While differential privacy (DP) contributes to pre serving data privacy during federated learning (FL), DP-FL suffers from either premature convergence or underutilized privacy budgets and subsequently degraded accuracy. Some recent studies heuristically adjusted the variance of the DP noises but offered no guarantee of optimality, little insight, and limited scalability. This paper presents a new control framework for (ε, δ)-DP FL to address the prevalent issues of DP-FL, i.e., premature convergence or underutilized privacy budgets. The key idea is to interpret the DP perturbation of DP-FL as a control process, where the DP noise variance and communication rounds are interdependent and jointly and adaptively determined. An optimal control framework is proposed to adjust the communication rounds and DP noise variance, adapting to the training accuracy of DP-FL. The optimality gap of (ε, δ)-DP FL is derived under the optimal control framework. The importance of joint orchestration of the DP noise and communication rounds is delineated. Experiments on MLP, CNN, and ResNet-9 models show that, given a privacy level, our control framework allows DP-FL to converge much faster with better accuracy than existing techniques, including those with persistent or heuristically reconfigurable DP noise variances.

The Mean Opinion Score (MOS) is a standard metric for assessing the Quality of Experience (QoE) in Voice over IP (VoIP) applications. Accurate prediction of how network conditions influence MOS is critical for network planning, operation, and optimization. This requires modeling traffic flows with application-level granularity, which significantly increases both the dimensionality and structural complexity of the learning task. The ability to achieve efficient, robust, and generalizable data-driven learning in the presence of such complexity depends critically on the careful design of model architectures. This paper presents NetMOS, a Graph Neural Network (GNN) architecture specifically crafted to model IP networks and predict VoIP MOS scores. NetMOS models IP networks as heterogeneous graphs and designs a two-stage Message Passing Neural Network (MPNN) to capture both permutation invariant and sequential dependencies in traffic flow and network interactions. It uses a Gated Recurrent Unit (GRU) layer to model the ordered influence of links along a traffic path and introduces a customized attention layer with Sigmoid activations to model the cumulative effects of multiple flows on the links. Simulations demonstrate that NetMOS consistently outperforms conventional GNN-based baselines across diverse network topologies in Mean Absolute Error (MAE), R² score, Pearson correlation, and Spearman correlation. NetMOS generalizes effectively beyond the training topology, maintaining high prediction accuracy on unseen network topologies and varying network activity durations without retraining. NetMOS also provides MOS predictions 44×–170× faster than packet-level simulations.

As deep learning–based AI proliferates, model theft and plagiarism pose increasing Intellectual Property (IP) risks. However, watermarking alters model weights and can degrade performance, while fingerprinting often merely verifies uniqueness or requires heavy computation. In this article, we propose a Neural Network Fingerprinting-Based Model Authentication Code (NNFMAC) scheme that verifies both model uniqueness and ownership without affecting performance. NNFMAC extracts key weights from a trained model, applies a median-based method to generate a unique binary fingerprint, and uses this fingerprint as a codebook to encode ownership information via a newly designed index-based function with expansion, producing reliable authentication codes. This non-intrusive approach integrates fingerprinting for uniqueness verification and authentication coding for ownership verification, delivering comprehensive model IP protection while preserving the model’s original performance. Extensive experiments demonstrate that NNFMAC preserves model accuracy without additional training overhead, unlike other watermarking schemes that degrade accuracy by 0.36–1.53%. It achieves bit error rates of 0.12 under weight perturbation, 0.03 under fine-tuning, 0.08 under pruning, and 0.09 under weight shifting attacks, which are substantially lower than the 0.51, 0.49, 0.46, and 0.22 reported in prior work, while consistently outperforming state-of-the-art schemes in effectiveness, efficiency, and robustness.

Model training is increasingly offered as a service for resource-constrained data owners to build customized models. Split Learning (SL) enables such services by offloading training computation under privacy constraints, and evolves towardserverlessandmulti-clientsettings where model segments are distributed across training clients. This cooperative mode assumes partial trust: data owners hide labels and data from trainer clients, while trainer clients produce verifiable training artifacts and ownership proofs. We presentCliCooper, a multi-clientcooperative SL framework tailored for cooperative model training services in heterogeneous and partially trusted environments, where one client contributes data, while others collectively act as SL trainers.CliCooperbridges the privacy and trust gaps through two new designs. First, Differential Privacy–based activation protection and secret label obfuscation safeguard data owners' privacy without degrading model performance. Second, a dynamic chained watermarking scheme cryptographically links training stages on model segments across trainers, ensuring verifiable training integrity, robust model provenance, and copyright protection. Experiments show thatCliCooperpreserves model accuracy while enhancing resilience to privacy and ownership attacks. It reduces the success rate of clustering attacks (which infer label groups from intermediate activation) to 0%, decreases inversion-reconstruction (which recovers training data) similarity from 0.50 to 0.03, and limits model-extraction–based surrogates to about 1% accuracy, comparable to random guessing.

We introduce Split Unlearning, a novel machine unlearning technology designed for Split Learning (SL), enabling the first-ever implementation of Sharded, Isolated, Sliced, and Aggregated (SISA) unlearning in SL frameworks. Particularly, the tight coupling between clients and the server in existing SL frameworks results in frequent bidirectional data flows and iterative training across all clients, violating the ''Isolated'' principle and making them struggle to implement SISA for independent and efficient unlearning. To address this, we propose SplitWiper with a new one-way-one-off propagation scheme, which leverages the inherently ''Sharded'' structure of SL and decouples neural signal propagation between clients and the server, enabling effective SISA unlearning even in scenarios with absent clients. We further design SplitWiper+ to enhance client label privacy, which integrates differential privacy and label expansion strategy to defend the privacy of client labels against the server and other potential adversaries. Experiments across diverse data distributions and tasks demonstrate that SplitWiper achieves 0% accuracy for unlearned labels, and 8% better accuracy for retained labels than non-SISA unlearning in SL. Moreover, the one-way-one-off propagation maintains constant overhead, reducing computational and communication costs by 99%. SplitWiper+ preserves 90% of label privacy when sharing masked labels with the server.

In the Internet of Vehicles (IoV), safeguarding service privacy and communication efficiency is critical due to the vast exchange of sensitive data among connected entities. Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative model training without exposing raw data. However, a fundamental trade-off arises between the level of differential privacy (DP) protection and the number of communication rounds required. To address this, we propose the Unified State-Space Feedback Control Framework (USFCF), which introduces a dynamic feedback regulation mechanism that adaptively balances privacy protection and communication overhead. Roadside units (RSUs) serve as distributed coordinators, monitoring the evolving system state and adjusting the model aggregation frequency accordingly. When the system detects excessive privacy noise, it suppresses redundant communication; conversely, if privacy weakens, it increases update rounds to reinforce protection. The derivation of an optimization-driven update policy is constructed to realize the privacy-communication of the FL-DP system. Experimental evaluations on diverse datasets demonstrate that our method enhances privacy adaptability, reduces communication cost, and ensures robust performance for privacy-sensitive vehicular intelligence.

Trust management systems (TMS) are crucial for managing trust in distributed environments. The rise of decentralized systems and blockchain has sparked interest in credential-based decentralized trust management systems (DTMS). This paper bridges the gap between theory and practice through a systematic review of credential-based DTMS. We analyze existing DTMS solutions through multiple dimensions, including their architectural designs, credential mechanisms, and trust evaluation models. Our survey provides a detailed taxonomy of credential-based DTMS approaches and establishes comprehensive evaluation criteria for assessing DTMS implementations. Through extensive analysis of current systems and implementations, we identify critical challenges and promising research directions in the field. Our examination offers valuable insights for researchers and practitioners working on DTMS, particularly in areas such as access control, reputation systems, and blockchain-based trust frameworks.

Unlearning is challenging in generic learning frameworks with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel unlearning framework that enables fully parallel unlearning among models exhibiting inheritance. We use a chronologically Directed Acyclic Graph (DAG) to capture various unlearning scenarios occurring in model inheritance networks. Central to our framework is the Fisher Inheritance Unlearning (FIUn) method, designed to enable efficient parallel unlearning within the DAG. FIUn utilizes the Fisher Information Matrix (FIM) to assess the significance of model parameters for unlearning tasks and adjusts them accordingly. To handle multiple unlearning requests simultaneously, we propose the Merging-FIM (MFIM) function, which consolidates FIMs from multiple upstream models into a unified matrix. This design supports all unlearning scenarios captured by the DAG, enabling one-shot removal of inherited knowledge while significantly reducing computational overhead. Experiments confirm the effectiveness of our unlearning framework. For single-class tasks, it achieves complete unlearning with 0% accuracy for unlearned labels while maintaining 94.53% accuracy for retained labels. For multi-class tasks, the accuracy is 1.07% for unlearned labels and 84.77% for retained labels. Our framework accelerates unlearning by 99% compared to alternative methods.

Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. This complexity is amplified when blockchain is employed to ensure the integrity and traceability of FL, where the need to edit multiple interlinked blockchain records and update all inherited models complicates the process. In this paper, we introduce Blockchained Federated Unlearning (BlockFUL), a novel framework with a dual-chain structure— comprising a live chain and an archive chain—for enabling unlearning capabilities within Blockchained FL. BlockFUL introduces two new unlearning paradigms, i.e., parallel and sequential paradigms, which can be effectively implemented through gradient-ascent-based and re-training-based unlearning methods. These methods enhance the unlearning process across multiple inherited models by enabling efficient consensus operations and reducing computational costs. Our extensive experiments validate that these methods effectively reduce data dependency and operational overhead, thereby boosting the overall performance of unlearning inherited models within BlockFUL on CIFAR-10 and Fashion-MNIST datasets using AlexNet, ResNet18, and MobileNetV2 models.

Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers’ long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.

Drones are evolving into highly capable and adaptable devices, prompting the development of advanced control frameworks. This paper introduces a novel online control framework tailored for a multi-task drone, explicitly addressing the simultaneous execution of in-situ sensing and goods delivery. To tackle this complex scenario, a finite-horizon Markov decision process (FH-MDP) is formulated to ensure not only the prompt delivery of goods but also the minimization of energy consumption and the maximization of the drone's reward for in-situ sensing. A significant contribution lies in establishing the monotonicity and subadditivity of the FH-MDP. This mathematical foundation provides evidence for the existence of an optimal, monotone, deterministic Markovian policy. The crux of the optimal policy revolves around flight distance- and time-related thresholds, determining the precise points at which the drone should switch its optimal action. This unique feature empowers the multi-task drone to make real-time decisions, such as adjusting flight speed or engaging in in-situ sensing, by comparing its current state with these predefined thresholds. This process can be accomplished with a linear complexity, ensuring efficiency in decision-making. The optimality of our approach is rigorously demonstrated through numerical validation, where it is compared against a computationally expensive, dynamic programming-based alternative. Under the considered simulation settings, our approach reduces drone energy consumption by a substantial 19.8% compared to existing benchmarks. This not only highlights the practical effectiveness of the proposed framework but also underscores its potential for significant advancements in the field of drone operations and energy efficiency.

Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL's further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process. This design eliminates the need for central coordinators to achieve fully decentralized operations. IronForge runs in a public and open network and launches a fair incentive mechanism by enabling state consistency in the DAG. Hence, the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of IronForge. Experimental results based on a newly developed test bed FLSim highlight the superiority of IronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, IronForge is the first secure and fully decentralized FL (DFL) framework that can be applied in open networks with realistic network and training settings.

During the deployment and utilization of federated models, they are susceptible to unauthorized theft or misuse. To address this issue, researchers have proposed the use of watermarking techniques to protect the Intellectual Property (IP) of the federated models. Nevertheless, traditional watermarking methods in federated learning have certain limitations. It is highly likely that different clients may embed watermarks in the same region of the model. During the aggregation of the watermarked weights, the watermarks from various clients may overlap, resulting in conflicts between the embedded watermarks. To overcome these challenges, we propose a novel method called Non-Interfering Fragmented Watermarking for Federated Models (FedNIFW). In the proposed scheme, each client node is assigned a specific segment of the neural network layer where watermarking can be applied. During training, each client is allowed to embed watermarks only within their designated segments, while other segments intended for watermarking by different clients are frozen. Experimental results demonstrate that this segmented watermarking scheme effectively prevents conflicts between client watermarks and does not significantly impact the accuracy of the federated models. These findings underscore the feasibility of the proposed watermarking scheme.

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it is vulnerable to the 1% attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (TbDd) framework, crafted to counter collusion attack risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, TbDd discerns node types and performs targeted resharding against potential threats. The TbDd framework maximizes the tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Extensive evaluations validate TbDd’s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions.

The distributed nature of Federated Learning (FL) introduces security vulnerabilities and issues related to the heterogeneous distribution of data. Traditional FL aggregation algorithms often mitigate security risks by excluding outliers, which compromises the diversity of shared information. In this paper, we introduce a novel filtering-and-voting framework that adeptly navigates the challenges posed by non-iid training data and malicious attacks on FL. The proposed framework integrates a filtering layer for defensive measures against the intrusion of malicious models and a voting layer to harness valuable contributions from diverse participants. Moreover, by employing Deep Reinforcement Learning (DRL) for dynamic aggregation weight adjustment, we ensure the optimized aggregation of participant data, enhancing the diversity of information used for aggregation and improving the performance of the global model. Experimental results demonstrate that the proposed framework presents superior accuracy over traditional and contemporary FL aggregation methods as diverse models are utilized. It also shows robust resistance against malicious poisoning attacks.

As the primary standard protocol for modern cars, the controller area network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. As the decoding specification of CAN is a proprietary black-box maintained by original equipment manufacturers (OEMs), conducting related research and industry developments can be challenging without a comprehensive understanding of the meaning of CAN messages. In this article, we propose a fully automated reverse-engineering system, named ByCAN, to reverse engineer CAN messages. ByCAN outperforms the existing research by introducing byte-level clusters and integrating multiple features at both the byte and bit levels. ByCAN employs the clustering and template matching algorithms to automatically decode the specifications of CAN frames without the need for prior knowledge. Experimental results demonstrate that ByCAN achieves high accuracy in slicing and labeling performance, i.e., the identification of CAN signal boundaries and labels. In the experiments, ByCAN achieves slicing accuracy of 80.21%, slicing coverage of 95.21%, and labeling accuracy of 68.72% for the general labels when analysing the real-world CAN frames.

