The Open Radio Access Network (O-RAN) represents a significant advancement in cellular networks, promoting openness, intelligence, and flexibility in 5G deployment. However, designing a scheduler for 5G O-RAN presents significant challenges due to its unique network architecture, large scheduling space, and stringent timing requirements of various control loops. Existing model-based and model-free schedulers both have inherent drawbacks that hinder their performance and adoption in 5G O-RAN. Model-based schedulers struggle because accurately modeling wireless system is often impossible, and they usually suffer from high complexity due to the NP-hard problem structure and large scheduling space. On the other hand, model-free schedulers often face convergence issue under a large scheduling space, and they usually cannot provide performance guarantees or even satisfy constraints. In this paper, we present MOGUL—a MOdel-GUided Learning approach that retains the strengths of both model-based and model-free methods while avoiding their pitfalls. MOGUL employs a model-based optimization problem to derive a reduced yet promising scheduling space, which is then used as the action space for model-free online Deep Multi-agent Reinforcement Learning (DMARL) to determine the final scheduling decision. Moreover, MOGUL is specifically tailored to the O-RAN architecture, allowing seamless integration into various control loops while meeting their stringent timing requirements. Experimental results demonstrate that MOGUL outperforms both state-of-the-art model-based and model-free algorithms.

Open-Radio Access Network (O-RAN) has become an important paradigm for 5G and beyond radio access networks. This paper presents an xApp calledxSlicefor the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5G O-RANs.xSliceis an online learning algorithm that adaptively adjusts MAC-layer resource allocation in response to dynamic network states, including time-varying wireless channel conditions, user mobility, traffic fluctuations, and changes in user demand. To address these network dynamics, we first formulate the Quality-of-Service (QoS) optimization problem as a regret minimization problem by quantifying the QoS demands of all traffic sessions through weighting their throughput, latency, and reliability. We then develop a deep reinforcement learning (DRL) framework that utilizes an actor-critic model to combine the advantages of both value-based and policy-based updating methods. A graph convolutional network (GCN) is incorporated as a component of the DRL framework for graph embedding of RAN data, enablingxSliceto handle a dynamic number of traffic sessions. We have implementedxSliceon an O-RAN testbed with 10 smartphones and conducted extensive experiments to evaluate its performance in realistic scenarios. Experimental results show thatxSlicecan reduce performance regret by 67% compared to the state-of-the-art solutions. Source code is available athttps://github.com/xslice-5G/code

Anomaly-Based Intrusion Detection Systems (IDSs) have been extensively researched for their ability to detect zero-day attacks. These systems establish a baseline of normal behavior using benign traffic data and flag deviations from this norm as potential threats. They generally experience higher false alarm rates than signature-based IDSs. Unlike image data, where the observed features provide immediate utility, raw network traffic necessitates additional processing for effective detection. It is challenging to learn useful patterns directly from raw traffic data or simple traffic statistics (e.g., connection duration, package inter-arrival time) as the complex relationships are difficult to distinguish. Therefore, some feature engineering becomes imperative to extract and transform raw data into new feature representations that can directly improve the detection capability and reduce the false positive rate. We propose a geometric feature learning method to optimize the feature extraction process. We employ contrastive feature learning to learn a feature space where normal traffic instances reside in a compact cluster. We further utilize H-Score feature learning to maximize the compactness of the cluster representing the normal behavior, enhancing the subsequent anomaly detection performance. Our evaluations using the NSL-KDD and N-BaloT datasets demonstrate that the proposed IDS powered by feature learning can consistently outperform state-of-the-art anomaly-based IDS methods by significantly lowering the false positive rate. Furthermore, we deploy the proposed IDS on a Raspberry Pi 4 and demonstrate its applicability on resource-constrained Internet of Things (IoT) devices, highlighting its versatility for diverse application scenarios.

Federated learning, while being a promising approach for collaborative model training, is susceptible to backdoor attacks due to its decentralized nature. Backdoor attacks have shown remarkable stealthiness, as they compromise model predictions only when inputs contain specific triggers. As a countermeasure, anomaly detection is widely used to filter out backdoor attacks in FL. However, the non-independent and identically distributed (non-IID) data distribution nature of FL clients presents substantial challenges in backdoor attack detection, as the data variety introduces variance among benign models, making them indistinguishable from malicious ones. In this work, we propose a novel distribution-aware backdoor detection mechanism, BoBa, to address this problem. To differentiate outliers arising from data variety versus backdoor attacks, we propose to break down the problem into two steps: clustering clients utilizing their data distribution, and followed by a voting-based detection. We propose a novel data distribution inference mechanism for accurate data distribution estimation. To improve detection robustness, we introduce an overlapping clustering method, where each client is associated with multiple clusters, ensuring that the trustworthiness of a model update is assessed collectively by multiple clusters rather than a single cluster. Through extensive evaluations, we demonstrate that BoBa can reduce the attack success rate to lower than 0.001 while maintaining high main task accuracy across various attack strategies and experimental settings.

With the proliferation of LEO satellite communications (SatCom) serving rural areas, there is a strong interest on exploring the performance limit (e.g., throughput) with such a service. This problem is challenging due to highly dynamic satellite positions, limited satellite beams and spectrum bandwidth, and wide disparity in number of subscribers across a vast area. Most existing analytical models fail to capture real-world characteristics of operational satellite network, such as long time interval between satellite handover and polarization in transmission. This paper makes a major step in advancing this research area by formalizing an analytical framework for LEO SatCom based on real-world satellite network. Our analytical framework addresses architectural issues such as gateway service region (GSR) and scheduling problems such as satellite/beam/channel-to-cell allocation, interference issues such as co-channel interference avoidance, polarization, and performance issues such as throughput fairness. Simulation results on a real-world satellite ephemeris (Starlink) show that the optimal scheduling solution based on our analytical framework can offer 93% scheduling efficiency while satisfying all design requirements and system constraints.

The O-RAN architecture introduces unprecedented flexibility and openness into modern cellular networks, allowing mix-and-match of components from different vendors and the rapid deployment of innovative solutions across the RAN vertical. Despite its openness, some fundamental technical challenges associated with 5G/Next-G still remain in O-RAN. A well known example is joint optimization of Resource Block (RB) allocation, Modulation and Coding Scheme (MCS) selection, and Beamforming (BF) design. In this paper, we present Savitar—an O-RAN scheduler that jointly optimizes these components, with the objective of minimizing spectrum usage while meeting per-UE probabilistic data rate requirements. Following the multi-timescale design principle in O-RAN, we present three components (each at a different time scale) of Savitar that can be seamlessly integrated with O-RAN RICs: (i) hyperparameter tuning in the Non-Real-Time (Non-RT) RIC, (ii) parallel RB Group (RBG) allocation and MCS selection in the Near-RT RIC, and (iii) BF vector design in the RT Open Distributed Unit (O-DU). A unique design in these components is our handling of CSI uncertainty with limited data samples. Experimental results show that Savitar achieves competitive spectrum efficiency performance while meeting our design requirements (i.e., per-UE probabilistic data rate requirement and real-time requirement in O-DU).

The scarcity of spectrum and the proliferation of data-intensive applications in 5G/Next-G networks call for innovations of new techniques that are capable of offering UE-level data rate guarantee with minimum required spectrum usage. This is a challenging problem due to the complexity of mechanisms involved in the process, such as Resource Block (RB) allocation, modulation and coding scheme (MCS) selection, and MU-MIMO beamforming (BF) design. Further complicating the problem is the random, unknown nature of Channel State Information (CSI) and the errors involved in its estimation. In this paper, we present Rudra, which offers a comprehensive solution to these challenges. Rudra formulates the bandwidth minimization problem by incorporating probabilistic data rate guarantee through a chance constraint, which embeds RB allocation, MCS selection, and MU-MIMO BF mechanisms. The CSI uncertainty problem is addressed through a novel error-embedded (EE)-Wasserstein ambiguity set based on a small set of data samples. We show that the solution by Rudra meets our design objective and outperforms a modified state-of-the-art algorithm.

We study an Age-of-Information (AoI) scheduling problem where users can tolerate occasional violations of AoI for each source at the base station. Each user’s AoI is associated with a violation tolerance constraint. We are interested in determining whether a set of users, each with a given AoI deadline, a violation tolerance constraint, and a packet loss rate (due to channel condition) is schedulable, and if so, find a feasible scheduler. For this problem, we study two cases: 1) the stable tolerant case where the tolerance rate is higher than the packet loss rate for each source and 2) the unstable tolerant case where the tolerance rate is lower than the packet loss rate for at least one source. For the stable tolerant case, we design an algorithm called stable tolerant scheduler (STS), which can find a feasible scheduler for any network when the system load is no greater than$\ln 2$(roughly 70%). When the system load is between$\ln 2$and 1, we offer a necessary and sufficient condition for STS to find a feasible scheduler by solving an optimization problem. Likewise, for the unstable tolerance case, we develop a scheduler called unstable tolerant scheduler (UTS) and its corresponding schedulability conditions. Through extensive simulations, we show that STS and UTS match our theoretical results.

Age of Information (AoI) is a metric that can be used to measure the freshness of information. Since its inception, there have been active research efforts on designing scheduling algorithms to AoI-related problems. These problems vary in specific AoI-based objectives and network settings. For each problem, typically a custom-designed scheduler was developed. Instead of following the (custom-design) path, we envision and pursue a general framework that can be applied to design a wide range of schedulers to solve AoI-related problems. As a first step toward this vision, we present a general framework—Eywa, that can be applied to construct high-performance schedulers for a family of AoI-related optimization and decision problems, all sharing a common setting of an IoT data collection network. We show how to apply Eywa to solve three important problems: to minimize weighted sum of AoIs, to minimize bandwidth requirement under AoI constraints, and to determine the existence of feasible schedulers to satisfy AoI constraints. We show that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms or provide new (or general) results that are not available in the literature.

Vehicle-to-Everything (V2X) communication enables vehicles to communicate with other vehicles and roadside infrastructure, enhancing traffic management and improving road safety. However, the open and decentralized nature of V2X networks exposes them to various security threats, especially misbehaviors, necessitating a robust Misbehavior Detection System (MBDS). While Machine Learning (ML) has proved effective in different anomaly detection applications, the existing ML-based MBDSs have shown limitations in generalizing due to the dynamic nature of V2X and insufficient and imbalanced training data. Moreover, they are known to be vulnerable to adversarial ML attacks. On the other hand, Generative Adversarial Networks (GAN) possess the potential to mitigate the aforementioned issues and improve detection performance by synthesizing unseen samples of minority classes and utilizing them during their model training. Therefore, we propose the first application of GAN to design an MBDS that detects any misbehavior and ensures robustness against adversarial perturbation. In this article, we present several key contributions. First, we propose an advanced threat model for stealthy V2X misbehavior where the attacker can transmit malicious data and mask it using adversarial attacks to avoid detection by ML-based MBDS. We formulate two categories of adversarial attacks against the anomaly-based MBDS. Later, in the pursuit of a generalized and robust GAN-based MBDS, we train and evaluate a diverse set of Wasserstein GAN (WGAN) models and present Ve hicular GAN ( VehiGAN ), an ensemble of multiple top-performing WGANs, which transcends the limitations of individual models and improves detection performance. We present a physics-guided data preprocessing technique that generates effective features for ML-based MBDS. In the evaluation, we leverage the state-of-the-art V2X attack simulation tool VASP to create a comprehensive dataset of V2X messages with diverse misbehaviors. Evaluation results show that in 20 out of 35 misbehaviors, VehiGAN outperforms the baseline and exhibits comparable detection performance in other scenarios. Particularly, VehiGAN excels in detecting advanced misbehaviors that manipulate multiple fields in V2X messages simultaneously, replicating unique maneuvers. Moreover, VehiGAN provides approximately 92% improvement in false positive rate under powerful adaptive adversarial attacks, and possesses intrinsic robustness against other adversarial attacks that target the false negative rate. Finally, we make the data and code available for reproducibility and future benchmarking, available at https://github.com/shahriar0651/VehiGAN .

Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range of applications. However, a lack of adequate security in IoT devices renders IoT systems vulnerable to various network-based cyberattacks, potentially causing severe damage. Recent works have explored using machine learning to build anomaly detection models for defending against such attacks. In this paper, we propose FeCo, a federated-contrastive-learning framework that coordinates in-network IoT devices to jointly learn intrusion detection models. FeCo utilizes federated learning to alleviate users’ privacy concerns as participating devices only submit their model parameters rather than raw local data. Compared to previous works, we develop a novel representation learning method based on contrastive learning that is able to learn a more accurate model for the benign class. FeCo significantly improves the intrusion detection accuracy compared to previous works. In addition, we implement a two-step feature selection scheme to avoid overfitting and reduce computation time. Through extensive experiments on the NSL-KDD dataset and the BaIoT dataset, we demonstrate that FeCo achieves as high as 8% accuracy improvement compared to the state-of-the-art and is robust to non-independent and identically distributed (non-IID) data. Our implementation of FeCo on a Raspberry Pi device further confirms the applicability of FeCo for resource-constrained IoT devices.

Federated learning (FL) has been shown vulnerable to a new class of adversarial attacks, known asmodel poisoning attacks (MPA), where one or more malicious clients try to poison the global model by sending carefully crafted local model updates to the central parameter server. Existing defenses that have been fixated on analyzing model parameters show limited effectiveness in detecting such malicious models. In this work, we proposeFLARE, a robust model aggregation mechanism for FL, which is resilient against state-of-the-art MPAs. Instead of solely depending on model parameters,FLAREleverages thepenultimate layer representations (PLRs)of the model for characterizing the adversarial influence on each local model update. We further propose a trust evaluation method that estimates a trust score for each model update based on pairwise PLR discrepancies among all model updates. Under the assumption of honest majority,FLAREassigns a low trust score to model updates that are far from the benign cluster.FLAREthen aggregates the model updates weighted by their trust scores and finally updates the global model. Extensive experimental results demonstrate the effectiveness ofFLAREin defending FL against various MPAs, including semantic backdoor attacks, trojan backdoor attacks, and untargeted attacks, in various FL systems.

MU-MIMO beamforming is a key technology for 5G networks, relying on Channel State Information (CSI). However, in practice, the estimated CSI in reality is prone to uncertainty. Further, a MU-MIMO beamforming solution must be derived within a millisecond to be useful for real-time 5G applications. We present ReDBeam—a real-time data-driven beamforming solution for MU-MIMO using limited CSI data samples. The main novelties of ReDBeam are a parallel algorithm and an optimized GPU implementation. ReDBeam delivers a MU-MIMO beamforming solution within 1 millisecond to meet the probabilistic data rate requirements from the users, and minimize a base station’s power consumption. Through extensive experiments, we show that ReDBeam consistently meets the stringent 1-millisecond real-time requirement and is orders of magnitude faster than other state-of-the-art algorithms. ReDBeam conclusively demonstrates that MU-MIMO beamforming with data rate requirements can be achieved in real-time using only limited CSI data samples.

To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem --- the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize fault-tolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms.

Blockchain’s decentralization, transparency, and tamper-resistance properties have facilitated the system’s use in various application fields. However, the low throughput and high confirmation latency hinder the widespread adoption of Blockchain. Many solutions have been proposed to address these issues, including first-layer solutions (or on-chain solutions) and second-layer solutions (or off-chain solutions). Among the proposed solutions, the blockchain sharding system is the most scalable one, where the nodes in the network are divided into several groups. The nodes in different shards work in parallel to validate the transactions and add them to the blocks, and in such a way, the throughput increases significantly. However, previous works have not adequately summarized the latest achievements in blockchain sharding, nor have they fully showcased its state-of-the-art. Our study provides a systemization of knowledge of public blockchain sharding, including the core components of sharding systems, challenges, limitations, and mechanisms of the latest sharding protocols. We also compare their performance and discuss current constraints and future research directions.

MU-MIMO beamforming is a key technology for 5G/NextG networks. In practice, MU-MIMO beamforming requires Channel State Information (CSI) and is prone to uncertainty. Furthermore, a beamforming solution must be derived within a millisecond (ms) to be useful for real-time (RT) 5G applications. We present ReDBeam-a RT data-driven beamforming solution for MU-MIMO using limited CSI data samples. The main contribution of ReDBeam is a parallel algorithm and an optimized GPU implementation. ReDBeam minimizes the base station (BS)'s power consumption while offering a probabilistic guarantee of users' data rates. It is purposefully designed to take advantage of the vast parallel processing capability in commercial off-the-shelf GPUs. Through extensive experiments, we show that ReDBeam can meet the 1 ms RT requirement and is orders of magnitude faster than other state-of-the-art algorithms for the same problem.

Multiplexing Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) traffic on the same 5G New Radio (NR) air interface poses significant challenges due to extreme latency requirement of URLLC packets. This paper investigates the direct puncturing of URLLC traffic over eMBB transmissions, a method that, while guaranteeing immediate URLLC packet delivery, can severely degrade eMBB performance. To alleviate the adverse impact on eMBB, we present Cyrus—a deep reinforcement learning (DRL)-based puncturing solution for eMBB and URLLC multiplexing. Cyrus is tailored for the Open RAN (O-RAN) architecture and unifies the three control loops of O-RAN synergistically in its design of DRL-based solution. Not only does Cyrus meet the real-time requirements for URLLC but also it continuously updates and improves its scheduling policy based on changing network conditions. The effectiveness of Cyrus is demonstrated through link-level simulations for 5G NR, showing significant improvement in eMBB performance over the state-of-the-art, particularly as URLLC traffic increases.

Vehicle-to-Everything (V2X) communication enables vehicles to communicate with other vehicles and roadside infrastructure, enhancing traffic management and improving road safety. However, the open and decentralized nature of V2X networks exposes them to various security threats, necessitating a robust misbehavior detection system (MBDS). While machine learning (ML) has proved effective in different anomaly detection applications, the existing ML-based MBDSs have shown limitations in generalizing due to the dynamic nature of V2X and insufficient and imbalanced training data. Moreover, they are known to be vulnerable to adversarial ML attacks. On the other hand, generative adversarial networks (GAN) possess the potential to mitigate such issues and improve detection performance by synthesizing unseen samples of minority classes and utilizing them during their model training. Therefore, we propose the first application of GAN to design an MBDS. Our contributions are manifold. In the pursuit of an effective GAN-based MBDS, we train and evaluate a diverse set of Wasserstein GAN (WGAN) models and present VEhicular GAN (VEHIGAN), an ensemble of multiple top-performing WGANs, which transcends the limitations of individual models and improves detection performance and adversarial robustness. We present a physics-guided data preprocessing technique that generates effective features for ML-based misbehavior detection. To evaluate the adversarial robustness, we formulate two categories of adversarial attacks against the WGAN-based MBDS. In the evaluation, we leverage the state-of-the-art V2X attack simulation tool VASP to create a comprehensive dataset of V2X messages with diverse misbehaviors. Evaluation results show that in 20 out of 35 misbehaviors, VehigAnoutperforms the baselines and exhibits comparable detection performance in other scenarios. Particularly, VehigAnexcels in detecting advanced misbehaviors that manipulate multiple fields in V2X messages simultaneously, replicating unique maneuvers. Moreover, VehigAnprovides approximately 92% improvement in false positive rates under powerful adaptive adversarial attacks and possesses intrinsic robustness against other adversarial attacks that target false negative rates. Finally, we make the data and code available for reproducibility and future benchmarking, available at https://eithub.com/shahriar0651/VehiGAN.

Age of Information (AoI) is a promising metric to measure information freshness and optimizing AoI through scheduling is one of the most intensely studied areas in AoI research. To date, the vast majority of research on AoI scheduling has been based on simplified communication models that often fail to capture the complexities found in real-world network systems such as 5G. While there are some limited efforts on AoI scheduling that have ventured into exploring OFDMA-based data transmission models similar to those in 5G, they tend to neglect essential elements, such as channel-dependent resource block (RB) allocation and modulation and coding scheme (MCS) assignment, rendering limited utility to real-world 5G systems. In this article, we focus on developing 5G-compliant AoI schedulers. We study a specific problem with the objective of minimizing outdated information across all source nodes. This problem arises from practice where there is a specific information freshness requirement, known as AoI deadline for each source node. We present Aequitas, an innovative 5G scheduler designed to optimize this objective through joint RB allocation and MCS assignment, both of which are dependent on frequency and time-selective channel fading. We exploit a property called “uniform fairness,” derived from the analysis of an optimal offline scheduler, to develop Aequitas. To meet stringent timing requirement in 5G, Aequitas leverages the parallel computing capability of a commercial off-the-shelf GPU. Extensive evaluations demonstrate that Aequitas closely approaches the theoretical lower bound in terms of objective performance, while maintaining operational times below the 5G timing requirement.

Supporting ultra reliable low-latency communication (URLLC) is an extremely challenging problem, due to the excessive requirements on reliability and latency. To date, few of the existing research efforts have successfully addressed the ultra reliability problem for URLLC. This article investigates this problem through the design of a URLLC scheduler for industrial automation under the open radio access network (O-RAN) architecture. We cast the URLLC data transmission problem as a resource scheduling problem, where a set of resource blocks (RBs) from a set of O-RAN radio units (O-RUs) must be allocated to a set of user equipments (UEs) for information transmission. The challenge is to find a scheduling solution in each mini-slot (sub millisecond time scale) based on dynamic channel conditions and satisfy the ultra reliability requirement (e.g., 99.9999%, or six-nine). We present Pistis—a novel scheduler design that fully utilizes the three control loops in O-RAN. Pistis exploits channel slow fading and PHY-layer properties to reduce the search space in its design of the near-real time (near-RT) component. It further leverages GPU parallel computing in its design of the real time (RT) component, which takes into account of fast fading in channel dynamics. We implement Pistis on commercial off-the-shelf hardware and demonstrate that Pistis is able to meet the six-nine reliability requirement for 4 O-RUs, 40 RBs, and 40 UEs within 0.5 ms.

Open radio access network (O-RAN) enables cooperative signal processing among multiple cells at a centralized O-RAN distributed unit (O-DU). It is a key technology for cellular networks to increase spectrum efficiency. To achieve cooperative signal processing across multiple cells, a new scheduler is needed. Specifically, the scheduler must jointly determine RB allocation, MCS assignment, and beamforming matrices for all users from all the cells that are involved in multi-cell processing. In addition, the scheduler must obtain its scheduling solution within each TTI (i.e., at most 1 ms) to be useful for the frame structure defined by 5G NR. In this paper, we present O-$\mathbf M^{3}$—a real-time scheduler formulti-cellMIMO networks under the O-RAN architecture. O-$\mathbf M^{3}$can meet the stringent timing requirement with joint optimization of beamforming matrices, RB allocation, and MCS assignment among multiple cells. O-$\mathbf M^{3}$is developed through a novel multi-pipeline design that exploits parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is performed for cell-center users to explore MU-MIMO transmission. We implement O-$\mathbf M^{3}$on a commercial off-the-shelf (COTS) GPU. Experimental results show that O-$\mathbf M^{3}$is capable of offering a scheduling solution within 500$\mu \text{s}$for an O-RAN system with 7 O-RAN radio units (O-RUs), 100 users, 100 RBs, and$2\times 8$MIMO. O-$\mathbf M^{3}$can also meet the 1 ms requirement for$2\times 12$MIMO systems. Meanwhile, O-$\mathbf M^{3}$can provide ~40% throughput gain on average through joint transmission across multiple cells.

Channel State Information (CSI) is a critical piece of information for MU-MIMO beamforming. However, CSI estimation errors are inevitable in practice. The random and uncertain nature of CSI estimation errors poses significant challenges to MU-MIMO beamforming. State-of-the-art works addressing such a CSI uncertainty can be categorized into model-based and data-driven works, both of which have limitations when providing a performance guarantee to the users. In contrast, this paper presents Limited Sample-based Beamforming (LSBF)—a novel approach to MU-MIMO beamforming that only uses a limited number of CSI data samples (without assuming any knowledge of channel distributions). Thanks to the use of CSI data samples, LSBF enjoys flexibility similar to data-driven approaches and can provide a theoretical guarantee to the users—a major strength of model-based approaches. To achieve both, LSBF employs chance-constrained programming (CCP) and utilizes the$\infty $-Wasserstein ambiguity set to bridge the unknown CSI distribution with limited CSI samples. Through problem decomposition and a novel bilevel formulation for each subproblem based on limited CSI data samples, LSBF solves each subproblem with a binary search and convex approximation. We show that LSBF significantly improves the network performance while providing a probabilistic data rate guarantee to the users.

The use of mobile phones while driving is a major source of distraction for vehicle drivers and has resulted in a large number of car accidents. While surveillance cameras can be used to detect the violation of phone use, they do not work well in some scenarios (e.g., darkness and blockage) and may raise privacy concerns. In this paper, we present PhoLoc, a roadside device to detect the violation of phone use in personal vehicles using the cellular signals emitted by cellphones. PhoLoc is equipped with two sensors: a multi-antenna radio receiver and a low-cost lidar. It jointly processes the multimodal data from the two sensors to estimate the relative location of a phone in a vehicle. The enabler of PhoLoc is a new near-field localization scheme, which is capable of estimating the location of a moving phone at a specific time moment by overhearing its cellular signals. We have built a prototype of PhoLoc and evaluated its performance in realistic scenarios. Experimental results show that PhoLoc achieves 4.2% false positive rate and 13.8% false negative rate in the detection of phone call violation.

This paper investigates a bandwidth minimization problem with Age of Information (AoI) constraints—a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment while, at the same time, there is an expectation of AoI requirement on the application side. We present a novel polynomial-time algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure calledFractional Consecutively Divisible(FCD). In the general case when the given AoI constraint vector is not FCD, we show that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. We validate the performance of Aion through a large number of simulations and all results confirm our theoretical findings. The results from this paper lay a foundation for future research on bandwidth minimization with AoI guarantee.

Open Radio Access Network (O-RAN) offers a new paradigm for the design and deployment of future RANs. The unique architecture of O-RAN presents two main challenges when designing a scheduler. First, it is impractical to obtain accurate and full Channel State Information (CSI) due to estimation errors and limited bandwidth of the fronthaul link between Open Radio Unit (O-RU) and Open Distributed Unit (O-DU). Second, the large-scale processing at an O-DU introduces difficulties in meeting the stringent time requirement in O-RAN, especially in the real-time (RT) control loop. To address these challenges, we propose R3—a real-time robust Multi-user, Multiple Input, Multiple Output (MU-MIMO) scheduler for O-RAN. R3 serves as a comprehensive scheduling solution encompassing RB allocation, MCS selection, and beamforming calculation. Most notably, R3 utilizes a limited number of CSI samples to offer probabilistic QoS guarantees. To meet the timing requirements of O-RAN, R3 decomposes the scheduling problem into two distinct sub-problems and integrates them into separate control loops. Moreover, each sub-problem is designed with a parallel structure, utilizing a reduced search space, and implemented on a GPU platform to accelerate the computation time. Experimental results demonstrate that R3 offers competitive throughput performance as the state-of-the-art while simultaneously fulfilling the QoS guarantees. Further, R3 meets the timing requirements of various control loops in O-RAN over a wide range of operating conditions.

Age of Information (AoI) is a metric that can be used to measure the freshness of information. Since its inception, there have been active research efforts on designing scheduling algorithms to various AoI-related optimization problems. For each problem, typically a custom-designed scheduler was developed. Instead of following the (custom-design) path, we pursue a general framework that can be applied to design a wide range of schedulers to solve AoI-related optimization problems. As a first step toward this vision, we present a general framework—Eywa, that can be applied to construct high-performance schedulers for a family of AoI-related optimization problems, all sharing a common setting of an IoT data collection network. We show how to apply Eywa to solve two important AoI-related problems: to minimize the weighted sum of AoIs and to minimize the bandwidth requirement under AoI constraints. We show that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms or provide new results that are not available in the literature.

When a blockchain application runs on data from the real world, it relies on an oracle mechanism that transports data from external sources to the blockchain. The blockchain oracle problem arises around the need to procure trustworthy data from external sources. Previous works have addressed data authenticity/integrity by building a secure channel between blockchain and external sources while employing a decentralized oracle network to avoid a single point of failure. However, the truthful data challenge, which emerges when legitimate external sources submit fraudulent or deceitful data, remains unsolved. In this paper, we introduce a new decentralized truth-discovering oracle architecture called DecenTruth to address the truthful data challenge using a data-centric approach. DecenTruth aims to elevate the "truthfulness" of external data input by enabling decentralized oracle nodes to discover and reach consensus on truthful values of common data objects from multi-sourced inputs in an off-chain manner. It harmonizes techniques in both the data plane and consensus plane—truth discovery (TD) and asynchronous BFT consensus—and enables nodes to finalize the same estimated truths on data objects with high accuracy, amid the harsh asynchronous network condition and presence of Byzantine sources and nodes. We implemented DecenTruth and evaluated its performance in a simulated oracle service scenario. The results demonstrate significantly higher Byzantine resilience and long-term data feed accuracy of DecenTruth, compared to existing median-based aggregation methods.

