Clustered cell-free networking paves a new way for enabling scalable joint transmission among access points (APs) by partitioning the whole network into non-overlapping sub-networks. Previous works adopted clustering algorithms, graph partitioning methods or conventional continuous optimization theories to partition a network based on the channels between all users and all APs, resulting in huge channel measurement and computational costs. This makes these methods difficult to be implemented in practical systems since the optimal network partition could vary frequently due to user mobility. In addition, existing methods were usually designed for specific clustered cell-free networking problems with different optimization algorithms employed. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movements in dynamic environments, and propose a deep deterministic policy gradient based clustered cell-free networking (DDPG-C2F) framework that can be adapted in various application scenarios. Moreover, in our framework, only one single channel needs to be estimated at each AP as the input of the neural network, which greatly reduces the channel measurement costs for clustered cell-free networking, and the training and inference costs of our framework. The proposed DDPG-C2F framework is then applied to various clustered cell-free networking problems with different objectives and constraints to demonstrate its performance. Simulation results show that our framework outperforms existing baselines in all scenarios. Moreover, we show that the proposed framework can reduce the handover cost over user mobility, and is robust to dynamic scenarios with random user joining or leaving.

Decentralized Edge Computing (DEC) has emerged as a computing paradigm leveraging computational resources of edge nodes for complex, data-intensive applications. Decentralized task offloading decides when and at which edge node each task is executed without a central coordinator. However, ensuring reliability for decentralized task offloading is crucial, especially in critical applications like video analytics. Existing centralized approaches often face single points of failure and high communication overhead. Current decentralized methods often ignore task dependencies and bandwidth allocation, leading to suboptimal resource utilization and low reliability. We address the Reliability-aware Dependent Task Offloading (RDTO) problem in DEC, jointly optimizing bandwidth allocation, to maximize task success rate. The challenge of RDTO lies in optimizing dynamic task offloading and bandwidth allocation with task dependencies. We propose a Digital Twin assisted Multi-agent Reinforcement Learning (DT-MARL) algorithm. Our approach integrates a novel digital twin model that provides real-time estimation of task completion time and edge node failure rates. By integrating digital twin with multi-agent reinforcement learning, we enable each edge node to make informed decisions for offloading strategies, effectively improving the task success rate. Extensive experiments using real-world and synthetic datasets demonstrate that DT-MARL outperforms state-of-the-art baselines on task success rate up to 32.00% and 32.43%, respectively.

Vehicular Edge Computing (VEC) is an essential part of the Internet of Vehicles (IoV) due to its low latency by moving the computational resources close to the edge. Although the introduction of network slicing into VEC improves resource utilization through dynamic resource allocation based on real-time demands and priorities, it increases the deployment and operational costs. In view of this, this paper envisions a resource allocation strategy for VEC based on network slicing technique, in which the tasks involved are not only dynamic but also heterogeneous. To minimize the system cost (including resource consumption and computation, network slice maintenance and reconfiguration costs), this paper proposes CST-RL, a confidence-based self-adjusting two-timescale reinforcement learning algorithm. This solution performs resource allocation and activation scheduling for network slices on a large timescale, while allocating slices to heterogeneous tasks on a short timescale to meet dynamic demands. In addition, we innovatively utilize critic in reinforcement learning to predict and compare the expected benefits of network slices with versus without reconfiguration. We introduce the Random Network Distillation (RND) technique to assess the confidence level of these benefits, thus providing guidance for network slices to automatically decide whether and when to undergo reconfiguration. Finally, we demonstrate the effectiveness and superiority of CST-RL through simulations. Results show that CST-RL yields 27.77% lower system cost compared to the scheme without network slicing and 15.15% lower system cost compared to performing constant network slicing configuration, with guaranteed Quality-of-Service.

The rise of 6G networks brings ultra-fast communication and broad connectivity, enabling intelligent applications such as IoT, smart cities, and autonomous vehicles. However, training AI models in these environments often faces privacy concerns and limited labeled data. Federated Learning (FL) provides a privacy-preserving solution by allowing clients to collaboratively train models without sharing raw data. Yet, FL still struggles with the scarcity of labeled data due to privacy constraints. Semi-Supervised Learning (SSL) can alleviate this issue by leveraging both labeled and unlabeled data. While combining SSL and FL (FSSL) offers promise, it also introduces new challenges, such as confirmation bias and degraded performance under non-IID data distributions. Most existing FSSL methods assume uniform labeling capabilities across clients, which is rarely the case in practice. This leads to a new challenge only occur in FSSL called annotation heterogeneity, where some clients have many labeled samples while others have few or none. When combined with data heterogeneity, this dual-data heterogeneity (data and annotation heterogeneity) severely affects global model performance. Therefore, FSSL must learn under label scarcity while preserving privacy, remain stable when non-IID data is compounded by annotation heterogeneity, and stay communication-efficient for mobile deployment. In this work, we propose Federated Fisher Focus Filtering (FedF3), an enhanced framework built upon our previous SynFMPL method, to address the combination effect of dual-data heterogeneity. FedF3 introduces a two-stage strategy, Adaptive Loss Filtering stabilizes early training by suppressing unreliable contributions under dual heterogeneity, and Fisher Focus Selection preserves accuracy with Fisher-guided sparsity to meet communication budgets. Our method demonstrates robust performance improvements across diverse and heterogeneous FL settings, mitigating the negative effects of dual-data heterogeneity while preserving personalization and privacy.

Space-air-ground integrated network (SAGIN) represents a pivotal architecture for the future evolution of global mobile communications. However, its inherent high dynamics and stochastic nature pose significant challenges to conventional routing mechanisms. Moreover, the vast spatial-scale openness of SAGIN makes it particularly vulnerable to eavesdropping attacks. This paper presents a novel sleeping multi-armed bandit (SMAB) framework, designed to enable secure routing in SAGIN. Specifically, we first establish channel models for all types of links in SAGIN. Then, we theoretically analyze the statistical properties of secrecy capacity and end-to-end (E2E) delay for message transmission over arbitrary routes, and formulate the secure routing problem to maximize cumulative secure transmission throughput under the delay constraint. The uncertainty in the network state of SAGIN, along with the complexity of the optimization objective and constraint (non-convex, non-linear, and coupled), renders the solution to the secure routing problem highly intractable. To this end, we leverage the MAB model to transform the secure routing problem into a budget-constrained arm-pulling problem and introduce the “sleeping” mode to capture route unavailability due to intermittent link failures. To effectively balance route exploration and exploitation, we further apply the upper confidence bound (UCB) method to design the SMAB-based secure routing algorithm (SMAB-SR), and derive its regret upper bound theoretically. Finally, extensive simulations verify that the SMAB-SR algorithm exhibits significant advantages in E2E secure transmission throughput compared to benchmarks and can maintain highly effective across various SAGIN configurations.

With the rapid advancement of intelligent IoT services, the need for high-quality and efficient wireless communication has become increasingly critical for information exchanges and collaborations among intelligent edge devices such as autonomous mobile robots (AMRs), robots in smart factories, and surveillance cameras, etc., Federated Learning (FL) is a promising distributed learning framework by exploring the computation capability on edge devices while protecting data privacy of users. On the other hand, Over-the-Air (OTA) computation techniques is a new paradigm of integrated computation and communication. OTA based FL (OTA-FL) can enhance spectrum efficiency by leveraging the superposition properties of wireless channels for model aggregations. The implementation of OTA-FL on edge devices can further protect user data privacy for clients, improve the throughput of inter-device communications, and enhance the distributed learning performance. However, despite these advantages, the convergence of OTA-FL systems remain vulnerable to model poisoning attacks, presenting significant challenges. In this study, we address these vulnerabilities by proposing a two-phase detection mechanism that secures OTA-FL system while preserving high spectral efficiency. Our simulation results demonstrate that under varying conditions, our approach can make distributed learning more secure and communication-efficient.

The over-the-air (OTA) computation which utilizes the waveform-superposition property of wireless signals has been considered as a promising approach to simultaneously accomplish communication and computing tasks in multiple access channels. Equipping federated learning (FL) with the OTA computation allows distributed Artificial Intelligence of Things (AIoT) devices to collaboratively train machine learning models over wireless environment, while preserving data privacy without excessive bandwidth consumption. In fact, the appearance of jamming attacks in OTA-FL systems can severely disrupt the convergence. This paper studies the problem of jamming attacks and its countermeasures in over-the-air federated learning (OTA-FL). To this end, we propose the coordinated dropout strategy (CoDrop), which enables AIoT devices to collaboratively drop out (i.e., to refrain from transmitting) part of their gradients so that the jamming signals aggregated in received signals can be accurately measured and mitigated. Our simulation results reveal that CoDrop is effective in alleviating the negative impacts of jamming signals with significantly low dropout rates, and it is shown to converge well as compared to existing solutions under various parameter settings.