Web3 is leading a wave of the next generation of web services that even many Web2 applications are keen to ride. However, the lack of Web3 background for Web2 developers hinders easy and effective access and transition. On the other hand, Web3 applications desire encouragement and advertisement from conventional Web2 companies and projects due to their low market shares. In this article, we propose a seamless transition framework that transits Web2 to Web3, named WEBTTCOM [WEBTTCOM stands for Web2 (two)–Web3 (three) Communicator], after exploring the connotation of Web3 and the key differences betweenWeb2 andWeb3 applications.We also provide a full-stack implementation as a use case to support the proposed framework, followed by performance evaluation and surveys with ~1000 participants that show ~80% positive and ~20% neutral responses. We confirm that the proposed framework WEBTTCOM addresses the defined research question, and the implementation well satisfies the framework WEBTTCOM in terms of strong necessity,usability, andcompletenessbased on the survey results.

Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedules in a decentralized fashion. The framework allows each UAV to minimize its energy usage and connectivity outage, maximize its recharged energy, ensure its timely arrival, and preserve its privacy concerning its trajectory and destination. The key idea is that we obfuscate probabilistically the destination of each UAV, and design a new noncooperative Bayesian game among the UAVs to find their best routes and charging schedules toward the obfuscated destinations. Another important aspect is that we prove the game is a potential Bayesian game with a pure-strategy Bayesian Nash equilibrium and the best response yielded with the Bellman–Ford algorithm. This new framework preserves the UAVs’ privacy in the sense that an UAV only shares the probability of its visit to a charging station at different times, and its best response is based on an obfuscated destination. Simulations demonstrate that the framework ensures timely arrivals with near-optimal routes and substantially lower complexity than a centralized routing scheme based on brute force.

Full-duplex (FD) integrated sensing and communication (ISAC) has great potential in future vehicular networks. However, the FD requirement and the ISAC functions make the receiver processing extremely complicated, particularly when multiple transmissions are uncoordinated. In this paper, we study frequency-hopping (FH) based receivers in an FD ISAC system, where the arrivals of backscattered signals from one node may overlap with those of signals from another node. To mitigate the interferences caused by the overlapping signals, we consider two receiver options based on either conventional communications or frequency-modulated continuous-wave radars, and two signal modulations based on either fast FH or un-slotted ALOHA FH. Based on the different signal modulations, we develop two parameter estimation schemes via using FH-decoding and de-chirp operations, respectively. To further improve the sensing accuracy, we proceed to propose an iterative algorithm, which refines the estimates of all parameters via using short-time-Fourier transform and maximizing the received power in desired frequency bands. After obtaining all channel parameters in sensing, bilateral communications between two nodes are realized by differential phase-shift keying. Finally, simulation results are provided and verify that the proposed FD ISAC can obtain parameters in high resolution and realize robust communication links.

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming. It is non-trivial to perform holistic selections of users, data streams, and modulation-coding modes for all subchannels, and RIS configuration in a downlink multiuser OFDMA system under jamming attacks, because of a mixed-integer program nature and difficulties in acquiring the channel state information (CSI) of the channels to and from the RIS and from an uncooperative jammer. We propose a new deep reinforcement learning (DRL)-based approach that learns through changes in the data rates of the users to reject jamming and maximize the sum rate. The key idea is to decouple the continuous RIS configuration from the discrete selections of users, data streams, subchannels, and modulation-coding modes. Another critical aspect is that we show the optimal selections almost surely follow a winner-takes-all strategy. Accordingly, the new DRL framework learns the RIS configuration with a twin-delayed deep deterministic policy gradient and takes the winner-takes-all strategy to evaluate the reward, thereby reducing the action space and accelerating learning. Simulations show the framework converges fast and fulfills the benefit of the RIS. With no need for the CSI of the channels to and from the RIS and from the jammer, the framework offers practical value.

Latent graph structure and stimulus of graph-structured data contain critical private information, such as brain disorders in functional magnetic resonance imaging data, and can be exploited to identify individuals. It is critical to perturb the latent information while maintaining the utility of the data, which, unfortunately, has never been addressed. This paper presents a novel approach to obfuscating the latent information and maximizing the utility. Specifically, we first analyze the graph Fourier transform (GFT) basis that captures the latent graph structures, and the latent stimuli that are the spectral-domain inputs to the latent graphs. Then, we formulate and decouple a new multi-objective problem to alternately obfuscate the GFT basis and stimuli. The difference-of-convex (DC) programming and Stiefel manifold gradient descent are orchestrated to obfuscate the GFT basis. The DC programming and gradient descent are employed to perturb the spectral-domain stimuli. Experiments conducted on an attention-deficit hyperactivity disorder dataset demonstrate that our approach can substantially outperform its differential privacy-based benchmark in the face of the latest graph inference attacks.

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights —in terms of the model accuracy, ℓ 2 -distance-based model distance, and level of data privacy—and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a tradeoff between the model accuracy and privacy level and a tradeoff between the model difference and privacy level. The results indicate broad application prospects for training outsourcing and guarding against attacks in federated learning both of which have been increasingly attractive in many areas, particularly learning in edge computing.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

This paper puts forth a new application of an unmanned aerial vehicle (UAV) to joint goods delivery and in-situ sensing, and proposes a new algorithm that jointly optimizes the route and sensing task selection to minimize the UAV’s energy consumption, maximize its sensing reward, and ensure timely goods delivery. This problem is new and non-trivial due to its nature of mixed integer programming. The key idea behind the new algorithm is that we interpret the possible waypoints of the UAV as location-dependent tasks to incorporate routing and sensing in one task selection process. Another critical aspect is that we construct a new task-time graph to describe the process, where each vertex corresponds to a task associated with its location, time and reward, and each edge indicates the propulsion energy required for the UAV to travel between two tasks. By redistributing the weight of a vertex to its incoming edges, the new UAV routing and sensing task selection problem can be converted to a weighted routing problem in the new task-time graph and solved optimally using the Bellman-Ford algorithm. Validated by a real-world case study, our approach can outperform its alternatives by over 18% in task reward.

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

Network Function Virtualization (NFV), which decouples network functions from hardware and transforms them into Virtual Network Functions (VNFs), is a crucial technology for data center (DC) networks. A service function chain (SFC) is composed of an ordered set of VNFs and virtual links (VLs) connecting them. To optimize the resource allocation in DC networks, we need to efficiently map SFCs onto the physical network. Nevertheless, the dynamics and diversity of SFC requests in multi-datacenter (MDC) networks pose a significant challenge in embedding SFCs. To overcome this challenge, we design a two-stage graph convolutional network (GCN) assisted deep reinforcement learning (DRL) scheme. This framework aims to maximize the overall acceptance ratio of SFC requests while minimizing the total cost in an MDC network. In the first stage, we propose a GCN-based DRL algorithm as a coarse granularity solution to the SFC embedding problem from the macro perspective. This solution outlines a local observation scope (LOS) for each agent in the multi-agent system of the second stage, where all agents simultaneously handle SFC requests from their respective DCs using a multi-agent framework from the micro perspective. Numerical evaluations show that, compared to state-of-the-art methods, the proposed scheme improves the acceptance ratio by approximately 13% compared with the Kolin algorithm and 18% compared with the DQN algorithm and saves the cost by around 28% compared with the Kolin and the DQN.

Existing permissionless sharded-Blockchains come on the scene. However, there is a lack of systematic formulations and experiments regarding the behaviors of individual miners. In this article, we interpret block mining in a permissionless sharded-Blockchain as a repeated$M$-player noncooperative game with finite actions, and propose a new multiagent deep reinforcement learning (MADRL) framework to allow the miners to maximize their profits in a decentralized fashion by scheduling their resources across the shards without centralized coordination. We formulate the rewards, and design a two-scale action space for each miner to reduce the action space and expedite convergence. We also propose a new MADRL model, named Rainbow-WoLF-PHC, which allows each miner to learn its resource allocation online and converge fast to a mixed strategy Nash equilibrium. Extensive experiments show the superiority of the Rainbow-WoLF-PHC to its alternatives in terms of convergence, stability, and profitable actions. This work provides a prosperous design of an end-user-friendly permissionless sharded-Blockchain.

The proliferation of delay-sensitive Internet-of-Things (IoT) applications has ushered in a need for the statistical delay quality-of-service (QoS) guarantee for the applications. In this paper, we first derive an upper bound for the queuing delay violation probability (UB-QDVP) in uplink non-orthogonal multiple access (NOMA) by applying stochastic network calculus (SNC) to the Mellin transforms of service processes. A closed-form asymptotic approximation of the UB-QDVP is developed by proving the asymptotic convergence of the Mellin transform and its finite-length truncations. Given the closed-form asymptotic UB-QDVP, we propose two power allocation schemes. The first scheme minimizes the transmit power of a NOMA user pair while guaranteeing the statistical delay QoS of the pair. The second maximizes the$\alpha $-utility function of the effective capacity of the user pair, striking a balance between the energy efficiency and user fairness of uplink NOMA systems. Simulations validate the UB-QDVP and show the superiority of the proposed schemes to conventional power allocation schemes in terms of energy efficiency and fairness.

The undisclosed Controller Area Network (CAN) decoding specification is important to the in-vehicle network (IVN) research for both industry and academia. Researchers have developed several CAN reverse engineering systems to predict signal boundaries and labels in order to map out CAN signal decoding specifications. Existing works mainly use one parameter (i.e., bit flip rate) to determine CAN signals boundary, which results in biased slicing and labelling of CAN signals. In this paper, we propose a multi-layer CAN reverse engineering system to cluster signal boundary at byte-level and label sliced CAN signal blocks at bit-level. The proposed system avoids biased signal slicing and labelling by introducing multiple parameters in signal classification, while existing works only use the bit flip rate and the number of unique value. The feasibility and adaptability of the proposed system is assessed by deploying it into a web application as a functionality module. We evaluate the proposed system with CAN messages from real cars. Compared with existing reverse engineering models, the proposed system introduces multi-layer signal processing to avoid over-slicing and over-labelling problem.

The legacy design of the Controller Area Network (CAN) weakens the encryption and authentication of the In-Vehicle Networks (IVN). Anomaly detection systems, e.g. the Long-Short Term Memory (LSTM) based Intrusion Detection System (IDS), are employed to remedy the defection of CAN. Existing works feed the LSTM-based IDS with the byte values of the data payload of CAN to train and test the LSTM model. In this paper, we propose an LSTM-based IDS leveraging byte-level features, i.e., byte flip rate, byte-level change rage, and byte-level distinct value rate, to augment the sensitivity of proposed LSTM-based IDS when distinguishing malicious CAN messages. By using the byte-level signal features, the proposed system achieves high accuracy with a small size of the training dataset. The experiment results show that the model with the byte-level features can achieve a performance gain of the$F$1Score up to 20% over the model without the byte-level features.

The development of location-based services (LBS) leads to the rapid growth of location data, potentially increasing the threat to location privacy. Existing location obfuscation techniques focus on two-dimensional (2D) planar areas and overlook the features of road networks. In this paper, we leverage differential privacy and propose a new notion of Road Network-Indistinguishability (RN-Indistinguishability) to measure the indistinguishability of locations in road networks. With the RN-Indistinguishability, we design a Cloaking Region Obfuscation (CRO) mechanism to protect the location privacy of vehicles on roads. With the CRO mechanism, vehicle locations in a cloaking region are obfuscated following the same obfuscation distribution. The proposed CRO mechanism is proved to achieve RN-Indistinguishability and can be generalized with road network features holding the triangle inequality. Comprehensive experiments show that the CRO mechanism outperforms existing 2D obfuscation mechanisms in real-world road networks.

Network slicing is one of the core techniques of the current 5G networks. To accommodate as many network slices as possible with limited hardware resources, service providers need to avoid over-provisioning of resources. In this paper, we first propose a Deep Q-Network (DQN) based network slicing algorithm to maximize the acceptance ratio and ensure prior placement of higher-priority requests for Ultra-Reliable Low-Latency Communication (URLLC) services. Specifically, we model the network slicing as a Markov Decision Process (MDP), where we consider Virtual Network Function (VNF) placements to be the actions of the MDP, and define a reward function based on service priority. For every service request, we use the DQN to choose an MDP action for performing the VNF placement. The placement results in an MDP reward that we can use to train the DQN. Once trained, the DQN approximates the optimal solution of the MDP. Considering the over-provisioning of resources, we then propose a Binary Search Assisted Transfer Learning algorithm (BSATL), in which the available hardware resources are scaled down/up and the knowledge learned from the source task is transferred to the target task in each iteration, to achieve automated and closed-loop optimization for the ever changing infrastructure, a scenario of 6G Event Defined uRLLC (EDuRLLC). Numerical evaluations show that our proposed scheme can significantly improve cost-utility while maintaining the optimal acceptance ratio.

Abstract The ability of Fog computing to admit and process huge volumes of heterogeneous data is the catalyst for the fast expansion of Internet of things (IoT). The critical challenge is secure and differentiated access to the data, given limited computation capability and trustworthiness in typical IoT devices and Fog servers, respectively. This paper designs and develops a new approach for secure, efficient and differentiated data access. Secret sharing is decoupled to allow the Fog servers to assist the IoT devices with attribute-based encryption of data while preventing the Fog servers from tampering with the data and the access structure. The proposed encryption supports direct revocation and can be decoupled among multiple Fog servers for acceleration. Based on the decisional $q$-parallel bilinear Diffie–Hellman exponent assumption, we propose a new extended $q$-parallel bilinear Diffie–Hellman exponent (E$q$-PBDHE) assumption and prove that the proposed approach provides ‘indistinguishably chosen-plaintext attacks secure’ data access for legitimate data subscribers. As numerically and experimentally verified, the proposed approach is able to reduce the encryption time by 20% at the IoT devices and by 50% at the Fog network using parallel computing as compared to the state of the art .