Time-sensitive applications, such as 5G and IoT, are imposing increasingly stringent security and reliability requirements on network time synchronization. Precision time protocol (PTP) is a de facto solution to achieve high precision time synchronization. It is widely adopted by many industries. Existing efforts in securing the PTP focus on the protection of communication channels, but little attention has been given to the threat of malicious insiders. In this paper, we first present the security vulnerabilities of PTP and discuss why the current defense mechanisms are unable to counter Byzantine insiders. We demonstrate how a malicious insider can spoof a time source to arbitrarily shift the system time of a victim node on an IoT testbed. We further demonstrate the harmful consequence of the attack on a real Turtlebot3 robotic platform as the robot fails to locate itself and follows a false trajectory. As a countermeasure, we propose multi-source PTP, in short, MS-PTP, a Byzantine-resilient network time synchronization mechanism that relies on time crowdsourcing. MS-PTP changes the current PTP's single source hierarchy to a multi-source client-server architecture, in which PTP clients take responses from multiple time servers and apply a novel secure aggregation scheme to eliminate the effect of malicious responses from unreliable sources. MS-PTP is able to counter f Byzantine failures when the total number of time sources n used by a client satisfies n>=3f+1. We provide rigorous proof for its non-parametric accuracy guarantee---achieving bounded error regardless of the Byzantine population. We implemented a prototype of MS-PTP on our IoT testbed and the results show its resilience against Byzantine insiders while maintaining high synchronization accuracy.

Recently, has been recognized that there is a practical limitation with the original notion of Age of Information (AoI) metric in terms of quantifying the freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. In this article, we introduce the notion of AoII+ metric by modifying AoII with practical considerations. Then, we investigate a scheduling problem to minimize AoII+ in an IoT data collection network. We derive a theoretical lower bound for the minimum AoII+. Then, we present Heh—a low-complexity online scheduler to minimize AoII+. The design of Heh is based on the estimation of a novel offline scheduling priority metric without any future knowledge. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes AoII+. Through extensive simulations, we show that the lower bound is very tight and that the AoII+ obtained by Heh is close to optimal.

In this article, we consider a discrete-time information-update system, where a service provider can proactively retrieve information from the information source to update its data and users query the data at the service provider. One example is crowdsensing-based applications. In order to keep users satisfied, the application desires to provide users with fresh data, where the freshness is measured by the age-of-information (AoI). However, maintaining fresh data requires the application to update its database frequently, which incurs an update cost (e.g., incentive payment). Hence, there exists a natural tradeoff between the AoI and the update cost at the service provider who needs to make update decisions. To capture this tradeoff, we formulate an optimization problem with the objective of minimizing the total cost, which is the sum of the staleness cost (which is a function of the AoI) and the update cost. Then, we provide two useful guidelines for the design of efficient update policies. Following these guidelines and assuming that the aggregated request arrival process is Bernoulli, we prove that there exists a threshold-based policy that is optimal among all online policies and thus focus on the class of threshold-based policies. Furthermore, we derive the closed-form formula for computing the long-term average cost under any threshold-based policy and obtain the optimal threshold. Finally, we perform extensive simulations using both synthetic data and real traces to verify our theoretical results and demonstrate the superior performance of the optimal threshold-based policy compared with several baseline policies.

Modern vehicles rely on a fleet of electronic control units (ECUs) connected through controller area network (CAN) buses for critical vehicular control. With the expansion of advanced connectivity features in automobiles and the elevated risks of internal system exposure, the CAN bus is increasingly prone to intrusions and injection attacks. As ordinary injection attacks disrupt the typical timing properties of the CAN data stream, rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal/semantic level, while looking innocuous by the pattern/frequency of the CAN messages. The rule-based IDS, as well as the anomaly-based IDS, are built merely on the sequence of CAN messages IDs or just the binary payload data and are less effective in detecting such attacks. Therefore, to detect such intelligent attacks, we propose CANShield, a deep learning-based signal-level intrusion detection framework for the CAN bus. CANShield consists of three modules: a data preprocessing module that handles the high-dimensional CAN data stream at the signal level and parses them into time series suitable for a deep learning model; a data analyzer module consisting of multiple deep autoencoder (AE) networks, each analyzing the time-series data from a different temporal scale and granularity, and finally an attack detection module that uses an ensemble method to make the final decision. Evaluation results on two high-fidelity signal-based CAN attack datasets show the high accuracy and responsiveness of CANShield in detecting advanced intrusion attacks.

Task offloading is a powerful tool in Mobile Edge Computing (MEC). However, in many practical scenarios, the number of required processing cycles of a task is unknown beforehand and only known until its completion. This poses a serious challenge in making offloading decisions as the number of processing cycles is a key parameter to determine whether a task’s deadline can be met. To cope with such processing uncertainty, we formulate a Chance-Constrained Program (CCP) that offers probabilistic guarantees to task deadlines. The goal is to minimize energy consumption for the users while meeting the probabilistic task deadlines. We assume that only the means and variances of the random processing cycles are available, without any knowledge of distribution functions. We employ a powerful tool called Exact Conic Reformulation (ECR) that reformulates probabilistic deadline constraints into deterministic ones. Subsequently, we design an online solution called EPD (Energy-minimized solution with Probabilistic Deadline guarantee) for periodic scheduling and schedule updates during run-time. We show that EPD can address the processing uncertainty with probabilistic deadline guarantees while minimizing the users’ energy consumption.

With the rapid advancement in machine learning (ML), ML-based Intrusion Detection Systems (IDSs) are widely deployed to protect networks from various attacks. One of the biggest challenges is that ML-based IDSs suffer from adversarial example (AE) attacks. By applying small perturbations (e.g., slightly increasing packet inter-arrival time) to the intrusion traffic, an AE attack can flip the prediction of a well-trained IDS. We address this challenge by proposingMANDA, a MANifold and Decision boundary-based AE detection system. Through analyzing AE attacks, we notice that 1) an AE tends to be close to its original manifold (i.e., the cluster of samples in its original class) regardless of which class it is misclassified into; and 2) AEs tend to be close to the decision boundary to minimize the perturbation scale. Based on the two observations, we designMANDAfor accurate AE detection by exploiting inconsistency between manifold evaluation and IDS model inference and evaluating model uncertainty on small perturbations. We evaluateMANDAon both binary IDS and multi-class IDS on two datasets (NSL-KDD and CICIDS) under three state-of-the-art AE attacks. Our experimental results show thatMANDAachieves high true-positive rate (98.41%) with a 5% false-positive rate.

Hybrid beamforming (HB) architecture has been widely considered for 5G mmWave systems. It reduces hardware complexity by allowing the number of RF chains to be far fewer than the number of antennas. A major practical challenge for HB is to obtain a beamforming solution in real-time. In 5G NR, new frame structures with short TTIs are employed to support mmWave communications. Under such frame structures, it is necessary to obtain a beamforming solution with a time resolution varying from 1 ms to 125$\mu$s – an extremely stringent time requirement considering the complexity involved in HB. In this paper, we present the design and implementation ofTurbo-HB– a novel beamforming design under the HB architecture that is capable of offering the beamforming matrices in less than 500$\mu$s. The key ideas of Turbo-HB include: (i) reducing the complexity of computation-intensive SVD operations by exploiting channel sparsity at mmWave frequencies, and (ii) achieving large-scale parallel computation with minimal memory access. We implement Turbo-HB on an off-the-shelf Nvidia GPU and conduct extensive experiments. Our experimental results demonstrate that Turbo-HB can obtain a beamforming solution in 500$\mu$s for up to 100 RBs and 10 MU-MIMO users on each RB while offering competitive throughput performance compared to state-of-the-art (non-real-time) algorithms.

Degree-of-freedom (DoF)-based models have been proven to be highly successful in modeling and analysis of MIMO systems. Among existing DoF-based models, the number of DoFs used for interference cancellation (IC) is solely based on the number of interfering data streams. However, from both experimental and simulation results, we find that signal strengths of an interference link vary significantly in different directions in the eigenspace. In this paper, we exploit the difference in interference signal strengths in the eigenspace and perform IC with DoFs only on those directions with strong signals. To differentiate interference signal strengths on an interference link, we introduce a novel concept called “effective rank threshold.” Based on this threshold, DoFs are consumed only to cancel strong interferences in the eigenspace while weak interferences are treated as noise in throughput calculation. To better understand the benefits of this approach, we study a fundamental trade-off between network throughput and effective rank threshold for an MU-MIMO network. Our simulation results show that network throughput under optimal rank threshold is significantly higher than that under existing DoF IC models. To ensure the new DoF IC model is feasible at PHY layer, we propose an algorithm to set the weights for all nodes that can offer our desired DoF allocation.

Multi-User (MU)-MIMO technology plays a vital role in 5G NR. Under MU-MIMO transmission, multiple users can share the same time-frequency resources simultaneously. For 5G MU-MIMO systems, it is challenging to design a scheduler. The scheduler needs to determine resource block (RB) allocation, the number of data streams and modulation and coding scheme (MCS) for each user in each transmission time interval (TTI). In particular, multiple users can be co-scheduled on the same RB for MU-MIMO transmission. In addition, it is necessary for the scheduler to find a scheduling solution within each TTI to be useful. In this paper, we present mCore$+$, a novel design and implementation that can achieve$\sim$500$\mu$s timing performance for 5G MU-MIMO systems. mCore$+$is meticulously designed with a multi-phase optimization and heavily leverages large-scale parallel computation. In each phase, mCore$+$either decomposes the optimization problem into a number of independent sub-problems, or reduces the search space into a smaller but most promising subspace, or both. mCore$+$is validated on a commercial-off-the-shelf GPU platform. Experimental results show that mCore$+$can offer a scheduling solution in$\sim$500$\mu$s for up to 100 RBs, 100 users, 29 MCS levels and$4 \times 12$MIMO systems. Also, mCore$+$can achieve better or comparable throughput performance compared to other state-of-the-art algorithms.

Underlay coexistence is a spectrum efficient mechanism to roll out 5G picocells within a macrocell on the same spectrum. Due to a lack of cooperation between the primary users (PUs) in the macrocell and secondary users (SUs) in the picocells, it is impossible to have complete knowledge of channel conditions between them. Under such a circumstance, chance-constrained programming (CCP) has been shown to be an ideal optimization tool to address such a channel uncertainty. However, solutions to CCP are computationally intensive and cannot meet 5G’s timing requirement (125$\mu s$). To address this problem, we propose a novel scheduler called GPU-based Underlay Coexistence (GUC) with the goal of finding an approximate solution to CCP in real-time. The essence of GUC is to decompose the original optimization problem into a large number of small subproblems that are suitable for parallel computation on GPU platforms. By selecting a subset of promising subproblems and solving them in parallel with fast algorithms, we are able to leverage GPU parallel computing and develop a real-time solution. Through extensive experiments, we show that GUC meets the 125$\mu s$requirement while achieving 90% optimality on average.

Federated Learning (FL) is a promising technique to enhance the safety and efficiency of intelligent transportation systems. While FL has been extensively studied, the communication and networking challenges related to the operations of FL in dynamic yet dense vehicular networks remain under-explored. Limited storage and communication capacities of individual vehicles throttle the timely training of an FL model in distributed vehicular networks. In this paper, we present a communication framework for FL (CF4FL) in transportation systems. CF4FL aims to accelerate the convergence of FL training process through the innovation of two complementary networking components: (i) a deadline-driven vehicle scheduler (DDVS), and (ii) a concurrent vehicle polling scheme (CVPS). DDVS identifies a subset of vehicles for local model training in each iteration of FL, with the aim of minimizing data loss while respecting the deadline constraints derived from vehicles’ storage, computation, and energy budgets. CVPS takes advantage of multiple antennas on an edge server to enable concurrent local model transmissions in dynamic vehicular networks, thereby reducing the airtime overhead of each FL iteration. We have evaluated CF4FL through a blend of experimentation and simulation. Trace-driven simulation shows that, compared to existing scheduling and transmission schemes, CF4FL reduces the convergence time of FL training by 39%.

Federated meta-learning has emerged as a promising AI framework for today’s mobile computing scenes involving distributed clients. It enables collaborative model training using the data located at distributed mobile clients and accommodates clients that need fast model customization with limited new data. However, federated meta-learning solutions are susceptible to inference-based privacy attacks since the global model encoded with clients’ training data is open to all clients and the central server. Meanwhile, differential privacy (DP) has been widely used as a countermeasure against privacy inference attacks in federated learning. The adoption of DP in federated meta-learning is complicated by the model accuracy-privacy trade-off and the model hierarchy attributed to the meta-learning component. In this paper, we introduce DP-FedMeta, a new differentially private federated meta-learning architecture that addresses such data privacy challenges. DP-FedMeta features an adaptive gradient clipping method and a one-pass meta-training process to improve the model utility-privacy trade-off. At the core of DP-FedMeta are two DP mechanisms, namely DP-AGR and DP-AGRLR, to provide two notions of privacy protection for the hierarchical models. Extensive experiments in an emulated federated meta-learning scenario on well-known datasets (Omniglot, CIFAR-FS, and Mini-ImageNet) demonstrate that DP-FedMeta accomplishes better privacy protection while maintaining comparable model accuracy compared to the state-of-the-art solution that directly applies DP-based meta-learning to the federated setting.

Federated learning (FL) has been shown vulnerable to a new class of adversarial attacks, known as model poisoning attacks (MPA), where one or more malicious clients try to poison the global model by sending carefully crafted local model updates to the central parameter server. Existing defenses that have been fixated on analyzing model parameters show limited effectiveness in detecting such carefully crafted poisonous models. In this work, we propose FLARE, a robust model aggregation mechanism for FL, which is resilient against state-of-the-art MPAs. Instead of solely depending on model parameters, FLARE leverages the penultimate layer representations (PLRs) of the model for characterizing the adversarial influence on each local model update. PLRs demonstrate a better capability to differentiate malicious models from benign ones than model parameter-based solutions. We further propose a trust evaluation method that estimates a trust score for each model update based on pairwise PLR discrepancies among all model updates. Under the assumption that honest clients make up the majority, FLARE assigns a trust score to each model update in a way that those far from the benign cluster are assigned low scores. FLARE then aggregates the model updates weighted by their trust scores and finally updates the global model. Extensive experimental results demonstrate the effectiveness of FLARE in defending FL against various MPAs, including semantic backdoor attacks, trojan backdoor attacks, and untargeted attacks, and safeguarding the accuracy of FL.

In this paper, we consider a discrete-time information-update system, where a service provider can proactively retrieve information from the information source to update its data and users query the data at the service provider. One example is crowdsensing-based applications. In order to keep users satisfied, the application desires to provide users with fresh data, where the freshness is measured by the Age-of-Information (AoI). However, maintaining fresh data requires the application to update its database frequently, which incurs an update cost (e.g., incentive payment). Hence, there exists a natural tradeoff between the AoI and the update cost at the service provider who needs to make update decisions. To capture this tradeoff, we formulate an optimization problem with the objective of minimizing the total cost, which is the sum of the staleness cost (which is a function of the AoI) and the update cost. Then, we provide two useful guidelines for the design of efficient update policies. Following these guidelines and assuming that the aggregated request arrival process is Bernoulli, we prove that there exists a threshold-based policy that is optimal among all online policies and thus focus on the class of threshold-based policies. Furthermore, we derive the closed-form formula for computing the long-term average cost under any threshold-based policy and obtain the optimal threshold. Finally, we perform extensive simulations using both synthetic data and real traces to verify our theoretical results and demonstrate the superior performance of the optimal threshold-based policy compared with several baseline policies.

Cloud Radio Access Network (C-RAN) is a novel centralized architecture for cellular networks. C-RAN can significantly improve spectrum efficiency by performing cooperative signal processing for multiple cells at a centralized baseband unit (BBU) pool. However, a new resource scheduler is needed before we can take advantage of C-RAN's multi-cell processing capability. Under C-RAN architecture, the scheduler must jointly determine RB allocation, MCS assignment, and beamforming matrices for all users under all covering cells. In addition, it is necessary to obtain a scheduling solution within each TTI (at most 1 ms) to be useful for the frame structure defined by 5G NR. In this paper, we present M3—a sub-ms scheduler for multi-cell MIMO networks under C-RAN architecture. M3addresses the stringent timing requirement through a novel multi-pipeline design that exploits parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is for cell-center users to explore MU-MIMO transmission. Experimental results show that M3is capable of offering a scheduling solution within 1 ms for 7 remote radio heads (RRHs), 100 users, 100 RBs, and 2×12 MIMO. Meanwhile, M3provides ~40%. throughput gain on average by employing joint transmission.

Accurate estimation of Channel State Information (CSI) is essential to design MU-MIMO beamforming. However, errors in CSI estimation are inevitable in practice. State-of-the-art works model CSI as random variables and assume certain specific distributions or worst-case boundaries, both of which suffer performance issues when providing performance guarantees to the users. In contrast, this paper proposes a Data-Driven Beamforming (D2BF) that directly handles the available CSI data samples (without assuming any particular distributions). Specifically, we employ chance-constrained programming (CCP) to provide probabilistic data rate guarantees to the users and introduce ∞-Wasserstein ambiguity set to bridge the unknown CSI distribution with the available (limited) data samples. Through problem decomposition and a novel bilevel formulation for each subproblem, we show that each subproblem can be solved by binary search and convex approximation. We also validate that D2BF offers better performance than the state-of-the-art approach while meeting probabilistic data rate guarantees to the users.

Recently, it has been recognized that there is a serious limitation with the original Age of Information (AoI) metric in terms of quantifying true freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. By further refining this new metric with practical considerations, we introduce Age of Outdated Information (Ao2I) metric. In this paper, we investigate a scheduling problem for minimizing Ao2I in an IoT data collection network. We derive a theoretical lower bound for the minimum Ao2I that any scheduler can achieve. Then we present Heh—a low-complexity online scheduler. The design of Heh is based on the estimation of a novel offline scheduling priority metric in the absence of knowledge of the future. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes Ao2I. Through extensive simulations, we show that the lower bound is very tight and that the Ao2I obtained by Heh is close-to-optimal.

Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range of applications. However, a lack of sufficient security features in IoT devices renders IoT ecosystems vulnerable to various network intrusion attacks, potentially causing severe damage. Previous works have explored using machine learning to build anomaly detection models for defending against such attacks. In this paper, we propose FeCo, a federated-contrastive-learning framework that coordinates in-network IoT devices to jointly learn intrusion detection models. FeCo utilizes federated learning to alleviate users’ privacy concerns as participating devices only submit their model parameters rather than local data. Compared to previous works, we develop a novel representation learning method based on contrastive learning that is able to learn a more accurate model for the benign class. FeCo significantly improves the intrusion detection accuracy compared to previous works. Besides, we implement a two-step feature selection scheme to avoid overfitting and reduce computation time. Through extensive experiments on the NSL-KDD dataset, we demonstrate that FeCo achieves as high as 8% accuracy improvement compared to the state-of-the-art and is robust to non-IID data. Evaluations on convergence, computation overhead, and scalability further confirm the suitability of FeCo for IoT intrusion detection.

With the increasing diversity in the requirement of wireless services with guaranteed Quality of Service (QoS), radio access network (RAN) slicing becomes an important aspect in implementation of next-generation wireless systems (5G). RAN slicing involves the division of network resources into many logical segments where each segment has specific QoS and can serve users of the mobile virtual network operator (MVNO) with these requirements. This allows the network operator (NO) to provide service to multiple MVNOs each with different service requirements. Efficient allocation of the available resources to slices becomes vital in determining the number of users and therefore, the number of MVNOs that a NO can support. In this work, we study the problem of the modulation and coding scheme (MCS)-aware RAN slicing (MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement. Channel quality indicator (CQI) report sent from each user in the network determines the MCS selected, which in turn determines the achievable data rate. But the channel conditions might not remain the same for the entire duration of a user being served. For this reason, we consider the channel conditions to be dynamic where the choice of the MCS level varies at each time instant. We model the MaRS problem as a NonLinear Programming problem and show that it is NP-Hard. Next, we propose a solution based on the greedy algorithm paradigm. We then develop an upper performance bound for this problem and finally evaluate the performance of the proposed solution by comparing it against the upper bound under various channel and network configurations.

Ultra-Reliable and Low Latency Communications (URLLC) is an important use case in 5G NR that targets at 1-ms level delay sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by enhanced Mobile BroadBand (eMBB) service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through modulation and coding scheme (MCS) selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE – the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implemented DELUXE through a link-level 5G NR simulator with GPU and MathWorks 5G toolbox. Through extensive experiments, we show that DELUXE can successfully choose MCS for eMBB transmissions to maintain the desired link reliability while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement ($< 125 \mu \text{s}$) in 5G NR.

Since big data becomes a main impetus to the next generation of IT industry, data privacy has received considerable attention in recent years. To deal with the privacy challenges, differential privacy has been widely discussed and related private mechanisms are proposed as privacy-enhancing techniques. However, with today’s differential privacy techniques, it is difficult to generate a sanitized dataset that can suit every machine learning task. In order to adapt to various tasks and budgets, different kinds of privacy mechanisms have to be implemented, which inevitably incur enormous costs for computation and interaction. To this end, in this article, we propose two novel schemes for outsourcing differential privacy. The first scheme efficiently achieves outsourcing differential privacy by using our preprocessing method and secure building blocks. To support the queries from multiple evaluators, we give the second scheme that employs a trusted execution environment to aggregately implement privacy mechanisms on multiple queries. During data publishing, our proposed schemes allow providers to go off-line after uploading their datasets, so that they achieve a low communication cost which is one of the critical requirements for a practical system. Finally, we report an experimental evaluation on UCI datasets, which confirms the effectiveness of our schemes.

5G NR is designed to operate over a broad range of frequency bands and support new applications with ultra-low latency requirements. To support its extremely diverse operating conditions, multiple OFDM numerologies have been defined in the 5G standards. Under these numerologies, it is necessary to perform scheduling with a time resolution of$\sim 100 \mathrm {\mu s}$. This requirement poses a new challenge beyond existing LTE and cannot be satisfied by any existing LTE schedulers. In this paper, we present the design of GPF+, which is a GPU-based proportional fair (PF) scheduler with timing performance under$100 \mathrm {\mu s}$. GPF+ is an improvement over our GPF in Huanget al.(2018). The key ideas include decomposing the original scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU. By implementing GPF+ on an off-the-shelf NVIDIA Tesla V100 GPU, we show that GPF+ is able to achieve near-optimal PF performance with timing performance under$100 \mathrm {\mu s}$. GPF+ represents the fastest GPU-based PF scheduler that can meet the new real-time requirement in 5G NR.

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when there is an AoI requirement for each source node, which we call Maximum AoI Threshold (MAT). Specifically, we want to determine whether or not a vector of MATs corresponding to the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure calledCyclic Scheduler Detection(CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, calledFictitious Polynomial Mapping(FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under$\ln 2$. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm.

We study the problem of channel uncertainty on wireless transmissions from different users with mutual interference. Specifically, the channel gains from the transmitters to the receivers are available only through their mean and covariance rather than complete distributions. Our goal is to maximize the energy efficiency among all transmitter-receiver pairs while guaranteeing their capacity requirements. For this problem, we employ chance-constrained programming (CCP), which allows occasional violation of target capacity threshold as long as the probability of such violation is below a small tolerable constant (risk level). We propose a solution based on a novel reformulation technique that converts the original CCP into a deterministic optimization problem without relaxation errors. Then the deterministic optimization problem is approximated into a Geometric Program (GP) based on tight polynomial approximations, which can be solved optimally. We prove that our proposed solution achieves near-optimal performance with polynomial time complexity.

URLLC is an important use case in 5G NR that targets at 1-ms level delay-sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by eMBB service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through MCS selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE - the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implement DELUXE through a link-level 5G NR simulator. Extensive experimental results show that DELUXE can successfully maintain the desired link reliability for eMBB while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement (<; 125 μs) in 5G NR.

MU-MIMO technology enables a base station (BS) to transmit signals to multiple users simultaneously on the same frequency band. It is a key technology for 5G NR to increase the data rate. In 5G specifications, an MU-MIMO scheduler needs to determine RBs allocation and MCS assignment to each user for each TTI. Under MU-MIMO, multiple users may be coscheduled on the same RB and each user may have multiple data streams simultaneously. In addition, the scheduler must meet the stringent real-time requirement (~1 ms) during decision making to be useful. This paper presents mCore, a novel 5G scheduler that can achieve ~1 ms scheduling with joint optimization of RB allocation and MCS assignment to MU-MIMO users. The key idea of mCore is to perform a multi-phase optimization, leveraging large-scale parallel computation. In each phase, mCore either decomposes the optimization problem into a number of independent sub-problems, or reduces the search space into a smaller but most promising subspace, or both. We implement mCore on a commercial-off-the-shelf GPU. Experimental results show that mCore can offer the best scheduling performance for up to 100 RBs, 100 users, 29 MCS levels and 4 × 12 antennas when compared to other state-of-the-art algorithms. It is also the only algorithm that can find its scheduling solution in ~1 ms.

We study an Age of Information (AoI) scheduling problem where AoI for each source at the base station (BS) can tolerate occasional violations, which we define as a violation tolerance constraint. The problem is to determine whether a set of users with given AoI deadlines, tolerance rates, and packet loss rates (due to each source's channel condition) is schedulable, and if so find a feasible scheduler. We study two cases: (i) the stable tolerant case where the tolerance rate is higher than the packet loss rate for all sources; (ii) the unstable tolerant case where the tolerance rate is lower than the packet loss rate for at least one source. For stable tolerant case, we design an algorithm called stable tolerant scheduler (STS), which can find a feasible scheduler for any network when system load is no greater than ln 2. For unstable tolerance case, we develop unstable tolerant scheduler (UTS) and identify a schedulability condition for it. Through extensive simulations, we show that STS and UTS match our theoretical results.

This paper investigates bandwidth minimization under AoI constraints - a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment when AoI is used as a constraint. We present a novel fast algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure called Fractional Consecutively Divisible (FCD). In the general case when the given AoI constraint vector is not FCD, we prove that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. The results from this paper lay the foundation for future research on bandwidth minimization with AoI guarantee.

With the rapid advancement in machine learning (ML), ML-based Intrusion Detection Systems (IDSs) are widely deployed to protect networks from various attacks. Yet one of the biggest challenges is that ML-based IDSs suffer from adversarial example (AE) attacks. By applying small perturbations (e.g. slightly increasing packet inter-arrival time) to the intrusion traffic, an AE attack can flip the prediction of a well-trained IDS. We address this challenge by proposing MANDA, a MANifold and Decision boundary-based AE detection system. Through analyzing AE attacks, we notice that 1) an AE tends to be close to its original manifold (i.e., the cluster of samples in its original class) regardless which class it is misclassified into; and 2) AEs tend to be close to the decision boundary so as to minimize the perturbation scale. Based on the two observations, we design MANDA for accurate AE detection by exploiting inconsistency between manifold evaluation and IDS model inference and evaluating model uncertainty on small perturbations. We evaluate MANDA on NSL-KDD under three state-of-the-art AE attacks. Our experimental results show that MANDA achieves as high as 98.41% true-positive rate with 5% false-positive rate and can be applied to other problem spaces such as image recognition.

Due to ease-of-deployment, autonomous control and low cost, unmanned aerial vehicles (UAVs), as fixed aerial base stations, are increasingly being used as relays to collect time-sensitive information (i.e., status updates) from IoT devices and deliver it to the nearby terrestrial base station (TBS), where the information gets processed. In order to ensure timely delivery of information to the TBS (from all IoT devices), optimal scheduling of time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical challenge. To address this, we propose scheduling policies for Age of Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and Maximum AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS (hop 2). We show that MAF-MAD is the optimal scheduler under ideal conditions, i.e., error-free channels and generate-at-will traffic generation at IoT devices. On the contrary, for realistic conditions, we propose a Deep-Q-Networks (DQN) based scheduler. Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i.e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10’s of IoT devices). However, it does not scale well with network size whereas MAF-MAD outperforms all other schedulers under all considered scenarios for larger networks.

