of the background data traffic, and the channel throughput can also be improved.

Cognitive radio (CR) technology is a promising solution to enhance the spectrum utilization by enabling unlicensed users to exploit the spectrum in an opportunistic manner. Since unlicensed users are considered as temporary visitors to the licensed spectrum, they are required to vacate the spectrum when a licensed user appears. Due to the randomness of the appearance of licensed users, disruptions to both licensed and unlicensed communications are difficult to prevent, which lead to low throughput and high spectrum switching overhead. In this paper, a proactive spectrum handoff framework in a CR ad hoc network is proposed to address these concerns. In the proposed framework, channel switching policies and a proactive spectrum handoff protocol are proposed to let unlicensed users perform spectrum switching before a licensed user uses the spectrum to avoid interference. A network coordination scheme for unlicensed users is also incorporated into the spectrum handoff protocol design to realize network rendezvous. Moreover, a distributed channel selection scheme to eliminate collisions among unlicensed users in a multi-user spectrum handoff scenario is proposed. Simulation results reveal that the proposed proactive spectrum handoff protocol outperforms the conventional reactive spectrum handoff approach in terms of higher throughput and fewer collisions to licensed users. It is also shown that the proposed channel selection scheme results in higher packet delivery rate compared with existing channel selection schemes.

Inter-domain handoffs in wireless mesh networks (WMNs) must evaluate the resource conditions of the target handoff domain in order to make QoS handoff decisions. Performance degradations to either or both handoff and existing QoS-aware traffic occur when resources on gateways and mesh routers in the handoff domain are over-utilized, while on the other hand, global resources across domains might still be underutilized. In this paper, we introduce a QoS-handoff framework for WMNs which includes 1) a resilient mesh architecture that offers dynamic gateway selection for inter-gateway handoffs under the assumption that different gateways belong to different domains and 2) a resilient forwarding scheme that allows intermediate mesh routers to make resilient next-hop decisions for traffic forwarding. With an integrated design and inter-dependency linkage of network architecture and traffic management, handoff resource utilization across domains can be realized. QoS requirements for both handoff and existing traffic can be achieved before and after handoffs in the inter-gateway WMN environment.

Handoff management plays an important role in wireless mesh networks (WMNs) in delivering Quality of Service to mobile users. Inter-gateway (across subnets) movement in WMNs usually requires the handoff support from multilayers and thus causes nonnegligible delays and packet loss. Previous solutions on handoff management in infrastructure WMNs mainly focus on intra-gateway mobility (e.g., single gateway is assumed in IEEE 802.11s WMNs) and exert the reduction of handoff delay so as to reduce packet loss. Furthermore, some handoff issues involved in inter-gateway mobility in WMNs (e.g., the network-layer handoff detection issue) have not been properly addressed. In this paper, we present a novel architectural design, namely Explicit multicast-based (Xcast-based) WMNs (XMesh), to facilitate inter-gateway handoff management. The proposed XMesh architecture enables parallel executions of handoffs from multilayers, in conjunction with a Xcast-based caching mechanism which builds on top of mesh routing protocols to guarantee minimum packet loss during handoffs in WMNs. The required number and optimal placement of special mesh routers that form the XMesh architecture are modeled as a set covering problem which is solved based on a greedy algorithm. A comprehensive simulation study shows that the XMesh architecture enables fast handoffs and re-establishment of session communications in the inter-gateway mobility environment. With both the parallel handoff execution and data caching mechanism, our architecture offers a seamless handoff for supporting real-time applications.

In a cognitive radio (CR) network, CR users intend to operate over the same spectrum band licensed to legacy networks. A tradeoff exists between protecting the communications in legacy networks and maximizing the throughput of CR transmissions, especially when CR links are unstable due to the mobility of CR users. Because of the non-zero probability of false detection and implementation complexity of spectrum sensing, in this paper, we investigate a sensing-free spectrum sharing scenario for mobile CR ad hoc networks to improve the frequency reuse by incorporating the location awareness capability in CR networks. We propose an optimal power control algorithm for the CR transmitter to maximize the concurrent transmission region of CR users especially in mobile scenarios. Under the proposed power control algorithm, the mobile CR network achieves maximized throughput without causing harmful interference to primary users in the legacy network. Simulation results show that the proposed optimal power control algorithm outperforms the algorithm with the fixed power policy in terms of increasing the packet delivery ratio in the network.