Accurate assessment of fish quality is difficult in practice due to the lack of trusted fish provenance and quality tracking information. Working with Sydney Fish Market (SFM), we develop a Blockchain-enabled fish provenance and quality tracking (BeFAQT) system. A multilayer Blockchain architecture based on attribute-based encryption (ABE) is proposed to tackle the privacy issue caused by applying Blockchain to secure supply chain data and achieve trusted and confidential data sharing among parties in fish supply chains. An Internet-of-Things (IoT) chain saves encrypted fish provenance and quality tracking data, and an ABE chain is specifically designed for the access control to the data in the IoT chain. Latest IoT and artificial intelligence (AI) technologies, including NarrowBand-IoT, image processing, and biosensing, are developed for fish origin proof, supply chain tracking, and objective fish quality assessment. As proven by field trials with SFM and a local fish supply chain, the BeFAQT is able to provide trusted and comprehensive fish provenance and quality tracking information in real time.

Consensus is one of the most important issues of a blockchain system because it is a necessary process to reach an agreement between a group of separated nodes that do not trust each other in a decentralized framework. Most existing blockchain consensus works assume that the time of block propagation among separated nodes during the consensus process is ignorable, i.e., a block always successfully reaches every participating node during a period of time that is far shorter than the mining time. However, when blockchain is used in vehicularad hocnetworks (VANETs), the block propagation time is no longer negligible since the dynamic connectivity of the moving nodes in a wireless environment brings opportunistic communication to blockchain consensus. In this article, we study the impact of mobility on block propagation under the single-chain structure in VANET. Specifically, we investigate the dynamics of block propagation from the macroscopic view and derive the closed-form expression of the single-block propagation time. Then, we characterize the blockchain forking as the multiblock competitive propagation. In this way, an approximate result on multiblock propagation time is discussed. An interesting finding is that higher mobility and more moving vehicles can speed up the block propagation. In addition, we also discover that distinct propagation capabilities of moving nodes contribute to the forking reduction in the blockchain consensus.

In the Internet of Things (IoT) era, various IoT devices are equipped with sensing capabilities and employed to support clinical applications. The massive electronic health records (EHRs) are expected to be stored in the cloud, where the data are usually encrypted, and the encrypted data can be used for disease diagnosis. There exist some numeric health indicators, such as blood pressure and heart rate. These numeric indicators can be classified into multiple ranges, and each range may represent an indication of normality or abnormity. Once receiving encrypted IoT data, the CS maps it to one of the ranges, achieving timely monitoring and diagnosis of health indicators. This article presents a new approach to identify the range that an encrypted numeric value corresponds to without exposing the explicit value. We establish the sufficient and necessary condition to convert a range query to matchings of encrypted binary sequences with the minimum number of matching operations. We further apply the minimization of range queries to design and implement a secure range query system, where numeric health indicators encrypted independently by multiple IoT devices can be cohesively stored and efficiently queried by using Lagrange polynomial interpolation. Comprehensive performance studies show that the proposed approach can protect both the health records and range query against untrusted cloud platforms and requires less computational and communication cost than existing techniques.

Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.

The proliferation of location-based services (LBS) leads to increasing concern about location privacy. Location obfuscation is a promising privacy-preserving technique but yet to be adequately tailored for vehicles in road networks. Existing obfuscation schemes are based primarily on the Euclidean distances and can lead to infeasible results, e.g., off-road locations. In this paper, we define Road Network-Indistinguishability (RN-I) to evaluate obfuscation-based location privacy-preserving schemes in road networks. To protect drivers’ location privacy in road networks, we propose a Personalized Location Privacy-Preserving (PLPP) scheme and prove it achieves RN-I. The PLPP scheme employs a dual-obfuscation algorithm, consisting of a connection perturbation and an interval perturbation, to obfuscate on-road locations. An efficient personalization algorithm is designed for the PLPP scheme to fine-tune location privacy budgets for capturing drivers’ sensitive locations and privacy requirements. Experiments upon two real-world datasets confirm the location privacy-preserving capability, data utility, and efficiency of the proposed PLPP scheme.

Smart home is an emerging form of the Internet of Things (IoT), enabling people to enjoy a convenient and intelligent life. The data generated by smart home devices are transmitted through the public channel, which is not secure enough, so the secret data in smart home are easily intercepted by malicious adversaries. In order to solve this problem, this paper proposes a smart home privacy protection method combining DES encryption and the improved Least Significant Bit (LSB) information hiding algorithm, changing the practice of directly exposing smart home secret information to the Internet, first, using Data Encryption Standard (DES) encryption to encrypt the smart home information and second, the improved LSB information hiding algorithm is used to hide the ciphertext, so that the adversary cannot detect the smart home secret information. The goal of the scheme is to provide a double protection for the secure transmission of the smart home secret information. If an attacker wants to carry out an attack, it has to break through at least two defense lines, which seems impossible to do. Experiment results show that the improved LSB algorithm is more robust than the existing algorithms, and it is very safe. Therefore, the scheme proposed in this paper is very practical for protecting the smart home secret information.

Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost- and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This article presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.

Vehicular ad hoc network (VANET) encounters a critical challenge of efficiently and securely authenticating massive on-road data while preserving the anonymity and traceability of vehicles. This paper designs a new anonymous authentication approach by using an attribute-based signature. Each vehicle is defined by using a set of attributes, and each message is signed with multiple attributes, enabling the anonymity of vehicles. First, a batch verification algorithm is developed to accelerate the verification processes of a massive volume of messages in large-scale VANETs. Second, replicate messages captured by different vehicles and signed under different sets of attributes can be dereplicated with the traceability of all the signers preserved. Third, the malicious vehicles forging data can be traced from their signatures and revoked from attribute groups. The security aspects of the proposed approach are also analyzed by proving the anonymity of vehicles and the unforgeability of signatures. The efficiency of the proposed approach is numerically verified, as compared to the state of the art.

Massive multiple-input multiple-output (MIMO) wireless communication technology with the characteristics of hyperconnectivity is an ideal channel to connect the industrial Internet of Things (IIoT) and the cyber-physical system. It provides stable and reliable connectivity from the data center to distributed user terminals and the IIoT. However, traditional massive MIMO suffers from high power consumption and fabrication cost. The design of energy-efficient massive MIMO technology is essential for larger scale industrial deployments. In this article, we design three types of nonlinear RF chain structures, which not only reduce the power consumption of massive MIMO systems but also save fabrication costs. Information theoretic analysis demonstrates the power efficiency performance of our nonlinear system design. Our nonlinear MIMO system designs can increase the power efficiency by up to 2.3 times compared with the traditional MIMO system. We have demonstrated that our systems can achieve the same uplink rate as traditional MIMO by increasing the number of receiving antennas but with less overall power consumption. We also proposed an algorithm to overcome the problem of low computational efficiency due to high-dimensional integration when calculating the uplink achievable rate of nonlinear MIMO. Moreover, we reveal that when the skew-normal distribution is used as signaling, the nonlinear MIMO systems can achieve better performance than the Gaussian distribution.

Cooperative caching can unify storage across edge clouds and provide efficient delivery of popular contents under effective content placement. However, the placement and delivery are non-trivial in cooperative caching due to the decentralized property of edge clouds, as well as the temporal and spatial correlation of the placement. We propose a new distributed online learning approach to jointly optimize content placement and delivery without the a-priori knowledge on file popularity and link availability. Content placement and delivery can be asymptotically optimized in real-time by running distributed online learning at individual edge servers by exploiting stochastic gradient descent (SGD). The proposed approach can allow operations at different timescales by integrating mini-batch learning for farsighted content placement. The optimality loss, stemming from the different timescales, can asymptotically reduce, as the SGD stepsize declines. Simulations confirm that the proposed approach outperforms existing techniques in terms of cache hit ratio and cost effectiveness. Insights are shed on the optimal placement of popular contents.

Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages.

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.

This paper aims to produce an effective online scheduling technique, where a base station (BS) schedules the transmissions of energy harvesting-powered Internet-of-Things (IoT) devices only based on the (differently outdated) in-band reports of the devices on their states. We establish a new primal-dual learning framework, which learns online the optimal proactive schedules to maximize the time-average throughput of all the devices. Batch gradient descent is designed to enable stochastic gradient descent (SGD)-based dual learning to learn the network dynamics from the outdated reports. Replay memory is deployed to allow online convex optimization (OCO)-based primal learning to predict channel conditions and prevent over-fitting. We also decentralize the online learning between the BS and devices, and speed up learning by leveraging the instantaneous knowledge of the devices on their states. We prove that the proposed framework asymptotically converges to the global optimum, and the impact of the outdated knowledge of the BS diminishes. Simulation results confirm that the proposed approach can increasingly outperform state of the art, as the number of devices grows.

Most existing optimization objectives considered in non-orthogonal multiple access (NOMA) power allocation schemes are non-delay-sensitive metrics. In order to apply NOMA to various Internet of Things scenarios, the delay must be considered. The effective capacity of users, which characterizes the capacity under specific expiration probabilities, can potentially be a performance metric of statistical delay quality of service (QoS). In this paper, we propose two novel dynamic power allocation schemes with statistical delay QoS guarantee in the uplink NOMA system with paired users. One of the schemes maximizes the sum effective capacity (SEC) of the strong and weak users, which is a non-convex nonlinear optimization problem and is solved by Lagrangian dual decomposition and successive convex approximation (SCA). The other one maximizes the effective energy efficiency (EEE) of uplink NOMA, which is a fractional optimization problem and is solved by integrating the Dinkelbach method, SCA, and Lagrangian dual decomposition. Numerical results show that the SEC and EEE can be significantly improved by the proposed schemes, compared to the existing NOMA and orthogonal multiple access power allocation schemes.

Highly accurate localization is important for wire-less communications. In this paper, we propose a new tensor-based positioning method for 5G wideband mmWave massive MIMO systems. We first develop an extended multidimensional interpolation (E-MI)-based method as the preprocessing step to suppress the frequency-dependence of the array steering vectors. By using this method, the data across the whole frequency band can be processed jointly, and the high temporal resolution offered by wideband mmWave signals can be exploited. Then, we propose a parameter decoupling (PD)-based tensor multiparameter estimation algorithm. This algorithm can suppress the noises in all of temporal, spatial and frequency domains, and thus all the parameters can be precisely estimated. A simplified perturbation term (S-PT)-based method is also presented to match the estimated parameters at low complexity. Based on the quasi-optical property of mmWave signals, we propose a novel method to compute the 3D coordinates of the target. Simulation results demonstrate the effectiveness of the proposed positioning method in the end.

Lyapunov optimization has shown to be effective for online optimization of fog computing, asymptotically approaching the optimality only achievable offline. However, it is not directly applicable to the Internet of Things, as inexpensive sensors have small buffers and cannot generate sufficient backlogs to activate the optimization. This article proposes an enabling technique for the Lyapunov optimization to operate under finite buffers without loss of asymptotic optimality. This is achieved by optimizing the biases (namely, “virtual placeholders”) of the buffers to create sufficient backlogs. The optimization of the placeholders is proved to be a new three-layer shortest path problem and solved in a distributed manner by extending the Bellman-Ford algorithm. The sizes of the virtual placeholders decline fastest along the shortest paths from the sensors to the data center, thereby preventing unnecessary detours and reducing end-to-end delays. Corroborated by simulations, the proposed approach is able to operate under the conditions the direct application of the Lyapunov optimization fails, and significantly increase the throughput and reduce the delays in other cases.

To enable ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT), a sparse-code multiple-access (SCMA)-enhanced full-duplex (FD) scheme (FD-SCMA) is proposed in this article. FD-SCMA can support short-packet transmissions of several SCMA users in the uplink (UL) and downlink (DL) simultaneously by an FD next generation node B (gNB). First, the gNB and UL users can generate and superpose signals according to the preconfigured SCMA codebooks, and simultaneously transmit the signals via occupied subcarriers in a joint SCMA pattern. The receivers at the gNB and DL users can demodulate and decode the signals with multiuser detection (MUD). With the imperfect self-interference suppression (SIS) of FD considered, the effective signal-to-noise ratio (SNR) of FD-SCMA at the gNB and DL users is formulated. The error probability of FD-SCMA in the UL and DL is also derived under a given transmission latency constraint of short-packet transmissions. In the stationary flat-fading channel, it is proved that FD-SCMA can achieve better reliability than the existing FD and SCMA schemes. In the time-invariant frequency-selective fading channel, the upper bounds for error probability of the UL and DL users in FD-SCMA are derived, respectively. Through the theoretical calculation and Monte Carlo simulation, it is verified that the superiority of FD-SCMA in supporting ultrareliable and low-latency short-packet transmissions in IoT.

It is challenging to satisfy the critical requirements of ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT) under severe channel fading. The emerging massive multiuser multiple-input-multiple-output (MU-MIMO) concept is applied in IoT networks under shadow fading, enabling URLLC with pilot-assisted channel estimation (PACE) and zero-forcing (ZF) detection. Assuming users are uniformly and randomly deployed under log-normal shadow fading, the probability density function (pdf) of postprocessing signal-to-noise ratios (SNRs) is derived for the uplink (UL) of massive MU-MIMO with perfect channel state information (CSI) and imperfect CSI obtained by PACE. Then, finite blocklength (FBL) information theory is utilized to derive the error probability of accessing users with a given latency, thereby evaluating the reliability of massive MU-MIMO for short-packet transmissions. Further, the length of pilots to minimize the error probability can be decided by the golden section search method (GSSM), which can converge rapidly. Numerical results verify that massive MU-MIMO can support a large number of UL URLLC users even when users are randomly deployed under shadow fading.

Edge computing allows an edge server to adaptively place virtual instances to serve different types of data. This article presents a new algorithm which jointly optimizes virtual service placement farsightedly and service data admission instantly to maximize the time-average service throughput of edge computing. The data admission is optimized, adapting to fast-changing data arrivals and wireless channels. The service placement is transformed into a two-dimensional knapsack problem by approximating future arrivals and channels with past observations, and solved over a slow timescale to allow services to be properly installed. Different from existing studies, our algorithm considers practical aspects of edge servers, such as finite memory size and bandwidth. We prove that the algorithm is asymptotically optimal and the optimality loss resulting from the approximation diminishes. Simulations show that our approach can improve the time-average throughput of existing alternatives by 16% for our considered simulation setup. The improvement becomes higher, as the memory size becomes increasingly tight. The number of services to be replaced is reduced without loss of throughput, after being placed farsightedly.