Mobile Edge Computing (MEC) has emerged to be an integral component of 5G infrastructure due to its potential to speed up task processing and reduce energy consumption for mobile devices. However, a major technical challenge in making offloading decisions is that the number of required processing cycles of a task is usually unknown in advance. Due to this processing uncertainty, it is difficult to make offloading decisions while providing any guarantee on task deadlines. To address this challenge, we propose EPD---Energy-minimized solution with Probabilistic Deadline guarantee to task offloading problem. The mathematical foundation of EPD is Exact Conic Reformulation (ECR), which is a powerful tool that reformulates a probabilistic constraint for task deadline into a deterministic one. In the absence of distribution knowledge of processing cycles, we use the estimated mean and variance of processing cycles and exploit ECR to the fullest extent in the design of EPD. Simulation results show that EPD successfully guarantees the probabilistic deadlines while minimizing the energy consumption of mobile users, and can achieve significant improvement in energy saving when compared to a state-of-the-art approach.

The Age of Information (AoI) is a key metric to measure the freshness of information for IoT applications. Most of the existing analytical models for AoI are overly idealistic and do not capture state-of-the-art transmission technologies such as 5G as well as channel dynamics in both frequency and time domains. In this article, we present Kronos, a real-time 5G-compliant scheduler that minimizes AoI for IoT data collection. Kronos is designed to cope with highly dynamic channel conditions. Its main function is to perform RB allocation and to select the modulation and coding scheme for each source node based on channel conditions, with the objective of minimizing long-term AoI. To meet the stringent real-time requirement for 5G, we develop a GPU-based implementation of Kronos on commercial off-the-shelf Nvidia GPUs. Through extensive experimentation, we show that Kronos can find near-optimal solutions under submillisecond time scale. To the best of our knowledge, this is the first real-time AoI scheduler that is 5G compliant.

The spectrum access system (SAS) is being deployed as a key component of the emerging spectrum sharing paradigm to address the spectrum crunch facing the U.S. wireless industry. Ensuring security and privacy of this system against potential attacks is a task of paramount importance. In this article, we first introduce the SAS system, describing its three-tier access model, its functional architecture, and the spectrum management protocol. We then provide a comprehensive analysis of a variety of security and privacy attacks that an SAS is vulnerable to, and discuss their countermeasures. We identify key challenges, formalize threat models, and organize the discussion of SAS security into four categories: 1) SAS server security and privacy; 2) citizens broadband radio service device security; 3) security of environment sensing capability; and 4) communication protocol security. Finally, we suggest future research directions for spectrum management security.

In the recent decade, deep learning techniques have been widely adopted for founding artificial Intelligent applications, which led to successes in many data analysis tasks, such as risk assessment, medical predictions, and face recognition. Since the effectiveness of deep learning is directly proportional to the amount of data available, a large-scale collection of massive data is essential. Considering privacy and security concerns often prevent data owners from contributing sensitive data for training, researchers proposed several techniques to provide privacy guarantees of data in machine learning systems that contains multiple parties. However, all these works incurred frequent interactions between data owners during training, such that they came at a high communicational cost for data owners. To this end, in this article, we propose a new server-aid framework called non-interactive privacy-preserving multi-party machine learning (NPMML), which supports secure machine learning tasks without the participation of data owners. The NPMML framework significantly reduces data owners’ communicational overheads in multi-party machine learning. Moreover, we design a concrete construction for multi-layer neural networks based on NPMML. Finally, we evaluate the performance of NPMML by prototype implementation. The experimental result demonstrates that NPMML is communicational-efficient for data owners.

We consider an underlay coexistence scenario where secondary users (SUs) must keep their interference to the primary users (PUs) under control. However, the channel gains from the PUs to the SUs are uncertain due to a lack of cooperation between the PUs and the SUs. Under this circumstance, it is preferable to allow the interference threshold of each PU to be violated occasionally as long as such violation stays below a probability. In this article, we employ Chance-Constrained Programming (CCP) to exploit this idea of occasional interference threshold violation. We assume the uncertain channel gains are only known by their mean and covariance. These quantities are slow-changing and easy to estimate. Our main contribution is to introduce a novel and powerful mathematical tool called Exact Conic Reformulation (ECR), which reformulates the intractable chance constraints into tractable convex constraints. Further, ECR guarantees an equivalent reformulation from linear chance constraints into deterministic conic constraints without the limitations associated with Bernstein Approximation, on which our research community has been fixated on for years. Through extensive simulations, we show that our proposed solution offers a significant improvement over existing approaches in terms of performance and ability to handle channel correlations (where Bernstein Approximation is no longer applicable).

Automotive communication networks, represented by the CAN bus, are acclaimed for enabling real-time communication between vehicular ECUs but also criticized for their lack of effective security mechanisms. Various attacks have demonstrated that this security deficit renders a vehicle vulnerable to adversarial control that jeopardizes passenger safety. A recent standardization effort led by AUTOSAR has provided general guidelines for developing next-generation automotive communication technologies with built-in security mechanisms. A key security mechanism is message authentication between ECUs for countering message spoofing and replay attack. While many message authentication schemes have been proposed by previous work, the important issue of session key establishment with AUTOSAR compliance was not well addressed. In this paper, we fill this gap by proposing an AUTOSAR-compliant key management architecture that takes into account practical requirements imposed by the automotive environment. Based on this architecture, we describe a baseline session key distribution protocol called SKDC that realizes all designed security functionalities, and propose a novel secret-sharing-based protocol called SSKT that yields improved communication efficiency. Both SKDC and SSKT are customized for CAN/CAN-FD bus deployment. We implemented the two protocols on commercial microcontroller boards and evaluated their performance with hardware experiment and extrapolation analysis. The result shows while both protocols are performant, SSKT achieves superior computation and communication efficiency at scale.

Big data analytics is having a profound impact on many sectors of the economy by transforming raw data into actionable intelligence. However, increased use of sensitive business and private personal data with no or limited privacy safeguards has raised great concerns among individuals and government regulators. To address the growing tension between the need for data utility and the demand for data privacy, trusted execution environment (TEE) is being used in academic research as well as industrial application as a powerful primitive to enable confidential computation on the private data with only the result disclosed but not the original private data. While much of the current research has been focusing on protecting the TEE against attacks (e.g. side-channel information leakage), the security and privacy of the applications executing inside a TEE enclave has received little attention. The general attitude is that the application is running inside a trusted computing base (TCB), and therefore can be trusted. This assumption may not be valid when it comes to unverified third-party applications. In this paper, we present PrivacyScope, a static code analyzer designed to detect leakage of private data by an application code running in a TEE. PrivacyScope accomplishes this by analyzing the application code and identifying violations of a property called nonreversibility. We introduce nonreversibility since the classical noninterference property falls short of detecting private data leakage in certain scenarios, e.g., in machine learning (ML) programs where the program output is always related to (private) input data. Given its strict reliance on observable state, the noninterference falls short of detecting private data leakage in these situations. By design, PrivacyScope detects both explicit and implicit information leakage. The nonreversibility property is formally defined based on the noninterference property. Additionally, we describe the algorithms for PrivacyScope as extensions to the runtime semantics of a general language. To evaluate the efficacy of our approach and proof-of-feasibility prototype, we apply PrivacyScope to detect data leakage in select open-source ML code modules including linear regression, k-means clustering and collaborative filtering. Also, PrivacyScope can detect intentional data leakage code injected by a programmer. We responsibly disclosed all the discovered vulnerabilities leading to disclosure of private data in the open-source ML program we analyzed.

Hybrid beamforming (HB) architecture has been widely recognized as the most promising solution to mmWave MIMO systems. A major practical challenge for HB is to obtain a solution in ~1 ms, which is an extremely stringent but necessary time requirement for its deployment in the field. In this paper, we present the design and implementation of Turbo-HB, codename for a novel beamforming design under the HB architecture that can obtain the beamforming matrices in about 1 ms. The key ideas in Turbo-HB include (i) reducing the complexity of SVD techniques by exploiting the limited number of channel paths at mmWave frequencies, and (ii) designing and implementing a parallelizable algorithm for a large number of matrix transformations. We validate Turbo-HB by implementing it on an off-the-shelf Nvidia GPU. Through extensive experiments, we show that Turbo-HB can meet ~1 ms timing requirement while delivering competitive throughput performance compared to state-of-the-art algorithms.

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when each source node has an AoI requirement (which we call Maximum AoI Threshold (MAT)). Specifically, we want to determine whether or not a vector of MATs for the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure called Cyclic Scheduler Detection (CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, called Fictitious Polynomial Mapping (FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under ln 2. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm.

Blockchain, the technology behind the popular Bitcoin, is considered a "security by design" system as it is meant to create security among a group of distrustful parties yet without a central trusted authority. The security of blockchain relies on the premise of honest-majority, namely, the blockchain system is assumed to be secure as long as the majority of consensus voting power is honest. And in the case of proof-of-work (PoW) blockchain, adversaries cannot control more than 50% of the network's gross computing power. However, this 50% threshold is based on the analysis of computing power only, with implicit and idealistic assumptions on the network and node behavior. Recent researches have alluded that factors such as network connectivity, presence of blockchain forks, and mining strategy could undermine the consensus security assured by the honest-majority, but neither concrete analysis nor quantitative evaluation is provided. In this paper we fill the gap by proposing an analytical model to assess the impact of network connectivity on the consensus security of PoW blockchain under different adversary models. We apply our analytical model to two adversarial scenarios: 1) honest-but-potentially-colluding, 2) selfish mining. For each scenario, we quantify the communication capability of nodes involved in a fork race and estimate the adversary's mining revenue and its impact on security properties of the consensus protocol. Simulation results validated our analysis. Our modeling and analysis provide a paradigm for assessing the security impact of various factors in a distributed consensus system.

This article investigates the dynamic multiplexing of enhanced mobile broadband (eMBB) and ultrareliable and low latency communications (URLLC) on the same channel in 5G NR. Due to significant difference in transmission time scale, URLLC employs a preemptive puncturing technique to multiplex its traffic onto eMBB traffic for transmission. The optimization problem to solve is to minimize the adverse impact of such preemptive puncturing on eMBB users. We present DEMUX - a model-free deep reinforcement learning (DRL)-based solution to this problem. The essence of DEMUX is to use deep function approximators (neural networks) to learn an optimal algorithm for determining the preemption solution in each eMBB transmission time interval (TTI). Our novel contributions in the design of DEMUX include the first use of the DRL method with a large and continuous action domain for resource scheduling in NR, a mechanism to ensure fast and stable learning convergence by exploiting the intrinsic properties of the problem, and a mechanism to obtain a feasible preemption solution from the unconstrained output of a neural network while minimizing loss of information. The experimental results show that DEMUX significantly outperforms state-of-the-art algorithms proposed in the 3GPP standards body and the literature.

Degree-of-freedom (DoF) based models have become prevalent in studying MIMO-based wireless networks. However, most existing DoF-based models assume the channel matrix is of full-rank. Such a simplifying assumption has gradually become problematic, particularly when the number of antennas increases and the propagation environment is not close to ideal. In this paper, we address this problem by developing a general theory for the DoF-based model under general channel rank conditions. We start with a fundamental understanding on how MIMO's DoFs are consumed at each node for spatial multiplexing (SM) and interference cancellation (IC) in the presence of rank-deficient channels. Based on this understanding, we develop a DoF model that can be used for identifying the DoF region of a multi-link MIMO network and for studying DoF scheduling in MIMO networks under general channel rank conditions. Specifically, we find that for IC, shared DoF consumption at both transmit and receive nodes is critical for efficient DoF allocation. Further, we show that DoF consumption under the existing full-rank assumption is a special case of our generalized DoF model. Based on case studies, we show that the general IC model can achieve larger feasible DoF regions or improved objective values than existing unilateral IC models. The findings of this paper pave the way for future research of many-antenna networks under general channel rank conditions.

Underlay coexistence is an effective mechanism to improve spectrum effïciency by having picocells coexist with macrocell on the same spectrum. Due to a lack of cooperation between the primary users (PUs) in the macrocell and secondary users (SUs) in the picocell, it is impossible to have complete knowledge of channel gains between them. Under such circumstance, chance-constrained programming (CCP) is shown to be the ideal optimization tool to address such uncertainty. However, solutions to CCP are computationally intensive and cannot meet 5G's timing requirement. To address this problem, we propose a novel scheduler called GUC (stands for GPU-based Underlay Coexistence) to find an approximate solution to CCP in real-time. The essence of GUC is to decompose the original optimization problem into a large number of small problems that are suitable for parallel computation on GPU platforms. Through extensive experiments, we show that GUC reduces the scheduling computation time by at least 10,000 times comparing to commercial solvers (on CPU) while achieving an average of 90% optimality.

Age of information (AoI) is a powerful new metric to quantify the freshness of information and has gained increasing popularity in IoT applications. Existing models on AoI remain primitive and do not consider state-of-the-art transmission technologies such as 5G. They also fail to consider the impact of dynamic channel conditions. In this paper, we present Kronos, a 5G-compliant AoI scheduling algorithm that can cope with highly dynamic channel conditions. Kronos is capable of performing RB allocation and selecting MCS for each source node based on channel conditions, with the objective of minimizing long-term AoI. To meet the stringent real-time requirement for 5G, we propose a GPU-based implementation of Kronos on low-cost offthe-shelf GPUs. Through simulations and experiments, we show that Kronos can find near-optimal AoI scheduling solutions in sub-millisecond time scale. To the best of our knowledge, this is the first 5G-compliant real-time AoI scheduler that can cope with dynamic channel conditions.

Degree-of-Freedom (DoF) based models have been widely used to study MIMO networks. To cancel interference, the number of DoFs used in the state-of-the-art DoF models is solely based on the number of interfering data streams. However, by decomposing an interference into the eigenspace, we find that signal strengths varies significantly in different directions for the same interference link. In this paper, we exploited the difference in interference signal strength in the eigenspace and differentiate strong and weak interference signals via their singular values. By introducing a concept of effective rank threshold, we propose to use DoFs only to cancel strong interference in the eigenspace based on this threshold while treating weak interference signals as noise in throughput calculation. We explore a fundamental tradeoff between network throughput and effective rank threshold. Using simulation results on MU-MIMO networks, we show that network throughput under optimal rank threshold setting is significantly higher than that under existing DoF IC models. To ensure feasibility at the PHY layer, we present an algorithm that can find Tx and Rx weights at each node that can offer our desired DoF allocation.

Recently, a new metric, called Age of Information (AoI), has become popular to quantify the freshness of information collected at network edge. AoI research is still in its infancy and most prior efforts assume overly simplified models in their investigation. In this paper, we consider a more general model for AoI research that is closer to what happens in the real world. Specifically, we consider general and heterogeneous sampling behaviors among source nodes, varying sample size, and multiple data transmission units in each time slot. Under this much general setting, we develop new theoretical results (in terms of properties and performance bounds) and a new near-optimal low-complexity scheduling algorithm. Our results make a major advance of AoI research in terms of more realistic models.

In underlay coexistence, secondary users (SUs) attempt to keep their interference to the primary users (PUs) under a threshold. Due to the absence of cooperation from the PUs, there exists much uncertainty at the SUs in terms of channel state information (CSI). An effective approach to cope such uncertainty is to introduce occasional interference threshold violation by the SUs, as long as such occasional violation can be tolerated by the PUs. This paper exploits this idea through a chance constrained programming (CCP) formulation, where the knowledge of uncertain CSI is limited to only the first and second order statistics rather than its complete distribution information. Our main contribution is the introduction of a novel and powerful technique, called Exact Conic Reformulation (ECR), to reformulate the intractable chance constraints. ECR guarantees an equivalent reformulation for linear chance constraints into deterministic conic constraints and does not suffer from the limitations associated with the state-of-the-art approach -- Bernstein Approximation. Simulation results confirm that ECR offers significant performance improvement over Bernstein Approximation in uncorrelated channels and a competitive solution in correlated channels (where Bernstein Approximation is no longer applicable).

In the mobile edge computing (MEC) network, the applications of devices can be offloaded to the MEC server via the wireless link and then processed through the computation resource, to satisfy the computation and latency demand. Thus, a two-stage tandem queue is formed in the MEC network, consisting of the transmission queue and computation processing queue. However, the fluctuating wireless channel environment not only leads to the stochasticity of service in the first transmission queue, but also brings random computation task arrival in the second computation processing queue, which makes it difficult to guarantee the end-to-end quality of service (QoS) requirement. In this paper, we firstly derive the effective capacity of MEC with the two-stage tandem queue. Further, we formulate the joint bandwidth and computation resource allocation problem under the statistical QoS guarantee, to maximize the total revenue of network. This problem is proven to be NP-hard by the reduction to the two-dimensional knapsack problem. Then we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to reduce the computation complexity, where the complicated problem can be decomposed and transformed into some convex subproblems. Simulation results reveal the inherent relationship between the required bandwidth and computation resource in terms of the supported arrival rate and end-to-end delay, and also demonstrate the proposed scheme can achieve better performance than other schemes.

As an important application of the Internet of Things, many remote monitoring systems adopt a device-to-cloud network paradigm. In a remote patient monitoring case, various resource-constrained devices are used to measure the health conditions of a target patient in a distant non-clinical environment and the collected data are sent to the cloud backend of an authorized health care service for processing and decision making. As the measurements involve private patient information, access control and trustworthy processing of the confidential data become very important. Software-based solutions that adopt advanced cryptographic tools, such as attribute-based encryption and fully homomorphic encryption, can address the problem, but they also impose substantial computation overhead on both client and server sides. In this paper, we deviate from the conventional software-based solutions and propose a secure and efficient remote monitoring framework, called SRM, using the latest hardware-based trustworthy computing technology, such as Intel SGX. In addition, we present a robust and lightweight “heartbeat” protocol to handle notoriously difficult key revocation problem. We implemented a prototype of the framework for SRM and show that SRM can protect user data privacy against unauthorized parties, with minimum performance cost compared to existing software-based solutions.

Duplicated data storage wastes memory resources and brings extra data-management load and cost to cloud service providers (CSPs). Various feasible schemes to deduplicate encrypted cloud data have been reported. However, their successful deployment in practice depends on whether all system players or stakeholders are willing to accept and execute them in a cooperative way, which was scarcely investigated in the previous literature. In this paper, we employ a noncooperative game to model the interactions in a client-side server-controlled deduplication scheme (S-DEDU) and construct an incentive mechanism based on payment discount to motivate its final acceptance. The experimental results based on a real-world dataset demonstrate the individual rationality, incentive compatibility, profitability, and robustness of our incentive mechanism.

With the emergence of Internet of Things, mobile devices are generating more network traffic than ever. TrustZone is a hardware-enabled trusted execution environment for ARM processors. While TrustZone is effective in providing the much-needed memory isolation, we observe that it is possible to derive secret information from secure world using the cache contention, due to its high-performance cache sharing design. In this work, we propose TruSense to study the timing-based cache side-channel information leakage of TrustZone. TruSense can be launched from not only the normal world operating system but also a non-privileged user application. Without access to virtual-to-physical address mapping in user applications, we devise a novel method that uses the expected channel statistics to allocate memory for cache probing. We also show how an attacker might use the less accurate performance event interface as a timer. Using the T-table based AES implementation in OpenSSL 1.0.1f as an example, we demonstrate how a normal world attacker can steal fine-grained secret in the secure world. We also discuss possible mitigations for the information leakage.

In recent years, degree-of-freedom (DoF) based models were proven to be very successful in studying MIMO-based wireless networks. However, most of these studies assume channel matrix is of full-rank. Such assumption, although attractive, quickly becomes problematic as the number of antennas increases and propagation environment is not close to ideal. In this paper, we address this problem by developing a general theory for DoF-based model under rank-deficient conditions. We start with a fundamental understanding on how MIMO's DoFs are consumed for spatial multiplexing (SM) and interference cancellation (IC) in the presence of rank deficiency. Based on this understanding, we develop a general DoF model that can be used for identifying DoF region of a multi-link MIMO network and for studying DoF scheduling in MIMO networks. Specifically, we found that shared DoF consumption at transmit and receive nodes is critical for optimal allocation of DoF for IC. The results of this paper serve as an important tool for future research of many-antenna based MIMO networks.

Search over encrypted data (SE) enables a client to delegate his search task to a third-party server that hosts a collection of encrypted documents while still guaranteeing some measure of query privacy. Software-based solutions using diverse cryptographic primitives have been extensively explored, leading to a rich set of secure search indexes and algorithm designs. However, each scheme can only implement a small subset of information retrieval (IR) functions and often with considerable search information leaked. Recently, the hardware-based secure execution has emerged as an effective mechanism to securely execute programs in an untrusted software environment. In this paper, we exploit the hardware-based execution environment (TEE) and explore a software and hardware combined approach to address the challenging secure search problem. For functionality, our design can support the same spectrum of plaintext IR functions. For security, we present oblivious keyword search techniques to mitigate the index search trace leakage. We build a prototype of the system using Intel SGX. We demonstrate that the proposed system provides broad support of a variety of search functions and achieves computation efficiency comparable to plaintext data search with elevated security protection.

5G New Radio (NR) is designed to operate under a broad range of frequency bands and support new applications with ultra-low latency. To support its diverse operating conditions, a set of different OFDM numerologies has been defined in the standards body. Under this numerology, it is necessary to perform scheduling with a time resolution of ∼100 μs. This requirement poses a new challenge that does not exist in LTE and cannot be supported by any existing LTE schedulers. In this paper, we present the design of GPF -- a GPU-based proportional fair (PF) scheduler that can meet the ∼100 μs time requirement. The key ideas include decomposing the scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU. By implementing GPF on an off-the-shelf Nvidia Quadro P6000 GPU, we show that GPF is able to achieve near-optimal performance while meeting the ∼100 $\mathrmμs time requirement. GPF represents the first successful design of a GPU-based PF scheduler that can meet the new time requirement in NR.

WiFi-based motion sensing has received a lot of research attention in recent years. Taking advantage of Channel State Information(CSI) collected from physical layer, previous techniques are able to extract useful information from CSI values to infer human movements. However, these works concentrate either on coarse-grained motion sensing or on fine-grained but context-related motion sensing. In this paper, we propose WiTalk, a new fine-grained human motion sensing technique with the distinct context-free character. To profile human motion using CSI, WiTalk generates CSI spectrograms using signal processing techniques and extracts features by calculating the contours of the CSI spectrograms. We verify the proposed technique in the application scenario of lip reading, where the fine-grained motion is the mouth movements. We implement WiTalk on a commercial laptop. Experiment results show that WiTalk can achieve over 92.3% recognition accuracy to discern a set of 12 syllables and 74.3% accuracy to discern a set of short sentences up to six words.

With the popularity of sensor-rich mobile devices (e.g., smart phones and wearable devices), mobile crowdsourcing (MCS) has emerged as an effective method for data collection and processing. Compared with traditional wireless sensor networking, MCS holds many advantages such as mobility, scalability, cost-efficiency, and human intelligence. However, MCS still faces many challenges with regard to security, privacy, and trust. This paper provides a survey of these challenges and discusses potential solutions. We analyze the characteristics of MCS, identify its security threats, and outline essential requirements on a secure, privacy-preserving, and trustworthy MCS system. Further, we review existing solutions based on these requirements and compare their pros and cons. Finally, we point out open issues and propose some future research directions.

The recent proliferation of mobile devices such as smartphones and wearable devices has given rise to crowdsourcing Internet of Things (IoT) applications, such as urban mobility monitoring, virtual/augmented reality, smart city management, and indoor floor plan reconstruction and mapping. Various data collected by mobile devices with small or big volumes can be further processed, analyzed, and mined in order to support multifarious promising services with intelligence.

Interference neutralization (IN) is regarded as a promising interference management techniques for multi-hop wireless networks. Yet most existing results of IN are limited to two-hop networks such as the relay-aided cellular network. Little progress has been made so far in the exploration of IN in generic multi-hop (more than two hops) networks. This paper aims to bridge this gap by developing an optimization framework for IN in a generic multi-hop network with the objective of maximizing the end-to-end throughput of multiple coexisting communication sessions. We first derive a mathematical model for IN in a special one-hop network to characterize the capability of IN, and then generalize this model to a multi-hop network. Based on the IN model, we develop a cross-layer optimization framework for a multi-hop network with the objective of fully translating the benefits of IN to the end-to-end throughput of the multi-hop sessions. To evaluate the performance of IN in multi-hop networks, we compare its performance against the case where IN is not employed. Simulation results show that the use of IN can significantly (more than 50%) increase the session throughput and, more notably, the throughput gain of IN increases with the node density and traffic intensity in the network.

With increasingly complex cyber attacks occurring every day, memory-based forensic techniques are becoming instrumental in digital investigations. Forensic examiners can unravel what happened on a system by acquiring and inspecting in-memory data. However, the foundation of this analysis can be invalidated if the memory acquisition has been altered. In this paper, we study the feasibility of malicious software misusing architectural features to sabotage memory forensics. The misuse of two architectural features, namely, physical address layout and secure containers, is presented. The first architectural feature explored in this paper is the physical address layout. It is used by the northbridge to route memory access to either physical memory or I/O devices on x86 platforms. Observing this design choice, we propose Hidden in I/O Space (HIveS), which manipulates CPU registers to alter the physical address layout to conceal memory. The system uses a novel I/O shadowing technique to lock a memory region named HIveS memory into I/O address space to prevent access. Two novel techniques, blackbox write and TLB camouflage, are developed to further protect the unlocked HIveS memory against memory forensics while allowing access for attackers. The second architectural feature explored in this paper is hardware-aided secure execution technology. More specifically, hardware-enforced memory encryption in Intel secure guard extension is used in malicious enclave software (Malclaveware) to prevent introspection and memory forensics. A prototype of HIveS is built and tested against a set of memory acquisition tools for both Windows and Linux running on the x86 platform. Malclaveware is also prototyped in Windows to demonstrate the risk. More importantly, we proposed countermeasures and mitigations for the newly discovered attacks. Through these discussions, we aim to raise the awareness of the potential risks of misusing hardware architectural features.

Cooperation between primary and secondary networks offers significant benefits in data forwarding. But, cost implication in such cooperation is not well understood. In this paper, we explore cost incurred in both primary and secondary networks when they are allowed to relay each other's traffic in a cooperative manner. We model costs in both networks and formulate a multiobjective optimization problem. For this problem, we present a novel algorithm to construct an ε-approximation curve and prove its error bounds in both cost dimensions. Based on the ε-approximation curve, we develop three important applications. The first application is to show the minimum cost value for a single objective (by fixing the other objective as constant) or the relationship between the two objectives over the entire range of possible values. The second application is to address different cost parameters in the primary and secondary networks. We show how to obtain a new approximation curve by scaling the original ε-approximation curve with appropriate factors and quantify its error bounds. The third application is to use the ε-approximation curve to study a single objective optimization problem with a guaranteed error bound. The results in this paper offer some deep theoretical insights on potential costs incurred in both networks when they are allowed to relay each other's traffic cooperatively.

There are different forms of uncertainty in satellite communications, including cognitive interferers, channel conditions, packet traffic, and spectrum occupancy of users across channels. In addition, delay (such as propagation delay observed over satellite links) increases spectrum uncertainty and makes spectrum sensing and spectrum access two challenging tasks. To address such challenges, this paper presents a regret minimization solution for primary user (PU) and secondary user (SU) spectrum access to satellite resources in the presence of cognitive interferers. This robust game theoretic solution supports hierarchical spectrum sharing and dynamic spectrum access over multiple channels. Users select channels for data transmission and perform power control to optimize individual utility functions that are random due to different forms of uncertainty. The proposed game engine based on regret minimization framework provides a low-complexity and fast solution compared with traditional game solutions based on expected utility maximization. Detailed numerical results evaluate throughput and delay of PUs and SUs in the presence of cognitive interferers and compare the robust game theory-enabled approach with two benchmark schemes (with and without knowledge on channel availability). To support controllable and repeatable test and evaluation with real radios, an emulation testbed is built with software-defined radios connected with a network channel emulator that generates channel, mobility, and interference effects for satellite communications. GNU Radio modules are developed for cognitive network functionalities and run on USRP N210 radios that represent SU, PU, interferer, and satellite nodes. Emulation tests validate the effectiveness of the proposed solution under real radio effects.