Wireless mesh networks (WMNs) have recently emerged to be a cost-effective solution to support large-scale wireless Internet access. One important component of realizing large-scale WMNs is mobility management. Although a considerable amount of research on handoff management for cellular and Mobile IP networks has been proposed, handoff management for IP-based WMNs remains largely unexplored. Conventional handoff mechanisms can cause significant performance degradation when directly applied to WMNs due to overlooking the key features of WMNs. They also do not address the special design challenges in WMNs. In this paper, a new architectural design is proposed to facilitate inter-gateway handoffs in WMNs. The proposed Planned Multicast Group (PMG)-based architecture can facilitate cross-layer handoffs and hence reduce the total handoff delay caused from multiple layers. Extensive simulations are conducted to evaluate the feasibility and efficiency of the proposed PMG approach. Simulation results demonstrate that the proposed PMG-based cross-layer handoff scheme outperforms the conventional WMN handoff schemes and is suitable for realtime multimedia applications in WMNs.

Abstract Anonymity is an important security aspect of wireless communications and has continuously attracted significant attention. Implementing anonymity of mobile users not only protects their privacy but also reduces the chances of attacks based on impersonation; therefore security can be improved. Untraceability is a related issue to anonymity. If a user is traceable, its hidden identity can be revealed through profiling the activities associated to a user. In this paper, we conduct a survey on anonymity issues of wireless communication systems. We first discuss general issues of anonymity in wireless communication systems. Then we survey some protocols in the literature, which are designed for wireless mobile systems as well as wirelessad hocnetworks. Copyright © 2008 John Wiley & Sons, Ltd.

The proliferation of wireless local area network (WLAN) deployments in enterprises, public areas, and homes will likely cause frequent geographical coverage overlap among multiple networks. A recent growing interest is the coordination among WLAN providers for efficient network management over a large coverage area. While radio resource management for a single WLAN has been studied extensively, little research work addresses resource management over multiple domains. When multiple WLANs co-locate in a small geographic vicinity, the lack of cooperative multi-domain resource management can cause significant performance degradation due to inter-domain interference. Unbalanced loads among multiple networks can incur congestion in a few WLANs while foregoing unused excess resources in others. Moreover, since WLANs often operate in unlicensed frequency bands shared by various public and private networks, they must cope with the dynamic RF environment involving a diverse set of interference sources. This paper introduces a new cooperative load balancing framework for multi-domain WLANs operating in an interference environment. A third-party-based architecture is proposed to facilitate fair radio resource allocation among multiple networks. The proposed third-party agent collects observed network state information from different WLANs and optimizes resource usage. Under the proposed scheme, resource utilization and co-channel interference can be adaptively balanced across the entire integrated system. The impact of other co-located interference sources in the operational environment are taken into account in the optimization process. The proposed load balancing framework for interference environments is a complement to the interference mitigation mechanisms operated at the PHY/MAC layer. Simulation results show that the proposed multi-domain load balancing scheme outperforms other schemes which do not consider interdomain interference or environmental interference.

Wireless mesh networks (WMNs) have recently emerged to be a cost-effective solution to support large-scale wireless Internet access. In this paper, the handoff support in Internet-based infrastructure mesh networks is investigated. Current handoff schemes concentrate on the link-layer handoff support and have not considered the Internet-domain handoff due to the mobility from mesh networks. This paper focuses on the handoff overhead analysis. A new analytical model is proposed to evaluate the signaling overhead percentage during handoffs in infrastructure mesh networks. The proposed model considers overhead from both the link layer and network layer. The designed analytical model is validated by simulation results. Conclusions from this study can provide great insights in designing new cost-effective handoff schemes in WMNs.