Channel estimation is challenging for hybrid millimeter wave (mmWave) large-scale antenna arrays which are promising in 5G/B5G applications. The challenges are associated with angular resolution losses resulting from hybrid front-ends, beam squinting, and susceptibility to the receiver noises. Based on tensor signal processing, this paper presents a novel multi-dimensional approach to channel parameter estimation with large-scale mmWave hybrid uniform circular cylindrical arrays (UCyAs) which are compact in size and immune to mutual coupling but known to suffer from infinite-dimensional array responses and intractability. We design a new resolution-preserving hybrid beamformer and a low-complexity beam squinting suppression method, and reveal the existence of shift-invariance relations in the tensor models of received array signals at the UCyA. Exploiting these relations, we propose a new tensor-based subspace estimation algorithm to suppress the receiver noises in all dimensions (time, frequency, and space). The algorithm can accurately estimate the channel parameters from both coherent and incoherent signals. Corroborated by the Cramér-Rao lower bound (CRLB), simulation results show that the proposed algorithm is able to achieve substantially higher estimation accuracy than existing matrix-based techniques, with a comparable computational complexity.

Motivated to understand the increasingly severe threat of unmanned aerial vehicles (UAVs) to the confidentiality of terrestrial radio links, this paper analyzes the ergodic and E-outage secrecy capacities of the links in the presence of multiple cooperative aerial eavesdroppers flying autonomously in three-dimensional (3D) spaces and exploiting selection combining (SC) or maximal ratio combining (MRC). The “cut-off” density of the eavesdroppers under which the secrecy capacities vanish is identified. By decoupling the analysis of the random trajectories from the random channel fading, closed-form approximations with almost sure convergence to the secrecy capacities are devised. The analysis is extended to study the impact of the oscillator phase noises and finite memories of the aerial eavesdroppers on the secrecy performance of the ground link. Validated by simulations, the cut-off density only depends on the range of the link in the case of SC eavesdropping, while it depends on the flight region of the eavesdroppers in the case of MRC eavesdropping.

Disconnecting impaired or suspicious nodes and rewiring to those reliable, adaptive networks have the potential to inhibit cascading failures, such as DDoS attack and computer virus. The weights of disconnected links, indicating the workload of the links, can be transferred or redistributed to newly connected links to maintain network operations. Distinctively different from existing studies focused on adaptive unweighted networks, this paper presents a new mean-field model to analyze the reliability of adaptive weighted networks against cascading failures. By taking mean-field approximation, we develop a new continuous-time Markov model to capture the propagations of cascading failures and the rewiring actions that individual nodes can take to bypass failed neighbors. We analyze the stability of the model to identify the critical conditions, under which the cascading failures can be eventually inhibited or would proliferate. The conditions are evaluated under different link weight distributions and rewiring strategies. Our model reveals that preferentially disconnecting suspicious peers with high weights can effectively inhibit virus and failures.

Frame-based listen-before-talk (FB-LBT) has been adopted as one of the channel access mechanism for Wi-Fi/LTE coexistence.We aim to explore the limits of FB-LBT by developing theoretical models to characterise the FB-LBT channel access performance under the coexistence of LTE and Wi-Fi.We first derive a steady-state model to calculate the spectrum share occupied by LTE under the assumption that the Wi-Fi transmissions have stationary distributions.The assumption does not hold when the time between two LTE transmissions is short, where the system is dominated by a dynamic phenomenon.A second model is developed that accounts for the dynamics of the Wi-Fi channel access mechanism.Our models, validated by simulation results, accurately calculate the spectrum share occupied by LTE over a range of FB-LBT frame periods and Wi-Fi traffic loads.We obtain upper bounds on the FB-LBT spectrum share when competing with heavy Wi-Fi traffic, which confirm the weakness of FB-LBT.Moreover, we demonstrate that our models can be used to control the FB-LBT spectrum share within a modest range.

This paper proposes a novel 3D localization method for wideband mmWave massive MIMO systems. A high dimensional linear interpolation (HDLI)-based preprocessing is first proposed to transform the frequency-associated dynamical array response vectors into the common counterparts at the reference frequency. Through this method, the received data in all frequency bands can be processed jointly, and thus the high temporal resolution provided by wideband mmWave systems can be fully exploited for position estimation. To reduce the computational complexity in the process of the parameter estimation, we then present a wideband beamspace (WBS)-based parameter estimation algorithm to estimate the angle and delay in the low-dimensional beamspace. By exploiting the quasi- optical propagation at the mmWave frequencies, a novel positioning scheme is also designed to determine the 3D location of the target. According to our analysis and simulation results, the proposed method is capable of achieving significantly reduced computational complexity, while maintaining high localization accuracy.

Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the a-priori knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.

Deep neural networks (DNNs) have widely used in various Internet-of-Things (IoT) applications. Pursuing superior performance is always a hot spot in the field of DNN modeling. Recently, feature reuse provides an effective means of achieving favorable nonlinear approximation performance in deep learning. Existing implementations utilizes a multilayer perception (MLP) to act as a functional unit for feature reuse. However, determining connection weight and bias of MLP is a rather intractable problem, since the conventional back-propagation learning approach encounters the limitations of slow convergence and local optimum. To address this issue, this paper develops a novel DNN considering a well-behaved alternative called reservoir computing, i.e., reservoir in network (ResInNet). In this structure, the built-in reservoir has two notable functions. First, it behaves as a bridge between any two restricted Boltzmann machines in the feature learning part of ResInNet, performing a feature abstraction once again. Such reservoir-based feature translation provides excellent starting points for the following nonlinear regression. Second, it serves as a nonlinear approximation, trained by a simple linear regression using the most representative (learned) features. Experimental results over various benchmark datasets show that ResInNet can achieve the superior nonlinear approximation performance in comparison to the baseline models, and produce the excellent dynamic characteristics and memory capacity. Meanwhile, the merits of our approach is further demonstrated in the network traffic prediction related to real-world IoT application.

To enable machine learning at the edge of wireless networks (such as edge cloud), close to mobile users, is critical for future wireless networks, but challenging since the lower layers in edge cloud are substantially different from existing machine learning configurations in the cloud. In such geo-distributed computing environment, streaming data need to be evenly and cost-efficiently partitioned for different workers to produce an unbiased learning model with reduced parameter synchronization frequency. This paper presents a new online approach to optimally partitioning streaming data under time-varying network conditions. A new measure is proposed to quantify the evenness of data partitioning and restrain the optimization of data admission, partitioning, and processing. Stochastic gradient descent is applied to learn the optimal decisions online and asymptotically maximize the time-average utility of data partitioning. A new protocol is designed to further reduce the measurements of link costs, while preserving the asymptotic optimality, data evenness, and stability of the platform. Simulation results show that the proposed approach is superior to the state of the art in terms of throughput and cost efficiency, while only 24% of the links need to be measured to achieve the asymptotic optimality.

Antenna design and angle-of-arrival (AoA) estimation are critical to the efficiency of wireless information and power transfer. The AoA estimation is challenging for energy-efficient lens antenna arrays (LAAs), due to discrete sets of fixed discrete Fourier transform (DFT) beams. This paper presents a novel fast and accurate approach for the AoA estimation of LAAs. The key idea is that we prove the two differential outputs of three adjacent lens beams, referred to as “DFT beam differences (DBDs),” that are the strongest at the two sides of an AoA. They are easy to identify and robust to noises, and their powers are proved to provide an accurate estimate of the AoA. Another important aspect is a new beam synthesis technique which produces different beam widths based on DFT beams and practical 1-bit phase shifts in real time. As a result, the angular region containing the AoA can exponentially narrow down, and the two strongest DBDs can be quickly identified. The proposed approach can operate in coupling with successive interference cancellation to estimate the AoAs of multiple paths. Simulations show that the proposed approach is able to outperform the state of the art by orders of magnitude in terms of accuracy. The power transfer efficiency can be dramatically improved.

With the emergence of the mission-critical Internet of Things applications, ultra-reliable low-latency communications are attracting a lot of attentions. Non-orthogonal multiple access (NOMA) with multiple-input multiple-output (MIMO) is one of the promising candidates to enhance connectivity, reliability, and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in the downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove that the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA are Cauchy convergent and can be asymptotically approached by a finite truncated binomial series in the closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. The extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition.

Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.

This paper studies the threat that an aerial eavesdropper can pose to terrestrial wireless communications, from an information-theoretic point of view. The achievable ergodic and the average ε-outage secrecy rates with no channel state information at the transmitter (i.e., with no CSIT) are analyzed for a transmitter-receiver pair on the ground, in the presence of an aerial eavesdropper which flies a random trajectory following a smooth turn (ST) mobility model in a three-dimensional (3D) space. The ST mobility model induces a uniform distribution (of the eavesdropper's waypoints) within the considered 3D volume. Closed-form asymptotic approximations of the achievable secrecy rates are derived based on the almost sure convergence and non-trivial mathematical manipulations. Validated by simulations, our analysis is tight and reveals that the ground transmission is particularly vulnerable to aerial eavesdropping which can be carried out in a distance without being noticed. 3D spherical regions are identified, within which the secrecy rates vanish. This sheds useful insights to protect terrestrial wireless networks from aerial eavesdropping.

Cooperative caching can unify network storage to improve efficiency, but the effective placement and search of contents are challenging especially in distributed edge clouds with neither a-priori knowledge on content requests nor instantaneous global view. This paper establishes a new profitable cooperative region for every content request admitted at an edge server, within which the content, if cached, can be retrieved with guaranteed profit against a direct retrieval from the network backbone. This narrows down the search for the content. The caching density of the content can also be significantly reduced, e.g., to a cached copy per region. The regions are based on a novel distributed framework which allows individual servers to spontaneously admit/dispatch requests and deliver/forward contents, while asymptotically maximizing the time-average profit of caching. The cooperative region for content is erected at individual servers by comparing the upper and lower bounds for the backlogs of unsatisfied requests of the content. Simulations show the substantially improved profit of the proposed approach over existing solutions. The regions can help automate the placement of contents with reduced density and improved efficiency.

Attribute-based encryption has been a promising encryption technology to secure personal health records (PHRs) sharing in cloud computing. PHRs consist of the patient data often collected from various sources including hospitals and general practice centres. Different patients' access policies have a common access sub-policy. In this paper, we propose a novel attribute-based encryption scheme for fine-grained and flexible access control to PHRs data in cloud computing. The scheme generates shared information by the common access sub-policy, which is based on different patients' access policies. Then, the scheme combines the encryption of PHRs from different patients. Therefore, both time consumption of encryption and decryption can be reduced. Medical staff require varying levels of access to PHRs. The proposed scheme can also support multi-privilege access control so that medical staff can access the required level of information while maximizing patient privacy. Through implementation and simulation, we demonstrate that the proposed scheme is efficient in terms of time. Moreover, we prove the security of the proposed scheme based on security of the ciphertext-policy attribute-based encryption scheme.

Network Function Virtualization (NFV) can cost-efficiently provide network services by running different virtual network functions (VNFs) at different virtual machines (VMs) in a correct order. This can result in strong couplings between the decisions of the VMs on the placement and operations of VNFs. This paper presents a new fully decentralized online approach for optimal placement and operations of VNFs. Building on a new stochastic dual gradient method, our approach decouples the real-time decisions of VMs, asymptotically minimizes the time-average cost of NFV, and stabilizes the backlogs of network services with a cost-backlog tradeoff of [ε, 1/ε], for any ε > 0. Our approach can be relaxed into multiple timescales to have VNFs (re)placed at a larger timescale and hence alleviate service interruptions. While proved to preserve the asymptotic optimality, the larger timescale can slow down the optimal placement of VNFs. A learn-and-adapt strategy is further designed to speed the placement up with an improved tradeoff [ε, log2(ε)/ε]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 23 percent and reduce the queue length (or delay) by 74 percent, as compared to existing benchmarks.

Cloud has yet to be applied to computationally intensive radio signal processing, due to closely coupled computing tasks resulting from interference. This paper presents a new cloud-assisted joint beamforming architecture, where computations are decoupled for individual wireless users and pipelined for cloud execution, using Difference of Convex (DC), ℓ1-norm approximations, and dual decompositions. User-specific tasks are constructed and aligned with the cloud to leverage computation reuses and minimize overhead. The time-complexity is dramatically improved to support networks with tens to hundreds of base stations and users, without compromising the sum rate and quality-of-service. Further, the superiority of DC to the state-of-the-art Weighted Minimum Mean Square Error (WMMSE) in terms of convex relaxation is observed and discussed. Corroborated by simulations, the reason is revealed as WMMSE aggressively increases the data rate at interim stages, hence adversely interacting with ℓ1-norm approximation and reducing the feasible solution regions at later stages.

Arrays of Butler matrices provide a promising front-end design for massive MIMO transceivers with low cost and low complexity. However, this advanced design does not necessarily translate to effective applications, unless the angle-of-arrival (AoA) of signals avails to the Butler matrices. This paper presents an efficient approach to the unprecedented AoA estimation for the arrays of Butler matrices. Specifically, we design a new beam synthesis method to recursively narrow down and increasingly focus on the angular region of interest, and hence achieving robust estimation of the phase offset between Butler matrices. With the phase offset canceled in the received signals, we are able to identify the set of critical Butler beams with the dominating effect on the AoA estimation, and estimate the AoA accordingly with minimum signaling. The mean squared error of the proposed estimation is analyzed in the presence of non-negligible noises, with closed-form lower bounds derived. Validated by simulations, the proposed algorithm is able to indistinguishably approach the lower bounds, and significantly outperforms the state-of-the-art developed for discrete antenna arrays by orders of magnitude in terms of accuracy, especially in low signal-to-noise regimes.

Fog computing enables resource-limited network devices to help each other with computationally demanding tasks, but has yet to be implemented in large scales due to sophisticated control and network inhomogeneity. This paper presents a new fully distributed online optimization to asymptotically minimize the time-average cost of fog computing, where tasks are selected to be offloaded and processed independently between different links and devices by measuring their cost effectiveness at each time slot. A key contribution is that we optimize the cost-effectiveness measures which achieve the asymptotic optimality over infinite time. Another contribution is that we optimize placeholders at the devices; which create collaborative computing regions of tasks in the vicinity of the point of capture, prevent tasks being offloaded beyond, preserve the asymptotic optimality and reduce delay. This is achieved in a distributed fashion by discovering the optimal substructure of the placeholders. Simulations show that the average size of collaborative regions is only 3.2 out of total 500 servers, and the system income increases by 43% as compared with existing techniques.