The integration of space, air, and ground networks into a fully decoupled radio access network (FD-RAN) is emerging as a promising approach for 6 G, driven by the need for seamless coverage, flexible spectrum allocation, and efficient collaboration among heterogeneous nodes. However, the inherent differences in wireless environments across terrestrial, aerial, and satellite segments pose challenges for traditional feedbackbased transmission. In response to these challenges, this paper introduces a space-air-ground integrated FD-RAN architecture where users can be served by multiple nodes with adaptive resource block (RB) allocation. For downlink transmissions in base stations (BSs), a feedback-free approach is developed by employing a deep learning-based channel state information (CSI) prediction framework, allowing BSs to perform multipleinput multiple-output (MIMO) transmissions using only user geolocation. To enhance cooperation and RB allocation among heterogeneous nodes, a many-to-one matching model is proposed, achieving stable matching with low complexity and fast convergence. Simulation results validate the effectiveness of the proposed framework, showing a 70 % improvement in spectrum efficiency compared to single-connection networks based on optimal path loss and round-robin resource scheduling.

Smart grids rely on massive data from networked smart meters to enable fine-grained energy analytics and improve sustainability. As a representative, Non-Intrusive Load Monitoring (NILM) can infer individual appliance usage from aggregated meter readings, offering energy-saving insights without the need for per-appliance sensors. However, practical deployment at scale is hindered by three challenges: (1) limited per-device resources such as on-board computation and mobile data plans; (2) scarce labeled data due to the high cost of manual annotation; and (3) significant variability across households, including temporal changes in user behavior and appliance status.To address these challenges, we propose UNITY, a semi-supervised meta-learning NILM framework that unifies labeled and unlabeled data across diverse households to learn generalizable representations. UNITY minimizes user-side computation and communication overhead by reformulating inference as a lightweight sequence-retrieval task, accelerated by Discrete Haar Wavelet transforms. Only uncertain samples are uploaded to the server for refinement. To overcome label scarcity, UNITY employs entropy-based transductive learning, gradually enhancing model confidence on unlabeled data. Furthermore, to handle household diversity and temporal distribution shift, UNITY adopts a meta-learning approach that treats each household as a distinct task and incrementally adapts to behavioral drift over time, enabling robust long-term deployment. Experiments on public NILM datasets demonstrate that UNITY achieves 95.21% accuracy, outperforming existing methods under real- world resource constraints.

The widespread adoption of the Internet of Things (IoT) in our technology-driven society raises significant security and privacy concerns, highlighting the need for collaborative Intrusion Detection Systems (IDS) that leverage Deep Learning (DL) methods to detect suspicious network traffic. Using both the computing power and local data available at distributed end devices, Federated Learning (FL) provides a decentralized learning paradigm that preserves data privacy. However, the system heterogeneity and the Independent and Identically Distributed (non-IID) distribution of FL clients’ data introduce various challenges that impact training efficiency. To address these issues, we propose an adaptive approach named FLAIR, which leverages a semi-synchronous FL mechanism and a Decision Transformer (DT) to select clients that not only enhance the global model’s performance but also reduce communication and computation overhead. DT helps to make client selection decisions by considering both current and historical information, with the model trained offline using various client selection policies. The process begins by generating an offline database, which will be used to train the DT offline. Then, the DT is deployed for online client selection under a semi-synchronous protocol, enabling a fully adaptive FL system. Extensive experiments on IDS datasets show that FLAIR significantly reduces computation and communication time by up to 94% and 93%, respectively, while maintaining comparable classification performance to the baseline models.

The over-the-air (OTA) computation which utilizes the waveform-superposition property of wireless signals has been considered as a promising approach to simultaneously accomplish communication and computing tasks in multiple access channels. Equipping federated learning (FL) with the OTA computation allows distributed Artificial Intelligence of Things (AIoT) devices to collaboratively train machine learning models over wireless environments, while preserving data privacy without excessive bandwidth consumption. However, the appearance of wardens in OTA-FL can result in model eavesdropping and the risk of information leakage. In this paper, we investigate how OTAFL can be fulfilled among AIoT devices while ensuring the covertness (i.e., the prevention of eavesdropping) against wardens. To this end, we formulate an optimization problem to maximize achievable data rates subject to covertness and transmit power constraints. Then, we first derive optimal detection thresholds to simplify the covertness constraints, and then propose the covertness-aware transmit power control (CAMEO) algorithm, which iteratively explores transmit powers that yield high target signal-to-interference-plus-noise ratios while satisfying both the covertness and transmit power constraints. Simulation results demonstrate that the CAMEO algorithm outperforms comparison schemes in terms of test accuracy, and it is also shown to converge well under various parameter settings.

Clustered cell-free networking paves a new way for enabling joint transmission among access points (APs) by decomposing the whole network into non-overlapping subnetworks. Previous works adopted clustering algorithms or conventional optimization theories to group users and APs into subnetworks, which usually require high computational costs and have long running time. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movement in dynamic scenarios. To effectively reduce the joint processing complexity of each subnetwork, we aim to maximize the balance of the subnetworks in addition to the sum rate. With such an objective, we propose a novel deep deterministic policy gradient based clustered cell-free networking (DDPG-C2F) framework. It is worth mentioning that the proposed framework requires much lower channel measurement overhead and computational complexity compared to the conventional approaches. Simulation results demonstrate the superior performance of the proposed DDPG-C2F framework in various scenarios.

Autonomous driving technology has achieved remarkable advancements, offering substantial potential to revolutionize traffic safety and smart mobility. However, when faced with rare scenarios (weather, accident scenes, and lighting), autonomous driving systems can still only play a limited role due to insufficient learning in these rare situations. To address this challenge, we propose a novel approach that leverages low-rank adaptation (LoRA) and Mixture of Experts (MoEs) technologies to enhance the performance of pretrained autonomous driving models in handling rare situations. Specifically, we first use LoRA to fine tune the pretrained model of autonomous driving to focus on capturing knowledge related to rare scenarios and enhance the model’s ability to handle rare situations. Furthermore, we introduce MoEs and propose local, global, and hybrid adaptive solutions to overcome LoRA’s fixed intrinsic rank limitation. These approaches enable adaptive adjustment of LoRA’s rank, and improve the model’s performance from both local and global perspectives. Finally, we design detailed algorithms for different adaptation schemes. Extensive experiments demonstrate that our proposed solutions not only effectively improve the performance of the autonomous driving perception model in rare scenarios but also maintain lower inference latency compared to baseline methods.

Symbiotic Internet of Things (IoT) systems built upon existing 5G infrastructure are increasingly adopted due to their high capacity, low latency, and wide coverage. The small cell architecture inherent in 5G networks enables efficient spectrum utilization but also introduces challenges, such as complex intercell interference, particularly in deployments with low-cost and compact IoT base stations. While analog beamforming is effective for interference management, it incurs high hardware costs and energy consumption due to the requirement for RF power amplifiers and phase shifters (PSs). To address this issue, on–off analog beamforming (OABF) has emerged as a cost-efficient alternative, replacing PSs with simple RF on–off switches. OABF offers key advantages, including affordability, compactness, rapid speed, and most importantly, low power consumption. In this article, we propose an energy-efficient hybrid OABF coordination strategy for multicell multiple-input and single-output (MISO) downlink symbiotic IoT systems by leveraging deep reinforcement learning. The goal is to maximize the overall system energy efficiency (EE) by jointly optimizing antenna element activation and transmit power allocation at each IoT base station. Through extensive simulations, we compare the proposed approach against conventional antenna selection and PS-based analog beamforming schemes under various power constraint models. The simulation results unequivocally demonstrate the superiority of our method, exhibiting higher average EE across diverse network configurations.

Because of the ambiguous and subjective property of the facial expression, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current methods often directly predict whether the label is noised or not, aiming to reduce the contribution of the noised data. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning. In this paper, we propose a more reliable noise-label suppression method called ReSup. First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on popular datasets shows the effectiveness of ReSup.

Uncrewed aerial vehicle (UAV)-enabled ISAC systems have received widespread attention due to the high mobility of UAVs with good line-of-sight (LoS) paths to ensure communication and sensing performance. However, the existing works on UAV-enabled ISAC mainly focus on optimizing communication performance (e.g., sum rate) and sensing performance, resulting in excessive energy consumption and reducing the flight endurance of the UAV. Motivated by this, we draw a trade-off between such performance and energy consumption to achieve robust and efficient UAV-enabled ISAC. In this work, we aim to maximize the worst-case energy efficiency in UAV-enabled ISAC by jointly designing the beamforming and the UAV trajectory, while ensuring the UAV energy constraints and the ISAC performance. Nevertheless, solving this problem is non-trivial due to its non-convex nature, and the high coupling of the transmit beamforming vectors and the UAV dynamics adds an additional layer of complexity. To effectively address this non-convex issue, we alternately optimize the transmit communication and sense beamforming, as well as the UAV dynamic variables to obtain a sub-optimal solution, and the algorithm complexity is lower than the existing algorithms. Experimental results show a trade-off between energy efficiency and average sum rate. Furthermore, they indicate the superiority of the proposed algorithm to enhance energy efficiency by significantly reducing energy consumption without causing excessive sum rate loss.