Wireless mesh networks (WMNs) have recently emerged to be a cost-effective solution to support large-scale wireless Internet access. One important component of realizing large-scale WMNs is mobility management. Although mobility management has been extensively studied in cellular and Mobile IP networks, these mobility management schemes can cause significant performance degradation when directly applied to WMNs. In this paper, a performance study of handoffs in IEEE 802.11-based hybrid WMNs is conducted. Since no existing handoff schemes are specifically designed for hybrid WMNs taking into consideration the multihop wireless links in both the wireless mesh backbone and mesh client networks, new handoff schemes for hybrid WMNs are designed. A simulation approach for handoff performance analysis based on OPNET Modeler v12 is adopted and new OPNET models to enable handoff simulations, including both layer-2 and layer-3 handoff models, are developed. A comprehensive study on the handoff performance is investigated and important factors affecting the handoff delay are explained. Performance results demonstrate that the multihop routing protocol plays an important role in the handoff performance.

Internet protocol-based multimedia applications are gaining momentum thanks to the great proliferation of the Internet and mass deployment of broadband. The abundance of rich content, heterogeneity of networks, user terminals, and services, along with the surges of multimedia traffic, have convoluted ever-increasing complexity and costs in the operation and management of multimedia systems. Autonomic communications (AutoComm) promotes the autonomy of communication networks with minimum human administration. In a multimedia system that features AutoComm, the networks and their elements strive to realize efficiency, immunity, resilience, and evolvability through intrinsic self-governance. By encapsulating complexity within the networks, exploiting their intelligence, and using technology to manage technology, human administrative efforts can be greatly reduced while management efficiency can be maximized. In this paper, we want to illustrate how traditional way of management and control of multimedia networks can be gradually replaced by network autonomy and self-management. Related work is extensively reviewed and compared to our approach. Initial experiment results are presented as proof of concept.

The rapid growth of the Internet leads to antic- ipated depletion of Internet addresses in the existing Internet Protocol version 4 (IPv4). Internet Protocol version 6 (IPv6) provides increased address space and desired features that meet the predicted growth of the Internet. However, the current widespread deployment of IPv4 equipment means that IPv4- based applications will coexist with IPv6-based applications for an extended period of time. The crux of the problem associated with the co-existence of IPv4 and IPv6 is the IP header incom- patibility. Incompatibility can fail the Internet connectivity and seriously degrade the overall performance. The current proposed IPv4/IPv6 transition mechanisms are not designed to operate in a mobile environment. Mobility adds a new dimension to the transition and interoperability problems. This paper conducts a comprehensive case study on mobility support for IPv4/IPv6 transition mechanisms over IPv6 backbone networks. Design challenges for all the communication scenarios between IPv4 and IPv6 hosts are investigated. Solutions for transition mechanisms with mobility support are proposed.

This paper investigates cooperative radio resource management for multiple cognitive radio networks in interference environments. The objective of this research is to manage shared radio resources fairly among multiple non- cooperative cognitive radio networks to optimize the overall performance. We emphasize the underlying predictability of network conditions and promote management solutions tailored to different interference environments. A multi-agent-system- based approach is proposed to achieve information sharing and decision distribution among multiple cognitive radio networks in a distributed manner. We address the distributed constraint optimization problem (DCOP) in cognitive radio networks and study the effectiveness of DCOP algorithms to find the optimal radio resource assignment through communications between distributed agents.

The rapid growth of the Internet has led to the anticipated depletion of the addresses in the current version of the Internet protocol (IP), IPv4. This depletion has given rise to a newer version of the Internet protocol, internet protocol version 6 (IPv6). IPv6 provides sufficient address space to meet the predicted increase of the internet. Since IPv4 has been widely deployed, it is required that the existing IPv4 and the newly added IPv6 can co-exist and interoperate. Various mechanisms have been proposed for supporting the interoperability between IPv4 and IPv6. However, they are mostly designed for a static environment. Mobility introduces a new dimension to the problem of IPv4/IPv6 transition and interoperability. The mobility support of mobile terminals in a mixed IPv4/IPv6 environment remains largely unexplored. In this paper, we analyze various handoff scenarios for a dual stack mobile node with a predominant IPv6 home address (DSMNv6) roaming in a mixed IPv4/IPv6 environment. We investigate how handoffs can be supported and derive the handoff procedures for all scenarios. We also design an analytical model for the handoff signaling cost analysis. Numerical results are provided to demonstrate the signaling cost of various handoff scenarios. Conclusions from this study can give great insights in designing new cost-effective mobility-support mechanisms for IPv4/IPv6 transition and interoperability.