Decentralized orchestration of the control plane is critical to the scalability and reliability of software-defined network (SDN). However, existing orchestrations of SDN are either one-off or centralized, and would be inefficient the presence of temporal and spatial variations in traffic requests. In this paper, a fully distributed orchestration is proposed to minimize the time-average cost of SDN, adapting to the variations. This is achieved by stochastically optimizing the on-demand activation of controllers, adaptive association of controllers and switches, and real-time request processing and dispatching. The proposed approach is able to operate at multiple timescales for activation and association of controllers, and request processing and dispatching, thereby alleviating potential service interruptions caused by orchestration. A new analytic framework is developed to confirm the asymptotic optimality of the proposed approach in the presence of non-negligible signaling delays between controllers. Corroborated from extensive simulations, the proposed approach can save up to 73% the time-average operational cost of SDN, as compared to the existing static orchestration.

Accurate estimation of angle-of-arrival (AoA) is critical to wideband satellite communications, but is susceptible to receive noises and can be ambiguous due to space/cost-effective hybrid antenna array designs with localized analog phased subarrays. As a matter of fact, there has yet to be an unambiguous estimator even for narrow-band systems. This paper proposes a new design of subarray-specific time-varying phase shifts, which enables unambiguous and noise-tolerant estimation of AoA in localized hybrid arrays. Particularly, the new phase shifts deliver deterministic phase changes in the cross-correlations of receive signals between subarrays, and enable the cross-correlations to be coherently accumulated across subarrays and sub-carriers to eliminate ambiguities and tolerate noises. Another important contribution of the paper is that we optimize the frequency interval for coherent accumulation across sub-carriers, leveraging between estimation errors, and accumulation gains. Evident from simulations, our approach is able to dramatically improve the estimation accuracy by orders of magnitudes with significantly reduced requirements of complexities and training symbols, as compared with the state of the art. The approach is robust against noises, with estimation errors asymptotically achieving a rigorously developed lower bound.

The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. In this study, we propose a novel trust model for UAVNs that is based on the behavior and mobility pattern of UAV nodes and the characteristics of inter-UAV channels. The proposed trust model consists of four parts: direct trust section, indirect trust section, integrated trust section, and trust update section. Based on the trust model, the concept of a secure link in UAVNs is formulated that exists only when there is both a physical link and a trust link between two UAVs. Moreover, the metrics of both the physical connectivity probability and the secure connectivity probability between two UAVs are adopted to analyze the connectivity of UAVNs. We derive accurate and analytical expressions of both the physical connectivity probability and the secure connectivity probability using stochastic geometry with or without Doppler shift. Extensive simulations show that compared with the physical connection probability with or without malicious attacks, the proposed trust model can guarantee secure communication and reliable connectivity between UAVs and enhance network performance when UAVNs face malicious attacks and other security risks.

Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1-ϵ)-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. ϵ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an [O(ϵ), O(1/ϵ)]-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.

A major challenge in distributed mobile networks is network integrity, resulting from short link duration and severe transmission collisions. This paper analyzes the impact of link duration and transmission collisions on a range of on-the-fly authentication protocols, which operate based on predistributed keys and can instantly verify and forward messages. All unexpired messages within a link duration can be verified retrospectively, once the keys are matched on-the-air. We develop a new general 4D Markov model which, apart from the first three dimensions modeling a cycle of the protocols, is able to unprecedentedly capture unexpired messages between cycles in the fourth dimension. Validated by simulation, our analysis reveals that the on-the-fly authentication is efficient under short link duration, but is susceptible to transmission collisions. The authentication requires holistic cross-layer designs of retransmission and rekeying. The proposed model is able to facilitate the design of the protocol parameters, which allows the protocols to significantly outperform the state of the art.

By performing fog computing, a device can offload delay-tolerant computationally demanding tasks to its peers for processing, and the results can be returned and aggregated. In distributed wireless networks, the challenges of fog computing include lack of central coordination, selfish behaviors of devices, and multi-hop signaling delays, which can result in outdated network knowledge and prevent effective cooperations beyond one hop. This paper presents a new approach to enable cooperations of N selfish devices over multiple hops, where selfish behaviors are discouraged by a tit-for-tat mechanism. The titfor-tat incentive of a device is designed to be the gap between the helps (in terms of energy) the device has received and offered; and indicates how much help the device can offer at the next time slot. The tit-for-tat incentives can be evaluated at every device by having all devices broadcast how much help they offered in the past time slot, and used by all devices to schedule task offloading and processing. The approach achieves asymptotic optimality in a fully distributed fashion with a timecomplexity of less than O(N2). The optimality loss resulting from multi-hop signaling delays and consequently outdated titfor-tat incentives is proved to asymptotically diminish. Simulation results show that our approach substantially reduces the timeaverage energy consumption of the state of the art by 50% and accommodates more tasks, by engaging devices hops away under multi-hop delays.

Hybrid array is able to leverage array gains, transceiver sizes, and costs for massive multiple-input-multiple-output systems in millimeter-wave frequencies. Challenges arise from the estimation of angle-of-arrival (AoA) in localized hybrid arrays, due to the array structure and the resultant estimation ambiguities and susceptibility to noises. This paper eliminates the ambiguities and enhances the tolerance to the noises based on our new discoveries. Particularly, by designing new subarray-specific time-varying phase shifts, we discover that the cross-correlations between the gains of consecutive subarrays have consistent signs except the strongest. This enables the cross-correlations to be deterministically calibrated and constructively combined for the noise-tolerant estimation of the propagation phase offset between adjacent subarrays. Given the phase offset, the AoA can be estimated unambiguously with few training symbols. We also derive a closed-form lower bound for the mean square error of AoA estimation. Corroborated by simulations, our approach is able to dramatically improve estimation accuracy by orders of magnitude while reducing complexity and training symbols, as compared to the state of the art. With the ambiguities eliminated, the estimation errors of our method asymptotically approach the lower bound, as training symbols increase.

Decoupling network services from underlying hardware, network function virtualization (NFV) is expected to significantly improve agility and reduce network cost. However, network services, sequences of network functions, need to be processed in specific orders at specific types of virtual machines (VMs), which couples decisions of VMs on processing or routing network services. Built on a new stochastic dual gradient method, our approach suppresses the couplings, minimizes the time-average cost of NFV, stabilizes queues at VMs, and reduces the backlogs of unprocessed services through online learning and adaptation. Asymptotically optimal decisions are instantly generated at individual VMs, with a cost-delay tradeoff [ε,log2(ε)/√ε]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 30% and reduce the queue length (or delay) by 83%, as compared to existing non-stochastic approaches.

Vehicular Ad Hoc Networks (VANET) is an important network technology. Relay communication can effectively improve the connectivity and coverage of VANET, especially in distributed environments. Challenges arise from intense collision resulting from inherently synchronized relays. In this paper, we propose a decentralized relay scheme without collecting neighbor nodes' information. Particularly, we design a new score function to prioritize the relays based on their reception quality from source and channel conditions towards intended destination. A closed-form expression for packet delivery ratio (PDR) is derived based on time-out probabilities. Our analyses, validated by simulations, show that the proposed scheme, in terms of PDR, is much better than DAFMAC protocol.

This paper presents a novel access control scheme for personal health record(PHR) data in cloud computing. The scheme utilizes attribute-based encryption(ABE), hash function and symmetric encryption to realize a fine-grained, multi- privilege access control to PHR. The patients can share their PHR with medical staff from various departments with different privileges securely. The experimental results show the efficiency of our scheme in terms of running-time, communication cost and storage overhead.

Smallcells have great potential to enhance cellular networks, complementing macrocells. Severe interference may occur, as smallcells are expected to be deployed and operated uncoordinatedly. However, existing resource management methods require significant overhead to suppress interference. We propose a new resource management approach which is able to mitigate the cross-tier and co-tier interference with substantially reduced overhead. The key idea is to categorize the smallcells into two regions based on a judiciously designed cross-tier interference criterion. Smallcells in the high-interference zone occupy orthogonal radio resources with the macrocell; smallcells in the other zone can reuse the resources that the macrocell is using. Another crucial aspect is that we formulate the resource sharing between the macrocell and smallcells in the low-interference zone to a multi-agent Q-learning process which assigns adequate transmit power levels in a decentralized manner to suppress the co/cross-tier interference. As a result, our approach is able to reduce the outage probabilities of macrocell users significantly to 0%, respectively, in a dense smallcell deployment (200 smallcells), as evidenced by simulation results.

The logically-centralized controllers have largely operated as the coordination points in software-defined networking(SDN), through which applications submit network operations to manage the global network resource. Therefore, the validity of these network operations from SDN applications are critical for the security of SDN. In this paper, we analyze the mechanism that generates network operations in SDN, and present a fine-grained access control model, called Access Control Protector(AC-PROT),that employs an attribute-based signature scheme for network applications. The simulation result demonstrates that AC-PROT can efficiently identify and reject unauthorized network operations generated by applications.

Packet collision and packet delay are considered to be critical for safety applications in vehicular networks. This paper designs a new analytical model to evaluate the performance of channel switching for IEEE 802.11p/WAVE in vehicular networks. Under this model, it explicitly expresses the WAVE channel switching, and constructs contention window size and number of vehicles as packet collision probability and packet delay time function of variables. Finally, we evaluate accuracy of the designed model of collision caused by channel switching and transmission delay in vehicular networks. The results show that the model could analyzes perfectly packet collision which is caused by channel switching and packet delay in vehicular networks.

This study investigates the performance of practical wireless exclusive OR (XOR) two‐way relay (TWR) system, in which finite buffer, lossy wireless channels and non‐negligible signalling overhead are considered. Specifically, the authors develop a new analytical model to explicitly characterise the transmissions of both the end‐nodes and the relay. The impact of scheduling on the throughput, queuing delay, power consumption and buffer overflow probability of XOR‐TWR is evaluated. Validated by simulations, the model can precisely quantify the performance of XOR‐TWR and adequately allocate the relay's buffer adapting to the wireless link qualities and signalling overhead.

License-assisted access (LAA) is a promising technology to offload dramatically increasing cellular traffic to unlicensed bands. Challenges arise from the provision of quality-of-service (QoS) and the quantification of capacity, due to the distributed and heterogeneous nature of LAA and legacy systems (such as Wi-Fi) coexisting in the bands. In this paper, we develop new theories of the effective capacity to measure LAA under statistical QoS requirements. A new four-state semi-Markovian model is developed to capture transmission collisions, random backoffs, and lossy wireless channels of LAA in distributed heterogeneous network environments. A closed-form expression for the effective capacity is derived to comprehensively analyze LAA. The four-state model is further abstracted to an insightful two-state equivalent which reveals the concavity of the effective capacity in terms of transmit rate. Validated by simulations, the concavity is exploited to maximize the effective capacity and effective energy efficiency of LAA, and provide significant improvements of 62.7% and 171.4%, respectively, over existing approaches. Our results are of practical value to holistic designs and deployments of LAA systems.

Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.

Exploiting software-defined networking techniques, randomly and instantly mutating routes can disguise strategically important infrastructure and protect the integrity of data networks. Route mutation has been to date formulated as NP-complete constraint satisfaction problem where feasible sets of routes need to be generated with exponential computational complexities, limiting algorithmic scalability to large-scale networks. In this paper, we propose a novel node-centric route mutation method which interprets route mutation as a signature matching problem. We formulate the route mutation problem as a three-dimensional earth mover’s distance (EMD) model and solve it by using a binary branch and bound method. Considering the scalability, we further propose that a heuristic method yields significantly lower computational complexities with marginal loss of robustness against eavesdropping. Simulation results show that our proposed methods can effectively disguise key infrastructure by reducing the difference of historically accumulative traffic among different switches. With significantly reduced complexities, our algorithms are of particular interest to safeguard large-scale networks.

The existence of relatively long LTE data blocks within the licensed-assisted access (LAA) framework results in bursty machine-type communications (MTC) packet arrivals, which cause system performance degradation and present new challenges in Markov modeling. We develop an embedded Markov chain to characterize the dynamic behavior of the contention arising from bursty MTC and Wi-Fi data traffic in the LAA framework. Our theoretical model reveals a high-contention phenomenon caused by the bursty MTC traffic, and quantifies the resulting performance degradation for both MTC and Wi-Fi data traffic. The Markov model is further developed to evaluate three potential solutions aiming to alleviate the contention. Our analysis shows that simply expanding the contention window, although successful in reducing congestion, may cause unacceptable MTC data loss. A TDMA scheme instead achieves better MTC packet delivery and overall throughput, but requires centralized coordination. We propose a distributed scheme that randomly spreads the MTC access processes through the available time period. Our model results, validated by simulations, demonstrate that the random spreading solution achieves a near TDMA performance, while preserving the distributed nature of the Wi-Fi protocol. It alleviates the MTC traffic contention and improves the overall throughput by up to 10%.

Challenges of authentication in decentralized mobile networks arise from frequently changing topologies and unreliable contention-based transmissions. We propose a new protocol to speed up authentications, reduce communication costs, and support opportunistic routing under fast-changing topologies. Key pairs are predistributed across the network. Nodes that predistributed the same pair can instantly verify and route messages for each other in an opportunistic and cooperative fashion, combating fast-changing topologies. We also enable a node to increasingly combine unauthenticated messages and a new message for signature or message authentication code generation, while trying different keys on-the-fly. The messages can be verified altogether, once a key is matched. The communication overhead, thus, becomes independent of the number of keys tried. Closed-form expressions for authentication rate, delay, and throughput are derived through a new three-dimensional Markov model. Validated by simulations, analytical results corroborate the robustness of the proposed protocol against changing topologies, as well as the substantially improved resistance to collusion attacks, as compared with the state of the art.

Biological epidemic models, widely used to model computer virus propagations, suffer from either limited scalability to large networks, or accuracy loss resulting from simplifying approximations. In this paper, a discrete-time absorbing Markov process is constructed to precisely characterize virus propagations. Conducting eigenvalue analysis and Jordan decomposition to the process, we prove that the virus extinction rate, i.e., the rate at which the Markov process converges to a virus-free absorbing state, is bounded. The bounds, depending on the infection and curing probabilities, and the minimum degree of the network topology, have closed forms. We also reveal that the minimum curing probability for a given extinction rate requirement, specified through the upper bound, is independent of the explicit size of the network. As a result, we can interpret the extinction rate requirement of a large network with that of a much smaller one, evaluate its minimum curing requirement, and achieve simplifications with negligible loss of accuracy. Simulation results corroborate the effectiveness of the interpretation, as well as its analytical accuracy in large networks.