Unutilized spectrum, i.e. spectrum holes, are opportunities that may be used for communication or other RF services. In this paper, we explore adversarial attacks that reduce the size of spectrum holes by showing their advantage compared to a random jammer. Using a game-theoretical approach, we design an optimal scanning strategy that provides an increased probability of detecting such an attack. The advantage of our strategy is achieved by focusing scanning efforts on bands that are more likely to be attacked, and neglecting the others. However, such focused scanning is a disadvantage since, if the adversary has a different objective, he can safely sneak usage of the bands neglected by such a specially-tuned spectrum scanner. To deal with this problem, we also derive the optimal scanning allocation that balances between applying the anti-spectrum holes attack scanning strategy and scanning the neglected bands so as to prevent the possibility of the adversary using those bands without being detected.

As computing system continues to play an increasing role in daily life, user authentication is now an important component. One of the most widely accepted methods for user authentication is through proof of knowledge of a piece of secret information, such as password. However, entering this non-mutable secret for authentication in public space often allows attackers to steal the secret by shoulder surfing or video recording. We observe that it is possible to block attacker's access to user input using augmented reality (AR) display, which is only available to the user. Based on this intuition, we present AugAuth, an authentication scheme in AR using commercial off-the-shelf(COTS) gesture control sensors as an input device. AugAuth can resist against shoulder surfing by presenting user input interface that is only visible to the user and is unique every time. To enable user input with finger movement using the gesture control armband, Myo, we have solved several challenges in electromyogram signal processing, such as annotating the start of signal and finger classification. The experiment results for our input system of a group of volunteers show that our finger classification function has high accuracy and AugAuth is practical for use in real life authentication scenarios.

As the cost of human full genome sequencing continues to fall, we will soon witness a prodigious amount of human genomic data in the public cloud. To protect the confidentiality of the genetic information of individuals, the data has to be encrypted at rest. On the other hand, encryption severely hinders the use of this valuable information, such as Genome-wide Range Query (GRQ), in medical/genomic research. While the problem of secure range query on outsourced encrypted data has been extensively studied, the current schemes are far from practical deployment in terms of efficiency and scalability due to the data volume in human genome sequencing. In this paper, we investigate the problem of secure GRQ over human raw aligned genomic data in a third-party outsourcing model. Our solution contains a novel secure range query scheme based on multi-keyword symmetric searchable encryption (MSSE). The proposed scheme incurs minimal ciphertext expansion and computation overhead. We also present a hierarchical GRQ-oriented secure index structure tailored for efficient and large-scale genomic data lookup in the cloud while preserving the query privacy. Our experiment on real human genomic data shows that a secure GRQ request with range size 100,000 over more than 300 million encrypted short reads takes less than 3 minutes, which is orders of magnitude faster than existing solutions.

Personalized medicine performs diagnoses and treatments according to the DNA information of the patients. The new paradigm will change the health care model in the future. A doctor will perform the DNA sequence matching instead of the regular clinical laboratory tests to diagnose and medicate the diseases. Additionally, with the help of the affordable personal genomics services such as 23andMe, personalized medicine will be applied to a great population. Cloud computing will be the perfect computing model as the volume of the DNA data and the computation over it are often immense. However, due to the sensitivity, the DNA data should be encrypted before being outsourced into the cloud. In this paper, we start from a practical system model of the personalize medicine and present a solution for the secure DNA sequence matching problem in cloud computing. Comparing with the existing solutions, our scheme protects the DNA data privacy as well as the search pattern to provide a better privacy guarantee. We have proved that our scheme is secure under the well-defined cryptographic assumption, i.e., the sub-group decision assumption over a bilinear group. Unlike the existing interactive schemes, our scheme requires only one round of communication, which is critical in practical application scenarios. We also carry out a simulation study using the real-world DNA data to evaluate the performance of our scheme. The simulation results show that the computation overhead for real world problems is practical, and the communication cost is small. Furthermore, our scheme is not limited to the genome matching problem but it applies to general privacy preserving pattern matching problems which is widely used in real world.

Millimeter-wave communication in 60 GHz band is considered a promising technology to meet the explosive growth of data demand in Wi-Fi based WLAN. To address potential blockage for 60 GHz signals, multiple APs are proposed for such WLAN. This paper addresses the important problem of AP assignment and transmission scheduling for a multi-AP 60 GHz WLAN. We propose two AP assignment schemes with different complexity and study how to maximize user throughput with joint consideration of AP assignment and transmission scheduling. We advocate to use one-shot AP assignment-based scheduling due to its simplicity for implementation. To address real-time online traffic and human blockage, we propose an online algorithm to implement the one-shot AP assignment scheme without altering the AP assignment for other existing users. Through performance evaluation, we show that the proposed online algorithm is competitive when compared to the offline algorithm.

In recent years, there has been great interest from the cellular service providers to use the unlicensed spectrum for their service offerings. On the other hand, existing unlicensed users in these bands (e.g., Wi-Fi in the 5-GHz band) have serious concern that such coexistence will jeopardize their service quality. Although there are some proposals on how to achieve coexistence, they are driven by the service providers and as such there remain many issues and skepticism. In this paper, we take a novel human-centric approach to understand coexistence between Wi-Fi and LTE by focusing on human satisfaction. Through mathematical modeling, problem formulation, and extensive simulations studies, we show that in terms of maximizing total human satisfaction function, there does not appear to be any advantage with the coexistence of unlicensed spectrum for Wi-Fi and LTE under static partitioning of unlicensed spectrum. This finding serves as a powerful counter argument to some LTE service providers' proposal to share the unlicensed spectrum with Wi-Fi through static partitioning. On the other hand, we find that there is a significant improvement in human satisfaction in coexistence between Wi-Fi and LTE under adaptive spectrum partitioning. Since adaptive spectrum partitioning may require a user to change its service provider whenever there is a change among the users, we propose a practical (semi-adaptive) algorithm for implementation without affecting existing users' service providers. Through performance evaluation, we show that the proposed semi-adaptive algorithm is highly competitive.

Underwater acoustic (UWA) networks are a key form of communications for human exploration and activities in the oceanographic space of the earth. A fundamental issue of UWA communications is large propagation delays due to water medium, which has posed a grand challenge in UWA network protocol design. Conventional wisdom of addressing this issue is to live with this disadvantage by inserting a guard interval to introduce immunity to propagation delays. Recent advances in interference alignment (IA) open up a new direction to address this issue and promise a great potential to improve network throughput by exploiting large propagation delays. In this paper, we investigate propagation delay-based IA (PD-IA) in multi-hop UWA networks. We first develop a set of simple constraints to characterize PD-IA feasible region at the physical layer. Based on the set of PD-IA constraints, we develop a distributed PD-IA scheduling algorithm to greedily maximize interference overlapping possibilities in a multi-hop UWA network. Simulation results show that the proposed PD-IA algorithm yields higher throughput than an idealized benchmark algorithm without propagation delays, indicating that large propagation delays are not adversarial but beneficial for network throughput performance.

Interference alignment (IA) is widely regarded as a promising interference management technique in wireless networks. Despite its rapid advances in cellular networks, most results of IA are limited to information-theoretic exploration or physical-layer signal design. Little progress has been made so far to advance IA in cellular networks from a networking perspective. In this paper, we aim to fill this gap by studying IA in large-scale cellular networks. For the uplink, we propose an OFDM-based IA scheme and prove its feasibility at the physical layer by showing that all data streams in the IA scheme can be transported free of interference. Based on the IA scheme, we develop a cross-layer IA optimization framework that can fully translate the benefits of IA to throughput gain in cellular networks. Furthermore, we show that the IA optimization problem in the downlink can be solved in the exactly same way as that in the uplink. Simulation results show that our OFDM-based IA scheme can significantly increase the user throughput and the throughput gain increases with user density in the network.

A location proximity test service allows mobile users to determine whether they are in close proximity to each other, and has found numerous applications in mobile social networks. Unfortunately, existing solutions usually reveal much of users' private location information during a proximity test. They are also vulnerable to location cheating where an attacker reports false locations to gain an advantage. Moreover, the initial trust establishment among unfamiliar users in large scale mobile social networks has been a challenging task. In this paper, we propose a novel scheme that enables a user to perform (1) a location based handshake that establishes secure communications among strangers, who do not have a pre-shared secret, and (2) a privacy-preserving proximity test without revealing the user's actual location to the server or other users not within the proximity. The proposed scheme is based on a novel concept, i.e., spatial-temporal location tags, and we put forward a location tag construction method using environmental signals that provides an unforgeable location proof. We use Bloom filters to efficiently represent users' location tags and vicinity regions. We exploit fuzzy extractor, a lightweight cryptographic primitive, to extract shared secrets between matching location tags. We conduct extensive analysis, simulation, and real experiments to demonstrate the feasibility, security, and efficiency of our scheme.

With the increasing popularity of augmented reality (AR) services, providing seamless human-computer interactions in the AR setting has received notable attention in the industry. Gesture control devices have recently emerged to be the next great gadgets for AR due to their unique ability to enable computer interaction with day-to-day gestures. While these AR devices are bringing revolutions to our interaction with the cyber world, it is also important to consider potential privacy leakages from these always-on wearable devices. Specifically, the coarse access control on current AR systems could lead to possible abuse of sensor data. Although the always-on gesture sensors are frequently quoted as a privacy concern, there has not been any study on information leakage of these devices. In this article, we present our study on side-channel information leakage of the most popular gesture control device, Myo. Using signals recorded from the electromyography (EMG) sensor and accelerometers on Myo, we can recover sensitive information such as passwords typed on a keyboard and PIN sequence entered through a touchscreen. EMG signal records subtle electric currents of muscle contractions. We design novel algorithms based on dynamic cumulative sum and wavelet transform to determine the exact time of finger movements. Furthermore, we adopt the Hudgins feature set in a support vector machine to classify recorded signal segments into individual fingers or numbers. We also apply coordinate transformation techniques to recover fine-grained spatial information with low-fidelity outputs from the sensor in keystroke recovery. We evaluated the information leakage using data collected from a group of volunteers. Our results show that there is severe privacy leakage from these commodity wearable sensors. Our system recovers complex passwords constructed with lowercase letters, uppercase letters, numbers, and symbols with a mean success rate of 91%.

There have been some rapid advances on the design of full duplex (FD) transceivers in recent years. Although the benefits of FD have been studied for single-hop wireless communications, its potential on throughput performance in a multi-hop wireless network remains unclear. As for multi-hop networks, a fundamental problem is to compute the achievable end-to-end throughput for one or multiple communication sessions. The goal of this paper is to offer some fundamental understanding on end-to-end throughput performance limits of FD in a multi-hop wireless network. We show that through a rigorous mathematical formulation, we can cast the multi-hop throughput performance problem into a formal optimization problem. Through numerical results, we show that in many cases, the end-to-end session throughput in a FD network can exceed 2x of that in a half duplex (HD) network. Our finding can be explained by the much larger design space for scheduling that is offered by removing HD constraints in throughput maximization problem. The results in this paper offer some new understandings on the potential benefits of FD for end-to-end session throughput in a multi-hop wireless network.

Existing spectrum sharing paradigms have set clear boundaries between the primary and secondary networks. There is either no or very limited node-level cooperation between the primary and secondary networks. In this paper, we develop a new and bold spectrum-sharing paradigm beyond the state of the art for future wireless networks. We explore network cooperation as a new dimension for spectrum sharing between the primary and secondary users. Such network cooperation can be defined as a set of policies under which different degrees of cooperation are to be achieved. The benefits of this paradigm are numerous, as they allow integrating resources from two networks. There are many possible node-level cooperation policies that one can employ under this paradigm. For the purpose of performance study, we consider a specific policy called United cooperation of Primary and Secondary (UPS) networks. UPS allows a complete cooperation between the primary and secondary networks at the node level to relay each other's traffic. As a case study, we consider a problem with the goal of supporting the rate requirement of the primary network traffic while maximizing the throughput of the secondary sessions. For this problem, we develop an optimization model and formulate a combinatorial optimization problem. We also develop an approximation solution based on a piece-wise linearization technique. Simulation results show that UPS offers significantly better throughput performance than that under the interweave paradigm.

This paper presents a game theoretic solution for hierarchical spectrum sharing between primary users (PUs) and secondary users (SUs) in the presence of cognitive interferers. There exist several forms of uncertainty, including channel conditions, packet traffic, and spectrum occupancy of users across channels. This uncertainty is further aggravated by delays (such as propagation delays observed over satellite links) that make spectrum-efficient communication a challenging task. A robust game theoretic framework is developed for dynamic spectrum access (DSA) management over multiple channels. Cognitive functionalities employed in the game solution include selecting channels for data transmission and performing power control at each user to sustain target rates. By considering random utility functions, the game engine based on regret minimization provides low complexity and fast solutions compared to traditional game solutions based on expected utility maximization. Detailed numerical results with comparison to benchmark schemes (the ideal case and the random case where users have perfect or no knowledge on channel availability, respectively) are provided to show the effectiveness of robust game theory-enabled approach.

With the growing importance of networked embedded devices in the upcoming Internet of Things, new attacks targeting embedded OSes are emerging. ARM processors, which power over 60% of embedded devices, introduce a hardware security extension called TrustZone to protect secure applications in an isolated secure world that cannot be manipulated by a compromised OS in the normal world. LeveragingTrustZone technology, a number of memory integrity checking schemes have been proposed in the secure world to introspect malicious memory modification of the normal world. In this paper, we first discover and verify an ARM TrustZone cache incoherence behavior, which results in the cache contents of the two worlds, secure and non-secure, potentially being different even when they are mapped to the same physical address. Furthermore, code in one TrustZone world cannot access the cache content in the other world. Based on this observation, we develop a new rootkit called CacheKit that hides in the cache of the normal world and is able to evade memory introspection from the secure world. We implement a CacheKit prototype on Cortex-A8 processors after solving a number of challenges. First, we employ the Cache-as-RAM technique to ensure that the malicious code is only loaded into the CPU cache and not RAM. Thus, the secure world cannot detect the existence of the malicious code by examining the RAM. Second, we use the ARM processor's hardware support on cache settings to keep the malicious code persistent in the cache. Third, to evade introspection that flushes cache content back into RAM, we utilize physical addresses from the I/O address range that is not backed by any real I/O devices or RAM. The experimental results show that CacheKit can successfully evade memory introspection from the secure world and has small performance impacts on the rich OS. We discuss potential countermeasures to detect this type of rootkit attack.

In this paper, we present MobTrack, a single device system which aims to locate interfering radios on unlicensed ISM band in indoor environments. Compared with existing techniques which require a deployment of dense access points (APs), MobTrack only demands a single device equipped with multiple antennas. The location of an interfering signal source are estimated by computing the angle of arrival (AoA) of Line of Sight (LoS) component using an antenna array. Taking advantage of cyclostationary property, MobTrack differentiates interfering signals from working signals. By moving the device around for a short distance within one meter, it depresses multipath effects and determines the LoS component. Simultaneously, the AoAs on the moving trace are recorded to estimate the location of the interfering radio by triangulation. We evaluate the performance of MobTrack by setting up a prototype experimental system. Compared with recent interference localization schemes, MobTrack has much lower hardware complexity and gets better localization accuracy with a median of 0.55 meters.

Interference neutralization (IN) is an interference management technique that allows simultaneous transmission of multiple links by nullifying their mutual interference in the air via cooperation among the transmitters. Although IN has been studied from information theoretic perspective, its potential for a general multi-hop wireless network has not been explored. The goal of this paper is to understand IN in a multi-hop wireless network from networking perspective. We first establish an IN reference model. Based on this reference model, we develop a set of feasibility constraints for a subset of links to be active simultaneously. By identifying each eligible neutralization node (called neut), we study IN in a general multi-hop network and develop a set of necessary constraints to characterize neut selection, IN, and scheduling. These constraints allow us to study the performance of multi-hop networks without the need of getting involved into onerous signal design issues at the physical layer. Finally, we apply our IN model and constraints to study a throughput maximization problem and show that the use of IN can generally increase network throughput. In particular, throughput gain is most significant when the node density increases.

Recognizing the pressing demands to secure embedded applications, ARM TrustZone has been adopted in both academic research and commercial products to protect sensitive code and data in a privileged, isolated execution environment. However, the design of TrustZone cannot prevent physical memory disclosure attacks such as cold boot attack from gaining unrestricted read access to the sensitive contents in the dynamic random access memory (DRAM). A number of system-on-chip (SoC) bound execution solutions have been proposed to thaw the cold boot attack by storing sensitive data only in CPU registers, CPU cache or internal RAM. However, when the operating system, which is responsible for creating and maintaining the SoC-bound execution environment, is compromised, all the sensitive data is leaked. In this paper, we present the design and development of a cache-assisted secure execution framework, called CaSE, on ARM processors to defend against sophisticated attackers who can launch multi-vector attacks including software attacks and hardware memory disclosure attacks. CaSE utilizes TrustZone and Cache-as-RAM technique to create a cache-based isolated execution environment, which can protect both code and data of security-sensitive applications against the compromised OS and the cold boot attack. To protect the sensitive code and data against cold boot attack, applications are encrypted in memory and decrypted only within the processor for execution. The memory separation and the cache separation provided by TrustZone are used to protect the cached applications against compromised OS. We implement a prototype of CaSE on the i.MX53 running ARM Cortex-A8 processor. The experimental results show that CaSE incurs small impacts on system performance when executing cryptographic algorithms including AES, RSA, and SHA1.

Physical layer security for wireless communication is broadly considered as a promising approach to protect data confidentiality against eavesdroppers. However, despite its ample theoretical foundation, the transition to practical implementations of physical-layer security still lacks success. A close inspection of proven vulnerable physical-layer security designs reveals that the flaws are usually overlooked when the scheme is only evaluated against an inferior, single-antenna eavesdropper. Meanwhile, the attacks exposing vulnerabilities often lack theoretical justification. To reduce the gap between theory and practice, we posit that a physical-layer security scheme must be studied under multiple adversarial models to fully grasp its security strength. In this regard, we evaluate a specific physical-layer security scheme, i.e. orthogonal blinding, under multiple eavesdropper settings. We further propose a practical "ciphertext-only attack" that allows eavesdroppers to recover the original message by exploiting the low entropy fields in wireless packets. By means of simulation, we are able to reduce the symbol error rate at an eavesdropper below 1% using only the eavesdropper's receiving data and a general knowledge about the format of the wireless packets.

Network-coded cooperative communications (NC-CC) refers to the use of network coding (NC) in cooperative communications (CC). Prior studies have shown that NC has the potential to improve the performance of CC when there are multiple sessions in the wireless network. These studies were done for the case when multiple sessions are sharing a single relay node. However, how NC-CC behaves when multiple relay nodes are employed remains an open problem. In this paper, we explore this problem by analyzing the achievable rate of each session in this setting. We develop closed form formulas for the mutual information and the achievable data rate for each session. We show that prior results for a single relay is a special case of our result. Based on these findings, we then study a network optimization problem that requires joint optimization of session grouping, relay node grouping, and matching of session/relay groups. We show that this problem is NP-hard, and present a polynomial time heuristic algorithm to solve this problem. Using simulation results, we show this algorithm is highly competitive and can produce results that are near to optimality.

Cooperation has become an essential element in spectrum sharing between the primary and secondary networks. A new trend in cooperation is to allow the primary and secondary networks to cooperate on the node level for data forwarding. This new paradigm allows to pool network resources from both the primary and secondary networks and allows users in each network to access a much richer network infrastructure in a combined network. This paper offers an in-depth study of such node-level cooperation by explaining its optimal throughput curve—the maximum achievable throughput for both the primary and secondary users. We formulate the problem as a multicriteria optimization problem with the goal of maximizing the throughput of both the primary and secondary users. Through a novel approach based on weighted Chebyshev norm, we transform the multicriteria optimization problem into a single criteria optimization problem and find a sequence of Pareto-optimal points iteratively. Based on the Pareto-optimal points, we construct the throughput curve and show that it provides an $\varepsilon $ -approximation to the optimal curve. We prove some important properties of the optimal throughput curve. Through a case study, we show that the throughput region (the area under the throughput curve) under node-level cooperation is substantially larger than that when there is no node-level cooperation.

Jamming attack is a serious threat to the wireless communications. Reactive jamming maximizes the attack efficiency by jamming only when the targets are communicating, which can be readily implemented using software-defined radios. In this paper, we explore the use of the multi-input multi-output (MIMO) technology to achieve jamming resilient orthogonal frequency-division multiplexing (OFDM) communication. In particular, MIMO interference cancellation treats jamming signals as noise and strategically cancels them out, while transmit precoding adjusts the signal directions to optimize the decoding performance. We first investigate the reactive jamming strategies and their impacts on the MIMO-OFDM receivers. We then present a MIMO-based anti-jamming scheme that exploits MIMO interference cancellation and transmit precoding technologies to turn a jammed non-connectivity scenario into an operational network. We implement our jamming resilient communication scheme using software-defined radios. Our testbed evaluation shows the destructive power of reactive jamming attack, and also validates the efficacy and efficiency of our defense mechanisms in the presence of numerous types of reactive jammers with different jamming signal powers.

Interference alignment (IA) is a powerful technique to handle interference in wireless networks. Since its inception, IA has become a central research theme in the wireless communications community. Due to its intrinsic nature of being a physical layer technique, IA has been mainly studied for point-to-point or single-hop scenario. There is a lack of research of IA from a networking perspective in the context of multi-hop wireless networks. The goal of this paper is to make such an advance by bringing IA technique to multi-hop MIMO networks. We develop an IA model consisting of a set of constraints at a transmitter and a receiver that can be used to determine IA for a subset of interfering streams. We further prove the feasibility of this IA model by showing that a DoF vector can be supported free of interference at the physical layer as long as it satisfies the constraints in our IA model. Based on the proposed IA model, we develop an IA design space for a multi-hop MIMO network. To study how IA performs in a multi-hop MIMO network, we compare the performance of a network throughput optimization problem based on our developed IA design space against the same problem when IA is not employed. Simulation results show that the use of IA can significantly decrease the DoF consumption for IC, thereby improving network throughput.

Recently, a new MIMO degree-of-freedom (DoF) model was proposed to allocate DoF resources for spatial multiplexing (SM) and interference cancellation (IC) in a multi-hop network. Although this DoF model promises many benefits, it hinges upon a global node ordering to keep track of IC responsibilities among all the nodes. An open question about this model is whether its global ordering property can be achieved among the nodes in the network through distributed operations. In this paper, we explore this question by studying DoF scheduling in a multi-hop MIMO network, with the objective of maximizing the minimum throughput among a set of sessions. We propose an efficient DoF scheduling algorithm to solve it and show that our algorithm only requires local operations. We prove that the resulting DoF scheduling solution is globally feasible and show that there exists a corresponding feasible global node ordering for IC, albeit such global ordering is implicit. Simulation results show that the solution values obtained by our algorithm are relatively close to the upper bound values computed by CPLEX solver, thereby indicating that our algorithm is highly competitive.

Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e., multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e., file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. To build confidence of data user in the proposed secure search system, we also design a search result verification scheme. Finally, performance evaluation shows the efficiency of our scheme.

This paper studies the coexistence of heterogeneous multi-hop networks, which use different physical-layer technologies. We propose a new paradigm, called cooperative interference mitigation (CIM), which exploits recent advancement in interference cancellation (IC), such as technology-independent multiple output. CIM makes it possible for disparate networks to cooperatively mitigate the interference to/from each other to enhance everyone's performance. We first show the feasibility of CIM among heterogeneous multi-hop networks by exploiting only channel-ratio information. Then, we establish two tractable models to characterize the CIM behaviors of both networks by using full IC and receiver-side IC only. We propose two bi-criteria optimization problems aiming at maximizing both networks' throughput, while cooperatively canceling the interference between them based on our two models. Several simulations are carried out to compare the Pareto-optimal throughput curves by using our CIM paradigms and traditional interference-avoidance (IAV) paradigm. By comparing the results from CIM and IAV, we show that CIM could remarkably improve the coexisting networks' throughput in different network settings.

The classical approach to interference management in wireless medium access is based on avoidance. Recently, there is a growing interest in exploiting interference (rather than avoiding it) to increase network throughput. This was made possible by a number of advances at the physical layer. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we aim to close this gap by offering a systematic study of SIC in a multi-hop wireless network. After gaining a fundamental understanding of SIC's capability and limitation, we propose a cross-layer optimization framework for SIC that incorporates variables at physical, link, and network layers. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC behaves in a multi-hop wireless network.

Recently, degree-of-freedom (DoF)-based models have been widely used to study MIMO network performance. Existing DoF-based models differ in their interference cancellation (IC) behavior and many of them suffer from either loss of solution space or possible infeasible solutions. To overcome these limitations, a new DoF-based model, which employs an IC scheme based on node-ordering was proposed. In this paper, we apply this new DoF IC model to study a throughput maximization problem in a multihop MIMO network. The problem formulation involves joint consideration of flow routing and DoF allocation and falls in the form of a mixed-integer linear program (MILP). Our main contribution is an efficient polynomial time algorithm that offers a competitive solution to the MILP through a series of linear programs (LPs). The algorithm employs a sequential fixing framework to obtain an initial feasible solution and then improves the solution by exploiting: 1) the impact of node ordering on DoF consumption for IC at a node and 2) route diversity in the network. Simulation results show that the solutions obtained by our proposed algorithm are competitive and feasible.

The transparent coexistence (TC) paradigm allows simultaneous activation of the secondary users with the primary users as long as their interference to the primary users can be properly canceled. This paradigm has the potential to offer much more efficient spectrum sharing than the traditional interweave paradigm. In this paper, we design a distributed algorithm to achieve this paradigm for a secondary multi-hop network. For interference cancelation (IC), we employ MIMO at secondary nodes. We present a distributed iterative algorithm to maximize each secondary session's throughput while meeting all IC requirements under TC. By maintaining two local sets for each node, we can keep track of the node's IC responsibility. Although no explicit node ordering is maintained in our distributed algorithm, we prove that our distributed data structure at each node (with the use of two local sets) can be mapped to an explicit global node ordering for IC among all nodes in the network. This guarantees that each active node's degree-of-freedoms allocated for IC is feasible at the physical layer. Our algorithm is iterative in nature and all steps can be accomplished based on local information exchange among the neighboring nodes. We present the simulation results to show that the performance of our distributed algorithm is highly competitive when compared with an upper bound solution from the corresponding centralized problem.

With the growing complexity of computing systems, memory based forensic techniques are becoming instrumental in digital investigations. Digital forensic examiners can unravel what happened on a system by acquiring and inspecting in-memory data. Meanwhile, attackers have developed numerous anti-forensic mechanisms to defeat existing memory forensic techniques by manipulation of system software such as OS kernel. To counter anti-forensic techniques, some recent researches suggest that memory acquisition process can be trusted if the acquisition module has not been tampered with and all the operations are performed without relying on any untrusted software including the operating system.

Cyclostationary analysis is a powerful tool to study the Signal-Selective Direction Estimation (SSDE) problem as different types of wireless signals have different cyclostationary patterns. Generally speaking, each type of wireless signal has unique cyclic frequencies with the frequency-selective property, which distinguishes itself from other types of signals. The cyclostationary property of a signal may be induced by its modulation method, its carrier frequency, and/or its frame structure. In this paper, we study the cyclostationary property of IEEE 802.11 (WiFi) signals induced by their underlying OFDM frame structure, which includes pilots, cyclic prefix (CP), and preambles. We first analyze the pilot-induced, CP-induced, and preamble-induced cyclostationary properties of WiFi signals, respectively. We then derive their spectral correlation function (SCF) and investigate their applicability to solving the SSDE problem. Simulation results show that the pilot-induced cyclostationary property of WiFi signals is a promising feature that can be used to solve the SSDE problem.

Encrypted data search allows cloud to offer fundamental information retrieval service to its users in a privacy-preserving way. In most existing schemes, search result is returned by a semi-trusted server and usually considered authentic. However, in practice, the server may malfunction or even be malicious itself. Therefore, users need a result verification mechanism to detect the potential misbehavior in this computation outsourcing model and rebuild their confidence in the whole search process. On the other hand, cloud typically hosts large outsourced data of users in its storage. The verification cost should be efficient enough for practical use, i.e., it only depends on the corresponding search operation, regardless of the file collection size. In this paper, we are among the first to investigate the efficient search result verification problem and propose an encrypted data search scheme that enables users to conduct secure conjunctive keyword search, update the outsourced file collection and verify the authenticity of the search result efficiently. The proposed verification mechanism is efficient and flexible, which can be either delegated to a public trusted authority (TA) or be executed privately by data users. We formally prove the universally composable (UC) security of our scheme. Experimental result shows its practical efficiency even with a large dataset.