Uncrewed aerial vehicle (UAV)-assisted integrated sensing, computation, and communication (ISCC) network enables the entire data analysis process for practical applications. The existing works of UAV-assisted ISCC merely consider a single data source, and there still exist gaps in the collection of environmental data via multiple sources. Motivated by this, we envision a novel UAV-assisted air-ground collaborative ISCC network that fully explores the cooperation between aerial UAVs and ubiquitous ground Internet of Things (IoT) devices. To achieve effective, efficient, and fair joint task scheduling and resource allocation, an optimization is established to minimize two novel indicators, i.e., computation offloading and sensing penalty indices, subject to constraints of boundary, anti-collision, and UAV energy consumption. To tackle this problem, a deep reinforcement learning (DRL) framework is proposed where three advanced DRL algorithms are included under centralized and decentralized control schemes. In former scheme, the central controller makes globally optimal decisions. In latter scheme, multiple agents decide independently based on local information. We demonstrate a forest fire monitoring use case simulated in a national forest park. Results show the mutually interfering, competitive, and beneficial relationships among triple functionalities. Besides, our solution outperforms three state-of-the-art baselines in terms of effectiveness and efficiency.

As the adoption of electric vehicles continues to grow, the demand for extensive charging infrastructure in urban areas is concurrently rising. In response to the evolving charging infrastructure shortage, private charging piles have emerged as crucial supplementary energy sources, especially in areas lacking public charging infrastructure. The sharing of private charging piles, however, introduces several challenges. Notably, the variable availability time and extremely limited usage space of private charging piles pose scheduling complexities for charging pile owners. Furthermore, the completely peer-to-peer operation of private charging piles may lead to suboptimal solutions for fulfilling overall charging demand. To comprehensively address these challenges, we explore the potential for cooperation among geographically proximate charging piles. We introduce a novel online booking mechanism paired with specialized scheduling algorithms designed for scenarios involving both multiple private charging piles and single private charging piles. Our objective is to maximize the attained revenue of charging pile owners under fully dynamic conditions on both the supply and demand sides. Through meticulous theoretical proofs, we show that our mechanism achieves advantageous competitive ratios for both scenarios when compared to the offline optimal solutions. Numerous experiments, conducted with real charging sessions, consistently demonstrate that the proposed mechanism achieves the highest revenue, providing substantial evidence for its superior performance.

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution.

The robustness of Internet of Things (IoT) topologies measures a network structure's tolerance to random failures, or attacks, which is crucial for stable network communication. Research on optimizing network topology robustness has shifted from empirical rules and heuristics to machine learning, which can extract the features of robust network from topology data, thereby reducing the complexity of traditional topology optimization. However, machine learning approaches typically require a large number of parameters, resulting in high costs associated with parameter tuning and inference. To address these issues, this paper combines parameterized quantum circuits, and proposes a Quantum-Driven efficient Learning Model (QDLM) for enhancing robustness of IoT topology. This model leverages quantum exponential states to significantly reduce the number of training parameters while preserving learning performance. For inputs, QDLM integrates arithmetic encoding and quantum state encoding based on topological adjacency matrix, reducing the number of neurons. In training phase, parameterized quantum rotation gates and controlled quantum gates are used to achieve efficient training. A quantum measurement method is designed to ensure the output topology is a connected graph with the required number of edges. Compared to existing topology learning models, QDLM achieves an order-of-magnitude reduction in training parameters while maintaining topology learning effectiveness.

Reinforcement learning (RL) has been successfully applied in many fields for building autonomous systems, such as robotics and telecommunications. With its self-learning ability, RL provides a framework for learning from historical experience and adapting to dynamic environments. In response to the surge in network traffic and the evolving nature of traffic behavior, RL has emerged as a crucial technique for developing intelligent and adaptive traffic engineering (TE) solutions. However, most prior studies have focused on using a centralized unit (i.e., a single agent) to construct RL-based TE systems. While the centralized approach leverages global network information for solid performance, it encounters challenges related to scalability, dynamic network topology, and high monitoring overhead for collecting network information. This paper addresses these issues by introducing a jointly trained multi-agent reinforcement learning-based traffic engineering (MATE-JT) system, which operates as a distributed TE solution. Our approach utilizes multiple agents within a network node so that each agent can make independent routing decisions for a subset of flows. We take the approach of sharing parameters among agents and introduce a joint-training technique that facilitates simultaneous learning from multiple agents’ experiences. As a result, our proposed method enhances system performance while reducing training time. We evaluate the proposed approach using various network traffic datasets and demonstrate that MATE-JT improves the performance of TE (about 6.5%) and achieves faster convergence (about 35%) in large-scale networks when compared to state-of-the-art methods.

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

Federated Learning has emerged as a transformative paradigm in the realm of collaborative machine learning, enabling the training of global models across decentralized devices without the need for centralizing data. While Federated Learning has shown remarkable promise, a critical limitation lies in its ability to personalize models to individual clients. Current approaches predominantly emphasize improving the accuracy of trained clients, inadvertently sidelining the significance of accommodating unseen clients. Furthermore, most of the existing personalized federated learning approaches require new clients to provide labeled data and undergo extensive retraining, posing a substantial barrier and hindering the broader adoption and engagement of potential users within these systems.In this paper, we introduce a novel and comprehensive solution to address these challenges: a generalized method for Personalized Federated Learning. Our approach transcends the limitations of conventional Federated Learning techniques by not only optimizing the accuracy of trained clients but also ensuring exceptional performance among unseen clients, even in diverse settings. Throughout extensive experiments, our method demonstrates significant improvements concerning the performance of seen and unseen clients, respectively, while eliminating the need for labeled data and model re-training among unseen clients.

Real-time Bidding (RTB) advertisers wish to know in advance the expected cost and yield of ad campaigns to avoid trial-and-error expenses.However, Campaign Performance Forecasting (CPF), a sequence modeling task involving tens of thousands of ad auctions, poses challenges of evolving user interest, auction representation, and long context, making coarse-grained and static-modeling methods sub-optimal.We propose AdVance, a time-aware framework that integrates local auction-level and global campaign-level modeling.User preference and fatigue are disentangled using a timepositioned sequence of clicked items and a concise vector of all displayed items.Cross-attention, conditioned on the fatigue vector, captures the dynamics of user interest toward each candidate ad.Bidders compete with each other, presenting a complete graph similar to the self-attention mechanism.Hence, we employ a Transformer Encoder to compress each auction into embedding by solving auxiliary tasks.These sequential embeddings are then summarized by a conditional state space model (SSM) to comprehend long-range dependencies while maintaining global linear complexity.Considering the irregular time intervals between auctions, we

Joint task scheduling and resource allocation for unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) in emergency rescue activities has become an essential and challenging problem. However, the existing works have only considered such a problem for standalone UAV networks without considering the cooperation between UAVs and ground base stations (BSs), nor have they considered the uncertainty in terms of the availability of BSs due to damage/reconstruction in disaster events. In this paper, we consider a novel post-disaster UAV-assisted ISAC system where the UAVs are used to supplement the networking capacity of out-of-service ground BSs while using their radio signals for sensing. We apply transfer learning with deep reinforcement learning (DRL) to learn task scheduling and resource allocation strategies that can rapidly adapt to uncertainty in the environment. Experimental results show that the proposed algorithm outperforms the state-of-the-art in both communication and sensing performance and convergence speed. Moreover, the transfer learning-based DRL shows faster convergence and better robustness when the availability of BSs suddenly changes.

Federated learning (FL) has emerged as a promising technique to realize distributed machine learning (ML) in practice. FL enables multiple clients to collaboratively train a common ML model without the need to collect raw data from clients, which therefore has merit in the protection of data privacy. On the other hand, it is known that quantum computing excels in solving specific problems that are computationally prohibitive on classical computers due to its ability to harness quantum superposition and entanglement. However, current noisy intermediate-scale quantum (NISQ) computers have difficulties in dealing with many-qubit computation due to the lack of reliable error-correction schemes, which renders hybrid quantum-classical computing with few qubits a promising alternative. In this paper, we investigate how hybrid quantum-classical computing can be applied to FL while considering the data heterogeneity among clients. To this end, we propose the two-qubit quantum circuit-embedded convolutional neural network (2QCNN) for each client, which incorporates parallel two-qubit variational quantum circuits (VQCs) between the fully connected layers of the CNN. Simulation results show that 2QCNN outperforms the comparison schemes in terms of test accuracies. In addition, the impacts of the number and depth of parallel two-qubit VQCs on the performance of 2QCNN under various degrees of data heterogeneity are further evaluated.

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm in the Artificial Intelligence of Things (AIoT). FL enables AIoT devices to collaboratively train an ML model on the network edge, while protecting data privacy and solving the problem of isolated data islands. Contemporary FL is typically realized through the model aggregation of locally trained models and the model dissemination of a globally averaged model; such a procedure iteratively proceeds until a predefined convergence criterion is met. However, FL necessitates frequent information exchanges between AIoT devices and a parameter server, which inevitably induces tremendous communication costs. Therefore, this article proposes a new design for efficient one-shot FL for AIoT systems, so that the model aggregation and dissemination can be completed within a single communication round. To this end, we leverage optimal transport (OT) theory to design the coupled model averaging (CODE) algorithm to fuse the model weights of the neural networks (NNs) on AIoT devices. The CODE algorithm initially performs OT-based layer-by-layer model averaging (MA) over two NNs to form a fused NN, which will then be averaged with another NN. The CODE algorithm progressively determines a pair of NNs, and continues until all NNs have been examined to achieve one-shot FL. In addition, we provide a detailed convergence analysis for the proposed solution. Our simulation results show that the proposed solution outperforms other one-shot MA mechanisms under various parameter settings.