Airborne relaying can extend wireless sensor networks (WSNs) to remote human-unfriendly terrains. However, lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs) are critical issues, adversely affecting success rate and network lifetime, especially in real-time applications. We propose an energy-efficient cooperative relaying scheme which extends network lifetime while guaranteeing the success rate. The optimal transmission schedule of the UAVs is formulated to minimize the maximum (min-max) energy consumption under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimisation techniques. We also propose a computationally efficient suboptimal algorithm to reduce the scheduling complexity, where energy balancing and rate adaptation are decoupled and carried out in a recursive alternating manner. Simulation results confirm that the suboptimal algorithm cuts off the complexity by orders of magnitude with marginal loss of the optimal network yield (throughput) and lifetime. The proposed suboptimal algorithm can also save energy by 50 percent, increase network yield by 15 percent, and extend network lifetime by 33 percent, compared to the prior art.

The communication systems in existing smart grids mainly take the request/reply interaction model, in which data access is under the direct control of data producers. This tightly controlled interaction model is not scalable to support complex interactions among smart grid services. On the contrary, the publish/subscribe system features a loose coupling communication infrastructure and allows indirect, anonymous and multicast interactions among smart grid services. The publish/subscribe system can thus support scalable and flexible collaboration among smart grid services. However, the access is not under the direct control of data producers, it might not be easy to implement an access control scheme for a publish/subscribe system. In this article, we propose a Data-Centric Access Control Framework (DCACF) to support secure access control in a publish/subscribe model. This framework helps to build scalable smart grid services, while keeping features of service interactions and data confidentiality at the same time. The data published in our DCACF is encrypted with a fully homomorphic encryption scheme, which allows in-grid homomorphic aggregation of the encrypted data. The encrypted data is accompanied by bloom-filter encoded control policies and access credentials to enable indirect access control. We have analyzed the correctness and security of our DCACF and evaluated its performance in a distributed environment.

Abstract Two‐way exclusive OR (XOR) relay can enable hidden nodes to exchange data with low delays and high data rate, while keeping signal processing simple. In this paper, we analyse practical two‐way XOR relaying systems, where finite relay buffer, non‐negligible signalling overhead, and lossy wireless channels are all captured. A two‐layer model is developed to characterise such practical two‐way relay systems, which is then reformulated into a Markov process after we project and combine inter‐layer state transitions of the two‐layer model. Using Markov techniques, we evaluate the steady state probabilities of the Markov process and, in turn, the key performance measures of two‐way XOR relaying, such as throughput, delay, and packet loss. The accuracy of our model is validated by simulations. Our model can also be used as an online tool to configure the buffer resources, adapting to wireless channel conditions and signalling requirements. Copyright © 2016 John Wiley & Sons, Ltd.

Airborne relaying is of potential to extend wireless sensor networks (WSN) to human-unfriendly terrains. Challenges arise due to lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs). We propose an energy-efficient relaying scheme to overcome the challenges. A swarm of UAVs are deployed to listen to remote sensors from distributed locations, improving packet reception over lossy channels. UAVs report their reception qualities to the base station where the optimal schedule with guaranteed success rates and balanced energy consumption can be generated. Such scheduling is an NP-hard binary integer programming. We develop a suboptimal solution by decoupling the processes of energy balancing and data rate adjustment. Simulations confirm that, in terms of network yield, our method is indistinguishable to the NP-hard optimal solution, 15% higher than greedy algorithms. Our method can reduce the complexity by orders of magnitude, and extend network lifetime by 33%.

Detection of Denial-of-Service (DoS) attacks has attracted researchers since 1990s. A variety of detection systems has been proposed to achieve this task. Unlike the existing approaches based on machine learning and statistical analysis, the proposed system treats traffic records as images and detection of DoS attacks as a computer vision problem. A multivariate correlation analysis approach is introduced to accurately depict network traffic records and to convert the records into their respective images. The images of network traffic records are used as the observed objects of our proposed DoS attack detection system, which is developed based on a widely used dissimilarity measure, namely Earth Mover's Distance (EMD). EMD takes cross-bin matching into account and provides a more accurate evaluation on the dissimilarity between distributions than some other well-known dissimilarity measures, such as Minkowski-form distance Lpand X2statistics. These unique merits facilitate our proposed system with effective detection capabilities. To evaluate the proposed EMD-based detection system, ten-fold cross-validations are conducted using KDD Cup 99 dataset and ISCX 2012 IDS Evaluation dataset. The results presented in the system evaluation section illustrate that our detection system can detect unknown DoS attacks and achieves 99.95 percent detection accuracy on KDD Cup 99 dataset and 90.12 percent detection accuracy on ISCX 2012 IDS evaluation dataset with processing capability of approximately 59,000 traffic records per second.

In Vehicular Ad Hoc Networks (VANETs), vehicles driving along highways can be grouped into clusters to facilitate communication. The design of the clusters, e.g., size and geographical span, has significant impacts on communication quality. Such design is affected by the Media Access Control (MAC) operations at the Data Link layer, the wireless channel conditions at the Physical layer, and the mobility of the vehicles. Previous works investigated these effects separately. In this paper, we present a comprehensive analysis that integrates the three important factors into one model. In particular, we model an unsaturated VANET cluster with a Markov chain by introducing an idle state. The wireless channel fading and vehicle mobility are integrated by explicitly deriving the joint distribution of inter-vehicle distances. Closed-form expressions of network performance measures, such as packet loss probability and system throughput, are derived. Our model, validated by extensive simulations, is able to accurately characterize VANET performance. Our analysis reveals intrinsic dependencies between cluster size, vehicle speed, traffic demand, and window size, as well as their impacts on the overall throughput and packet loss of the cluster. Performance evaluation results demonstrate the practical value of the proposed model in providing guidelines for VANET design and management.

To maximize power transfer for inductively charging electric vehicles (EVs), charger and battery coils must be aligned. Wireless sensors can be installed to estimate misalignments; however, existing ranging techniques cannot satisfy the precision requirements of the misalignment estimation. We propose a high-precision wireless ranging and misalignment estimation scheme, where high precision is achieved by iteratively measuring, estimating, and aligning the coils. Another key aspect is to convert the nonconvex misalignment estimation to a more tractable problem with a convex objective. We develop a conditional gradient descent method to solve the problem, which performs gradient descent (or conditional gradient descent on the boundary of the search space) and projects out-of-boundary points back into the space. Employing experimentally validated models, we show that our scheme can achieve 92% of the efficiency of perfectly aligned coils in 90% of operations, and tolerate correlated distance measurement errors. In contrast, the prior art is susceptible to correlation, undergoing a significant efficiency degradation of 18.5%.

Efficient discovery and effective sharing of spectrum opportunities are the most challenging issues in a multi-channel cognitive radio network (CRN) with multiple secondary users (SUs). In this paper, we propose a novel spectrum sensing and access protocol in CRNs where SUs are allowed to sequentially sense two channels in a single time slot and are coordinated to access the potentially unused channels. The proposed protocol is formulated as a channel selection problem coupled with a channel assignment problem. We subsequently investigate the myopic sensing policy and propose a Markov chain-based greedy channel assignment scheme (MCGA) to maximize the expected total SU throughput. Moreover, we extend our proposal to the scenario with imperfect spectrum sensing and obtain the optimal sensing time to enhance the performance improvement by considering spectrum sensing and spectrum access in a joint manner. Finally, we evaluate the performance of our proposal in a saturated network. Numerical and simulation results demonstrate that compared to the existing work, our approach can achieve significant performance improvements in SU throughput.

Plug-and-play femtocells will be an integrating part of future cellular networks. Resource management and interference mitigation become challenging, suffering from severely delayed network control in large-scale deployments. We propose a new game theoretic framework, where fast interference suppression is decoupled from the relatively slow frequency allocation process to tolerate the delayed control. The key idea is to cast femtocell clustering as an outer-loop evolutionary game coupled with bankruptcy channel allocation, which drives the cells to spontaneously switch to less interfered clusters. Within each cluster, we design an inner-loop non-cooperative power control game, such that the requirement of prompt control is eliminated. The two loops interact recursively with analytically confirmed stability. Simulations show that our framework can improve the throughput by 13.2% in a network of 200 cells, compared to the prior art. The gain grows further with the network size.

In Vehicular Ad Hoc Networks (VANETs), vehicles along highways can be grouped into clusters. The design of VANET clusters (i.e., size and geographical span) depends on the packet collision in MAC layer, the wireless channel conditions in PHY layer, and the mobility of the vehicles. Existing works investigated these effects separately. In this paper, we present a comprehensive analysis that combines these three important factors into one model. In particular, we model an unsaturated VANET cluster with a Markov chain by introducing an idle state. The wireless channel fading and vehicle mobility are integrated by explicitly deriving the joint distribution of inter-vehicle distances. Closed-form expressions of network performance measures, i.e., packet loss probability and system throughput, are derived. The proposed analytic model, validated by simulations, is able to accurately characterize VANET performance. Our model can be applied to the design of VANET clusters, and reveals a number of insights that provide guidelines for VANETs design and management.

The Internet of Things is a vision that broadens the scope of the internet by incorporating physical objects to identify themselves to the participating entities. This innovative concept enables a physical device to represent itself in the digital world. There are a lot of speculations and future forecasts about the Internet of Things devices. However, most of them are vendor specific and lack a unified standard, which renders their seamless integration and interoperable operations. Another major concern is the lack of security features in these devices and their corresponding products. Most of them are resource-starved and unable to support computationally complex and resource consuming secure algorithms. In this paper, we have proposed a lightweight mutual authentication scheme which validates the identities of the participating devices before engaging them in communication for the resource observation. Our scheme incurs less connection overhead and provides a robust defence solution to combat various types of attacks.

Virtual Multiple-Input Multiple-Output (MIMO) is a promising uplink technology that can meet the throughput demand of Long-Term Evolution-Advanced (LTE-A) systems. However, the complexity of scheduling virtual MIMO is a challenge; existing virtual MIMO is therefore limited to best effort applications. We investigate the resource allocation and scheduling problem in a heterogeneous virtual MIMO system where delay sensitive applications are present. The goal is to maximize the system throughput while maintaining delay bound for delay sensitive traffic. To tackle the complexity challenge, we propose two low-complexity suboptimal algorithms, where the key idea is to reduce the search space and iteratively minimize the rate loss respectively. Simulation results show that the rate loss minimization based heuristic algorithm converges to within 99% of the optimal throughput on average and maintains delay bound for delay sensitive users. It also achieves almost the same fairness performance as the optimal solution.

Quality of Service (QoS) routing is one of the key enabling techniques for multimedia wireless sensor networks (WSNs). However, the multi-constraints QoS routing problem is an NP-hard problem, and the computational complexity of an exhaustive search over all the paths is too high for large scale multimedia WSNs. In this paper, a novel parallel elite clonal quantum evolutionary algorithm is proposed to solve the multi-constraints QoS routing problem. The proposed algorithm minimizes the energy consumption, while guaranteeing QoS performance, including delay, bandwidth, delay jitter and packet loss rate, in multimedia WSNs. The algorithm is tested by extensive simulations and its performance is compared with the genetic algorithm and ant colony optimization. Simulation results demonstrate that the proposed algorithm achieves lower energy consumption at a faster convergence rate than the other two evolutionary algorithms.

Although differentiated services (DiffServ) networks have been well discussed in the past several years, a conventional Active Queue Management (AQM) algorithm still cannot provide low-complexity and cost-effective differentiated bandwidth allocation in DiffServ. In this paper, a novel AQM scheme called CHOKeR is designed to protect TCP flows effectively. We adopt a method from CHOKeW to draw multiple packets randomly from the output buffer. CHOKeR enhances the drawing factor by using a multistep increase and single-step decrease (MISD) mechanism. In order to explain the features of CHOKeR, an analytical model is used, followed by extensive simulations to evaluate the performance of CHOKeR. The analytical model and simulation results demonstrate that CHOKeR achieves proportional bandwidth allocation between different priority levels, fairness guarantee among equal priority flows, and protection of TCP against high-speed unresponsive flows when network congestion occurs.

Relays enabled with multiuser MIMO techniques have great potential to mining vehicle safety applications. However, they are yet to be practical due to high scheduling overhead in mobile, radio-unfriendly, mining environments. A new decentralized relay-assisted multiuser MIMO approach is proposed, which cuts the overhead by 80% and enables relay-assisted multiuser MIMO to be implemented in practice. This approach is a new distributed participatory downlink transmission method, where both the relays and destinations participate in the scheduling decisions. A new recursive algorithm is also developed to optimally quantize the channel conditions of the vehicles, thereby minimizing the feedback requirement. Analytical results, confirmed by simulations, show that the proposed approach is able to achieve 97.6% of the sum-rate upper bound of the network, using only three bits to characterize the channel condition of each vehicle. In terms of throughput, the proposed decentralized scheme can perform 45.2% better than the existing centralized scheme. The proposed approach is compatible with industrial communication standards and can be implemented with commercial industrial communication systems.

Link aggregation, or multi-band striping, has been used in wireless backhaul systems to overcome the limitations of bandwidth and transmission range. Work-conserving and in-sequence delivery are essential but conflicting requirements in multi-band striping systems. Previous schemes can achieve one or the other, but not both. The heterogeneous channel and time varying data rate in multi-band wireless backhaul systems present additional challenges. We aim at designing a multi-band striping algorithm to achieve both work-conserving and in-sequence delivery in the heterogeneous time-varying multi-band wireless backhaul system. We propose a parallel processing architecture for multi-band aggregation, and derive the necessary conditions for such system to achieve sequence preserving and work conserving. An optimum set of timing controls is developed and proved to provide the best performance. We design a work-conserving in-sequence striping algorithm and prove it to be sequence preserving and work conserving. The algorithm is then extended to practical data granularity. Performance analysis and simulation results demonstrate that our designs are able to preserve data sequence, reduce delay, and achieve 100% channel utilization.

Interconnected systems, such as Web servers, database servers, cloud computing servers and so on, are now under threads from network attackers. As one of most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses multivariate correlation analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area-based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 data set, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy.