With the growing awareness of data privacy, more and more cloud users choose to encrypt their sensitive data before outsourcing them to the cloud. Search over encrypted data is therefore a critical function facilitating efficient cloud data access given the high data volume that each user has to handle nowadays. Inverted index is one of the most efficient searchable index structures and has been widely adopted in plaintext search. However, securing an inverted index and its associated search schemes is not a trivial task. A major challenge exposed from the existing efforts is the difficulty to protect user's query privacy. The challenge roots on two facts: 1) the existing solutions use a deterministic trapdoor generation function for queries; and 2) once a keyword is searched, the encrypted inverted list for this keyword is revealed to the cloud server. We denote this second property in the existing solutions as one-time-only search limitation. Additionally, conjunctive multi-keyword search, which is the most common form of query nowadays, is not supported in those works. In this paper, we propose a public-key searchable encryption scheme based on the inverted index. Our scheme preserves the high search efficiency inherited from the inverted index while lifting the one-time-only search limitation of the previous solutions. Our scheme features a probabilistic trapdoor generation algorithm and protects the search pattern. In addition, our scheme supports conjunctive multi-keyword search. Compared with the existing public key based schemes that heavily rely on expensive pairing operations, our scheme is more efficient by using only multiplications and exponentiations. To meet stronger security requirements, we strengthen our scheme with an efficient oblivious transfer protocol that hides the access pattern from the cloud. The simulation results demonstrate that our scheme is suitable for practical usage with moderate overhead.

Advanced botnets adopt a peer-to-peer (P2P) infrastructure for more resilient command and control (C&C). Traditional detection techniques become less effective in identifying bots that communicate via a P2P structure. In this paper, we present PeerClean, a novel system that detects P2P botnets in real time using only high-level features extracted from C&C network flow traffic. PeerClean reliably distinguishes P2P bot-infected hosts from legitimate P2P hosts by jointly considering flow-level traffic statistics and network connection patterns. Instead of working on individual connections or hosts, PeerClean clusters hosts with similar flow traffic statistics into groups. It then extracts the collective and dynamic connection patterns of each group by leveraging a novel dynamic group behavior analysis. Comparing with the individual host-level connection patterns, the collective group patterns are more robust and differentiable. Multi-class classification models are then used to identify different types of bots based on the established patterns. To increase the detection probability, we further propose to train the model with average group behavior, but to explore the extreme group behavior for the detection. We evaluate PeerClean on real-world flow records from a campus network. Our evaluation shows that PeerClean is able to achieve high detection rates with few false positives.

Recent advances in MIMO degree-of-freedom (DoF)models allow us to study MIMO in a multi-hop network environment. On the other hand, successive interference cancellation (SIC) is a powerful physical layer technique used in multi-user detection. Based on the strengths and weaknesses of MIMO DoF and SIC, we propose a marriage between these two techniques so that DoF-based interference cancellation (IC) and SIC can help each other as follows: (i) SIC is exploited to decode multiple received signals to conserve DoF resources in IC, and (ii) DoFIC resolves the potential SINR barrier that SIC may encounter. In this paper, we develop the necessary mathematical models to realize the two ideas in a multi-hop wireless network. Together with scheduling and routing constraints, we develop a cross-layer optimization framework with joint DoF IC and SIC. By applying the framework on a throughput maximization problem, we find that SIC and DoF IC can indeed offer significant performance improvement by addressing each other's limitation.

Wireless energy transfer (WET) is a new technology that can be used to charge the batteries of sensor nodes without wires. Although wireless, WET does require a charging station to be brought to within reasonable range of a sensor node so that a good energy transfer efficiency can be achieved. On the other hand, it has been well recognized that data collection with a mobile base station has significant advantages over a static one. Given that a mobile platform is required for WET, a natural approach is to employ the same mobile platform to carry the base station for data collection. In this paper, we study the interesting problem of co-locating a wireless charger (for WET) and a mobile base station on the same mobile platform-the wireless charging vehicle (WCV). The WCV travels along a pre-planned path inside the sensor network. Our goal is to minimize energy consumption of the entire system while ensuring that 1) each sensor node is charged in time so that it will never run out of energy, and 2) all data collected from the sensor nodes are relayed to the mobile base station. We develop a mathematical model for this problem (OPT-t), which is time-dependent. Instead of solving OPT-t directly, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. Our results offer a solution on how to use a single mobile platform to address both WET and data collection in sensor networks.

The dominate spectrum sharing paradigm of today is interference avoidance, where a secondary network can use the spectrum only when such a use is not interfering with the primary network. However, with the advances of physical-layer technologies, the mindset of this paradigm is being challenged. This paper explores a new paradigm called “transparent coexistence” for spectrum sharing between primary and secondary nodes in a multihop network environment. Under this paradigm, the secondary network is allowed to use the same spectrum simultaneously with the primary network as long as their activities are “transparent” (or “invisible”) to the primary network. Such transparency is accomplished through a systematic interference cancelation (IC) by the secondary nodes without any impact on the primary network. Although such a paradigm has been studied in the information theory (IT) and communications (COMM) communities, it is not well understood in the wireless networking community, particularly for multihop networks. This paper offers an in-depth study of this paradigm in a multihop network environment and addresses issues such as scheduling (both in frequency channels and time slots) and IC (to/from primary network and within the secondary network). Through a rigorous modeling and formulation, problem formulation, solution development, and simulation results, we show that transparent coexistence paradigm offers significant improvement in terms of spectrum access and throughput performance as compared to the current prevailing interference avoidance paradigm.

Wireless energy transfer based on magnetic resonant coupling is a promising technology to replenish energy to a wireless sensor network (WSN). However, charging sensor nodes one at a time poses a serious scalability problem. Recent advances in magnetic resonant coupling show that multiple nodes can be charged at the same time. In this paper, we exploit this multi-node wireless energy transfer technology and investigate whether it is a scalable technology to address energy issues in a WSN. We consider a wireless charging vehicle (WCV) periodically traveling inside a WSN and charging sensor nodes wirelessly. Based on charging range of the WCV, we propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal cells. We pursue a formal optimization framework by jointly optimizing traveling path, flow routing, and charging time. By employing discretization and a novel Reformulation-Linearization Technique (RLT), we develop a provably near-optimal solution for any desired level of accuracy. Through numerical results, we demonstrate that our solution can indeed address the charging scalability problem in a WSN.

Cloud computing has become the real trend of enterprise IT service model that offers cost-effective and scalable processing. Meanwhile, Software-Defined Networking (SDN) is gaining popularity in enterprise networks for flexibility in network management service and reduced operational cost. There seems a trend for the two technologies to go hand-in-hand in providing an enterprise's IT services. However, the new challenges brought by the marriage of cloud computing and SDN, particularly the implications on enterprise network security, have not been well understood. This paper sets to address this important problem. We start by examining the security impact, in particular, the impact on DDoS attack defense mechanisms, in an enterprise network where both technologies are adopted. We find that SDN technology can actually help enterprises to defend against DDoS attacks if the defense architecture is designed properly. To that end, we propose a DDoS attack mitigation architecture that integrates a highly programmable network monitoring to enable attack detection and a flexible control structure to allow fast and specific attack reaction. The simulation results show that our architecture can effectively and efficiently address the security challenges brought by the new network paradigm.

This paper explores a new paradigm for the coexistence among heterogeneous multi-hop networks in unplanned deployment settings, called cooperative interference mitigation (CIM). CIM exploits recent advancements in physical layer technologies such as technology-independent multiple output (TIMO), making it possible for disparate networks to cooperatively mitigate the interference to each other to enhance everyone's performance, even if they possess different wireless technologies. This paper offers a thorough study of the CIM paradigm for unplanned multi-hop networks. We first show the feasibility of CIM among heterogeneous multi-hop networks by exploiting only channel ratio information, and then establish a tractable model to accurately characterize the CIM behaviors of both networks. We also develop a bi-criteria optimization formulation to maximize both networks' throughput, and propose a new methodology to compute the Pareto-optimal throughput curve as performance bound. Simulation results show that CIM provides significant performance gains to both networks compared with the traditional interference-avoidance paradigm.

Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e. multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e. file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. Finally, performance evaluation shows the efficiency of our scheme.

Enabling keyword search directly over encrypted data is a desirable technique for effective utilization of encrypted data outsourced to the cloud. Existing solutions provide multi-keyword exact search that does not tolerate keyword spelling error, or single keyword fuzzy search that tolerates typos to certain extent. The current fuzzy search schemes rely on building an expanded index that covers possible keyword misspelling, which lead to significantly larger index file size and higher search complexity. In this paper, we propose a novel multi-keyword fuzzy search scheme by exploiting the locality-sensitive hashing technique. Our proposed scheme achieves fuzzy matching through algorithmic design rather than expanding the index file. It also eliminates the need of a predefined dictionary and effectively supports multiple keyword fuzzy search without increasing the index or search complexity. Extensive analysis and experiments on real-world data show that our proposed scheme is secure, efficient and accurate. To the best of our knowledge, this is the first work that achieves multi-keyword fuzzy search over encrypted cloud data.

Reactive jamming is considered the most powerful jamming attack as the attack efficiency is maximized while the risk of being detected is minimized. Currently, there are no effective anti-jamming solutions to secure OFDM wireless communications under reactive jamming attack. On the other hand, MIMO has emerged as a technology of great research interest in recent years mostly due to its capacity gain. In this paper, we explore the use of MIMO technology for jamming resilient OFDM communication, especially its capability to communicate against the powerful reactive jammer. We first investigate the jamming strategies and their impacts on the OFDM-MIMO receivers. We then present a MIMO-based anti-jamming scheme that exploits interference cancellation and transmit precoding capabilities of MIMO technology to turn a jammed non-connectivity scenario into an operational network. Our testbed evaluation shows the destructive power of reactive jamming attack, and also validates the efficacy and efficiency of our defense mechanisms.

Transparent coexistence, also known as underlay, offers much more efficient spectrum sharing than traditional interweave coexistence paradigm. In a previous work, the transparent coexistence for a multi-hop secondary networks is studied. In this paper, we design a distributed solution to achieve this paradigm. In our design, we show how to increase the number of data streams iteratively while meeting constraints in the MIMO interference cancelation (IC) model and achieving transparent coexistence. All steps in our distributed algorithm can be accomplished based on local information exchange among the neighboring nodes. Our simulation results show that the performance of our distributed algorithm is highly competitive when compared to an upper bound solution for the centralized problem.

Recent advances in information theory (IT) have shown great promises of interference alignment (IA) for cellular networks. However, due to a number of assumptions, these IT results cannot be directly applied to address practical problems. The goal of this paper is to fill in this gap by studying IA for cellular networks with more practical settings. We propose an IA scheme that includes constraints at each user and each base station (BS) for the uplink communication of a cellular network. We prove the feasibility of the IA scheme by constructing the encoding and decoding vectors for each data stream so that it can be transported free of interference. Based on this IA scheme, we study an uplink user throughput maximization problem and show the throughput improvement of the IA scheme over two other schemes.

Network-coded cooperative communications (NC-CC) is a new paradigm for communications in wireless networks that employs network coding (NC) to improve the performance of CC. A key problem to harness the potential of NC-CC is how to put sessions into different groups, and assign a relay node for each group. In this paper, we study this joint grouping and relay node selection problem for NC-CC. We provide a formal proof of NP-hardness for this problem. Due to NP-hardness, we propose a distributed and online algorithm and show that it offers near-optimal solution to this problem. The key idea in this algorithm is to have each neighboring relay node of a new session calculate the best local group that it can offer and advertise this information; and then to have the source node of the new session select the best local group to join among all offers. We show that our distributed algorithm has polynomial time complexity. Using extensive numerical results, we show that our distributed algorithm adapts well to online network dynamics.

The rapid advances of MIMO to date have mainly stayed at the physical layer. Such fruits have not fully benefited MIMO research at the network layer mainly due to the computational complexity associated with the matrix-based model that MIMO involves. Recently, there have been some efforts to simplify link layer model for MIMO so as to facilitate research at the upper layers. These models only require simple numeric computations on MIMO's degrees-of-freedom (DoFs) to characterize spatial multiplexing (SM) and interference cancellation (IC). Thus, these models are much simpler than the original matrix-based model from the communications world. However, achievable DoF regions of these DoF-based models are not analyzed. In this paper, we re-visit this important problem of MIMO modeling. Based on accounting of how DoFs are consumed for SM and IC, we develop a tractable link layer model for multi-hop MIMO networks. We show that under common assumptions of DoF-based models and additional assumption of no dependency cycle, this model includes all the feasible solutions by the matrix-based model under SM and IC for any network topology. This work offers an important building block for theoretical research on multi-hop MIMO networks.

With the growing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a verifiable privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency- and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaptive methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. In addition, we devise a scheme upon the proposed index tree structure to enable authenticity check over the returned search results. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

Passive monitoring by distributed wireless sniffers has been used to strategically capture the network traffic, as the basis of automatic network diagnosis. However, the traditional monitoring techniques fall short in cognitive radio networks (CRNs) due to the much larger number of channels to be monitored and the secondary users' channel availability uncertainty imposed by primary user activities. To better serve CRNs, we propose a systematic passive monitoring framework, i.e., SpecMonitor, for traffic collection using a limited number of sniffers in Wi-Fi-like CRNs. We jointly consider primary user activity and secondary user channel access pattern to optimize the traffic capturing strategy. In particular, we exploit a nonparametric density estimation method to learn and predict secondary users' access pattern in an online fashion, which rapidly adapts to the users' dynamic behaviors and supports accurate estimation of merged access patterns from multiple users. We also design near-optimal monitoring algorithms that maximize two levels of quality-of-monitoring goals based on the predicted channel access patterns. The simulations and experiments show that SpecMonitor outperforms the existing schemes significantly.

With the increasing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaption methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

In this paper, we propose a privacy-preserving proximity-based security strategy for location-based services in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. More specifically, radio clients build their location tags according to the unique physical features of their ambient radio signals, which cannot be forged by attackers outside the proximity range. The proximity-based authentication and session key generation is based on the public location tag, which incorporates the received signal strength indicator (RSSI), sequence number and MAC address of the ambient radio packets. Meanwhile, as the basis for the session key generation, the secret location tag consisting of the arrival time interval of the ambient packets, is never broadcast, making it robust against eavesdroppers and spoofers. The proximity test utilizes the nonparametric Bayesian method called infinite Gaussian mixture model, and provides range control by selecting different features of various ambient radio sources. The authentication accuracy and key generation rate are evaluated via experiments using laptops in typical indoor environments.

Lacking trusted central authority, distributed systems have received serious security threats from Sybil attack, where an adversary forges identities of more than one node and attempts to control the system. By utilizing the real-world trust relationships between users, social network-based defense schemes have been proposed to mitigate the impact of Sybil attacks. These solutions are mostly built on the assumption that the social network graph can be partitioned into two loosely linked regions - a tightly connected non-Sybil region and a Sybil region. Although such an assumption may hold in certain settings, studies have shown that the real-world social connections tend to divide users into multiple inter-connected small worlds instead of a single uniformly connected large region. Given this fact, the applicability of existing schemes would be greatly undermined for inability to distinguish Sybil users from valid ones in the small non-Sybil regions. This paper addresses this problem and presents SybilShield, the first protocol that defends against Sybil attack utilizing multi-community social network structure in real world. Our scheme leverages the sociological property that the number of cutting edges between a non-Sybil community and a Sybil community, which represent human-established trust relationships, is much smaller than that among non-Sybil communities. With the help of agent nodes, SybilShield greatly reduces false positive rate of non-Sybils among multiple communities, while effectively identifying Sybil nodes. Analytical results prove the superiority of SybilShield. Our experiments on a real-world social network graph with 100,000 nodes also validate the effectiveness of SybilShield.

Wireless energy transfer is a promising technology to fundamentally address energy and lifetime problems in a wireless sensor network (WSN). On the other hand, it has been well recognized that a mobile base station has significant advantages over a static one. In this paper, we study the interesting problem of co-locating the mobile base station on the wireless charging vehicle (WCV). The goal is to minimize energy consumption of the entire system while ensuring none of the sensor nodes runs out of energy. We develop a mathematical model for this complex problem. Instead of studying the general problem formulation (OPT-t), which is time-dependent, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. The novelty of this research mainly resides in the development of several solution techniques to tackle a complex problem that is seemingly intractable at first glance. In addition to addressing a challenging and interesting problem in a WSN, we expect the techniques developed in this research can be applied to address other related networking problems involving time-dependent movement, flow routing, and energy consumption.

Passive monitoring by distributed wireless sniffers has been used to strategically capture the network traffic, as the basis of automatic network diagnosis. However, the traditional monitoring techniques fall short in cognitive radio networks (CRNs) due to the much larger number of channels to be monitored, and the secondary users' channel availability uncertainty imposed by primary user activities. To better serve CRNs, we propose a systematic passive monitoring framework for traffic collection using a limited number of sniffers in WiFi like CRNs. We jointly consider primary user activity and secondary user channel access pattern to optimize the traffic capturing strategy. In particular, we exploit a non-parametric density estimation method to learn and predict secondary users' access pattern in an online fashion, which rapidly adapts to the users' dynamic behaviors and supports accurate estimation of merged access patterns from multiple users. We also design near-optimal monitoring algorithms that maximize two levels of quality-of-monitoring goals respectively, based on the predicted channel access patterns. The simulations and experiments show that our proposed framework outperforms the existing schemes significantly.

Degree-of-Freedom (DoF)-based model is a simple yet powerful tool to analyze MIMO's spatial multiplexing (SM) and interference cancellation (IC) capabilities in a multi-hop network. Recently, a new DoF model was proposed and was shown to achieve the same rate region as the matrix-based model (under SM and IC). The essence of this new DoF model is a novel node ordering concept, which eliminates potential duplication of DoF allocation for IC. In this paper, we investigate DoF scheduling for a multi-hop MIMO network based on this new DoF model. Specifically, we study how to perform DoF allocation among the nodes for SM and IC so as to maximize the minimum rate among a set of sessions. We formulate this problem as a mixed integer linear programming (MILP) and develop an efficient DoF scheduling algorithm to solve it. We show that our algorithm is amenable to local implementation and has polynomial time complexity. More importantly, it guarantees the feasibility of final solution (upon algorithm termination), despite that node ordering establishment and adjustment are performed locally. Simulation results show that our algorithm can offer a result that is close to an upper bound found by CPLEX solver, thus showing that the result found by our algorithm is highly competitive.

Interference alignment (IA) is a major advance in information theory. Despite its rapid advance in the information theory community, most results on IA remain point-to-point or single-hop and there is a lack of advance of IA in the context of multi-hop wireless networks. The goal of this paper is to make a concrete step toward advancing IA technique in multi-hop MIMO networks. We present an IA model consisting of a set of constraints at a transmitter and a receiver that can be used to determine a subset of interfering streams for IA. Based on this IA model, we develop an IA optimization framework for a multihop MIMO network. For performance evaluation, we compare the performance of a network throughput optimization problem under our proposed IA framework and the same problem when IA is not employed. Simulation results show that the use of IA can significantly decrease the DoF consumption for IC, thereby improving network throughput.

There has been a growing interest to employ the so-called degree-of-freedom (DoF) based models to study multihop MIMO networks. Existing DoF-based models differ in their interference cancelation (IC) behavior and suffer from either loss of solution space or possible infeasible solutions. Recently, a DoF model based on a novel node-ordering concept was proposed to overcome the limitations of the exiting DoF models. In this paper, we apply this new DoF model to study a throughput maximization problem in a multi-hop network. The problem formulation jointly considers half duplex, node ordering, DoF consumption constraints and flow routing and is in the form of a mixed integer linear program (MILP). Our main contribution is the development of an efficient polynomial time algorithm that offers a competitive solution to the MILP through a series of linear programs (LPs). The key idea in the algorithm is to explore (i) the impact of node ordering on DoF consumption for IC at a node, and (ii) route diversity in the network while ensuring DoF constraints are satisfied at each node throughout the iterations. Simulation results show that our solutions by the proposed algorithm are competitive and feasible.

The dominant spectrum sharing paradigm of today is the interweave paradigm. This paper advocates a new and alternative paradigm called United network of Primary and Secondary networks (UPS). UPS allows a complete cooperation between primary and secondary networks at the node level to relay each other's traffic, in addition to existing dynamic spectrum access (DSA) in time, space, and frequency domains. Such cooperation allows the primary and secondary networks to access a much richer network resources from the combined network. As a case study, we consider a problem with the goal of supporting the rate requirement of the primary network traffic while maximizing the minimum throughput of the secondary sessions. For this problem, we develop an optimization model and formulate a combinatorial optimization problem. Although this problem is in the form of mixed integer linear program (MILP), we can use CPLEX to solve it efficiently. Simulation results show that the UPS paradigm offers much better throughput performance than the interweave DSA paradigm.

Wireless power transfer is a promising technology to fundamentally address energy problems in a wireless sensor network. To make such a technology work effectively, a vehicle is needed to carry a charger to travel inside the network. On the other hand, it has been well recognized that a mobile base station offers significant advantages over a fixed one. In this paper, we investigate an interesting problem of co-locating the mobile base station on the wireless charging vehicle. We study an optimization problem that jointly optimizes traveling path, stopping points, charging schedule, and flow routing. Our study is carried out in two steps. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem.

This paper explores the so-called “transparent coexistence” paradigm for spectrum sharing between primary and secondary nodes in a multi-hop network environment. Although such paradigm has been studied in the information theory and communications communities, it is not well understood in the wireless networking community, particularly for multihop networks. Under this paradigm, a secondary network is allowed to use the same spectrum simultaneously with the primary network as long as their activities are “transparent” (or “invisible”) to the primary network. Such transparency can be accomplished through a systematic interference cancellation (IC) by the secondary nodes without any impact on the primary network. This paper offers an in-depth study of this paradigm in a multi-hop network environment and addresses issues such as channel selection, IC to/from primary network, and IC within the secondary network. Through a rigorous modeling and formulation, we develop an optimization problem under this paradigm with the objective of maximizing secondary user's throughput. Through simulation results, we show that such paradigm offers significant improvement to a multi-hop network in terms of spectrum efficiency and throughput performance as compared to the prevailing interference-avoidance paradigm.

In this paper, we propose a privacy-preserving proximity-based security system for location-based services in wireless networks, without requiring any pre-shared secret, trusted authority, or public key infrastructure. In this system, the proximity-based authentication and session key establishment are implemented based on spatial temporal location tags. Incorporating the unique physical features of the signals sent from multiple ambient radio sources, the location tags cannot be easily forged by attackers. More specifically, each radio client builds a public location tag according to the received signal strength indicators, sequence numbers, and media access control (MAC) addresses of the ambient packets. Each client also keeps a secret location tag that consists of the packet arrival time information to generate the session keys. As clients never disclose their secret location tags, this system is robust against eavesdroppers and spoofers outside the proximity range. The system improves the authentication accuracy by introducing a nonparametric Bayesian method called infinite Gaussian mixture model in the proximity test and provides flexible proximity range control by taking into account multiple physical-layer features of various ambient radio sources. Moreover, the session key establishment strategy significantly increases the key generation rate by exploiting the packet arrival time of the ambient signals. The authentication accuracy and key generation rate are evaluated via experiments using laptops in typical indoor environments.

Network throughput and energy consumption are two important performance metrics for a multihop wireless network. Current state-of-the-art research is limited to either maximizing throughput under some energy constraint or minimizing energy consumption while satisfying some throughput requirement. Although many of these prior efforts were able to offer some optimal solutions, there is still a critical need to have a systematic study on how to optimize both objectives simultaneously. In this paper, we take a multicriteria optimization approach to offer a systematic study on the relationship between the two performance objectives. To focus on throughput and energy performance, we simplify link layer scheduling by employing orthogonal channels among the links. We show that the solution to the multicriteria optimization problem characterizes the envelope of the entire throughput-energy region, i.e., the so-called optimal throughput-energy curve. We prove some important properties of the optimal throughput-energy curve. For case study, we consider both linear and nonlinear throughput functions. For the linear case, we characterize the optimal throughput-energy curve precisely through parametric analysis, while for the nonlinear case, we use a piecewise linear approximation to approximate the optimal throughput-energy curve with arbitrary accuracy. Our results offer important insights on exploiting the tradeoff between the two performance metrics.

This paper tries to reconcile the tension between the physical model and the protocol model that have been used to characterize interference relationship in a multihop wireless network. The physical model (a.k.a. signal-to-interference-and-noise ratio model) is widely considered as a reference model for physical layer behavior but its application in multihop wireless networks is limited by its complexity. On the other hand, the protocol model (a.k.a. disk graph model) is simple but there have been doubts on its validity. This paper explores the following fundamental question: How to correctly use the protocol interference model? We show that, in general, solutions obtained under the protocol model may be infeasible and, thus, results based on blind use of protocol model can be misleading. We propose a new concept called "reality check” and present a method of using a protocol model with reality check for wireless networks. Subsequently, we show that by appropriate setting of the interference range in the protocol model, it is possible to narrow the solution gap between the two models. Our simulation results confirm that this gap is indeed small (or even negligible). Thus, our methodology of joint reality check and interference range setting retains the protocol model as a viable approach to analyze multihop wireless networks.

The cost of energy consumption is an important concern for network operators. In this paper, we study an energy-related problem that focuses on network-wide energy consumption. In the first part of this work, we study how to maximize throughput under a network-wide energy constraint. We formulate this problem as a mixed-integer nonlinear program (MINLP). This formulation differs from prior efforts as it considers a non-zero device power, which complicates the problem. We propose a novel piece-wise linear approximation to transform the nonlinear constraints into linear constraints. We prove that the solution developed under this approach is near-optimal with a guaranteed performance bound. In the second part, we generalize the problem in the first part via a multicriteria optimization framework, which simultaneously optimizes throughput and total network energy. We show how weakly Pareto-optimal solutions can characterize an optimal throughput-energy curve. We offer some interesting properties of the optimal throughput-energy curves, which are useful to both network operators and end-users. Our results fill in some important gaps in the current understanding on optimizing total network energy.

With the increasing adoption of cloud computing for data storage, assuring data service reliability, in terms of data correctness and availability, has been outstanding. While redundancy can be added into the data for reliability, the problem becomes challenging in the “pay-as-you-use” cloud paradigm where we always want to efficiently resolve it for both corruption detection and data repair. Prior distributed storage systems based on erasure codes or network coding techniques have either high decoding computational cost for data users, or too much burden of data repair and being online for data owners. In this paper, we design a secure cloud storage service which addresses the reliability issue with near-optimal overall performance. By allowing a third party to perform the public integrity verification, data owners are significantly released from the onerous work of periodically checking data integrity. To completely free the data owner from the burden of being online after data outsourcing, this paper proposes an exact repair solution so that no metadata needs to be generated on the fly for repaired data. The performance analysis and experimental results show that our designed service has comparable storage and communication cost, but much less computational cost during data retrieval than erasure codes-based storage solutions. It introduces less storage cost, much faster data retrieval, and comparable communication cost comparing to network coding-based distributed storage systems.

Conserving network-wide energy consumption is becoming an increasingly important concern for network operators. In this work, we study network-wide energy conservation problem which we hope will offer insights to both network operators and users. In the first part of this work, we study how to maximize throughput under a network-wide energy constraint. We formulate this problem as a mixed-integer nonlinear program (MINLP). We propose a novel piece-wise linear approximation to transform the nonlinear constraints into linear constraints. We prove that the solution developed under this approach is near-optimal with guaranteed performance bound. In the second part, we generalize the problem in the first part by exploring throughput and network-wide energy optimization via a multi-criteria optimization framework. We show that the weakly Pareto-optimal points in the solution can characterize an optimal throughput-energy curve. We offer some interesting properties of the optimal throughput-energy curve which are useful to both network operators and end users.

There is a growing interest in exploiting interference (rather than avoiding it) to increase network throughput. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we try to answer the following fundamental questions. What are the limitations of SIC? How to overcome such limitations? How to optimize the interaction between SIC and interference avoidance? How to incorporate multiple layers (physical, link, and network) in an optimization framework? We find that SIC alone is not adequate to handle interference in a multi-hop wireless network, and advocate the use of joint SIC and interference avoidance. To optimize a joint scheme, we propose a cross-layer optimization framework that incorporates variables at physical, link, and network layers. This is the first work that combines successive interference cancellation and interference avoidance in multi-hop wireless network. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC and interference avoidance can complement each other in an optimal manner.