This is Part II of the two-part Special Issue (SI) on 5G/6G Precise Positioning on Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV). The SI aims at bringing together contribution from both academia and industry to highlight the recent progress in various aspects of positioning systems. We have included 30 original contributions in this two-parts SI. We kindly refer readers to Part I of this SI for a comprehensive overview written by the Guest Editorial Team.

Unsourced multiple access (UMA) is a novel approach to support a large number of devices in a massive Machine-Type Communication (mMTC) system. UMA enables devices to concurrently encode their data using the same codebook to transmit without being individually identified, resulting in reduced signaling and computational overhead at the base station. Hybrid-domain non-orthogonal multiple access (NOMA), which combines power-domain NOMA with code-domain NOMA, is another technique that enhances the spectral efficiency of mMTC. While the study of hybrid-domain NOMA has been conducted, its integration with UMA has not been thoroughly investigated. Considering that mMTC traffic primarily consists of sporadic short packets in the uplink direction, employing a fully distributed mMTC multiple access protocol can substantially decrease signaling overhead and latency. In this work, a multi-armed bandits (MAB) paradigm is adopted to create a distributed power selection policy for devices that using UMA. Particularly, an MAB algorithm called Thompson Sampling (TS) is used to allow mMTC devices to minimize the transmission power without violating the minimum receiving signal-to-noise constraint needed to correctly decode the UMA codewords back to the original messages. A mean-field modeling technique is used to approximate the learned policies. The knowledge gained from the approximated policies can be transferred to new devices by initializing their prior distribution, which is called Mean-field Transfer Thompson Sampling (MFTTS). Simulations show that the mean-field approximation is indeed accurate and effective. Interestingly, MFTTS performs better than TS without knowledge transfer as well as other distributed power allocation methods.

Traffic engineering (TE) is one of the most critical issues in networking, as it enables efficient and reliable network operations. With the advent of Machine Learning (ML) techniques, many ML-based TE methods have emerged in recent years, especially those employing Deep Neural Networks for future traffic prediction to enhance the performance of traditional approaches. However, current methods suffer from two major issues. Firstly, most prior works only solve the TE problem based on short-term traffic prediction, neglecting the network traffic dynamics over an extended time period. This oversight results in high network disturbance when numerous traffic flows need to be rerouted to adapt to traffic changes. Secondly, although traffic prediction models rely on historical traffic data to perform future prediction, ML-based TE studies often ignore the high overhead for network traffic monitoring. To address these issues, we propose a traffic prediction-based routing algorithm in which the routing rules can be applied to multiple time-steps without requiring changes, ultimately leading to reduced network disturbance. We employ the segment routing (SR) technique as the routing algorithm and formulate the multi-time-step segment routing method that incorporates future traffic prediction. To address the high monitoring overhead, we present an approach that combines partial traffic prediction and compressive sensing techniques to estimate unmeasured data. Through extensive experiments on real backbone network traffic datasets, we demonstrate that our proposal can achieve more than 80% of the optimal performance in reducing maximum link utilization while significantly reducing the number of routing changes and traffic monitoring cost.

Deep mutual learning (DML) is one of the most high-profile technologies emerging in the field of machine learning during the past few years. DML has the potential of exchanging knowledge on the premise of ensuring data privacy, while retaining the characteristics of local models. In this paper, we design a novel system named as decentralized mutual learning over unlicensed spectrum (DML-U), which allows neighbor mobile devices to learn from each other via bidirectional device-to-device links over unlicensed spectrum (D2D-U). On this basis, we formulate a non-convex optimization problem for the one-to-one pairing scenario with the goal of minimizing the average communication energy cost for sharing knowledge. We further propose a two-layer iterative algorithm that includes the outer layer based on the enumeration method and the inner layer based on the sum-of-ratios optimization, aiming to find the optimal pairing scheme between devices and obtain the global optimal communication resource allocation scheme, respectively. The numerical results validate the effectiveness of the proposed algorithm in improving the DML performance.

This paper addresses the power control problem of a cell-free uplink massive Multiple-Input Multiple-Output (MIMO) system with mobile users, aiming at global sum-rate maximization under individual user Quality of Service (QoS) constraints. To solve this problem, we propose a Deep Deter-ministic Policy Gradient (DDPG)-based power control algorithm, whose design is tailored given the static and mobile user cases, respectively. In particular, different partial state space designs are investigated for each mobility use case, so as to achieve the best tradeoff between network performance and required learning complexity. Numerical results validate the effectiveness of the proposed method, which outperforms benchmark schemes both in terms of sum-rate and number of QoS satisfied users. It is shown that it can combine the advantages of traditional uniform max power control and max-min power control schemes. Furthermore, the proposed method is flexible and adapts itself well to dynamic and mobile environments.

Open radio access network (O-RAN) slicing allows the flexible control of network components and resources to satisfy the ever increasing demand of mobile applications. To optimize service provisioning, efficient management of limited radio resources is challenging due to the orchestration among network slices in the long-timescale and the slice configurations according to the mobile user (MU) statistics in the short-timescale. In this paper, we first propose a novel meta Markov decision process framework to mathematically formulate the problem of two-timescale radio resource management (RRM) in O-RAN slicing. The original RRM problem is then decoupled into a long-timescale master problem and a short-timescale subproblem, which are solved by a hierarchical reinforcement learning (RL) mechanism. Our proposed hierarchical RL mechanism includes a deep RL algorithm, solving the optimal long-timescale RRM policy, and a linear-decomposition based meta-RL algorithm, solving the optimal short-timescale RRM policy. Numerical experiments verify the theoretical analysis and show that our proposed hierarchical RL mechanism outperforms the most representative state-of-the-art baselines.

In this paper, we design a novel system named as decentralized mutual learning over unlicensed spectrum (DML-U), where edge devices are allowed to learn from each other via bidirectional device-to-device communications over unlicensed spectrum. We further formulate a non-convex optimization problem to minimize energy consumption and accelerate knowledge sharing with constrained power, bandwidth and transmission latency. Under this context, we propose a two-layer iterative algorithm, which contains an enumeration-based outer layer for the pairing scheme and a sum-of-ratios-based inner layer for obtaining a globally optimal allocation of communication resources. Simulation results verify that our obtained algorithm converges fast and finds efficiently the balance between knowledge sharing and communication energy consumption.

Remote driving, an emergent technology enabling remote operation of vehicles, presents a significant challenge due to the necessity of transmitting substantial volumes of image data from the vehicle to a central server. This requirement outpaces the capacity of traditional communication methods, emphasizing the need for efficient data communication. We propose a framework using semantic communication, specifically through a semantic segmentation-based method, which reduces the communication cost by transmitting meaningful semantic information rather than bit-wise data. Addressing the challenge of inconsistencies across knowledge bases in semantic communication, we present a blockchain-based, edge-assisted knowledge base management system. This system leverages edge nodes to manage multiple, geographically and contextually diverse knowledge bases while ensuring security through blockchain's tamper-resistant nature. Furthermore, blockchain sharding is employed to manage different knowledge bases for varying tasks, thereby enhancing the blockchain's throughput. Experimental results showed a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

Federated learning (FL) has received explosive research attention in that it enables multiple clients to collaboratively train a global model without sharing raw data in between, thereby facilitating the protection of data privacy. Typically, FL can converge well after a couple of communication rounds, but its convergence is vulnerable to the model poisoning attacks induced by fake clients. Existing works have been devoted to designing various post-processing techniques to alleviate the adverse effects of the model poisoning attacks, they, however, fail to accurately trim off the local models of fake clients while keeping those of benign clients intact during model averaging. In this paper, we investigate the problem of model poisoning attacks in federated learning (FL). To cope with this problem, we design the federated adaptive trimmed mean (FedATM) algorithm, where the clients are sorted in accordance with the distances between local models, and a distance-based threshold is designed to detect the presence of fake clients, thereby preventing the fake local models from destroying the accuracy of model averaging. Simulation results show that the proposed FedATM algorithm is robust to model poisoning attacks as compared to several comparison schemes under various data heterogeneities.

Due to its ease of deployment and high mobility, unmanned aerial vehicles (UAVs) have gained great popularity for a variety of applications. To conduct high-level and complicated tasks such as search/rescue missions and target identification, deep learning functions at UAVs are required. To this end, distributed learning methods such as federated learning (FL) and split learning (SL) have been proposed. In this paper, we investigate the SL assisted image classification task in a multi-UAV system for applications such as area exploration and object detection. Specifically, the whole deep learning model is cut into the UAV-side model and BS (base station)-side model. Each UAV performs forward propagation on UAV-side model by using the locally gathered images, and sends the smashed data to the BS. The BS performs forward and backward propagation based on the smashed data, and sends back the gradients of the cut layer to the UAVs, which is used for the backward propagation of the UAV-side model. The performance was evaluated using an aerial perspective geographic dataset, and the effectiveness of the proposed system was validated by comparing with FL-based and centralized learning methods. It was found that SL can significantly reduce computation time at UAV compared with FL, and is particularly effective with non-IID (independent and identically distributed) dataset. SL also requires less data during the training initial phase and has a faster convergence speed compared to centralized learning.