Mobile IP is a solution for mobility support in the global Internet. However, it suffers from long handoff delay. Many solutions have been proposed to reduce the handoff delay of Mobile IP. Among these solutions, Fast Handovers for Mobile IPv6 (FMIPv6) and Hierarchical Mobile IPv6 (HMIPv6) are actively developed by the Internet Engineering Task Force for future IPv6 networks. A majority of the research on performance analysis of Mobile IP handoff latency either adopts a simulation approach or is based on experimental testbeds. This paper focuses on the analytical modeling of handoff latency for the two Mobile IPv6 fast handoff protocols, FMIPv6 and HMIPv6, using IEEE 802.11-based wireless local area networks as the wireless access networks. Different from other previously proposed analytical models for handoff latency analysis, our model considers comprehensive factors from both link layer and network layer which influence the Mobile IP handoff delay. Individual analytical models for each of these factors affecting the handoff latency are developed. The outcome of this research is the probability distribution of the handoff latency occurring within a certain range based on the offered traffic load and network conditions.

This paper proposes a new analytical model to evaluate the distributed coordination function (DCF) performance of a multi-cell wireless local area network (WLAN). Current DCF analytical models concentrate on the performance analysis of a single-cell WLAN and have not considered the interference impact from neighboring co-channel cells using the same frequency channels. This paper analyzes DCF performance in an environment of multiple cells and investigates the vital effects of co-channel interference. At the same time, a realistic propagation model with lognormal shadowing is taken into account. In our model, users are located at different positions in a cell and experience different signal-to-interference ratio (SIR) at the access point (AP). As a result, the performance of each user is distinct. The proposed model is able to evaluate the performance for each user in terms of SIR, packet error rate (PER), transmission probability (TP), collision probability (CP), etc. In addition, the system throughput (TH) is calculated to evaluate the overall efficiency of each cell.

The proliferation of wireless local area network (WLAN) deployments will likely cause frequent geographical coverage overlap among multiple networks. A recent growing interest is the coordination among WLAN providers for efficient network management. When multiple WLANs co-locate in a small geographic vicinity, the lack of cooperative multi- domain resource management can cause significant performance degradation due to inter-domain interference. Moreover, since WLANs often operate in unlicensed frequency bands shared by various public and private networks, they must cope with the dynamic RF environment involving a diverse set of interference sources. This paper introduces a new cooperative resource management framework for multi-domain WLANs operating in an interference environment. Under the proposed scheme, resource utilization and co-channel interference can be adaptively balanced across the entire integrated system. Simulation results show that the proposed scheme outperforms other schemes without inter-domain cooperation or without the consideration of environmental interference.

Multi-step paging has been widely proposed to reduce signaling overheads. This paper introduces a user-independent paging scheme for mobile IP where the paging criterion is not based on individual user information. The goal of user-independent paging is to provide satisfactory overall performance of the whole system, when personalized optimal performance for each user is hard to obtain. The paging criterion adopted is the mobility rate of each subnet determined by the aggregated movements of all mobile users. In order to implement the proposed scheme, a concept of "semi-idle state" is introduced and the detailed solution for obtaining the mobility rate is presented. Analytical results show that when paging one user at a time, the performance of the proposed scheme is comparable to that of the user-dependent paging schemes. When paging multiple users simultaneously, the proposed scheme has remarkable advantages.