Capacity scaling laws offer fundamental understanding on the trend of user throughput behavior when the network size increases. Since the seminal work of Gupta and Kumar, there have been active research efforts in developing capacity scaling laws for ad hoc networks under various advanced physical layer technologies. These efforts led to many custom-designed solutions, most of which were intellectually challenging and lacked universal properties that can be extended to address scaling laws of ad hoc networks with other physical layer technologies. In this paper, we present a set of simple yet powerful tool that can be applied to quickly determine the capacity scaling laws for various physical layer technologies under the protocol model. We prove the correctness of our proposed criteria and demonstrate their usage through a number of case studies, such as ad hoc networks with directional antenna, MIMO, multi-channel multi-radio, cognitive radio, and multiple packet reception. These simple criteria will serve as powerful tools to networking researchers to obtain throughput scaling laws of ad hoc networks under different physical layer technologies, particularly those to be developed in the future.

Cooperative spectrum sensing is key to the success of cognitive radio networks. Recently, fully distributed cooperative spectrum sensing has been proposed for its high performance benefits particularly in cognitive radio ad hoc networks. However, the cooperative and fully distributed natures of such protocol make it highly vulnerable to malicious attacks, and make the defense very difficult. In this paper, we analyze the vulnerabilities of distributed sensing architecture based on a representative distributed consensus-based spectrum sensing algorithm. We find that such distributed algorithm is particularly vulnerable to a novel form of attack called covert adaptive data injection attack. The vulnerabilities are even magnified under multiple colluding attackers. We further propose effective protection mechanisms, which include a robust distributed outlier detection scheme with adaptive local threshold to thwart the covert adaptive data injection attack, and a hash-based computation verification approach to cope with collusion attacks. Through simulation and analysis, we demonstrate the destructive power of the attacks, and validate the efficacy and efficiency of our proposed protection mechanisms.

Wireless energy transfer based on magnetic resonant coupling is a promising technology to replenish energy to sensor nodes in a wireless sensor network (WSN). However, charging sensor node one at a time poses a serious scalability problem. Recent advances in magnetic resonant coupling shows that multiple nodes can be charged at the same time. In this paper, we exploit this multi-node wireless energy transfer technology to address energy issue in a WSN. We consider a wireless charging vehicle (WCV) periodically traveling inside a WSN and charging sensor nodes wirelessly. We propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal cells. The WCV visits these cells and charge sensor nodes from the center of a cell. We pursue a formal optimization framework by jointly optimizing traveling path, flow routing and charging time. By employing discretization and a novel Reformulation-Linearization Technique (RLT), we develop a provably near-optimal solution for any desired level of accuracy.

It has been shown that cooperative communications (CC) has the potential to significantly increase the capacity of wireless networks. However, most of the existing results are limited to single-hop wireless networks. To explore the behavior of CC in multi-hop wireless networks, we study a joint optimization problem of relay node assignment and flow routing for a group of sessions. We develop a mathematical model and propose a solution procedure based on the branch-and-bound framework augmented with cutting planes (BB-CP). We design several novel components to speed-up the computational time of BB-CP. Via numerical results, we show the potential rate gain that can be achieved by incorporating CC in multi-hop networks.

A major benefit of employing network coding (NC) in cooperative communications (CCs) is its ability to reduce time-slot overhead. Such approach is called network-coded CC (or NC-CC). Most of the existing works have mainly focused on exploiting this benefit without considering its potential adverse effect. In this paper, we show that NC may not always benefit CC. We substantiate this important finding with two important scenarios: employing analog network coding (ANC) in amplify-and-forward (AF) CC, and digital network coding (DNC) in decode-and-forward (DF) CC. For both scenarios, we introduce the important concept of network coding noise (NC noise). We analyze the origin of this noise via a careful study of signal aggregation at a relay node and signal extraction at a destination node. We derive a closed-form expression for NC noise at each destination node and show that the existence of NC noise could diminish the advantage of NC in CC. Our results shed new light on how to use NC in CC most effectively.

The benefits of using a mobile base station to prolong sensor network lifetime have been well recognized. However, due to the complexity of the problem (time-dependent network topology and traffic routing), theoretical performance limits and provably optimal algorithms remain difficult to develop. This paper fills this important gap by contributing some theoretical results regarding the optimal movement of a mobile base station. Our main result hinges upon two key intermediate results. In the first result, we show that a time-dependent joint base station movement and flow routing problem can be transformed into a location-dependent problem. In the second result, we show that, for$(1- \varepsilon)$optimality, the infinite possible locations for base station movement can be reduced to a finite set of locations via several constructive steps [i.e., discretization of energy cost through a geometric sequence, division of a disk into a finite number of subareas, and representation of each subarea with a fictitious cost point (FCP)]. Subsequently, for each FCP, we can obtain the optimal sojourn time for the base station (as well as the corresponding location-dependent flow routing) via a simple linear program. We prove that the proposed solution can guarantee the achieved network lifetime is at least$(1- \varepsilon)$of the maximum (unknown) network lifetime, where$\varepsilon$can be made arbitrarily small depending on the required precision.

Wireless sensor networks are constrained by limited battery energy. Thus, finite network lifetime is widely regarded as a fundamental performance bottleneck. Recent breakthrough in the area of wireless power transfer offers the potential of removing this performance bottleneck, i.e., allowing a sensor network to remain operational forever. In this paper, we investigate the operation of a sensor network under this new enabling energy transfer technology. We consider the scenario of a mobile charging vehicle periodically traveling inside the sensor network and charging each sensor node's battery wirelessly. We introduce the concept of renewable energy cycle and offer both necessary and sufficient conditions. We study an optimization problem, with the objective of maximizing the ratio of the wireless charging vehicle (WCV)'s vacation time over the cycle time. For this problem, we prove that the optimal traveling path for the WCV is the shortest Hamiltonian cycle and provide a number of important properties. Subsequently, we develop a near-optimal solution by a piecewise linear approximation technique and prove its performance guarantee.

Network-coded cooperative communications (NC-CC) refers to the use of network coding (NC) in cooperative communications (CC). Prior studies have shown that NC has the potential to improve the performance of CC when there are multiple sessions in the wireless network. These studies were done for the case when multiple sessions are sharing a single relay node. However, how NC-CC behaves when multiple relay nodes are employed remains an open problem. In this paper, we explore this problem by analyzing the achievable rate of each session in this setting. We develop closed form formulas for the mutual information and the achievable data rate for each session and show that prior results for a single relay is a special case of our result. Our findings in this paper offer an important building block on the theory of NC-CC. To demonstrate the application of our theoretical result, we apply it in a numerical study to understand the impact on a session's achievable rate when different sets of relay nodes are employed in NC-CC.

Cognitive radio is envisioned to be an enabling radio technology for future wireless networks. In this paper, we study the capacity scaling laws for cognitive radio ad hoc networks (CRNs), i.e., how each individual node's capacity scales as the number of nodes in the network increases. This effort is critical to the fundamental understanding of the scalability of such network. However, due to the heterogeneity in available frequency bands at each node, the asymptotic capacity is much more difficult to develop than prior efforts for other types of wireless networks. To overcome this difficulty, we introduce two auxiliary networks ζ and α to analyze the capacity upper bound and lower bound. We derive the capacity results under both the protocol model and the physical model. Further, we show that the results developed by Gupta and Kumar for the simple single-channel single-radio (SC-SR) networks are special cases under the results for CRNs.

Abstract-Network throughput and energy consumption are two important performance metrics for a multi-hop wireless network. Current state-of-the-art is limited to either maximizing throughput under some energy constraint or minimizing energy consumption while satisfying some throughput requirement. In this paper, we take a multicriteria optimization approach to offer a systematic study on the relationship between the two performance objectives. We show that the solution to the multi criteria optimization problem is equivalent to finding an optimal throughput-energy curve, which characterizes the envelope of the entire throughput-energy region. We prove some important prop erties of the optimal throughput-energy curve. For case study, we consider both linear and nonlinear throughput functions. In the linear case, we characterize the optimal throughput-energy curve precisely through parametric analysis, while in the nonlinear case, we use a piece-wise linear approximation to approximate the optimal throughput-energy curve with arbitrary accuracy. Our results offer important insights on exploiting the trade-off between the two performance metrics. I.

Network-coded cooperative communications (NC-CC) is a new paradigm in wireless networks that employs network coding (NC) to improve the performance of CC. The core mechanism to harness the benefits of NC-CC is to appropriately combine sessions into separate groups, and then have each group select the most beneficial relay node for NC-CC. In this paper, we study this joint grouping and relay node selection problem for NC-CC. Due to NP-hardness of problem, we propose a distributed and online algorithm that offers near-optimal solution to this problem. The key idea in our algorithm is to have each neighboring relay node of a new session determine and offer its best local group; and then to have the source node of the new session select the best group among all offers. We show that our distributed algorithm has polynomial complexity. Using extensive numerical results, we show that our distributed algorithm adapts well to online network dynamics.

The rapid advances of MIMO to date have mainly stayed at the physical layer. Such fruits have not been fully benefited at the network layer mainly due to the computational complexity associated with the matrix-based model that MIMO involves. Recently, there are some efforts to simplify link layer model for MIMO so as to ease research for the upper layers. These models only require numeric computations on MIMO's degrees-of-freedom (DoFs) for spatial multiplexing (SM) and interference cancellation (IC) to obtain a feasible rate region. Thus, these models are much simpler than the original matrix-based model from the communications world. However, none of these DoF-based models is shown to achieve the same rate region as that by the matrix-based model. In this paper, we re-visit this important problem of MIMO modeling. Based on accurate accounting of how DoFs are consumed, we develop a simple link layer model for multi-hop MIMO networks. We show that this model is optimal in the sense of achieving the same rate region as that by the matrix-based model under SM and IC for any network topology. This work offers an important building block for theoretical research on multi-hop MIMO networks.

Traditional wireless sensor networks are constrained by limited battery energy. Thus, finite network lifetime is widely regarded as a fundamental performance bottleneck. Recent breakthrough in the area of wireless energy transfer offers the potential of removing such performance bottleneck, i.e., allowing a sensor network remain operational forever. In this paper, we investigate the operation of a sensor network under this new enabling energy transfer technology. We consider the scenario of a mobile charging vehicle periodically traveling inside the sensor network and charging each sensor node's battery wirelessly. We introduce the concept of renewable energy cycle and offer both necessary and sufficient conditions. We study an optimization problem, with the objective of maximizing the ratio of the wireless charging vehicle (WCV)'s vacation time over the cycle time. For this problem, we prove that the optimal traveling path for the WCV is the shortest Hamiltonian cycle and provide a number of important properties. Subsequently, we develop a near-optimal solution and prove its performance guarantee.

We study a multicast communication problem in a multi-hop ad hoc network where each node is equipped with a cognitive radio (CR). The goal is to minimize the required network-wide resource to support a set of multicast sessions, with a given bit rate requirement for each multicast session. The unique characteristics and complexity associated with CR distinguish this problem from existing multicast problems for ad hoc networks. In this paper, we formulate this problem via a cross-layer approach by taking consideration of scheduling and routing jointly. Although the problem formulation is in the form of a mixed-integer linear program, we develop a polynomial-time algorithm that offers highly competitive solutions. By comparing the solution values with a lower bound, we show that the proposed algorithm can provide a solution that is close to the optimum.

Cognitive radio (CR) and multiple-input multiple-output (MIMO) are two independent physical layer technologies that have made significant impact on wireless networking. CR operates on the channel/band level to exploit white space across spectrum dimension while MIMO operates within the same channel to improve spectral efficiency within the same band. In this paper, we explore MIMO-empowered CR network, which we call {\rm CRN}^{{\rm MIMO}}, to achieve the ultimate flexibility and efficiency in dynamic spectrum access and spectrum utilization. Given that CR and MIMO handle interference at different levels (across channels vs. within a channel), we are interested in how to jointly optimize both so as to maximize user throughput in a multihop network. To answer this question, we develop a tractable mathematical model for {\rm CRN}^{{\rm MIMO}}, which captures the essence of channel assignment (for CR) and degree-of-freedom (DoF) allocation (for MIMO) within a channel. Based on this mathematical model, we use numerical results to show how channel assignment in CRN and DoF allocation in MIMO can be jointly optimized to maximize throughput. More important, for a {\rm CRN}^{{\rm MIMO}} with A_{{\rm MIMO}} antennas at each node, we show that joint optimization of CR and MIMO offers more than A_{MIMO}-fold throughput increase than a CRN (without MIMO).

Cognitive radio networks (CRNs) have the potential to utilize spectrum efficiently and are positioned to be the core technology for the next-generation multihop wireless networks. An important problem for such networks is its capacity. We study this problem for CRNs in the SINR (signal-to-interference-and-noise-ratio) model, which is considered to be a better characterization of interference (but also more difficult to analyze) than disk graph model. The main difficulties of this problem are two-fold. First, SINR is a nonconvex function of transmission powers; an optimization problem in the SINR model is usually a nonconvex program and NP-hard in general. Second, in the SINR model, scheduling feasibility and the maximum allowed flow rate on each link are determined by SINR at the physical layer. To maximize capacity, it is essential to follow a cross-layer approach, but joint optimization at physical (power control), link (scheduling), and network (flow routing) layers with the SINR function is inherently difficult. In this paper, we give a mathematical characterization of the joint relationship among these layers. We devise a solution procedure that provides a (1- \varepsilon ) optimal solution to this complex problem, where \varepsilon is the required accuracy. Our theoretical result offers a performance benchmark for any other algorithms developed for practical implementation. Using numerical results, we demonstrate the efficacy of the solution procedure and offer quantitative understanding on the interaction of power control, scheduling, and flow routing in a CRN.

Recently, cooperative communications, in the form of having each node equipped with a single antenna and exploit spatial diversity via some relay node's antenna, is shown to be a promising approach to increase data rates in wireless networks. Under this communication paradigm, the choice of a relay node (among a set of available relay nodes) is critical in the overall network performance. In this paper, we study the relay node assignment problem in a cooperative ad hoc network environment, where multiple source-destination pairs compete for the same pool of relay nodes in the network. Our objective is to assign the available relay nodes to different source-destination pairs so as to maximize the minimum data rate among all pairs. The main contribution of this paper is the development of an optimal polynomial time algorithm, called ORA, that achieves this objective. A novel idea in this algorithm is a “linear marking” mechanism, which maintains linear complexity of each iteration. We give a formal proof of optimality for ORA and use numerical results to demonstrate its capability.

Multi-input multi-output (MIMO) is a key technology to increase the capacity of wireless networks. Although there has been extensive work on MIMO at the physical and link layers, there is limited work on MIMO at the network layer (i.e., multi-hop MIMO network), particularly results on capacity scaling laws. In this paper, we investigate capacity scaling laws for MIMO ad hoc networks. Our goal is to find the achievable throughput of each node as the number of nodes in the network increases. We employ a MIMO network model that captures spatial multiplexing and interference cancellation. We show that for a MIMO network with n randomly located nodes, each equipped with α antennas and a rate of W on each data stream, the achievable throughput of each node is Θ(αW/√(n ln n)).

MIMO-based communications have great potential to improve network capacity for multi-hop wireless networks. Although there has been significant progress on MIMO at the physical layer or single-hop communication, advances in the theory of MIMO for multi-hop wireless networks remain limited. This stagnation is mainly due to the lack of an accurate and more important, analytically tractable model that can be used by networking researchers. In this paper, we propose such a model to enable the networking community to carry out cross-layer research for multi-hop MIMO networks. In particular, at the physical layer, we develop a simple model for MIMO channel capacity computation that captures the essence of spatial multiplexing and transmit power limit without involving complex matrix operations and the water-filling algorithm. We show that the approximation gap in this model is negligible. At the link layer, we devise a space-time scheduling scheme called OBIC that significantly advances the existing zero-forcing beamforming (ZFBF) to handle interference in a multi-hop network setting. The proposed OBIC scheme employs simple algebraic computation on matrix dimensions to simplify ZFBF in a multi-hop network. As a result, we can characterize link layer scheduling behavior without entangling with beamforming details. Finally, we apply both the new physical and link layer models in cross-layer performance optimization for a multi-hop MIMO network.

It has been shown that cooperative communications (CC) have the potential to significantly increase the capacity of wireless networks. However, most of the existing results are limited to single-hop wireless networks. To illustrate the benefits of CC in multi-hop wireless networks, we solve a joint optimization problem of relay node assignment and flow routing for concurrent sessions. We study this problem via mathematical modeling and solve it using a solution procedure based on the branch-and-cut framework. We design several novel components to speed-up the computation time of branch-and-cut. Via numerical results, we show the significant rate gains that can be achieved by incorporating CC in multi-hop networks.

Network coding (NC) is a promising approach to reduce time-slot overhead for cooperative communications (CC) in a multi-session environment. Most of the existing works take advantage of the benefits of NC in CC but do not fully recognize its potential adverse effect. In this paper, we show that employing NC may not always benefit CC. We substantiate this important finding in the context of analog network coding (ANC) and amplify-and-forward (AF) CC. This paper, for the first time, introduces an important concept of network coding noise (NC noise). Specifically, we analyze the signal aggregation at a relay node and signal extraction at a destination node. We then use the analysis to derive a closed-form expression for NC noise at each destination node in a multi-session environment. We show that NC noise can diminish the advantage of NC in CC. Our results formalizes an important concept on using NC in CC.

We investigate the problem of scalable video multicast in emerging cognitive radio (CR) networks. Although considerable advances have been made in CR research, such important problems have not been well studied. Naturally, 'bandwidth hungry' multimedia applications are excellent candidates for fully capitalizing the potential of CRs. We propose a crosslayer optimization approach to multicast video in CR networks. Specifically, we consider an infrastructure-based CR network collocated with N primary networks and model CR video multicast over the N channels as a mixed integer nonlinear programming (MINLP) problem. The objective is three-fold: to optimize the overall received video quality; to achieve proportional fairness among multicast users; and to keep the interference to primary users below a prescribed threshold. We propose a sequential fixing algorithm and a greedy algorithm to solve the MINLP, while the latter has low complexity and proven optimality gap. Our simulations with MPEG-4 fine grained scalability (FGS) video demonstrate the efficacy and superior performance of the proposed algorithms.

Broadband multi-carrier MIMO (MC-MIMO) is a promising technology that could provide significant capacity gain for wireless ad hoc networks. For MC-MIMO networks, since the capacity is affected by potential mutual interference on subcarriers, scheduling for subcarriers and algorithms for power control/allocation become key problems to harness their potential. However, due to non-convexity and large size of the underlying problem, there are few results on this important problem. In this paper, we first show that the non-convex problem for MC-MIMO networks satisfies the so-called concave perturbation condition, which gives a zero duality gap for the problem. This important result allows us to tackle the problem in the dual domain. The dual approach has the highly desirable benefit of reducing the complexity of the underlying problem, which allows us to design a near-optimal off-line algorithm. In addition to the off-line algorithm, we also devise an online adaptive algorithm (OAA) without the need of channel distribution information (CDI). We show that OAA is able to achieve the same result as the off-line algorithm.

This paper tries to reconcile the tension between physical model and protocol model that have been used to characterize interference relationship in a multi-hop wireless network. The physical model (a.k.a. SINR model) is widely considered as a reference model for physical layer behavior but its application in multi-hop wireless networks is limited by its complexity. On the other hand, the protocol model (a.k.a. unified disk graph model) is simple but there have been doubts on its validity. This paper explores the following fundamental question: How to correctly use the protocol interference model? We show that in general, solutions obtained under the protocol model may be infeasible in practice and thus, results based on blind use of protocol model can be misleading. We propose a novel concept called "reality check" and present a method of using protocol model with reality check for wireless networks. Subsequently, we show that by appropriate setting of the interference range in the protocol model, it is possible to narrow the solution gap between the two models. Our simulation results confirm that this gap is indeed small (or even negligible). Thus, our methodology of joint reality check and interference range setting retains the protocol model as a viable approach to analyze multi-hop wireless networks.

More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer. Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance. Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multiple-input multiple-output radios.

Base station location has a significant impact on network lifetime performance for a sensor network. For a multihop sensor network, this problem is particularly challenging due to its coupling with data routing. This article presents an approximation algorithm that can guarantee (1 − ε)-optimal network lifetime performance for base station placement problem with any desired error bound ε > 0. The proposed (1 − ε)-optimal approximation algorithm is based on several novel techniques that makes it possible to reduce an infinite search space to a finite-element search space for base station location. The first technique used in this reduction is to discretize cost parameter (associated with energy consumption) with performance guarantee. Subsequently, the continuous search space can be broken up into a finite number of subareas. The second technique is to exploit the cost property of each subarea and represent it by a novel notion called fictitious cost point, each with guaranteed cost bounds. We give a proof that the proposed base station placement algorithm is (1− ε)-optimal. This approximation algorithm is simpler and faster than a state-of-the-art algorithm and represents the best known result to the base station placement problem.

Cognitive radio network (CRN) is a promising approach to improve spectrum efficiency for wireless networking. This paper investigates how to perform optimal power control on each node (or per-node based power control) in the network so as to optimize network performance. Per-node based power control is a difficult problem due to its large design space (i.e., interaction among the powers on different nodes in the network) and the coupling relationship between power control and upper layers (scheduling and routing). In this paper, we develop a formal mathematical model for joint power control, scheduling, and routing. We formulate a cross-layer optimization problem encompassing these three layers and develop a unified solution procedure based on branch-and-bound framework and convex hull relaxation. Using numerical results, we demonstrate the efficacy of the solution procedure and offer insights on the behavior of per-node based power control.

Recently, researchers showed that "dirty paper coding" (DPC) achieves the capacity region of MIMO Gaussian broadcast channels (MIMO-BC). So far, there has been little study on how this fundamental information-theoretic result will impact the cross-layer design for MIMO-based ad hoc networks. To fill this gap, we consider the problem of jointly optimizing DPC power allocation at the physical layer and multihop/multipath routing at the network layer for MIMO-based ad hoc networks. This optimization problem turns out to be a challenging non-convex problem. To address this difficulty, we transform the original problem to an equivalent problem by exploiting the uplink-downlink duality. For the transformed problem, we propose a solution procedure that integrates Lagrangian dual decomposition, conjugate gradient projection based on matrix differential calculus, and cutting-plane methods.

In this paper, we investigate the maximum weighted sum-rate problem (MWSR) of MIMO Gaussian broadcast channels (MIMO-BC). We propose an efficient algorithm that employs conjugate gradient projections (CGP) to solve the MWSR problem. The proposed CGP offers provable convergence. By deflecting gradient direction to its Hessian conjugate, CGP enjoys a superlinear convergence rate. Also, CGP has a modest memory requirement. It only needs the solution information from the previous step. More importantly, CGP is able to solve the MWSR problem with arbitrary number of antennas on both sides of a MIMO-BC.

Recently, there has been tremendous interest in exploring the capacity region of multiple-input multiple-output broadcast channels (MIMO-BC). However, fairness, a very important performance measure of multi-user communications systems and networks, has not been addressed for MIMO-BC in the literature. In this paper, we study how to determine the weighted proportional fairness (WPF) capacity of MIMO-BC. The difficulty of finding the WPF capacity of MIMO-BC lies in that it contains two difficult subproblems: 1) a complex combinatorial optimization problem to determine the optimal decoding order in the dual MIMO multiple access channel (MEMO-MAC) and 2) a nonconvex optimization problem in computing the optimal input covariance matrices to achieve WPF capacity. To circumvent the difficulty in the first subproblem, we derive a set of optimality conditions that the optimal decoding order must satisfy. Based on these optimality conditions, we design an efficient algorithm called iterative gradient sorting (IGS) to determine the optimal decoding order by iteratively sorting the gradient entries and moving across corner points. We also show that this method can be geometrically interpreted as sequential gradient projections. For the second subproblem, we propose an efficient algorithm based on conjugate gradient projection (CGP) technique, which employs the concept of Hessian conjugate. We also develop a polynomial time algorithm to solve the projection subproblem.

The benefits of using mobile base station to prolong sensor network lifetime have been well recognized. However, due to the complexity of the problem (time-dependent network topology and traffic routing), theoretical performance limit and provably optimal algorithms remain difficult to develop. This paper fills this important gap by contributing theoretical results regarding the optimal movement of a mobile base station. Our main result hinges upon a novel transformation of the joint base station movement and flow routing problem from time domain to space domain. Based on this transformation, we first show that if the base station is allowed to be present only on a set of pre-defined points, then we can find the optimal time span for the base station on each of these points so that the overall network lifetime is maximized. Based on this finding, we show that when the location of the base station is un-constrained (i.e., can move to any point in the two-dimensional plane), we can develop an approximation algorithm for the joint mobile base station location and flow routing problem such that the network lifetime is guaranteed to be at least (1-epsiv) of the maximum network lifetime, where epsiv can be made arbitrarily small depending on required precision.

Cognitive radio (CR) is a revolution in radio technology and is viewed as an enabling technology for dynamic spectrum access. This paper investigates how to design distributed algorithm for a future multi-hop CR network, with the objective of maximizing data rates for a set of user communication sessions. We study this problem via a cross-layer optimization approach, with joint consideration of power control, scheduling, and routing. The main contribution of this paper is the development of a distributed optimization algorithm that iteratively increases data rates for user communication sessions. During each iteration, there are two separate processes, a Conservative Iterative Process (CIP) and an Aggressive Iterative Process (AIP). For both CIP and AIP, we describe our design of routing, minimalist scheduling, and power control/scheduling modules. To evaluate the performance of the distributed optimization algorithm, we compare it to an upper bound of the objective function, since the exact optimal solution to the objective function cannot be obtained via its mixed integer nonlinear programming (MINLP) formulation. Since the achievable performance via our distributed algorithm is close to the upper bound and the optimal solution (unknown) lies between the upper bound and the feasible solution obtained by our distributed algorithm, we conclude that the results obtained by our distributed algorithm are very close to the optimal solution.

Recently, cooperative communications, in the form of keeping each node with a single antenna and having a node exploit a relay node's antenna, is shown to be a promising approach to achieve spatial diversity. Under this communication paradigm, the choice of relay node plays a significant role in the overall system performance. In this paper, we study the relay node assignment problem in a network environment, where multiple source-destination pairs compete for the same pool of relay nodes in the network. The main contribution of this paper is the development of a polynomial time algorithm to solve this problem. A key idea in this algorithm is a "linear marking" mechanism, which is able to offer a linear complexity for each iteration. We give a formal proof of optimality for this algorithm. We also show several attractive properties associated with this algorithm.

Cognitive radio (CR) capitalizes advances in signal processing and radio technology and is capable of reconfiguring RF and switching to desired frequency bands. It is a frequency-agile data communication device that is vastly more powerful than recently proposed multi-channel multi-radio (MC-MR) technology. In this paper, we investigate the important problem of multi-hop networking with CR nodes. For such a network, each node has a pool of frequency bands (typically of unequal size) that can be used for communication. The potential difference in the bandwidth among the available frequency bands prompts the need to further divide these bands into sub-bands for optimal spectrum sharing. We characterize the behavior and constraints for such a multi-hop CR network from multiple layers, including modeling of spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. We develop a mathematical formulation with the objective of minimizing the required network-wide radio spectrum resource for a set of user sessions. Since the formulated model is a mixed-integer non-linear program (MINLP), which is NP-hard in general, we develop a lower bound for the objective by relaxing the integer variables and using a linearization technique. Subsequently, we design a near-optimal algorithm to solve this MINLP problem. This algorithm is based on a novel sequential fixing procedure, where the integer variables are determined iteratively via a sequence of linear programs. Simulation results show that solutions obtained by this algorithm are very close to the lower bounds obtained via the proposed relaxation, thus suggesting that the solution produced by the algorithm is near-optimal.

MIMO-based communications systems have great potential to improve network capacity for wireless ad hoc networks. Due to unique physical layer characteristics associated with MIMO, network performance is tightly coupled with mechanisms at physical, link, and routing layers. So far, research on MIMO-based wireless ad hoc networks is still in its infancy and few results are available. In this paper, we consider the problem of jointly optimizing power and bandwidth allocation at each node and multi-hop/multi-path routing in a MIMO-based wireless ad hoc network. We develop a solution procedure to this cross-layer optimization problem and use simulations to validate the efficacy of this solution.

There is growing interest in employing ultra-wideband (UWB) communication systems at the physical layer for multihop wireless networks. Recent efforts show that networking problems involving UWB systems should follow a cross-layer approach with consideration at multiple layers. Due to the nonlinear nature of the optimization problem, there are very limited theoretical results for this important problem. In this paper, we address this problem by considering a UWB-based ad hoc network. We study how to maximize capacity (in the form of a data rate utility) for a set of communication sessions. Via a cross-layer approach, we formulate this utility maximization problem into a nonlinear programming (NLP) problem, which takes into consideration routing, scheduling, and power control. We develop a solution procedure based on the so-called branch-and-bound framework. Within this framework, we employ a powerful optimization technique called reformulation linearization technique (RLT). We use numerical results to validate the efficacy of this solution procedure and offer insights on UWB-based ad hoc networks. This work provides a theoretical result for the achievable performance bound for a UWB-based ad hoc network.