Federated learning (FL) enables edge devices (EDs) to collaboratively train a single machine learning (ML) model maintained by an edge server (ES) without sharing their raw data contents, thereby facilitating distributed ML while protecting data privacy. In fact, FL can be implemented in wireless environments by means of the over-the-air (OTA) computation, which takes advantage of the waveform-superposition property of wireless signals to receive local gradients from EDs without the needs of increased bandwidth. Despite the existing works devoted to power control and client selection in OTA-FL systems, they mostly neglect how to construct cohorts (i.e., a subset of EDs that have similar channel coefficients) to elevate the quality of the aggregated local gradients. In this paper, we investigate the problem of gradient aggregation and recovery in OTA-FL systems. To cope with this problem, we propose the cohort-based power scaling and gradient recovery (CRAIC) algorithm, where we first construct cohorts based on uplink channel coefficients, and then we adjust the transmit powers of EDs and recover the aggregated local gradients in a cohort basis. Simulation results show that our proposed solution outperforms several comparison schemes, and we further evaluate how it performs under various parameter settings.

Semantic communication, a promising candidate for 6G technology, has become a research hot spot. However, existing studies tend to focus more on image reconstruction rather than accurately transmitting semantic information at the pixel level. This paper introduces a novel approach using codec-based Masked AutoEncoders (MAE) for efficient image transmission. The proposed system compresses local information into low-dimensional latent vectors, improving system efficiency. We also design a selective module for enhanced image reconstruction and implement Noise Adversarial Training (NAT) to increase the system’s resilience to channel noise. Experimental results show that our method effectively improves downstream tasks while preserving image quality.

The advancement of connected intelligent transportation systems (C-ITS) and connected automated vehicles (CAV) has brought about a growing need for precise positioning solutions. Positioning technologies play a crucial role in many use cases such as emergency call systems, disaster rescue operations, automated robotics, and more. To ensure the availability, reliability, and quality of location systems both indoors and outdoors, the evolution of cellular technology, particularly in the form of 5G/6G networks, promises to provide a new pathway towards achieving high precision positioning.

Facial Expression Recognition (FER) has recently emerged as a crucial area in Human-Computer Interaction (HCI) system for understanding the user’s inner state and intention. However, feature- and label-noise constitute the major challenge for FER in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, in this paper, we propose a simple but effective Facial Expression Noise-tolerant Network (FENN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, FENN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, the discriminative ability of deep features is weakened by the subtle differences between expressions and the presence of feature noise. FENN utilizes a feature-noise mitigation module to extract compact intra-class feature representations under feature noise while preserving the intrinsic inter-class relationships. We conduct extensive experiments to evaluate the effectiveness of FENN on both original annotated images and synthetic noisy annotated images from RAF-DB, AffectNet, and FERPlus in-the-wild facial expression datasets. The results show that FENN significantly outperforms state-of-the-art FER methods.

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.

In recent years, the fast development of mobile communications and cloud systems has substantially promoted edge computing. By pushing server resources to the edge, mobile service providers can deliver their content and services with enhanced performance, and mobile-network carriers can alleviate congestion in the core networks. Although edge computing has been attracting much interest, most current research is application-specific, and analysis is lacking from a business perspective of edge cloud providers (ECPs) that provide general-purpose edge cloud services to mobile service providers and users. In this article, we present a vision of general-purpose edge computing realized by multiple interconnected edge clouds, analyzing the business model from the viewpoint of ECPs and identifying the main issues to address to maximize benefits for ECPs. Specifically, we formalize the long-term revenue of ECPs as a function of server-resource allocation and public data-placement decisions subject to the amount of physical resources and inter-cloud data-transportation cost constraints. To optimize the long-term objective, we propose an online framework that integrates the drift-plus-penalty and primal-dual methods. With theoretical analysis and simulations, we show that the proposed method approximates the optimal solution in a challenging environment without having future knowledge of the system.

Federated learning (FL) has been widely regarded as a promising distributed machine learning technology that utilizes on-device computation while protecting clients' data privacy. To adapt FL to wireless networks, the over-the-air (OTA) computation, which employs the superposition nature of wireless waveforms, can prevent excessive consumption of the communication resources. However, energy harvesting technology can overcome the energy limitation of clients to realize durable computation. Despite the existing works devoted to OTA FL from various aspects, they mostly neglect jointly performing client selection and energy management for energy harvesting devices. In this paper, we investigate the combined problem of client selection and energy management for OTA FL and formulate it as a nonlinear integer programming (NIP) problem to minimize the optimality gap. To solve the NIP problem, we propose a client selection scheme that jointly considers channel state information, residual battery capacities, and dataset size. Our simulation results show that the proposed solution outperforms other comparison schemes within various parameter settings.

This paper investigates secure communication in a two-hop relaying system based on physical layer security. The relay employs time-switching simultaneous wireless information and power transfer (SWIPT) to harvest energy and receive information from the source, and then transmits the source’s information and its own information to the destination. A passive eavesdropper exists and wiretaps information transmission over both hops. Under the general system configuration, we first provide performance modeling to reveal the secrecy rate of source and relay as well as identify their utilities. Then, we analyze the hierarchical competition behaviors between the source and relay from a game-theoretic perspective. In particular, we develop a Stackelberg game-based analytical framework to determine the optimal strategies for the source and relay by deriving the Stackelberg equilibrium. Furthermore, we summarize the feasible conditions of utilizing SWIPT-enabled relaying for secure communication and propose the end-to-end transmission scheme accordingly. Extensive numerical results are presented to demonstrate the performance of the proposed SWIPT-enabled relaying system.

Label-noise constitutes a major challenge for facial expression recognition in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, we propose a simple but effective Label-noise Robust Network (LRN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, LRN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, LRN utilizes a confidence-based label-free loss to extract compact intra-class feature representations under label noise while preserving the intrinsic inter-class relationships. Experiments on three in-the-wild facial expression datasets demonstrates the superiority of our method.

The great popularity of smartwatches leads to a growing demand for smarter interactive systems. Hand gesture is suitable for interaction due to its unique features. However, the existing single-modal gesture interactive systems have different biases in diverse scenarios, which makes it intractable to be applied in real life. In this paper, we propose a multi-modal smartwatch interactive system named Mudra, which fuses vision and Inertial Measurement Unit (IMU) signals to recognize and identify hand gestures for convenient and robust interaction. We carefully design a parallel attention multi-task model for different modals, and fuse classification results at the decision level with an adaptive weight adjustment algorithm. We implement a prototype of Mudra and collect data from 25 volunteers to evaluate its effectiveness. Extensive experiments demonstrate that Mudra can achieve 95.4% and 92.3% F1-scores on recognition and identification tasks, respectively. Meanwhile, Mudra can maintain stability and robustness under different experimental settings.

Human pulmonary function declines with age. Elders, especially those with lung or cardiovascular diseases, yearn for daily lung function tests for timely diagnosis and treatment. However, current clinical spirometers are cumbersome and ex-pensive while home-use portable ones’ accuracy is questionable. Moreover, both kinds require contact measurements and could cause cross infection, especially hazardous for contagious diseases like COVID-19. To this end, we propose SpiroFi, a contactless system that leverages WiFi Channel State Information (CSI) for convenient yet accurate Pulmonary Function Testing (PFT) out of clinic. The key enabler underlying SpiroFi is a set of algorithms that can extract chest wall movement from WiFi signal variations and interpret such information into lung function indices. We have realized SpiroFi on low-cost commodity WiFi devices and tested it in a home-like site where it achieves 2.55% monitoring error over healthy youths. Then, with the Ethics Committee (EC) approval, we conducted a 2-month clinic study in a city hospital over elders with basic diseases. SprioFi still yields 6.05% monitoring error despite elders’ degenerated pulmonary function and body control. Also, the correlation between lung function and age as well as chronic diseases has been revealed, highlighting the importance of daily PFT for the elderly.

Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing.

Distributed access control is a crucial component for massive machine type communication (mMTC). In this communication scenario, centralized resource allocation is not scalable because resource configurations have to be sent frequently from the base station to a massive number of devices. We investigate distributed reinforcement learning for resource selection without relying on centralized control. Another important feature of mMTC is the sporadic and dynamic change of traffic. Existing studies on distributed access control assume that traffic load is static or they are able to gradually adapt to the dynamic traffic. We minimize the adaptation period by training TinyQMIX, which is a lightweight multi-agent deep reinforcement learning model, to learn a distributed wireless resource selection policy under various traffic patterns before deployment. Therefore, the trained agents are able to quickly adapt to dynamic traffic and provide low access delay. Numerical results are presented to support our claims.

Analog beamforming with low-resolution phase shifters is a key technique for 5G networks due to its superior hardware complexity and power consumption advantages. However, the optimal beamforming coordination is an extremely challenging issue in a downlink multi-antenna base station and single-antenna user equipment scenario. To avoid using global channel state information and reduce the communication overhead, in this paper, we propose a deep reinforcement learning based distributed analog beamforming approach to improve the energy efficiency for a downlink multiple-input and single-output (MISO) system. Specifically, each base station trains a neural network to steer its beamformer by phase shifters according to its local and obtained neighbouring information, with the purpose of maximizing its own energy efficiency and minimizing the negative impacts to its neighbouring cells. We evaluate the performance of the proposed approach by simulations, and validate its superiority by comparing with baseline schemes.