Large scale deployment of machine-to-machine (M2M) communication networks hinges on the cost and energy efficient design of the embedded devices. Standard WLAN power save mechanisms, which are designed for human communications, experience performance degradation and unbalanced energy consumptions in M2M communication networks. We develop a novel analytical model that takes into account the fundamentally different network architecture and traffic patterns of M2M communications. Our model accurately characterizes the high contention and long packet delay found in M2M communication networks, while previous models underestimate such measures. To combat such performance decline, we propose a new algorithm that enhances existing power save mechanisms to extend the lifetime of a M2M communication network. We call this the Offset ListenInterval (OLi) Algorithm. The OLi algorithm spreads the M2M traffic evenly with calculated offsets to alleviate network contention and reduce packet delay. Our analytical model is then used to evaluate the energy efficiency of our OLi algorithm and compare with the standard power save mechanisms. Our results show that the proposed OLi algorithm extends the lifetime by up to 40%, and scales up to thousands of nodes in a M2M communication network.

Bandwidth-demanding mobile video applications are becoming increasingly popular in wireless networks, leading to a relentless growth in the demand for wireless throughput and quality of service (QoS). Multiuser Multiple-Input Multiple-Output (MIMO) has great potential to meet the growth of wireless throughput. However, this advancement in physical-layer technologies does not necessarily translate into better QoS for the applications, unless the design principles and operating protocols at the higher layers of the networking stack are adapted accordingly to fully capture this potential. We propose a new scheduling algorithm, which selects mobile users to form multiuser MIMO based on the priorities we carefully design to leverage the demands of wireless throughput and video quality. We also develop a new computationally efficient parallel technique to calculate the priorities precisely, which allows the users to be selected in a computationally effective way. Analyses and simulations show that the proposed scheme allows video applications to achieve close to the throughput upper bound of multiuser MIMO. Our scheme also improves the video quality by reducing the loss of video enhancement packets by an order of magnitude and by reducing the delay by 35%, compared to the state of the art.

Quality of Service (QoS) and queue management are critical issues for broadcast scheme of IEEE 802.11 systems in Vehicular Ad hoc Networks (VANETs). However, existing 1-dimensional models of broadcast scheme in VANETs are unable to capture the complete QoS performance and queueing behavior due to the lack of an adequate finite buffer model. We present a 2-dimensional Markov chain that integrates the broadcast scheme of the 802.11 system and queueing processes into one model. The extra dimension, that models the queue length, accurately capture important QoS measures for realistic 802.11 broadcast systems with finite buffer under finite load. We derive an simplified method for solving the steady state probabilities of the Markov chain. The solutions are validated by extensive simulations. Based on this model, we also show numerical results to analyze the performance of the broadcast scheme in VANETs in terms of collision probability, throughput, queue length, and QoS measures, including blocking probability and queueing delay.

Communication is an enabling technology for the efficient control and management of next-generation Smart Grids. Energy conservation of the communication devices is essential for future large scale deployment of Smart Grid communication networks. However, existing power save protocols experience high contention in Smart Grid communication networks that have a large number of nodes and periodic traffic. We design a new energy conservation protocol, Power Save with Offset Listen Interval (PS-OLi), to address such contention problems. PS-OLi avoids message collisions by controlling the station wake up time with a calculated offset. A new analytical model is developed to characterize the power save performance of networks with periodic traffic. Simulation results show that our analytical model accurately predicts the collision probability and packet delay. We use our model to evaluate the energy efficiency of PS-OLi and standard power save protocols. Our results show that PS-OLi extends the lifetime of a Smart Grid communication network by more than 10%.

Existing mobile video applications are continuously driving up the demand for throughput and better quality of service (QoS) for future Long Term Evolution-Advanced (LTE-A) networks. Multi-User Multiple-Input Multiple-Output (MU-MIMO) is one of the most promising technologies that would meet the throughput demand. Unfortunately, existing MU-MIMO schemes do not consider metrics such as delay, and therefore, cannot meet the QoS requirement of delay sensitive applications, such as mobile video. We propose a new cross-layer MU-MIMO scheduling algorithm, which is referred to as joint channel and delay aware user scheduling (CDAUS), satisfies both the throughput and delay requirements. The key idea of the CDAUS algorithm is to select users to form MU-MIMO based on the delay requirements of individual users, as well as their channel correlations. The priority of the users is carefully designed to leverage their delay and throughput. Simulation results show that the proposed CDAUS algorithm is able to reduce the average delay by up to 30% with a marginal 2% sacrifice of throughput, compared to previous work. It also reduces delay variations and improves fairness among the users.

Cooperative spectrum sensing has been proposed to significantly improve spectrum sensing accuracy by taking advantage of the cooperation among multiple secondary users (SUs). Most of existing work assumes that all SUs have the same SNR values of primary users' signal while the difference of SNR values among SUs, although it is very common in practice, is largely ignored. In this paper, we investigate two cooperative spectrum sensing scenarios where multiple geographically diverse SUs may have different SNR values. In the first scenario a cognitive radio network (CRN) with a single primary channel is considered. We aim to optimize the individual thresholds of SUs to maximize SU throughput subject to the constraint of missed detection probability. In the second scenario where there are multiple channels in a CRN, we jointly optimize the allocation of SUs to sense different channels and the individual detection thresholds of SUs. Our objective is to maximize total SU throughput over multiple channels while guaranteeing the missed detection probability below a given threshold. Simulation results demonstrate that our proposed schemes achieve significant improvements in SU throughput over the existing schemes.

Efficient data transmission in cognitive radio networks (CRNs) is critical for cognitive radio (CR) users to communicate with each other in an opportunistic manner. Even with successful access to required channels, the transmission could still suffer from failures due to channel fading. In this paper, we propose a random linear coded scheme for efficient data transmission in multi-channel CRNs under practical fading channel conditions. We develop theoretical analysis and derive general form solutions for the batch delay associated with the proposed scheme. We also use our theoretical model to analyze the performances of two multi-channel automatic repeat request (ARQ) based schemes. Simulation results validate the analysis and show that the coded scheme outperforms the ARQ based schemes in terms of batch transmission delay. Additionally, the coded scheme is less dependent on feedback channels than the other schemes.

The rapid growth of computational power demand from scientific, business, and Web applications has led to the emergence of cloud-oriented data centers. These centers use pay-as-you-go execution environments that scale transparently to the user. Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment. Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective. Load classification before prediction is necessary to improve prediction accuracy. In this paper, a novel approach is proposed to forecast the future load for cloud-oriented data centers. First, a hidden Markov model (HMM) based data clustering method is adopted to classify the cloud load. The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers. Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load. With the data from this cluster, a genetic algorithm optimized Elman network is used to forecast future load. Experimental results show that our algorithm outperforms other approaches reported in previous works.

The provision of location-based services with high positional accuracy requires the use of Time of Arrival (TOA)-based techniques. However, existing TOA-based WLAN location service schemes are inefficient due to the individual query and response ranging method employed. We present a highly efficient WLAN location service architecture which includes a modification to the Transmit Opportunity (TXOP) technique in the IEEE 802.11e standard. Our Location Service with TXOP (LSOP) scheme achieves high efficiency by minimizing the number of TOA transmissions and eliminating the contention overhead for TOA messages. The adaptation of TXOP technique also improves location accuracy by protecting TOA messages from collision and by grouping the TOA messages into one compact burst. Our analysis shows that the LSOP scheme achieves the highest location update rate compared to previous schemes. Our simulation results show that the LSOP scheme has minimum impact on data traffic and achieves higher accuracy than the previous schemes. Experimental results demonstrate the degradation in localization performance caused by packet collisions. These results validate that our LSOP scheme, which implements contention-free broadcast of TOA messages with a modified TXOP, provides the best combination of high location update rate, low network load, and high location accuracy compared to other schemes.

In practical radio transmissions, bit error and channel capture are two dominating factors that affect wireless network performance. Previous models have omitted the interaction between bit error and channel capture. We present a homogeneous-network performance-prediction model for a Rayleigh fading channel that incorporates both the capture effect and transmission error into a 3-D Markov Chain. We accurately characterise the interaction between packet error and capture by incorporating them both into the model of the receiver operations. We show how the model can be solved efficiently. The model provides quality of service measures, including packet delay and loss, which are difficult to achieve with other models. Simulation results confirm that our 3-D model accurately predicts the performance for practical SNRs and receiver sensitivities. We demonstrate that our model can be directly applied to call admission control of Voice over IP service with a QoS guarantee in a WiFi network. The model is then extended to heterogeneous networks, where different stations have different packet arrival rates and packet sizes.

The authors of the above titled paper (ibid., vol. 9, no. 8, pp. 2664-2675, Aug. 2010), have become aware that several of the equations in that paper were presented with errors. In this brief, we provide correct equations. The implementation of the model in the original paper was in accordance with the correct equations, so the implementation results presented in Section V. Performance Analysis are still valid.

Small cells with wireless backhaul are promising, whereas challenges of severe overlapping coverage and strong interference are yet to be addressed. Coordinating small cells could resolve the challenges; however, existing multicell coordinated beamforming techniques involve high cost of communication overhead, synchronization and backhaul. Such problems may deteriorate in practical cellular applications, where there could be many users, each generating high channel feedback overhead to compete for an opportunity of being scheduled, and the downlink data signals of the coordinated cells need to be precisely synchronized at each of the users. We propose a new scheme, which cuts the overhead by 80% and enables the coordination to be practically implemented in a decentralized manner. Our scheme is a user-centric downlink scheduling approach, where mobile terminals trigger and participate in the scheduling decisions of small cells. We also develop a new recursive algorithm to optimize the quantization levels of mobile terminals' feedback, minimizing the feedback requirement. Analysis, confirmed by simulations, shows that our scheme is able to achieve 94.4% of the sum-rate upper-bound which can only be approached by idealized centralized coordination. In terms of throughput, given the 80% reduced overhead, our scheme is 139.5% better than the idealized centralized coordination approach.

Cooperative spectrum sensing has been proposed to significantly improve spectrum sensing accuracy by taking advantage of the cooperation among secondary users (SUs), but also this incurs some sensing cost. In this paper, we present a cooperative spectrum sensing model with consideration to spectrum sensing cost in distributed cognitive radio networks where each SU aims to maximize its utility. Under the scenario with selfish SUs, we formulate cooperative spectrum sensing as a non-cooperative game and obtain the mixed strategy Nash equilibrium of the formulated spectrum sensing game by deriving the sensing probabilities of SUs. Under the scenario with limited collaboration of SUs, we formulate cooperative spectrum sensing as a nonlinear optimization problem and derive the optimal sensing strategy of SUs by using our proposed Newton-Raphson based algorithm. Numerical results demonstrate that SUs with limited collaboration are able to achieve much better performance than the outcome of the Nash equilibrium and by choosing the optimal sensing strategy SUs are able to maximize their utility, which is an effective tradeoff between SU throughput and sensing cost.

In this paper we present a novel dynamic spectrum sensing and access model in cognitive radio networks. This model allows secondary users (SUs) to sequentially sense two channels in a single time slot and provides coordinated access of multiple SUs to the available channels. The presented access model is formulated as a channel assignment optimization problem which is shown to be NP-hard. We subsequently propose and analyze a Markov chain based greedy channel assignment scheme (MCGA) which allows for sequential sensing of two channels with a priority order per time slot. Finally, we analyze and evaluate the performance of our approach in a saturated network. Our analytical results, validated by simulation, indicate that compared to the existing work, our approach can achieve significant improvements in terms of SU throughput and MAC delay.

A Wireless Body Area Network (WBAN) comprises a number of tiny devices implanted in/on the body that sample physiological signals of the human body and send them to a coordinator node for medical or other purposes. As these miniature devices run on built-in batteries, energy is the most valuable resource in WBANs. This makes signal interference between neighboring WBANs a serious threat because it causes energy waste in these systems. To mitigate this internetwork interference, we propose a dynamic power control mechanism in WBANs which employs reinforcement learning (RL) to learn from experience and improve its performance. This paper presents guidelines in designing efficient RL power controllers in WBANs and provides an analysis of the effect of the reward function, discount factor, learning rate and eligibility trace parameter where the main performance criteria used are convergence and solution optimality in terms of throughput and energy consumption per bit.

The threat of rogue Access Points (APs) has attracted significant attentions from both industrial and academic researchers. However existing solutions focus on rogue AP detection, rather than localization. We propose a Rogue AP Detection and Localization (RAPDL) architecture, which integrates rogue AP detection and localization into one software system. A RAPDL demonstration system has been developed in our laboratory. In the RAPDL system, the monitors identify potential rogue APs, measure their properties and report relevant information to the server. The RAPDL server collects information from all monitors, and runs a localization algorithm to identify and locate the rogue APs. We implemented two localization algorithms in the RAPDL system based on received signal strength (RSS) and compare their performance. Experimental results acquired in an office environment show that RAPDL can detect and locate rogue APs quickly and accurately.

Cloud computing plays an important role in current converged networks. It brings convenience of accessing services and information to users regardless of location and time. However, there are some critical security issues residing in cloud computing, such as availability of services. Denial of service occurring on cloud computing has even more serious impact on the Internet. Therefore, this paper studies the techniques for detecting Denial-of-Service (DoS) attacks to network services and proposes an effective system for DoS attack detection. The proposed system applies the idea of Multivariate Correlation Analysis (MCA) to network traffic characterization and employs the principal of anomaly-based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle area technique is proposed to enhance and speed up the process of MCA. The effectiveness of our proposed detection system is evaluated on the KDD Cup 99 dataset, and the influence of both non-normalized and normalized data on the performance of the detection system is examined. The results presented in the system evaluation section illustrate that our DoS attack detection system outperforms two state-of-the-art approaches.

802.11e enhanced distributed channel access (EDCA) introduces different parameters to provide differentiated QoS support for various traffic classes. This brings additional difficulties and complexities to the traditional per-slot based Markov chain modeling techniques, where the transmission probability relies on generic time slots. Channel access cycle based modeling, which does not depend on per-slot states, breaks new ground in 802.11e EDCA study. However, the current cycle based model only considers saturated traffic load conditions, while typical network conditions are unsaturated with significant idle periods. We define a new unsaturated channel access cycle that includes a potential idle period and post-backoff procedure, in addition to the backoff procedure and a transmission attempt of the saturated cycle. A new EDCA model for unsaturated traffic conditions is proposed based on this channel access cycle. Our model achieves high accuracy in describing channel access operations over a wide range of traffic conditions. The model solutions, validated by ns-2 simulations, accurately characterize the unique features of 802.11e EDCA in unsaturated traffic conditions.