Maximizing the total mutual information of multiuser multiple-input multiple-output (MIMO) systems with interference is a challenging problem. In this paper, we consider the power control problem of finding the maximum sum of mutual information for a multiuser network with mutually interfered MIMO links. We propose a new and powerful global optimization method using a branch-and-bound (BB) framework, coupled with a novel reformulation-linearization technique (RLT). The proposed BB/RLT guarantees finding a global optimum for multiuser MIMO networks with interference. To reduce the complexity of BB/RLT, we propose a modified BB variable selection strategy to accelerate the convergence process. Numerical examples are also given to demonstrate the efficacy of the proposed solution.

Software defined radio (SDR) capitalizes advances in signal processing and radio technology and is capable of reconfiguring RF and switching to desired frequency bands. It is a frequency-agile data communication device that is vastly more powerful than recently proposed multi-channel multi-radio (MC-MR) technology. In this paper, we investigate the important problem of multi-hop networking with SDR nodes. For such network, each node has a pool of frequency bands (not necessarily of equal size) that can be used for communication. The uneven size of bands in the radio spectrum prompts the need of further division into sub-bands for optimal spectrum sharing. We characterize behaviors and constraints for such multi-hop SDR network from multiple layers, including modeling of spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. We give a formal mathematical formulation with the objective of minimizing the required network-wide radio spectrum resource for a set of user sessions. Since such problem formulation falls into mixed integer non-linear programming (MINLP), which is NP-hard in general, we develop a lower bound for the objective by relaxing the integer variables and linearization. Subsequently, we develop a near-optimal algorithm to this MINLP problem. This algorithm is based on a novel sequential fixing procedure, where the integer variables are determined iteratively via a sequence of linear programming. Simulation results show that solutions obtained by this algorithm are very close to lower bounds obtained via relaxation, thus suggesting that the solution produced by the algorithm is near-optimal.

Software defined radio (SDR) is a revolution in radio technology that promises unprecedented flexibility in radio communications and is viewed as an enabling technology for dynamic spectrum access. This paper investigates how to support user communication sessions by jointly considering power control, scheduling, and flow routing for an SDR-based multi-hop wireless network. We develop a formal mathematical model for scheduling feasibility under the influence of power control. This model extends existing protocol interference model for wireless networks and can be used for a broad class of problems where power control (and thus transmission range and interference range) is part of the optimization space. We formulate a cross-layer optimization problem encompassing power control, scheduling, and flow routing. Subsequently, we develop an efficient solution procedure based on branch-and-bound technique and convex hull relaxation. Using simulation results, we demonstrate the efficacy of the solution procedure and offer insights on the impact of power control on scheduling feasibility, bandwidth efficiency, and bandwidth-footprint product (BFP).

Researchers have recently shown that the dirty-paper coding (DPC) is the optimal transmission strategy for multiple-input multiple-output Gaussian broadcast channels (MIMO BC). Moreover, by the channel duality, the nonconvex MIMO BC sum rate problem can be transformed to the convex dual MIMO multiple-access channel (MIMO MAC) problem with a sum power constraint. In this paper, we design an efficient algorithm based on conjugate gradient projection (CGP) to solve the MIMO BC maximum sum rate problem. Our proposed CGP algorithm solves the dual sum power MAC problem by utilizing the powerful concept of Hessian conjugate. We also develop a rigorous algorithm to solve the projection problem. We show that CGP enjoys provable convergence, scalability, and efficiency for large MIMO BC systems.

Ultra-wideband (UWB) has great potential for wireless communications in emerging applications such as sensor networks. This paper studies the following fundamental problems for UWB-based sensor networks: For a given network instance, what is the maximum data rate (network capacity) that can be received at the base-station (i.e., sink node)? What is the network capacity bound among arbitrary network instances? We show that these problems can be cast into a cross-layer formulation with joint consideration of routing, scheduling, power control, and rate assignment. For a given network instance, we find a closed-form network capacity as well as corresponding optimal routing, scheduling, power control, and rate assignment. We also find a network capacity bound among arbitrary network instances.

It has been shown in prior work that when used in multi-hop wireless networks, the 802.11 MAC suffers low throughput performance, especially when the number of hops is large. This paper clarifies the relation between exposed node and interference range, and proposes a location-assisted MAC protocol that schedules concurrent transmissions in a multi-hop wireless network. In the proposed algorithm, after identifying a node as an exposed node, a simple procedure is executed to validate the concurrent transmission of the exposed node (called scheduled transmission). Based on location information, the scheduled transmission is allowed if the current and scheduled transmitters are out of the interference range of each other's target receiver. Simulation results show that the proposed algorithm can effectively improve the throughput of multi-hop wireless networks.

In this paper, we consider the problem of optimal multipath routing for providing application performance guarantees in multi-hop wireless networks, using multiple description video streaming as our target application. We address this problem, which is shown to be NP-hard, with a novel reformulation-linearization technique (RLT) and branch-and-bound-based approach, and develop an algorithm that produces a pair of paths within the (1 - epsiv) range of the global optimum. The proposed algorithm is computationally efficient and this (1 - epsiv) optimal algorithm provides an elegant tradeoff between optimality and computational complexity.

MIMO-based systems have great potential to improve network capacity for wireless mesh networks (WMNs). Due to unique physical layer characteristics associated with MIMO systems, network performance is tightly coupled with mechanisms at physical layer and link layer. So far, research on MIMO-based WMNs is still in its infancy and little results are available in this important area. In this paper, we consider the problem of jointly optimizing power and bandwidth allocation at each node and multihop/multipath routing in a MIMO-based WMN where links operate in orthogonal channels. To solve this problem, we develop a mathematical solution procedure, which combines Lagrangian dual decomposition, gradient projection, and cutting-plane methods. We provide theoretical insights in deriving gradient projection and cutting plane methods. We also use simulations to verify the efficacy of our algorithm.

Traditionally, routing is considered solely as a network layer problem and has been decoupled from application layer objectives. Although such an approach offers simplicity in the design of the protocol stack, it does not offer good performance for certain applications such as video. In this paper, we explore the problem of how to perform routing with the objective of optimizing application layer performance. Specifically, we consider how to perform multipath routing for multiple description (MD) video in a multi-hop wireless network. We formulate this problem into an optimization problem with application performance metric as the objective function and routing and link layer considerations as constraints. We develop a formal branch-and-bound framework and exploit the so-called reformulation-linearization technique (RLT) in the solution procedure. We show that this solution procedure is able to produce a set of routes whose objective value is within (1 - e) of the optimum. We use simulation results to substantiate the efficacy of the solution procedure and compare the performance with that under non-cross-layer approach.

Current expectations on sensor node in terms of size, cost, and energy efficiency have led to a severely limited design space on hardware and software. In this paper, we explore capabilities at the network edge for sensor networks, aiming to reduce the hardware and software complexity of a sensor node without sacrificing network performance. We present a novel edge-based routing protocol, nicknamed BeamStar, for wireless sensor networks. Under BeamStar, the base station exploits some nice properties associated with directional antenna and power control at the base station. We devise a simple protocol so that each sensor node can determine its location information passively with minimum control overhead. We also show how to design a robust routing protocol based on the location information at each sensor node. Under the proposed protocol, sensor nodes are relieved of the activities (or burdens) that are associated with control and routing, thus enabling much simpler hardware and software implementation at sensor nodes. Simulation results demonstrate that BeamStar achieves high reliability at comparable energy consumptions as compared with prior work. It is a viable approach to pursue size and cost reduction for future sensor node design.

Since energy constraint is a fundamental issue for wireless sensor networks, network lifetime performance has become a key performance metric for such networks. In this paper, we consider a two-tier wireless sensor network and focus on the flow routing problem for the upper tier aggregation and forwarding nodes (AFNs). Specifically, we are interested in how to perform flow routing among the nodes when the bit rate from each source node is time-varying. We present an algorithm that can be used to construct a flow routing solution with the following properties: (1) If the average rate from each source node is known a priori, then flow routing solution obtained via such algorithm is optimal and offers provably maximum network lifetime performance; (2) If the average rate of each source node is unknown but is within a fraction (epsiv) of an estimated rate value, then network lifetime by the proposed flow routing solution is within 2epsiv/1-epsiv from the optimum. These results fill in an important gap in theoretical foundation for flow routing in energy-constrained sensor networks.

For media streaming in ad hoc networks, service replication has been demonstrated to be a quite effective countermeasure to streaming interruptions caused by fragile paths and dynamic topology. In this paper, we study the problem of joint routing and server selection for double description (DD) video streaming in ad hoc networks. We formulate the task as a combinatorial optimization problem and present tight lower and upper bounds for the achievable distortion. The upper bound provides a feasible solution to the formulated problem. Our extensive numerical results show that the bounds are very close to each other for all the cases studied, indicating the near-global optimality of the derived upper bounding solution. Moreover, we observe significant gains in video quality achieved by the proposed approach over existing server selection schemes. This justifies the importance of jointly considering routing and server selection for optimal MD video streaming

Maximizing the total mutual information of a multiuser multiple-input multiple-output (MIMO) system with interference is a well-known and challenging problem. In this paper, we consider the power control problem of finding the maximum sum of mutual information for multiuser MIMO systems with equal power allocation at each link. A new and powerful global optimization method using a branch-and-bound framework coupled with the reformulation-linearization technique (BB/RLT) is introduced. The proposed BB/RLT is the first such method that guarantees finding a global optimum for multiuser MIMO systems with interference. In addition, we propose a modified branch-and-bound (BB) variable selection strategy to accelerate the convergence process, and apply the proposed technique to several MIMO systems in order to demonstrate its efficacy.

Base station placement has significant impact on sensor network performance. Despite its significance, results on this problem remain limited, particularly theoretical results that can provide performance guarantee. This paper proposes a set of procedure to design (1 -- ε) approximation algorithms for base station placement problems under any desired small error bound ε > 0. It offers a general framework to transform infinite search space to a finite-element search space with performance guarantee. We apply this procedure to solve two practical problems. In the first problem where the objective is to maximize network lifetime, an approximation algorithm designed through this procedure offers 1 / ε2 complexity reduction when compared to a state-of-the-art algorithm. This represents the best known result to this problem. In the second problem, we apply the design procedure to address base station placement problem for maximizing network capacity. Our (1 -- ε) approximation algorithm is the first theoretical result on this problem.

Video communication is an important application area for a multihop wireless network. This paper studies the problem of finding the optimal encoding rates for a number of video sessions in such network. The objective is to maximize the video quality at the receivers and the optimization space takes into consideration, the interaction among all the active video sessions. A branch-and-bound solution procedure is proposed to solve this nonconvex, non-polynomial programming problem. Using analytical and simulation results, we show that this solution procedure is an effective approach for addressing such complex cross-layer optimization problem

This paper considers ultra-wideband (UWB)-based sensor networks and studies the following problem: given a set of source sensor nodes in the network each generating a certain data rate, is it possible to relay all these rates successfully to the base station? We will show that such problem is intrinsic cross-layer, and subsequently we formulate an optimization problem, with joint consideration of link-layer scheduling, power control, and network-layer routing. For large-sized networks, we propose an efficient heuristic algorithm by partitioning the given network into a core centered around the base station and a boundary edge. For the network core, we formulate a nonlinear programming problem, which can be solved by branch-and-bound approach. For data generated at network edge, we propose an algorithm to connect it to the network core. We use simulation results to demonstrate the efficacy of the proposed solution procedure, as well as the importance of cross-layer considerations.

Wireless sensor networks are becoming increasingly important in recent years due to their ability to detect and convey real-time, in-situ information for many civilian and military applications. A fundamental challenge for such networks lies in energy constraint, which poses a performance limit on the achievable network lifetime. We consider a two-tier wireless sensor network and address the network lifetime problem for upper-tier aggregation and forwarding nodes (AFNs). Existing flow routing solutions proposed for maximizing network lifetime require AFNs to split flows to different paths during transmission, which we call multisession flow routing solutions. If an AFN is equipped with a single transmitter/receiver pair, a multisession flow routing solution requires a packet-level power control at the AFN so as to conserve energy, which calls for considerable overhead in synchronization among the AFNs. In this paper, we show that it is possible to achieve the same optimal network lifetime by power control on a much larger timescale with the so-called single-session flow routing solutions, under which the packet-level power control and, thus, strict requirement on synchronization are not necessary. We also show how to perform optimal single-session flow routing when the bit-rate of composite flows generated by AFNs is time-varying, as long as the average bit-rate can be estimated

We study the problem of multipath routing for double description (DD) video in wireless ad hoc networks. We follow an application-centric cross-layer approach and formulate an optimal routing problem that minimizes the application layer video distortion. We show that the optimization problem has a highly complex objective function and an exact analytic solution is not obtainable. However, we find that a meta-heuristic approach such as genetic algorithms (GAs) is eminently effective in addressing this type of complex cross-layer optimization problems. We provide a detailed solution procedure for the GA-based approach. Simulation results demonstrate the superior performance of the GA-based approach versus several other approaches. Our efforts in this work provide an important methodology for addressing complex cross-layer optimization problems, particularly those involved in the application and network layers

Directional antenna has great potential to reduce power consumption in energy constrained wireless sensor networks. We consider a two-tier wireless sensor network where directional antenna is employed for upper-tier aggregation and forwarding nodes (AFNs). Existing flow routing solutions for maximizing network lifetime require each AFN to transmit multiple flows to different nodes at the same time, which we call multi-beam flow routing solution. In this paper, we show that it is possible to develop single-beam flow routing solution for nodes with directional antenna. More important, we show that the single-beam flow routing solution developed here is provably optimal in terms of network lifetime performance. This result is based on a novel technique to transform the optimal multi-beam flow routing into an equivalent single-beam flow routing solution. Numerical example illustrating how to obtain an optimal single-beam flow routing solution is also given.

We consider a two-tier wireless sensor network and focus on the flow routing problem for the upper tier aggregation and forwarding nodes (AFNs). Assuming each AFN is equipped with directional antennas for transmission, we are interested in how to perform flow routing at each node such that the network lifetime is maximized. We present a flow routing algorithm that provably has the following properties: (1) when the average source rate of each AFN is known a priori, the flow routing algorithm is optimal and gives maximum network lifetime performance; (2) when the average source rate of each AFN is unknown but is within a fraction, /spl epsiv/, of an estimated rate value, then the network lifetime given by the proposed flow routing algorithm is no more than 2/spl epsiv//(1-/spl epsiv/) from optimal. As a result, the proposed flow routing algorithm can provide predictable lifetime performance, even when the source bit rate can be time-varying.

Multiple description (MD) coding has a great potential for multimedia communications in wireless ad hoc networks. In this paper, we study the important problem of joint routing and server selection for MD video in ad hoc networks. We take a cross-layer approach to formulate the task as a combinatorial optimization problem and present tight lower and upper bounds for the achievable distortion. The upper bound also provides a feasible solution to the formulated problem. Our extensive numerical results show that the bounds are very close to each other for all the cases studied, indicating the near-global optimality of the derived upper bounding solution. Moreover, we observe significant gains in video quality achieved by the proposed approach over existing server selection schemes. This justifies the importance of jointly considering routing and server selection for optimal MD video streaming in wireless ad hoc networks. The proposed algorithms are computationally efficient and can be easily incorporated into existing routing protocols.

Real-time multimedia communication is an important service that should be supported in wireless ad hoc networks, In this paper we consider the problem of how to optimally support multiple concurrent video communication sessions in an ad hoc network. Our problem formulation follows an application-centric cross-layer approach with the objective of minimizing the average distortion for all video sessions via finding optimal paths for each session. Since this network-wide optimization problem is shown to be NP-complete, we pursue to develop competitive heuristic algorithms to address this problem. We find that genetic algorithms (GA) are eminently efficient in solving such cross-layer problems with complex objective functions and constraints. We describe a detailed solution procedure based on the GA approach and use numerical results to demonstrate its superior performance over other conventional approaches. Our efforts in this work provide an important methodology for addressing cross-layer network-wide optimal routing problems for video applications.

We consider a wireless ad hoc network where each node employs a single-beam directional antenna and is provisioned with limited energy. We are interested in an online multicast routing algorithm for successive multicast communication requests with the aim of maximizing network lifetime. The beamforming property associated with single-beam directional antenna introduces some unique problems that do not exist for omnidirectional antennas and therefore significantly increases the design space for routing algorithms. The contributions of this paper are twofold. First, we provide some important theoretical understanding on various multicast problems and deduce that even an offline version of this problem is NP-hard. Second, we develop a highly competitive online heuristic algorithm that takes network lifetime consideration directly into iterative calculations and show that an algorithm designed under this methodology provides consistently better performance than the current state-of-the-art algorithm that takes remaining energy into iterative calculations. The theoretical results and heuristic algorithm in this paper offer some important insights on algorithmic design for energy-constrained wireless ad hoc networks with directional antennas.

As developments in wireless ad hoc networks continue, there is an increasing expectation with regard to supporting content-rich multimedia communications (e.g., video) in such networks, in addition to simple data communications. The recent advances in multiple description (MD) video coding have made it highly suitable for multimedia applications in such networks. In this paper, we study the important problem of multipath routing for MD video in wireless ad hoc networks. We follow an application-centric cross-layer approach and formulate an optimal routing problem that minimizes the application layer video distortion. We show that the optimization problem has a highly complex objective function and an exact analytic solution is not obtainable. However, we find that a meta-heuristic approach such as genetic algorithms (GAs) is eminently effective in addressing this type of complex cross-layer optimization problems. We provide a detailed solution procedure for the GA-based approach, as well as a tight lower bound for video distortion. We use numerical results to demonstrate the superior performance of the GA-based approach and compare it to several other approaches. Our efforts in this work provide an important methodology for addressing complex cross-layer optimization problems, particularly those involving application and network layers.

Multipath transport provides higher usable bandwidth for a session. It has also been shown to provide load balancing and error resilience for end-to-end multimedia sessions. Two key issues in the use of multiple paths are (1) how to minimize the end-to-end delay, which now includes the delay along the paths and the resequencing delay at the receiver, and (2) how to select paths. In this paper, we present an analytical framework for the optimal partitioning of realtime multimedia traffic that minimizes the total end-to-end delay. Specifically, we formulate optimal traffic partitioning as a constrained optimization problem using deterministic network calculus, and derive its closed form solution. Compared with previous work, our scheme is simpler to implement and enforce. This analysis also greatly simplifies the solution to the path selection problem as compared to previous efforts. Analytical results show that for a given flow and a set of paths, we can choose a minimal subset to achieve the minimum end-to-end delay with O(N) time, where N is the number of available paths. The selected path set is optimal in the sense that adding any rejected path to the set will only increase the end-to-end delay.

Ultra-wideband (UWB) has great potential for wireless communications in emerging applications such as sensor networks. This paper considers UWB-based sensor networks and studies the following problem: given a set of source sensor nodes in the network each generating a certain data rate, is it possible to relay all these rates successfully to the base-station? We follow a cross-layer optimization approach, with joint consideration of link layer scheduling, power control, and network layer routing. The optimization problem is formulated as a non-linear programming problem. For small-sized networks, we develop a powerful approximation solution procedure to this problem based on the branch-and-bound approach and the novel Reformulation-Linearization Technique (RLT). For large-sized networks, we propose an efficient heuristic algorithm by partitioning the sensor network into a core centered around the base-station and an edge that is outside the core. We also provide a closed-form analysis for the maximum rate that a base-station can receive. Simulation results exhibit the efficacy of our proposed optimization solution procedure and demonstrate the importance of the cross-layer approach to UWB-based sensor networks.

Energy constraints have had a significant impact on the design and operation of wireless sensor networks. In this paper, we investigate base station selection (or anycast) problem in wireless sensor networks. We consider a wireless sensor network having multiple base stations (data sink nodes), where each source node must send all its locally generated data to only one base station. To maximize the network lifetime, it is essential to optimally match each source node to a particular base station in addition to finding an optimal routing solution. We propose a polynomial time heuristic for optimal base station selection for anycast via a sequential fixing procedure, under the assumption that the bit rate from each source node is constant. Through extensive simulation results, we show that this heuristic has excellent performance behavior and is a tight low bound that is very close to optimal solution for the original optimization problem.

Abstract — Wireless sensor networks that operate on batteries have limited network lifetime. There have been extensive recent research efforts on how to design protocols and algorithms to prolong network lifetime. However, due to energy constraint, even under the most efficient protocols and algorithms, the network lifetime may still be unable to meet the mission’s requirements. In this paper, we consider the energy provisioning problem for a two-tier wireless sensor network. In addition to provisioning additional energy on the existing nodes, we also consider deploying relay nodes (RNs) into the network to mitigate network geometric deficiency and prolong network lifetime. We formulate the joint problem of energy provisioning and relay node placement (EP-RNP) into a mixed-integer nonlinear programming (MINLP) problem. Since an MINLP problem is NP-hard in general, and even the state-of-the-art software and techniques are unable to offer satisfactory solutions, we develop a heuristic algorithm, called SPINDS, to address this problem. We show a number of novel algorithmic design techniques in the design of SPINDS that effectively transforms a complex MINLP problem into linear programming (LP) problems without losing critical points in its search space. Through numerical results, we show that SPINDS offers very attractive solution and some important insights to the EP-RNP problem. Index Terms — Energy provisioning, relay node placement, power control, network lifetime, flow routing, wireless sensor networks. I.

We consider generic two-tiered wireless sensor networks (WSNs) consisting of sensor clusters deployed around strategic locations, and base-stations (BSs) whose locations are relatively flexible. Within a sensor cluster, there are many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area, and there is at least one application node (AN) that receives raw data from these SNs, creates a comprehensive local-view, and forwards the composite bit-stream toward a BS. This paper focuses on the topology control process for ANs and BSs, which constitute the upper tier of two-tiered WSNs. Since heterogeneous ANs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. By proposing algorithmic approaches to locate BSs optimally, we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for ANs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different lifetime definitions according to the mission criticality of WSNs. By studying intrinsic properties of WSNs, we establish the upper and lower bounds of maximal topological lifetime, which enable a quick assessment of energy provisioning feasibility and topology control necessity. Numerical results are given to demonstrate the efficacy and optimality of the proposed topology control approaches designed for maximizing network lifetime of WSNs.

In this paper, we investigate the fundamental trade-offs in aggregate packet scheduling for support of guaranteed delay service. In our study, we consider two classes of aggregate packet scheduling algorithms: the static earliest time first (SETF) and dynamic earliest time first (DETF). Through these two classes of aggregate packet scheduling (and together with the simple FIFO packet scheduling algorithm), we show that, with additional timestamp information encoded in the packet header for scheduling purposes, we can significantly increase the maximum allowable network utilization level, while, at the same time, reducing the worst-case edge-to-edge delay bound. Furthermore, we demonstrate how the number of the bits used to encode the timestamp information affects the trade-off between the maximum allowable network utilization level and the worst-case edge-to-edge delay bound. In addition, the more complex DETF algorithms have far superior performance than the simpler SETF algorithms. These results illustrate the fundamental trade-offs in aggregate packet scheduling algorithms and shed light on their provisioning power in support of guaranteed delay service.

We consider the problem of multicasting multiple description (MD) video in wireless ad hoc networks. We follow an application-centric, cross-layer approach with the objective of minimizing video distortion. The contribution of this paper is twofold. First, we propose a practical MD video multicast scheme that uses multiple trees to achieve an improved error resilience performance. The proposed scheme also takes into account highly diverse wireless link bandwidths by using scalable coding for each description, thus further improving the overall video quality. Second, we formulate the optimized multicast routing as a combinatorial optimization problem and propose an efficient genetic algorithm (GA)-based metaheuristic solution procedure. Performance comparison with existing approaches show significant gains for a wide range of network operating conditions.

In this paper, we present a base station-assisted, location-aware routing protocol, which we call BeamStar, for wireless sensor networks. We make a major paradigm change by shifting computational intensive and energy consuming routing control overhead from sensor nodes to base stations. In BeamStar, each base station uses a directional antenna with power control. We show that these two capabilities are sufficient for each sensor node to determine its location, and the local location information is sufficient for power-efficient routing. Therefore, sensors are relieved of control and routing burdens, such as maintaining clusters and exchanging control information, yielding substantial energy savings. In addition, each data packet is forwarded in a constrained, loop-free mesh towards the base station, making data delivery robust to sensor failures and transmission errors. The proposed routing scheme is suitable for large-scale, dense sensor networks monitoring rare events.

Under the weak consistency paradigm, time-to-live (TTL)-based hierarchical caching systems are proposed to support Web content delivery. Within such systems, due to strictly hierarchical caching structure and linear TTL countdown function, a single user request may encounter consecutive cache miss events at cache servers of different levels in the hierarchy. This behavior, referred to as miss synchronization, is the main cause of a sudden increase in user-perceived response time. In this paper, we examine this undesirable behavior to gain a better understanding of its properties and characteristics. To mitigate this problem, we propose a family of nonlinear TTL countdown functions using a novel concept called extended lifetime. Performance analysis indicates that the proposed approach can effectively avoid miss synchronization in hierarchical caching systems. Further, a carefully-designed nonlinear countdown function can reduce cache miss ratio and user response time without any significant increase in outlived objects.

We study the network lifetime problem by considering not only the maximized time until the first node fails, but also the maximized lifetime for all the nodes in the network which we define as the lexicographic max-min (LMM) node lifetime problem. The main contributions of this paper are two-fold. First, we develop a polynomial-time algorithm to derive the LMM-optimal node lifetime vector, which effectively circumvents the computational complexity problem associated with an existing state-of-the-art approach, which is exponential. Second, we present a simple (also polynomial-time) algorithm to calculate the flow routing schedule such that the LMM-optimal node lifetime vector can be achieved. Our results in this paper advance the state-of-the-art algorithmic design to network-wide node lifetime problems.

Active queue management (AQM) is an effective method to enhance congestion control, and to achieve tradeoff between link utilization and delay. The de facto standard, random early detection (RED), and most of its variants use queue length as a congestion indicator to trigger packet dropping. The proportional-integral (PI), use both queue length and traffic input rate as congestion indicators; effective stability model and practical design rules built on the TCP control model and abstracted AQM model reveal that such schemes enhance the stability of a system. In this paper, we propose an AQM scheme with fast response time, yet good robustness. The scheme, called loss ratio based RED (LRED), measures the latest packet loss ratio, and uses it as a complement to queue length in order to dynamically adjust packet drop probability. Employing the closed-form relationship between packet loss ratio and the number of TCP flows, this scheme is responsive even if the number of TCP flows varies significantly. We also provide the design rules for this scheme based on the well-known TCP control model. This scheme's performance is examined under various network configurations, and compared to existing AQM schemes, including PI, random exponentially marking (REM), and adaptive virtual queue (AVQ). Our simulation results show that, with comparable complexity', this scheme has short response time, better robustness, and more desirable tradeoff than PI, REM, and AQV, especially under highly dynamic network and heavy traffic load.

This paper proposes a rate-based active queue management algorithm or RAQM. It uses the aggregated traffic input rate to calculate packet drop probability according to an exponential rule. We analyze the stability and investigate practical implementation issues of the RAQM. Simulations are carried out to study RAQM performance and to compare with other AQM algorithms, in particular PI and REM schemes. The results demonstrate that RAQM achieves better stability and faster response as it can quickly regulate the queue length to the expected value with small overshoot. RAQM also obtains better tradeoff between link utilization and queuing delay, and obtains higher goodput with the same buffer size as in PI and REM schemes. Finally RAQM has O(1) complexity, thus is independent of the number of flows.

An important performance consideration for wireless sensor networks is the amount of information collected by all the nodes in the network over the course of network lifetime. Since the objective of maximizing the sum of rates of all the nodes in the network can lead to a severe bias in rate allocation among the nodes, we advocate the use of lexicographical max-min (LMM) rate allocation for the nodes. To calculate the LMM rate allocation vector, we develop a polynomial-time algorithm by exploiting the parametric analysis (PA) technique from linear programming (LP), which we call serial LP with Parametric Analysis (SLP-PA). We show that the SLP-PA can be also employed to address the so-called LMM node lifetime problem much more efficiently than an existing technique proposed in the literature. More important, we show that there exists an elegant duality relationship between the LMM rate allocation problem and the LMM node lifetime problem. Therefore, it is sufficient to solve any one of the two problems and important insights can be obtained by inferring duality results for the other problem.