The applications of Internet of Things have emerged in every aspect of people’s life. The volume of data gathered can be enormous. Enterprises and personal consumers are increasingly reliant on cloud storage services instead of local storage. While they enjoy the convenience of cloud storage services, they also worry about the integrity of the cloud-stored data since they do not physically own the data. To enable public integrity auditing, third-party auditors as trusted ones verify data integrity on behalf of the data owner. However, the vulnerability of auditors should also be considered. We propose a consortium blockchain-based public integrity verification system (CBPIV). In CBPIV, the auditor behaviors are recorded in the consortium blockchain so that authorized parties can audit the auditor to see if the verification results are correct. A smart contract is deployed to check the behavior of the auditor automatically, which can trigger alerts for unusual behaviors. The evaluation on both security and performance shows that our proposed scheme is secure and alleviates the burden on data owners of limited computation capability.

This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). Under a business agreement with the InP, a third-party service provider provides computing services to the subscribed mobile users (MUs). MUs compete for the shared spectrum and computing resources over time to achieve their distinctive goals. From the perspective of an MU, we deliberately define the age of update to capture the staleness of information from refreshing computation outcomes. Given the system dynamics, we model the interactions among MUs as a stochastic game. In the Nash equilibrium without cooperation, each MU behaves in accordance with the local system states and conjectures. We can hence transform the stochastic game into a single-agent Markov decision process. As another major contribution, we develop an online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks to approximate the Q-factor and the post-decision Q-factor, respectively. The deep RL scheme allows each MU to optimize the behaviours with unknown dynamic statistics. Numerical experiments show that our proposed scheme outperforms the baselines in terms of the average utility under various system conditions.

Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The growth of extremely large and complex datasets—commonly referred to asbig data—and various advances in information and communications technology and computing now support more effective approaches to humanitarian relief, logistical coordination, overall disaster management, and long-term recovery in connection with natural disasters and emergency events. Leveraging big data and technological advances for emergency management has attracted considerable attention in the research community. However, the desired merging ofbig data and emergency management(BDEM) requires coordinated efforts to align and define interdisciplinary terminologies and methodologies. To date, the key concepts and technologies in this emerging research area have not been coherently discussed in a sufficiently broad and multidisciplinary manner. In this article, an international team presents an overview of the BDEM domain, highlighting a general framework and discussing key challenges from several perspectives. We introduce and summarize typical technologies and applications, organized into the six broad categories of remote sensing, resilient communication networks, mobile communication networks, human mobility and urban sensing, social network analysis, and knowledge graphs. Finally, we outline several directions of future research.

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing-enabled gesture recognition due to its inherent merits like remote sensing, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know “where to look” and “when to look.” To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi channel state information via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a deep residual network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

Real-time vital signs (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Current solutions are mostly based on wearable sensors or cameras, the former affects the quality of sleep, while the latter is not conducive to privacy protection, and the cost of these methods is usually expensive. In this paper, we propose Wital, a real-time vital signs monitoring system based on the low-cost and widespread COTS WiFi device. Most of the existing WiFi-based vital signs monitoring solutions utilize the line of sight (LOS) WiFi signals to achieve powerful performance. However, in our daily environments, NLOS sensing is more common. In this article, we first model the relationship between the energy ratio of LOS/NLOS signals and the ability to monitor vital signs based on the Ricean-K theory and theoretically prove that blocking LOS signals in NLOS sensing is more beneficial. We have also established a real-time vital signs monitoring system to verify our method, and the experimental results prove the effectiveness of our method.

Material identification is playing an increasingly important role in our daily lives such as public security checks. X-ray-based technologies are highly radioactive because they rely on specialized devices to transmit high-frequency signals. Ultrasound-based technologies are cumbersome due to their large size. RF-based approaches necessitate the use of RFID which is usually expensive to be used in home and office environments. To this end, WiFi-based material identification approach has emerged recently as a low-cost yet effective alternative. In this paper, we propose WiMate, a noncontact material identification system leveraging only off-the-shelf WiFi devices. The key enabler of WiMate is a novel theoretical model we build to characterize how the electromagnetic wave decays when penetrating different materials. Our model identifies a unique feature for each material that only depends on the material itself. Consequently, we can leverage this feature coupling with the machine learning techniques for robust and accurate material identification. We prototype WiMate using low-cost commodity WiFi devices and evaluate its performance in real-world. The empirical study shows that WiMate can identify six different materials, i.e., board, paperboard, nickel, wood chip, iron and titanium, with an average accuracy of 96.20%.

Missing values appear in most multivariate time series, especially in the monitored network traffic data due to high measurement cost and unavoidable loss. In the networking fields, missing data prevents advanced analysis and downgrades downstream applications such as traffic engineering and anomaly detection. Despite the great potential, existing imputation approaches based on tensor decomposition and deep learning techniques have shown limitations in addressing missing values of traffic data due to its dynamic behavior. In this paper, we propose Graph Convolutional Recurrent Neural Network for Imputing Network Traffic (GCRINT), a combination between Recurrent Neural Network (RNN) and Graph Convolutional Neural Network, for filling the missing values of network traffic data. We use a bidirectional Long Short-Term Memory network and Graph Neural Network to efficiently learn the spatial-temporal correlations in partially observed data. We conducted extensive experiments to evaluate our model by using two different datasets and various missing scenarios. The experiment results show that GCRINT achieves significantly low imputation errors and reduces the error by 35% compared to the state-of-the-art methods. GCRINT also helps to obtain a stable performance in the traffic engineering problem.

The proliferation of the Internet of Things (IoT) applications has resulted in the ever-increasing research attention in recent years. In light of the sensitivity to latency in various IoT applications, the age of information (AoI) has been widely regarded as a promising performance metric to quantify the timeliness (i.e., freshness or age) of information updates from IoT devices. In addition, serverless computing (also known as function as a service (FaaS)) evolves as a highly scalable and flexible computing architecture that can facilitate timely IoT analytics. In fact, the execution of serverless functions may rely on the data collected from IoT devices, therefore the freshness of information updates of IoT devices has prompt impacts on the age of service (AoS) of serverless functions. Although existing works have been devoted to various aspects of serverless computing, the issues regarding how information updates affect the AoS of serverless functions are rarely investigated. In this paper, we address the information update delivery and acquisition (IUDA) problem for IoT with serverless computing and formulate it an integer linear program (ILP), the objective of which is to minimize a weighted sum of AoS of serverless functions. To cope with the IUDA problem, we propose an offline and an online algorithm for scheduling information updates with and without the knowledge of the arrivals of serverless functions, respectively. Our simulation results demonstrate that the proposed solutions outperform existing solutions in terms of the AoS performance and effectively provide serverless functions with timely information updates under various parameter settings.

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user.

Fine-grained energy consumption analysis has great potential value in applications of Smart Grids, renewable energy, and Artificial Intelligence of Things. Non-Intrusive Load Monitoring (NILM) is a single-sensor alternative to the conventional one-sensor-for-one-appliance solution due to its ability to deduce individual appliances states from mixed measurements from the main power interface. Despite its advantages of low cost and easy maintenance, a few drawbacks hinders its widespread adoption. To enhance the Quality of Service (QoS) of NILM, four objectives should be achieved by careful designing: high accuracy, user transparency, low response delay, and low data redundancy.Inspired by observations of discriminative yet redundant current waveform and model sparsity, we propose LCL, a lightweight, contactless, plug-and-play solution for real-time load monitoring. The filtering module skips over unchanged input and compresses the measurements of interest using Compressed Sensing. The reconstruction-free inference module runs an attentional multi-label classification and returns all functioning appliance states directly from the compressed input. The compression module leverages model sparsity for real-time processing on edge devices. Evaluations based on our prototype deployed in real-life scenarios attest to the high QoS of LCL with a subset accuracy of 94.2% and a delay reduction of 52.2%. Our solution further filters out 96.8% of the redundant input and attains a Measurement Rate of 0.1 without noticeable impact on the performance.

The proliferation of Internet of Things (IoT) applications has prompted the continuous increase of research efforts in recent years. In light of the diversified use cases and service requirements, the information freshness [or Age of Information (AoI)] of IoT data is key for latency-sensitive IoT applications (e.g., industrial automation and intelligent transportation) because stale information may lead to delayed responses and catastrophic outcomes. In addition, various types of IoT applications require the analytics of IoT data collected from their constituent IoT devices. While previous AoI-related works have analyzed or optimized the information freshness of various communication systems, the problem that theinformation cofreshness[or Coage of Information (CoI)] of an IoT application is determined by the maximum AoI of the constituent IoT devices has been rarely investigated. In this article, we address the grant assignment and transmission scheduling (GATS) problem for IoT and formulate it as an integer linear program (ILP) to minimize a weighted sum of CoI. Due to the intractability of the original GATS problem, we transform it to an equivalent problem of the maximization of the number of the eliminated age blocks. Then, we propose the CoI-aware age block elimination (CABEL) algorithm in which information updates are selected progressively according to their coage efficiency (CE) values and prove that the achieved approximation factor depends on the relative service costs and uplink delays. Our simulation results demonstrate that the proposed solution can effectively perform information updates and utilize service budgets, thereby achieving low CoI compared with the existing solutions under various parameter settings.