Echo state network (ESN), which efficiently models nonlinear dynamic systems, has been proposed as a special form of recurrent neural network. However, most of the proposed ESNs consist of complex reservoir structures, leading to excessive computational cost. Recently, minimum complexity ESNs were proposed and proved to exhibit high performance and low computational cost. In this paper, we propose a simple deterministic ESN with a loop reservoir, i.e., an ESN with an adjacent-feedback loop reservoir. The novel reservoir is constructed by introducing regular adjacent feedback based on the simplest loop reservoir. Only a single free parameter is tuned, which considerably simplifies the ESN construction. The combination of a simplified reservoir and fewer free parameters provides superior prediction performance. In the benchmark datasets and real-world tasks, our scheme obtains higher prediction accuracy with relatively low complexity, compared to the classic ESN and the minimum complexity ESN. Furthermore, we prove that all the linear ESNs with the simplest loop reservoir possess the same memory capacity, arbitrarily converging to the optimal value.

We consider the impacts of channel fading on the quality of service (QoS) performance of the IEEE 802.11 system. Traditional 2-D Markov chain models, while suitable for throughput analysis, are unable to capture the QoS performance due to the lack of a proper queueing model. We present a 3-D Markov chain queueing model that incorporates channel fading effects and solve the Markov chain efficiently with our Collapsed Transition onto Basis approach. In doing so, we are able to investigate important QoS measures, packet delay and loss, as well as throughput, for a 802.11 system under practical channel fading conditions. The analytical results are validated by extensive simulations. Our 3-D model offers new insights in channel fading effects on system capacity and QoS performance. We demonstrate that our 3-D model can also be used to provides guidelines for traffic control.

Wireless tracking is being increasingly used in elite and professional sports for training and performance monitoring. Accurate and high update rate tracking in indoor venues is the challenge that we are addressing. In this paper we propose a new algorithm for network round-trip ranging based on the measurement of time of arrival that supports high update rates and tracks and removes errors caused by changes in propagation delay in the analog electronics. The localization performance of our proposed algorithm is validated by results acquired in a sporting venue using our localization system, which is based on a custom TDMA protocol. We then discuss the issues with implementing the scheme in an 802.11 wireless network, propose an extension to the standard to support our scheme, and present analysis and simulation results to demonstrate the benefit over other schemes.

A Wireless Body Area Network (WBAN) is made up of multiple tiny physiological sensors implanted in/on the human body with each sensor equipped with a wireless transceiver that communicates to a coordinator in a star topology. Energy is the scarcest resource in WBANs. Power control mechanisms to achieve a certain level of utility while using as little power for transmission as possible can play an important role in reducing energy consumption in such very energy-constrained networks. In this paper, we propose a novel power controller to mitigate internetwork interference in WBANs and increase the maximum achievable throughput with the minimum energy consumption. The proposed power controller employs reinforcement learning with approximation to learn from the environment and improve its performance. We compare the performance of the proposed controller to two other power controllers, one based on game theory and the other one based on fuzzy logic. Simulation results show that compared to the other two approaches, RLPC provides a substantial saving in energy consumption per bit, with a substantial increase in network lifetime.

We consider the downlink transmit power allocation problems in multi-user MIMO wireless networks using zeroforcing beamforming. Traditionally such problems are solved by water-filling algorithm under the assumption of perfect channel knowledge. However when channel information is not known a priori or time varying the water-filling solution is shown to be unstable. We use the sliding mode control theory to synthesize the transmit powers so that the target SINR requirements of all users are met. We synthesize the sliding mode controller for the case of zero-forcing beamforming. The synthesis problem is solved under time varying Rayleigh fading channel conditions. Our solutions and simulation results show that our sliding mode controller is stable and delivers better quality of service under practical channel conditions.

This paper studies optimal channel reservation in cooperative cognitive radio networks (CRNs) where secondary users (SUs) have access to the combined spectrum pool of cooperating CRNs. Motivated by SU high forced termination in cooperative CRNs, we propose two channel reservation schemes, Fixed Channel Placement Reservation (FCPR) and Dynamic Channel Placement Reservation (DCPR), and theoretically analyze their performances using the Markov chain approach. Our numerical results, validated by simulation, indicate that for a given number of reserved channels, the DCPR algorithm achieves better user experience by reducing the forced termination probability. Based on this analysis, we propose two enhanced reservation algorithms: Algorithm A maximizes the overall capacity of CRNs to enable network operators to increase their revenue; Algorithm B minimizes the user experience cost function to provide better services.

In multi-hop cognitive radio networks (CRNs), there can be dramatic increase in end-to-end delay when a traffic flow switches between a number of channels along its path. We propose a new Maximum Flow-Segment (MFS) based scheme to channel assignment in CRN by minimizing the number of times the channel is switched along a flow. Our MFS based scheme has been efficiently integrated into the AODV on-demand routing protocol. We demonstrate that our MFS based scheme reduces the number of channel switches for the traffic flows and reduces the end-to-end delay by 50%. Our scheme also minimizes the routing overhead, and achieves a higher and more stable throughput than the link based approach.

The capture effect can occur in IEEE 802.11 distributed coordination function (DCF) wireless systems when packets arrive with different powers. Packets with high power can effectively swamp low power packets, such that they are received successfully, when otherwise a collision would have occurred. We present a network performance prediction model that accurately incorporates the capture effect into a 3-D Markov Chain. The model is solved efficiently with the Collapsed Transition onto Basis (CTB) approach. The performance of the model is significantly better than existing models in terms of estimating important QoS measures, including packet delay and loss, as well as collision probability and throughput.

The major objective of an admission control in wireless mesh networks is to prevent excessive real-time traffic from over-utilizing the bandwidth resources. A key component of an admission control is an accurate estimator of the bandwidth available at each node and of the actual bandwidth (including the MAC overhead) required by a new flow. This estimation problem is particularly difficult when node localization information is not available. We propose a novel admission control based on active neighbor bandwidth reservation (AC-ANBR), by which active neighbors periodically broadcast bandwidth reservation information and flow setup requests carry their bandwidth reservation states. Our proposed AC-ANBR accurately estimates the available bandwidth at each node, as well as the bandwidth required by each new flow. The effectiveness of AC-ANBR in multi-hop wireless networks is demonstrated by NS-2 simulations.

Intrusion detection systems are widely used security tools to detect cyber-attacks and malicious activities in computer systems and networks. Hypertext Transport Protocol (HTTP) is used for new applications without much interference. In this paper, we focus on intrusion detection of HTTP traffic by applying pattern recognition techniques using our Geometrical Structure Anomaly Detection (GSAD) model. Experimental results reveal that features extracted from HTTP request using GSAD model can be used to distinguish anomalous traffic from normal traffic, and attacks carried out over HTTP traffic can be identified. We evaluate and compare our results with the results of PAYL intrusion detection systems for the test of DARPA 1999 IDS data set. The results show GSAD has high detection rates and low false positive rates.

To enable ubiquitous end-to-end quality of service over IP networks expanding to rural areas, we examine resource allocation problems of a MAC layer for an OFDM MIMO wireless base station (BS) using multiuser beamforming. We compare two linear beamforming techniques, zero-forcing (ZF-BF) and minimum mean square error (MMSE-BF). To guarantee minimum bandwidth and low packet delay in an environment where the number of users is much larger than the number of BS antennas, one needs to partition the users into several sets and combine space division multiple access (SDMA) with time division multiple access (TDMA). We study the impact on the optimal transmission power resulting from selecting the following design parameters: (i) ZF-BF vs. MMSE-BF; (ii) the number of user sets multiplexed by TDMA; and (iii) the number of BS antennas. Two notable results are observed: (1) power wise, ZF-BF is far more superior to MMSE-ZF; (2) substantial power can be saved by increasing the number of BS antennas; however, only by large increments, e.g., from 6 to 9 and further to 21.

Over-the-air programming (OAP) protocols play a key role in wireless sensor network maintenance and task assignment. Most existing OAP protocols are contention based and suffer from collisions and the hidden terminal problem. We propose a novel TDMA based code dissemination service called WCDS that has been implemented on our WASP platform, which is a high performance integrated sensing, communication, and positioning system. WCDS is designed as a background service without interfering with normal network operations, in contrast to the existing OAP protocols that freeze sensing and data service during code download. We implement hand-shaking, pipelining and time slot scheduling to achieve high reliability and fast completion time. Experimental results show that WCDS outperform Deluge by 30% in completion time and up to 40% in energy saving.

Experiments show that IEEE 802.11 DCF system exhibits unstable behavior in the congestion onset load range where the system starts to become saturated. This phenomenon is not well investigated due to the lack of proper models. In this paper, we propose a two-dimensional (2-D) Markov chain model by combining the well-known saturated model with newly created idle states. Closed-form solution to the 2-D Markov chain is derived, and system performances are validated by simulations. With the proposed 2-D model, we are able to characterize the unstable system behavior, and provide insights into 802.11 DCF system dynamics.

Quality of Service (QoS) and queue management are important issues for IEEE 802.11 systems. However, existing 2-dimensional (2-D) Markov chain models of 802.11 systems are unable to capture the complete QoS performance and queueing behavior due to the lack of an adequate finite buffer model. We present a 3-dimensional (3-D) Markov chain that integrates the 802.11 system contention resolution and queueing processes into one model. The 3rddimension, that models the queue length, allows us to accurately capture important QoS measures, delay and loss, plus throughput and queue length, for realistic 802.11 systems with finite buffer under finite load. We derive an efficient method for solving the steady state probabilities of the Markov chain. Our 3-D Markov chain is the first finite buffer model defined and solved for 802.11 systems. The solutions, validated by extensive simulations, capture the system dynamics over a wide range of traffic load, buffer capacity, and network size. Our 3-D model points to the existence of an effective maximum throughput and shows its relationship with buffer capacity. We demonstrate that our 3-D model can also be used in resource allocation to determine adequate buffer sizes under a particular QoS constraint.

We introduce a 3-dimensional Markov chain that integrates the IEEE 802.11 DCF contention resolution and queueing processes into one model. Important QoS measures, delay and loss, plus throughput and queue length, can be obtained for a realistic systems with finite buffer under finite load. We present an efficient method for solving the steady state probabilities of the Markov chain. Simulations confirm the accuracy of our model, and demonstrate that the model provides new insights into the 802.11 DCF protocol.

A common application of unattended sensor networks (WSN) is low data rate streaming from many scattered sensors to one or more sink nodes. To meet the stringent requirement of prolonged WSN lifetime, we introduce a new notion of statistical reliability for data streaming applications and propose several variants of stop-and-wait hop-by-hop ARQ with explicit and implicit ACKs. The energy-efficiency of the protocols are mathematically analyzed and compared. The analysis reveals that implicit ACKs should be applied with caution to prevent an "avalanche" of implicit ACK transmissions. It is further shown that a simple combined implicit/explicit ACK resolves the "avalanche" problem. Our proposal is further validated by simulation.

We derive two new energy efficient reliable data transport protocols for overcoming the negative impact of asymmetric radio links in wireless sensor networks. The energy efficiency of these algorithms is explicitly derived using our theoretical model, and validated by results obtained from simulations and field trials. The analytical, simulation and field trials demonstrate that our proposed protocols perform well in networks with asymmetric links and can save energy of up to 27% compared to conventional ARQ schemes.

We propose an efficient reliable data collection(eRDC) algorithm. The eRDC is designed for energy-constraint wireless sensor networks (WSN) to balance reliability and energy consumption. We derive energy efficiencies of the proposed reliability schemes, and evaluate their performances. These analyses provide a guideline to determine the number of retransmissions for reliable data delivery. Dynamic programming concept is used to find the optimal solution. We present a distributed eRDC implementation to dynamically control the maximum number of retransmissions based on the guideline provided. Discrete event simulations and field trials with wireless sensor nodes confirmed our results.

Reducing energy consumption has been a recent focus of wireless sensor network research. Topology control explores the potential that a dense network has for energy savings. One such approach is geographic adaptive fidelity (GAF) [1]. GAF is proved to be able to extend the lifetime of self-configuring systems by exploiting redundancy to conserve energy while maintaining application fidelity. However the properties of the grid topology in GAF have not been fully studied. In this paper it is shown that there exists an unreachable corner in the GAF grid architecture. Using an analytical model, we are able to calculate the unreachable probability and analyse its impacts on data delivery. After investigating a couple of lossless topologies, we propose to use the honeycomb virtual mesh (GAF-h) to replace the square grid. GAF-h is proved to be able to achieve zero loss with little extra cost compared to the original GAF scheme. An efficient honeycomb cell placement and node association algorithm is also proposed. It integrates nicely with the original GAF protocol with little computing overhead.

While Web services have been widely accepted as a platform-independent services-oriented technology, its performance remains a concern due to the verbosity and inefficiency inherent from using text-based XML. This paper presents a study of Web services performance by evaluating the current implementations of Web services and comparing them with a number of alternative technologies. This study gives a picture of the current Web services performance behaviors and develops a simple performance model that can be used to estimate Web services latencies

An important component of the differentiated services concept being developed by the IETF is traffic conditioning which includes the shaping of traffic at the boundaries of differentiated services domains, and also at interior nodes in order to match the requirements of particular link layer technologies. However shaping alters the characteristics of a traffic flow and is in danger of degrading individual flow's QoS, particularly where no per flow connection admission control is used, unless the source is able to respond to the shaping operation. The IETF's Assured Forwarding Specification defines a multi precedence level, probabilistic packet dropping mechanism to give controlled sources such as TCP the ability to respond to congestion. In this paper we propose combining this with a suitable shaping mechanism to enable controlled sources to respond to shaping operations. While we believe this to be generally applicable, we develop the idea in the specific context of differentiated services over an ATM link where traffic shaping at the ATM level is required to exploit ATM's QoS capability, for example using VBR. An experimental implementation is described, based on commercial equipment, which is used to demonstrate the essential features of an Assured Forwarding PHB with three levels of drop precedence. In particular we show that sources of traffic with different precedence levels can, simultaneously, be in different modes of the TCP control algorithm.