Caching is an important means to scale up the growth of the Internet. Weak consistency is a major approach used in Web caching and has been deployed in various forms. The paper investigates some fundamental properties and performance issues associated with an expiration-based caching system. We focus on a hierarchical caching system based on the time-to-live expiration mechanism and present a basic model for such system. By analyzing the intrinsic timing behavior of the basic model, we derive important performance metrics from the perspectives of the caching system and end users, respectively. Based on the results for the basic model, we introduce threshold-based and randomization-based techniques to enhance and generalize the basic model further. Our results offer some important insights into a hierarchical caching system based on the weak consistency paradigm.

We present a novel bandwidth broker architecture for scalable support of guaranteed services that decouples the QoS control plane from the packet forwarding plane. More specifically, under this architecture, core routers do not maintain any QoS reservation states, whether per-flow or aggregate. Instead, the QoS reservation states are stored at and managed by a bandwidth broker. There are several advantages of such a bandwidth broker architecture. Among others, it avoids the problem of inconsistent QoS states faced by the conventional hop-by-hop, distributed admission control approach. Furthermore, it allows us to design efficient admission control algorithms without incurring any overhead at core routers. The proposed bandwidth broker architecture is designed based on a core stateless virtual time reference system developed recently. This virtual time reference system provides a unifying framework to characterize, in terms of their abilities to support delay guarantees, both the per-hop behaviors of core routers and the end-to-end properties of their concatenation. We focus on the design of efficient admission control algorithms under the proposed bandwidth broker architecture. We consider both per-flow end-to-end guaranteed delay services and class-based guaranteed delay services with flow aggregation. Using our bandwidth broker architecture, we demonstrate how admission control can be done on a per domain basis instead of on a "hop-by-hop" basis. Such an approach may significantly reduce the complexity of the admission control algorithms. In designing class-based admission control algorithms, we investigate the problem of dynamic flow aggregation in providing guaranteed delay services and devise a new apparatus to effectively circumvent this problem. We conduct detailed analyses to provide theoretical underpinning for our schemes as well as to establish their correctness. Simulations are also performed to demonstrate the efficacy of our schemes.

We consider the fundamental problem of bandwidth allocation among flows in a packet-switched network. The classical max-min rate allocation has been widely regarded as a fair rate allocation policy. But, for a flow with a minimum rate requirement and a peak rate constraint, the classical max-min policy no longer suffices to determine rate allocation since it is not capable of supporting either the minimum rate or the peak rate constraint from a flow. We generalize the theory of the classical max-min rate allocation with the support of both the minimum rate and peak rate constraints for each flow. Additionally, to achieve generalized max-min rate allocation in a fully distributed packet network, we present a distributed algorithm that uses a feedback-based flow control mechanism. Our design not only offers a fresh perspective on flow marking technique, but also advances the state-of-the-art flow marking technique favored by other researchers. We provide proof that such a distributed algorithm, through asynchronous iterations, will always converge to the generalized max-min rate allocation under any network configuration and any set of link distances. We use simulation results to demonstrate the fast convergence property of the distributed algorithm.

For video broadcasting applications in a wireless environment, layered transmission is an effective approach to support heterogeneous receivers with varying bandwidth requirements. There are several important issues that need to be addressed for such layered video broadcasting systems. At the session level, it is not clear how to allocate bandwidth resources among competing video sessions. For a session with a given bandwidth, questions such as how to set up the video layering structure (i.e., number of layers) and how much bandwidth should be allocated to each layer remain to be answered. The solutions to these questions are further complicated by practical issues such as uneven popularity among video sessions and video layering overhead. This paper presents a systematic study to address these issues for a layered video broadcasting system in a wireless environment. The approach is to employ a generic utility function for each receiver under each video session. They cast the joint problem of layering and bandwidth allocation (among sessions and layers) into an optimization problem of total system utility among all the receivers. By using a simple two-step decomposition of intersession and intrasession optimization, they derive efficient algorithms to solve the optimal layering and bandwidth allocation problem. Practical issues for deploying the optimal algorithm in typical wireless networks are also discussed. Simulation results show that the optimal layering and bandwidth allocation improves the total system utility under various settings.

Adopting time-to-live (TTL) based hierarchical caching systems is considered to be a viable approach to support Web content delivery under the weak consistency paradigm. However, with a strictly hierarchical structure in these systems, a single user request may trigger multiple consecutive miss events at cache servers of different levels. This undesirable miss synchronization can cause a sudden degradation of user-perceived performance in terms of response time. We offer a comprehensive examination of this undesirable behavior and propose a randomized request redirection approach to circumvent the structural restrictions in TTL-based hierarchical caching systems. Performance analysis and evaluation indicate that the proposed approach can effectively rebalance server overhead and network delay, which in turn reduces end user response time.

Hierarchical caching is deployed to scale up the explosive Web growth, and the expiration-based mechanism is adopted as an economic means to support the weak consistency in this context. However, given a hierarchy, the user perceived performance heavily depends on its position. Normally, a user near the hierarchy leaf suffers higher miss rate and longer response time. Such an intrinsic property can discourage users from participating in any hierarchical caching systems. In this paper, we analyze the performance of a proposed approach, i.e., freshness and retrieval threshold based cache prefetching, to mitigate the bias against leaf users. We also use ns-2 to further substantiate our analysis. By adopting this approach with the appropriate parameters, the fairness among users within a caching hierarchy can be considerably improved.

For video broadcasting applications in a wireless environment, layered transmission is an effective approach to support heterogeneous receivers with varying bandwidth requirements. There are several important issues that need to be addressed for such layered video broadcasting systems. At the session level, it is not clear how to allocate bandwidth resources among competing video sessions. For a session with a given bandwidth, questions such as how to set up the video layering structure (i.e., number of layers) and how much bandwidth should be allocated to each layer remain to be answered. The solutions to these questions are further complicated by practical issues such as uneven popularity among video sessions and video layering overhead. This paper presents a systematic study to address these issues for a layered video broadcasting system in a wireless environment. Our approach is to employ a generic utility function for each receiver under each video session. We cast the joint problem of layering and bandwidth allocation (among sessions and layers) into an optimization problem of total system utility among all the receivers. By using a simple 2-step decomposition of inter-session and intra-session optimization, we derive efficient algorithms to solve the optimal layering and bandwidth allocation problem. Practical issues for deploying the optimal algorithm in wireless networks are also discussed. Simulation results show that the optimal layering and bandwidth allocation improves the total system utility.

We consider a two-tiered Wireless Sensor Network (WSN) consisting of sensor clusters deployed around strategic locations and base-stations (BSs) whose locations are relatively flexible. Within a sensor cluster, there are many small sensor nodes (SNs) that capture, encode and transmit relevant information from the designated area, and there is at least one application node (AN) that receives raw data from these SNs, creates a comprehensive local-view, and forwards the composite bit-stream toward a BS. In practice, both SN and AN are battery-powered and energy-constrained, and their node lifetimes directly affect the network lifetime of WSNs. In this paper, we focus on the topology control process for ANs and BSs, which constitute the upper tier of a two-tiered WSN. We propose approaches to maximize the topological network lifetime of the WSN, by arranging BS location and inter-AN relaying optimally. Based on an algorithm in Computational Geometry, we derive the optimal BS locations under three topological lifetime definitions according to mission criticality. In addition, by studying the intrinsic properties of WSNs, we establish the upper and lower bounds of their maximal topological lifetime. When inter-AN relaying becomes feasible and favorable, we continue to develop an optimal parallel relay allocation to further prolong the topological lifetime of the WSN. An equivalent serialized relay schedule is also obtained, so that each AN only needs to have one relay destination at any time throughout the mission. The experimental performance evaluation demonstrates the efficacy of topology control as a vital process to maximize the network lifetime of WSNs.

Providing quality-of-service (QoS) to video delivery in wireless networks has attracted intensive research over the years. A fundamental problem in this area is how to map QoS criterion at different layers and optimize QoS across the layers. In this paper, we investigate this problem and present a cross-layer mapping architecture for video transmission in wireless networks. There are several important building blocks in this architecture, among others, QoS interaction between video coding and transmission modules, QoS mapping mechanism, video quality adaptation, and source rate constraint derivation. We describe the design and algorithms for each building block, which either builds upon or extend the state-of-the-art algorithms that were developed without much considerations of other layers. Finally, we use simulation results to demonstrate the performance of the proposed architecture for progressive fine granularity scalability video transmission over time-varying and nonstationary wireless channel.

We advocate the notion of service overlay network (SON) as an effective means to address some of the issues, in particular, end-to-end quality of service (QoS), plaguing the current Internet, and to facilitate the creation and deployment of value-added Internet services such as VoIP, Video-on-Demand, and other emerging QoS-sensitive services. The SON purchases bandwidth with certain QoS guarantees from the individual network domains via bilateral service level agreement (SLA) to build a logical end-to-end service delivery infrastructure on top of the existing data transport networks. Via a service contract, users directly pay the SON for using the value-added services provided by the SON. In this paper, we study the bandwidth provisioning problem for a SON which buys bandwidth from the underlying network domains to provide end-to-end value-added QoS sensitive services such as VoIP and Video-on-Demand. A key problem in the SON deployment is the problem of bandwidth provisioning, which is critical to cost recovery in deploying and operating the value-added services over the SON. The paper is devoted to the study of this problem. We formulate the bandwidth provisioning problem mathematically, taking various factors such as SLA, service QoS, traffic demand distributions, and bandwidth costs. Analytical models and approximate solutions are developed for both static and dynamic bandwidth provisioning. Numerical studies are also performed to illustrate the properties of the proposed solutions and demonstrate the effect of traffic demand distributions and bandwidth costs on SON bandwidth provisioning.

Caching is an important means to scale up the growth of the Internet. Weak consistency is a major approach used in Web caching and has been deployed in various forms. The paper investigates some properties and performance issues of an expiration-based caching system. We focus on a hierarchical caching system based on the time-to-live (TTL) expiration mechanism and present a basic model for such a system. By analyzing the intrinsic TTL timing behavior in the basic model, we derive several important performance metrics from the perspective of the caching system and end users, respectively. Our results offer some basic understanding of a hierarchical caching system based on the weak consistency paradigm.

Effective service delivery capabilities are critical to the transformation of the Internet into a viable commercial infrastructure. At the same time, there are several design limitations that prevent this. We propose a novel service overlay architecture that serves as a flexible, unifying platform for delivering services over the Internet. We introduce a new addressing scheme and an associated service layer, which enables service-oriented routing and forwarding over the underlying IP network domain. We also describe the functionality of the network elements that are introduced by our architecture, namely service gateway (SG) and service point-of-presence (S-PoP). We also present examples to demonstrate the efficacy of our architecture.

We advocate the notion of service overlay network (SON) as an effective means to address some of the issues, in particular end-to-end QoS, plaguing the current Internet, and to facilitate the creation and deployment of value-added Internet services such as VoIP, video-on-demand, and other emerging QoS-sensitive services. A SON purchases bandwidth with certain QoS guarantees from individual network domains via a bilateral service level agreement (SLA) to build a logical end-to-end service delivery infrastructure on top of existing data transport networks. Via a service contract, users directly pay the SON provider for using the value-added services provided by the SON. We study the bandwidth provisioning problem for a service overlay network which is critical to the cost recovery in deploying and operating value-added services over the SON. We mathematically formulate the bandwidth provisioning problem, taking into account various factors such as SLA, service QoS, traffic demand distributions, and bandwidth costs. Analytical models and approximate solutions are developed for both static and dynamic bandwidth provisioning. Numerical studies are also performed to illustrate the properties of the proposed solutions and demonstrate the effect of traffic demand distributions and bandwidth costs on the bandwidth provisioning of a SON.

In this paper we study the scalability issue in the design of a centralized bandwidth broker model for dynamic control and management of QoS provisioning. We propose and develop a path-oriented, quota-based dynamic bandwidth allocation mechanism for efficient admission control operations under the centralized bandwidth broker model. We demonstrate that this dynamic bandwidth allocation mechanism can significantly reduce the overall number of QoS state accesses/updates, thereby increasing the overall call processing capability of the bandwidth broker. Based on the proposed dynamic bandwidth allocation mechanism, we also extend the centralized architecture with a single bandwidth broker to a hierarchically distributed architecture with multiple bandwidth brokers to further improve its scalability. Our study demonstrates that the bandwidth broker architecture can be designed in such a manner that it scales with the increase in the network capacity.

The paper presents a formal study on optimal bandwidth partitioning for multi-rate video broadcasting in a broadband wireless network. The formulation is generic in that it considers both inter-session and intra-session bandwidth partitions for layering as well as stream replication based broadcasting. It also takes into account the most fundamental issues associated with video transmission, including encoding overhead, non-linear relationship between the receiver perceived video quality and the delivered bandwidth, as well as intra-session and inter-session fairness. Specifically, we consider the bandwidth-constrained case with channelized allocation. We show that there exist polynomial time algorithms for both inter-session and intra-session partitioning problems.

This paper presents architecture and mechanisms to support multiple QoS under the DiffServ paradigm. On the data plane, we present a node architecture based on the virtual time reference system (VTRS), which is a unifying scheduling framework for scalable support of the guaranteed service. The key building block of our node architecture is the core-stateless virtual clock (CSVC) scheduling algorithm, which, in terms of providing delay guarantee, has the same expressive power as a stateful weighted fair queueing (WFQ) scheduler. Based on the CSVC scheduler, we design a node architecture that is capable of supporting integrated transport of the guaranteed service (GS), the premium service (PS), the assured service (AS), and the traditional best-effort (BE) service. On the control plane, we present a BE architecture to provide flexible resource allocation and QoS provisioning. Simulation results demonstrate that our architecture and mechanisms can provide scalable and flexible transport of integrated traffic of the GS, the PS, the AS, and the BE services.

We investigate the fundamental trade-offs in aggregate packet scheduling for the support of guaranteed delay service. Besides the simple FIFO packet scheduling algorithm, we consider two new classes of aggregate packet scheduling algorithms: the static earliest time first (SETF) and dynamic earliest time first (DETF). Through these two classes of aggregate packet scheduling, we show that, with additional time stamp information encoded in the packet header for scheduling purpose, we can significantly increase the maximum allowable network utilization level, while at the same time reducing the worst-case edge-to-edge delay bound. Furthermore, we demonstrate how the number of the bits used to encode the time stamp information affects the trade-off between the maximum allowable network utilization level and the worst-case edge-to-edge delay bound. In addition, the more complex DETF algorithms have far better performance than the simpler SETF algorithms. These results illustrate the fundamental trade-offs in aggregate packet scheduling algorithms and shed light on their provisioning power in support of guaranteed delay service.

With the emergence of broadband wireless networks and increasing demand of multimedia information on the Internet, wireless multimedia services are foreseen to become widely deployed in the next decade. Real-time video transmission typically has requirements on quality of service (QoS). However, wireless channels are unreliable and the channel bandwidth varies with time, which may cause severe degradation in video quality. In addition, for video multicast, the heterogeneity of receivers makes it difficult to achieve efficiency and flexibility. To address these issues, three techniques, namely, scalable video coding, network-aware adaptation of end systems, and adaptive QoS support from networks, have been developed. This paper unifies the three techniques and presents an adaptive framework, which specifically addresses video transport over wireless networks. The adaptive framework consists of three basic components: (1) scalable video representations; (2) network-aware end systems; and (3) adaptive services. Under this framework, as wireless channel conditions change, mobile terminals and network elements can scale the video streams and transport the scaled video streams to receivers with a smooth change of perceptual quality. The key advantages of the adaptive framework are: (1) perceptual quality is changed gracefully during periods of QoS fluctuations and hand-offs; and (2) the resources are shared in a fair manner.

Due to the explosive growth of the Internet and increasing demand for multimedia information on the Web, streaming video over the Internet has received tremendous attention from academia and industry. Transmission of real-time video typically has bandwidth, delay, and loss requirements. However, the current best-effort Internet does not offer any quality of service (QoS) guarantees to streaming video. Furthermore, for video multicast, it is difficult to achieve both efficiency and flexibility. Thus, Internet streaming video poses many challenges. In this article we cover six key areas of streaming video. Specifically, we cover video compression, application-layer QoS control, continuous media distribution services, streaming servers, media synchronization mechanisms, and protocols for streaming media. For each area, we address the particular issues and review major approaches and mechanisms. We also discuss the tradeoffs of the approaches and point out future research directions.

This paper presents a network node architecture and several traffic management mechanisms that are capable of achieving QoS provisioning for the guaranteed service (GS), the controlled-load (CL) service, and the best-effort (BE) service under IETF integrated services (IntServ) paradigm. Our architecture offers the attractive feature of in-sequence delivery for all packets, albeit some of which may be out-of-profile. Simulation results show that, once admitted into the network, our architecture and traffic management algorithms provide hard performance guarantees to GS flows under all conditions, consistent (or soft) performance to CL flows under both light load and heavy load conditions, and minimal negative impact to in-profile GS, CL and BE traffic should there be any out-of-profile behavior from some flows.

We present an end-to-end approach to generalize the classical theory of rate distortion (R-D) optimized mode selection for point-to-point video communication. We introduce a notion of global distortion by taking into consideration of both the path characteristics and the receiver behavior, in addition to the source behavior. We derive, for the first time, a set of accurate global distortion metrics for any packetization scheme. Equipped with the global distortion metrics, we design an R-D optimized mode selection algorithm to provide the best trade-off between compression efficiency and error resilience. As an application, we integrate our theory with point-to-point MPEG-4 video conferencing over the Internet. Simulation results conclusively demonstrate that our end-to-end approach offers superior performance over the classical approach for Internet video conferencing.

This paper proposes an adaptive quality-of-service (QoS) control to increase the robustness of MPEG-4 video communication over wireless channels. More specifically, the proposed adaptive QoS control consists of optimal mode selection and delay-constrained hybrid automatic repeat request (ARQ). The optimal mode selection is employed to provide QoS support on the compression layer while delay-constrained hybrid ARQ is used to provide QoS support on the link layer. Simulation results show that the proposed adaptive QoS control achieves satisfactory quality for MPEG-4 video under dynamically changing wireless channel conditions and utilizes network resources efficiently.

The IETF differentiated services (DiffServ) framework achieves scalability by moving complexity out of the core of the network into edge routers which process fewer number of flows. Previously, an end-to-end service called the premium service (PS) has been proposed under the DiffServ model to provide coarse grained guaranteed rate service. This paper presents a buffer management mechanism based on simple FIFO scheduling to support integrated transport of the PS and the traditional best effort (BE) service. A key feature in our buffer management is to perform selective packet discarding from an embedded queue at a shared buffer. We show that such a buffer management mechanism is capable of achieving the following objectives: (1) a core router does not maintain any state information for any flow (i.e., stateless); (2) the bandwidth for an PS flow is guaranteed (in conjunction with a bandwidth broker (BB) for admission control). Simulation results demonstrate that our buffer management mechanism can achieve integrated transport of the PS and the BE services.

There has been great interest in transporting real-time video over wireless IP networks from both industry and academia. Real-time video applications have quality-of-service (QoS) requirements. However, the fluctuations of wireless channel conditions pose many challenges to providing QoS for video transmission over wireless IP networks. It has been shown that scalable video coding and adaptive services are viable solutions under a time-varying wireless environment. We propose an adaptive framework to support quality video communication over wireless IP networks. The adaptive framework includes: (1) scalable video representations, (2) network-aware video applications, and (3) adaptive services. Under this framework, as wireless channel conditions change, the mobile terminal and network elements can scale the video streams and transport the scaled video streams to receivers with acceptable perceptual quality. The key advantages of the adaptive framework are: (1) perceptual quality is degraded gracefully under severe channel conditions; (2) network resources are efficiently utilized; and (3) the resources are shared in a fair manner.

Rate-distortion (R-D) optimized mode selection is a fundamental problem for video communication over packet-switched networks. The classical R-D optimized mode selection only considers quantization distortion at the source. Such an approach is unable to achieve global optimality under the error-prone environment since it does not consider the packetization behavior at the source, the transport path characteristics, and receiver behavior. This paper presents an end-to-end approach to generalize the classical theory of R-D optimized mode selection for point-to-point video communication. We introduce a notion of global distortion by taking into consideration both the path characteristics (i.e., packet loss) and the receiver behavior (i.e., the error concealment scheme), in addition to the source behavior (i.e., quantization distortion and packetization). We derive, for the first time, a set of accurate global distortion metrics for any packetization scheme. Equipped with the global distortion metrics, we design an R-D optimized mode selection algorithm to provide the best tradeoff between compression efficiency and error resilience. The theory developed in this paper is general and is applicable to many video coding standards, including H.261/263 and MPEG-1/2/4. As an application, we integrate our theory with point-to-point MPEG-4 video conferencing over the Internet, where a feedback mechanism is employed to convey the path characteristics (estimated at the receiver) and receiver behavior (error concealment scheme) to the source. Simulation results are discussed.

We propose and develop a novel virtual time reference system as a unifying scheduling framework to provide scalable support for guaranteed services. This virtual time reference system is designed as a conceptual framework upon which guaranteed services can be implemented in a scalable manner using the DiffServ paradigm. The key construct in the proposed virtual time reference system is the notion of packet virtual time stamps, whose computation is core stateless, i.e., no per-flow states are required for its computation. We lay the theoretical foundation for the definition and construction of packet virtual time stamps. We describe how per-hop behavior of a core router (or rather its scheduling mechanism) can be characterized via packet virtual time stamps, and based on this characterization establish end-to-end per-flow delay bounds. Consequently, we demonstrate that, in terms of its ability to support guaranteed services, the proposed virtual time reference system has the same expressive power and generality as the IntServ model. Furthermore, we show that the notion of packet virtual time stamps leads to the design of new core stateless scheduling algorithms, especially work-conserving ones. In addition, our framework does not exclude the use of existing scheduling algorithms such as stateful fair queuing algorithms to support guaranteed services.

Delivering real-time video over the Internet is an important component of many Internet multimedia applications. Transmission of real-time video has bandwidth, delay, and loss requirements. However the current Internet does not offer any quality of service (QoS) guarantees to video transmission over the Internet. In addition, the heterogeneity of the networks and end systems makes it difficult to multicast Internet video in an efficient and flexible way. Thus, designing protocols and mechanisms for Internet video transmission poses many challenges. In this paper, we take a holistic approach to these challenges and present solutions from both transport and compression perspectives. With the holistic approach, we design a framework for transporting real-time Internet video, which includes two components, namely, congestion control and error control. Specifically congestion control consists of rate control, rate-adaptive encoding, and rate shaping; error control consists of forward error correction (FEC), retransmission error resilience, and error concealment. For the design of each component in the framework, we classify approaches and summarize representative research work. We point out there exists a design space which can be explored by video application designers and suggest that the synergy of both transport and compression could provide good solutions.

With the success of the Internet and flexibility of MPEG-4, transporting MPEG-4 video over the Internet is expected to be an important component of many multimedia applications in the near future. Video applications typically have delay and loss requirements, which cannot be adequately supported by the current Internet. Thus, it is a challenging problem to design an efficient MPEG-4 video delivery system that can maximize the perceptual quality while achieving high resource utilization. This paper addresses this problem by presenting an end-to-end architecture for transporting MPEG-4 video over the Internet. We present a framework for transporting MPEG-4 video, which includes source rate adaptation, packetization, feedback control, and error control. The main contributions of this paper are: (1) a feedback control algorithm based on the Real Time Protocol (RTP) and the Real Time Control Protocol (RTCP); (2) an adaptive source-encoding algorithm for MPEG-4 video which is able to adjust the output rate of MPEG-4 video to the desired rate; and (3) an efficient and robust packetization algorithm for MPEG video bit-streams at the sync layer for Internet transport. Simulation results show that our end-to-end transport architecture achieves good perceptual picture quality for MPEG-4 video under low bit-rate and varying network conditions and efficiently utilizes network resources.

This paper presents an implementation architecture based on per flow queueing that is capable of achieving QoS provisioning for future integrated services networks consisting of the guaranteed service (GS), the controlled-load (CL), and the best-effort (BE) service classes. We propose several novel traffic management mechanisms, including adaptive rate allocation for controlled-load (ARC), a hybrid model-based and measurement-based admission control algorithm for GS and CL flows, and a quasi-pushout plus (QPO+) packet discarding mechanism. Simulation results show that our architecture and algorithms provide hard QoS guarantees to GS flows under all conditions, consistent (soft) QoS to CL flows under both light and heavy load conditions, and effective control of negative impact from non-conforming CL flows. Our architecture and algorithms also resolve several issues associated with the traditional class-based approach.

It is a challenging problem to design an efficient MPEG-4 video delivery system that can machine the perceptual quality while achieving high resource utilization. This paper addresses this problem by presenting an architecture of transporting MPEG-4 video over the Internet, which includes an end-to-end feedback control algorithm and a source encoding rate control algorithm. Our feedback control algorithm is capable of estimating the available bandwidth in the network based on the feedback information from the receiver, while our source encoding rate control algorithm is able to adjust the encoding rate of MPEG-4 video to the desired rate. Simulation results demonstrate that our architecture achieves good perceptual picture quality under low bit-rate and varying network conditions while efficiently utilizing network resources.

This paper presents a novel generic weight-based network bandwidth sharing policy and an available bit rate (ABR) algorithm that achieves this policy. Our policy supports the minimum cell rate (MCR) requirement and peak cell rate (PCR) constraint of each connection and allocates network bandwidth among all connections based on a weight associated with each connection. To achieve this policy for ABR connections, we design an ABR algorithm which employs per virtual connection (VC) accounting to keep track of the state information of each VC. Our ABR algorithm is proven to provide guaranteed convergence to our generic weight-based rate allocation policy under any network configuration and any set of link distances. Simulation results show that our ABR algorithm has a fast convergence property.

We generalize the classical max-min rate allocation policy with the support of the minimum rate requirement and peak rate constraint for each connection. Since a centralized algorithm for the generalized max-min (GMM) rate allocation requires global information, which is difficult to maintain and manage in a large network, we develop a distributed protocol to achieve the GMM policy using the available bit rate (ABR) flow control mechanism. We give a proof that our distributed protocol converges to the GMM rate allocation through distributed and asynchronous iterations under any network configuration and any set of link distances.

The operation of MPEG-2 systems is modeled and simulated when an MPEG-2 transport stream is delivered through an ATM network with jitter. End-to-end packet-based analysis is performed for delivery of MPEG-2 transport streams over ATM networks. A novel approach to analyzing the decoder buffer behaviour in the presence of network jitter is presented. The probability density function of the interarrival time of the ATM adaptation layer 5 (AAL5) protocol data unit (PDU) is derived from an MPEG-2 video source model and an ATM network jitter model. Based on a real-time decoding requirement of the MPEG-2 transport stream (TS) system target decoder (T-STD), the decoder buffer behaviour is simulated. In this simulation, the packets' arrivals follow the derived probability density function of the AAL5 PDU interarrival time. The modeling and simulation results show the interactions among packet loss ratio, decoder buffer size, and network jitter level. We found that jitter affects decoder buffer size and packet loss ratio in a significant way.

A novel concept in available bit rate (ABR) service model as defined by the ATM Forum is the minimum cell rate (MCR) bandwidth guarantee for each connection. In this paper, we present a network bandwidth allocation policy to support each ABR connection's MCR requirement, as well as its peak cell rate (PCR) constraint. Furthermore, we develop two explicit-rate (ER) based ABR algorithms consistent with the ATM Forum ABR traffic management framework to achieve this rate allocation policy. The first ABR implementation is a simple heuristic algorithm which does not require per-VC accounting. It requires minimal implementation complexity and offers satisfactory performance in a LAN environment. The second ABR implementation employs per-VC accounting and is proven to converge to our rate allocation policy for any network topology and any set of link distances.

In this paper, the operation of MPEG-2 systems is modeled and simulated when an MPEG-2 transport stream is delivered through a ATM network with jitter. A novel approach to analyzing the decoder buffer behavior in the presence of network jitter is presented. The probability density function of the interarrival time of the ATM adaptation layer 5 (AAL5) Protocol Data Unit (PDU) is derived from a MPEG-2 video source model and an ATM network jitter model. Based on a real-time decoding requirement of the MPEG-2 transport stream (TS) system target decoder (T-STD), the decoder buffer behavior is simulated. The modeling; and simulation results show that jitter affects decoder buffer size and packet loss ratio in a significant way.