Designing efficient and secure mutual authentication and key agreement (MAAKA) protocols for Internet of Medical Things (IoMT) has been shown to be challenging, mainly due to the different security and privacy requirements in complex settings. Existing schemes generally are subject to a number of limitations, ranging from performance to security issues. In this article, we introduce a provably secure and lightweight MAAKA (PSL-MAAKA) protocol for fully public channels in IoMT. First, the proposed scheme is lightweight since the major operations in the stage of authentication and key agreement are hash operation and XOR operation, respectively. Second, this article proves the security of the presented protocol taking the advantage of the random oracle model. Next, this article gives that security requirements in IoMT could be satisfied through our presented MAAKA protocol. Finally, we demonstrate that it enjoys optimal performance than other competing schemes, in terms of communication overhead, computation overhead, and storage overhead.

As an important driving factor of emerging Internet-of-Things (IoT) applications, machine learning algorithms are currently facing the challenge of how to “clean” data noise, that is introduced during the training process (e.g., asynchronous execution and lossy data compression and quantization). In an attempt to guarantee data quality, various data cleaning approaches have been proposed to filter out abnormal data entries based on the global data distribution. However, most existing data cleaning approaches are based on a centralized paradigm and thus cannot be applied to future edge-based IoT applications, where each edge node (EN) has only a limited view of the global data distribution. Moreover, the increasing demand for privacy preservation largely prevents ENs from combining their data for centralized cleaning. In this study, we propose a federated data cleaning protocol, coined as FedClean, for edge intelligence (EI) scenarios that is designed to achieve data cleaning without compromising data privacy. More specifically, different ENs first generate Boolean shares of their data and distribute them to two noncolluding servers. These two servers then run the FedClean protocol to privately and efficiently compute the attribute value frequency (AVF) scores of the collected data entries, which are then sorted in ascending order via a bitonic sorting network without revealing their values. As a result, data entries with lower AVF scores are considered as abnormal and filtered out. The security, efficiency, and effectiveness of the proposed approach are then demonstrated via concrete security analysis and comprehensive experiments.

Collaborative edge computing (CEC) is a recently popular paradigm enabling sharing of data and computation resources among different edge devices. Task offloading is an important problem to address in CEC as we need to decide when and where each task is executed. However, it is challenging to solve task offloading in CEC as tasks can be offloaded to a multihop neighboring device leading to bandwidth contention among network flows. Most existing works do not jointly consider network flow scheduling that can lead to network congestion and inefficient performance in terms of completion time. Another challenge is to formulate and solve the problem considering the dependencies among dependent tasks and conflicting network flows. Few recent works have considered multihop computation offloading; however, these works focus on independent tasks and do not jointly consider the dependencies with network flows. In this work, we mathematically formulate the problem of jointly offloading multiple tasks consisting of dependent subtasks and network flow scheduling in CEC to minimize the average completion time of tasks. We have proposed a joint dependent task offloading and flow scheduling heuristic (JDOFH) that considers both dependencies in task directed acyclic graph and start time of network flows. Performance comparison done using simulation for both real application task graph and simulated task graphs shows that JDOFH leads to up to 85% improvement in average completion time compared to benchmark solutions which do not make a joint decision.

Vehicle-based visual crowdsourcing is an emerging paradigm where the visual data collected from dash cameras are analyzed with the aim of measuring phenomena of common interest. To ensure the efficiency in vehicle-based visual crowdsourcing, there remain at least two technical challenges. First, to maximize the Quality of Information (QoI), which measures the amount of information extracted from the collected data, the context of data collection (e.g., camera position and orientation) must be taken into account in the process of task allocation. Second, intensive data collection from dense measurement points is key to ensure timely and accurate sensing of the targets of interest, whereas there exists a trade-off between the amount and rate of data collection and the computing and communication resources required to fulfill the latency constraint. To solve these challenges, we propose gathering and processing the collected data at the edge of the network and design a context-aware task allocation scheme, called FlexSensing, to jointly optimize the QoI and processing latency. We target application scenarios where commercial vehicles are turned into vehicular fog nodes (VFNs). These nodes gather and process the visual data collected from other vehicles within their coverage areas. The key idea of FlexSensing is to determine the rate of data collection for each sensing vehicle in the targeted area and to assign processing tasks to VFNs based on the estimated QoI and the workload of the VFNs. Given the excessive computational complexity of task allocation in this context, we formulate task allocation as a Markov decision process and apply a deep Q-network (DQN) to learn the optimized task allocation strategies for increasing the QoI of collected data while reducing the processing latency. To evaluate the effectiveness of FlexSensing, we simulate the mobility of different vehicles involved in the scenario at different times of the day based on real-world traffic data collected from the city of Helsinki and select a real-time object detection application for a case study. As compared with the existing task allocation strategies, the DQN-based task allocation strategies reduce the average processing latency by up to 51% and increase the QoI of the collected data by up to 34%.

Collaborative edge computing (CEC) is a recent popular paradigm where different edge devices collaborate by sharing data and computation resources. One of the fundamental issues in CEC is to make task offloading decision. However, it is a challenging problem to solve as tasks can be offloaded to a device at multi-hop distance leading to conflicting network flows due to limited bandwidth constraint. There are some works on multi-hop computation offloading problem in the literature. However, existing works have not jointly considered multi-hop partial computation offloading and network flow scheduling that can cause network congestion and inefficient performance in terms of completion time. This article formulates the joint multi-task partial computation offloading and network flow scheduling problem to minimize the average completion time of all tasks. The formulated problem optimizes several dependent decision variables including partial offloading ratio, remote offloading device, start time of tasks, routing path, and start time of network flows. The problem is formulated as an MINLP optimization problem and shown to be NP-hard. We propose a joint partial offloading and flow scheduling heuristic (JPOFH) that decides partial offloading ratio by considering both waiting times at the devices and start time of network flows. We also do the relaxation of formulated MINLP problem to an LP problem using McCormick envelope to give a lower bound solution. Performance comparison done using simulation shows that JPOFH leads to up to 32 percent improvement in average completion time compared to benchmark solutions which do not make a joint decision.

This paper focuses on the QoS-aware secure routing design based on the physical layer security technology for a multi-hop wireless network consisting of legitimate nodes, malicious eavesdroppers, and selfish jammers. We first provide theoretical modeling for a given route to reveal how its end-to-end security/QoS performance is related to the transmitting power of legitimate nodes along the route and the jamming power of jammers in the network. We then design an incentive mechanism that stimulates jammers to generate artificial jamming for security enhancement, and also develop a non-cooperative game framework to resolve the jamming power setting issue here. Based on the security/QoS performance modeling of the route and jamming power setting, we further propose a theoretical framework to determine the optimal transmitting power of nodes along the route such that its optimal transmission security can be achieved under a QoS constraint. Finally, with the help of the power setting results of the given route, we formulate a shortest weighted path-finding problem to identify the optimal route for data delivery in the network, which can be solved by employing the Bellman-Ford or Dijkstra's algorithm. It is demonstrated that the proposed routing scheme is individually rational, stable, distributed and computationally efficient.

By enabling direct communications between nearby user equipment (UE), device‐to‐device (D2D) communication has become one of the key technologies in 5th generation (5G) mobile networks. D2D communication brings new communication opportunities for mobile devices, especially in a highly dense network. In this paper, D2D communication in the unlicensed spectrum, namely, D2D‐Unlicensed (D2D‐U), is discussed. The use of unlicensed frequency bands can ease the shortage of spectrum resources and improve network performance. However, the D2D‐U in 5G has significant effects on the network performance of existing unlicensed networks sharing the same frequency bands, such as Wi‐Fi and Bluetooth. Therefore, it is necessary to design a fair coexistence scheme for D2D‐U. To understand the coexistence problem, in this paper, we first formulate the network performance of D2D‐U and Wi‐Fi under two different coexistence schemes, namely, listen before talk (LBT) and duty cycle mechanism (DCM). Then, we use computer simulations to investigate a mode selection scheme that switches between these two schemes and point out the best possible solution for the coexistence between D2D‐U and Wi‐Fi.

This paper investigates the problem of age of information (AoI)-aware resource awareness in an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, which is deployed by an infrastructure provider (InP). A service provider leases resources from the InP to serve the mobile users (MUs) with sporadic computation requests. Due to the limited number of channels and the finite shared I/O resource of the UAV, the MUs compete to schedule local and remote task computations in accordance with the observations of system dynamics. The aim of each MU is to selfishly maximize the expected long-term computation performance. We formulate the non-cooperative interactions among the MUs as a stochastic game. To approach the Nash equilibrium solutions, we propose a novel online deep reinforcement learning (DRL) scheme, which enables each MU to behave using its local conjectures only. The DRL scheme employs two separate deep Q-networks to approximate the Q-factor and the post-decision Q-factor for each MU. Numerical experiments show the potentials of the online DRL scheme in balancing the tradeoff between AoI and energy consumption.