Mobile IP is a solution mobility on the global Internet. However, the basic mobile IP does not support paging. The main benefit of providing paging services is to save the battery power consumption at mobile terminals. Next generation Internet is expected to support multimedia communications. For real-time data traffic, quality of service (QoS) provision must be guaranteed. The resource reservation protocol (RSVP) was proposed to support the signaling of end-to-end IP QoS. When both IP paging and RSVP are supported in the network, the signaling delay for connection setup is the sum of the paging delay and the time for RSVP path setup. This paper introduces a new scheme for fast connection setup with the help of mobile IP location registration and paging. Performance analysis shows that the proposed scheme reduces the overall signaling delay and the total number of signaling messages.

The capacity of a switch is built out of two factors: space parallelism and speedup. A switch has space parallelism if more than one input port can transmit simultaneously. Speedup is the ratio of the switch's internal link speed over the incoming link speed. An input-queuing switch uses only the first factor (space parallelism), and a share-medium or a share-memory output queuing switch uses only the second factor (speedup). However, to build a large switch, both factors are normally used. A large switch's capacity can be built with less space parallelism (the space factor), but more speedup (the time factor), or vise versa. Buffers are needed at both the input and the output ports. In this paper, we show how to divide the buffers between the input and the output queues and how the optimal division is affected by the (space, time) combinations.

Mobile IP is a solution for mobility on the global Internet. However, it does not extend well to highly mobile users. Mobile IP regional registration Is proposed to reduce the number of location updates to the home network, and reduce the signaling delay. This paper introduces an optimal regional location management mechanism for Mobile IP that results in the minimum signaling cost. A novel discrete analytical model is developed which captures the mobility and packet arrival pattern of a mobile terminal. This model does not impose any restrictions on the shape and the geographic location of Internet subnets. Given the average total location update and packet delivery cost, an iterative algorithm is then used to determine the optimal size of regional networks. Analytical results are also obtained to demonstrate how the optimal value changes under various parameters.

Mobile IP is a simple and scalable global mobility solution. However, it may cause excessive signaling traffic and long signaling delay. Mobile IP regional registration is proposed to reduce the number of location updates to the home network, and reduce the signaling delay. This paper introduces a novel distributed and dynamic regional location management for Mobile IP where the signaling burden is evenly distributed and the regional network boundary is dynamically adjusted according to the up-to-date mobility and traffic load for each terminal. In our distributed system, each user has its own optimized system configuration, which results in minimal signaling traffic. In order to find the signaling cost function, a new discrete analytical model is developed which captures the mobility and packet arrival pattern of a mobile terminal. This model does not impose any restrictions on the shape and the geographic location of subnets in the Internet. Given the average total location update and packet delivery cost, an iterative algorithm is then used to determine the optimal regional network size. Analytical results show that our distributed dynamic scheme outperforms the IETF Mobile IP regional registration scheme for various scenarios in terms of reducing the overall signaling cost. Using our approach, the system robustness is also enhanced.

Mobile IP is a simple and scalable global mobility solution. However, it may cause excessive signaling traffic and long signaling delay. Mobile IP regional registration is proposed to reduce the number of location updates to the home network and to reduce the signaling delay. This paper introduces a novel distributed and dynamic regional location management for Mobile IP where the signaling burden is evenly distributed and the regional network boundary is dynamically adjusted according to the up-to-date mobility and traffic load for each terminal. In our distributed system, each user has its own optimized system configuration which results in the minimal signaling traffic. In order to determine the signaling cost function, a new discrete analytical model is developed which captures the mobility and packet arrival pattern of a mobile terminal. This model does not impose any restrictions on the shape and the geographic location of subnets in the Internet. Given the average total location update and packet delivery cost, an iterative algorithm is then used to determine the optimal regional network size. Analytical results show that our distributed dynamic scheme outperforms the IETF Mobile IP regional registration scheme for various scenarios in terms of reducing the overall signaling cost.

In this paper, the capability of neural networks to call admission control in asynchronous transfer mode (ATM) networks is investigated. The general problem of call admission control (CAC) and its formulation as a functional mapping are discussed leading to applications of learning algorithms to CAC problems. A modified cascade-correlation network, which combines typical backpropagation and cascade-correlation algorithms together, is used as call admission controller. Its performances are compared with those of typical backup. Simulation results of basic call admission models illustrate the applicability of the proposed controller.