Energy saving in cellular systems has attracted the extensive attention of various studies due to ever-deteriorating global warming. In addition to network greenness, various emerging mobile applications (e.g., mixed reality and automated vehicles) further necessitate timely service provision. Recently, the age of information (AoI) has been regarded as a promising performance metric for quantifying the freshness (i.e., timeliness) of information updates in communication systems. Despite the existing works devoted to energy saving in cellular systems, how to leverage information freshness to ensure timely information updates while achieving network greenness is rarely investigated. In this paper, we investigate the problem of freshness-aware energy saving in cellular systems, where active base stations (BSs) can cooperatively update information to target devices (TDs). To address this problem, we formulate a mixed-integer nonlinear program (MINLP) to minimize average power consumption. Due to its intractability, we propose decomposing the MINLP into two subproblems by means of constraint reinterpretation and spatiotemporal decoupling. Then, we propose a two-stage solution that leverages LP techniques to sequentially determine update scheduling and BS activeness. Our simulation results show that the proposed solution can adequately perform update scheduling and BS activeness control, thereby effectively saving energy for cellular systems under various parameter settings.

After a disaster occurred, it is extremely important to reconstruct the network and provide the communication services to the victims immediately. Deploying MDRU (Movable and Deployable Resource Unit) in the disaster area, along with multiple access points to extend the service area of MDRU is a very promising solution. In this kind of heterogeneous disaster response networks, it is of great importance to minimize the packet delay from user terminals by performing optimal radio access control. In this paper, we propose a deep reinforcement learning based radio access control mechanism, which enables the smart relay selection and transmitting power control. We evaluate the performance by extensive simulations, and validate the superiority of the proposed mechanism by comparing with baseline schemes.

By integrating both energy harvesting and backscatter communication technologies, so-called `battery-free tag' emerges as a promising solution to the energy related issues in IoT. However, such tags present new challenges due to the unstable energy supply and excitation signals, which are critical to realize successful backscatter communications. In this paper, we present a novel system, denoted as ecUWB, which enables robust communication for battery-free tags where unstable WiFi signals act as the unified source for both energy supply and excitation signals. At tag side, we propose a charging-transmission division scheme to achieve better signal utilization, and introduce a re-transmission mechanism for possible excitation signal interruption. At receiver side, we implement a simply method which is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to predict the uncontrollable signals, and propose an energy-centric tag scheduling method according to the tag energy estimation results. Extensive experiments are carried out on customized tags and NI USRP platform. The results show that ecUWB outperforms the existing ones in terms of performance and efficiency under the unstable WiFi signals.

Nowadays, fifth-generation (5G) technology is promoted to support massive data transmissions and low-latency communications, which enables diverse vehicular applications. There have been some studies discussing the use of vehicle clustering to save scarce spectrum resources, and prevent network congestions in hybrid cellular/IEEE 802.11p vehicular environments. However, different types of applications could have different levels of quality-of-service (QoS) requirements, and thus this issue should be considered in the clustering of vehicles. We propose a novel vehicle clustering scheme that utilizes the global view and programmable advantage of software defined networking (SDN) technology to conduct an efficient clustering in vehicular environments. The proposed scheme first classifies the existing applications to three different types, the delay-sensitive type, traffic-intensive type, and computation-intensive type, according to the QoS requirements. Then the scheme forms three different clusters based on this classification to satisfy the QoS requirement for each type. We use computer simulations to show the advantage of the proposed scheme over the conventional approach.

In edge computing, computation offloading of video stream tasks and collaboration processing among edge nodes is a huge challenge. The previous research mainly focuses on the selection of computing modes and resource allocation, but taking no joint consideration of computation offloading and collaborative processing of edge node groups. In order to jointly tackle these issues in edge computing, we propose an innovative distributed collaborative video stream processing framework for edge computing(DCVP), where the video tasks are assigned to mobile edge computing (MEC) nodes or edge groups based on the offloading decision. First, we design a method for the group formation, which matches video subtasks to appropriate edge groups. In addition, we present two offloading modes for video streaming tasks, e.g., offloading to MEC nodes or edge groups, to handle computationally intensive video tasks. Furthermore, we formulate the joint optimization problem for offloading decision and collaborative processing of video subtasks into a distributed optimization problem. Finally, we employ an alternating direction method of multipliers (ADMM)-based algorithm to solve the problem. Simulation results under multiple parameters show the proposed schemes outperform other typical schemes.

Mobile Ad hoc Network (MANET) is an emerging technology that allows users to transmit data without any physical infrastructure. Among those MANET protocols, Temporally Ordered Routing Algorithm (TORA) is an on-demand MANET routing protocol that attempts to find routes according to the directed acyclic graph (DAG). However, the TORA protocol requires strict synchronization. The routing overhead of TORA will increase linearly with the packet transmission rate. Motivated by Apple Wireless Direct Link (AWDL), we propose an ad hoc link-layer protocol called TORA-based Wireless Protocol (TWP) in this article. TWP can be deployed on embedded devices with Linux-kernel systems. Besides, it has unique frame structures and mechanisms. Also, it can implement synchronization and a TORA-like routing function at the link layer. We analyze the performance of TWP via experiments on Raspberry Pis. The results show that TWP can perform routing and data transmission successfully. It performs well in synchronization and can effectively reduce the routing overhead during the process of network routing.

Edge computing is one of the revolutionary technologies that enable high-performance and low-latency modern applications, such as smart cities, connected vehicles, etc. Yet its adoption has been limited by factors including high cost of edge resources, heterogeneous and fluctuating demands, and lack of reliability. In this paper, we study resource provisioning in edge computing, taking into account these different factors. First, based on observations from real demand traces, we propose a time-varying stochastic model to capture the time-dependent and uncertain demand and network dynamics in an edge network. We then apply a novel robustness model that accounts for both expected and worst-case performance of a service. Based on these models, we formulate edge provisioning as a multi-stage stochastic optimization problem. The problem is NP-hard even in the deterministic case. Leveraging the multi-stage structure, we apply nested Benders decomposition to solve the problem. We also describe several efficiency enhancement techniques, including a novel technique for quickly solving the large number of decomposed subproblems. Finally, we present results from real dataset-based simulations, which demonstrate the advantages of the proposed models, algorithm and techniques.

Streaming 360° videos to a head-mounted display (HMD) client is challenging due to their high network resource consumption and computational load. This is due to the use of gaze point prediction or image saliency features from the field of view (FoV) since, in real-time scenarios, FoV extraction is computationally demanding. We propose a functional gaze prediction system that addresses these issues by relying on a tiling scheme for gaze prediction. We condition gaze point prediction on virtual reality (VR) content and long short-term memory (LSTM)-encoded eye movement history. Further, we encode image flow and saliency maps of RGB images via VGG16, using a convolutional neural network (CNN). Future gaze points are then predicted using a novel sinusoidal encoding technique. In experiments, our tile-based approach outperforms state-of-the-art FoV-based schemes in terms of computational load and predicted gaze position.

This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, in which the UAV provides complementary computation resource to the terrestrial MEC system. The UAV processes the received computation tasks from the mobile users (MUs) by creating the corresponding virtual machines. Due to finite shared I/O resource of the UAV in the MEC system, each MU competes to schedule local as well as remote task computations across the decision epochs, aiming to maximize the expected long-term computation performance. The non-cooperative interactions among the MUs are modeled as a stochastic game, in which the decision makings of a MU depend on the global state statistics and the task scheduling policies of all MUs are coupled. To approximate the Nash equilibrium solutions, we propose a proactive scheme based on the long short-term memory and deep reinforcement learning (DRL) techniques. A digital twin of the MEC system is established to train the proactive DRL scheme offline. Using the proposed scheme, each MU makes task scheduling decisions only with its own information. Numerical experiments show a significant performance gain from the scheme in terms of average utility per MU across the decision epochs.

Multiaccess edge computing (MEC) systems provide low-latency computing services for Internet of Things (IoT) applications by processing IoT data on edge servers. In the era of heterogeneous IoT environments, the success of IoT applications hinges on the processing of diversified IoT data. To leverage MEC systems to enable timely IoT services, we characterize IoT applications as cotasks, where each cotask is completed only if all its constituent subtasks (e.g., IoT data processing) are finished. Existing works have been devoted to the design of task offloading and scheduling decisions for MEC-enabled IoT applications, but they mostly neglect the cotask feature. In this article, we investigate the problem of cotask processing in MEC systems, and we formulate it as a nonlinear program (NLP) to minimize total cotask completion time (TCCT). In the light of uncertain communication latency, we transform the NLP to a parameterized and unconstrained version, based on which we propose the deep dual learning (DDL) method, where the learner keeps updating primal and dual variables based on randomly perturbed samples. Furthermore, we provide the duality gap and time complexity analyses for the DDL method. Our simulation results demonstrate that the proposed solution can gradually converge over iterations, and its TCCT performance outperforms other comparison schemes under various system settings.

Sleep is a major event of our daily lives. Its quality constitutes a critical indicator of people's health conditions, both mentally and physically. Existing sensor-based or vision-based sleep monitoring systems either are obstructive to use or fail to provide adequate coverage. With the fast expansion of wireless infrastructures nowadays, channel data, which is pervasive and transparent, emerges as another alternative. To this end, we propose Sleepy, a wireless channel data driven sleep monitoring system leveraging commercial WiFi devices. The key idea of Sleepy is that the energy feature of the wireless channel follows a Gaussian Mixture Model (GMM) derived from the accumulated channel data over a long period. Therefore, a GMM based foreground extraction method has been designed to adaptively distinguish motions like rollovers (foreground) from background (stationary postures), leading to certain major merits, e.g., no calibrations or target-dependent training needed. We prototype Sleepy and evaluate it in two real environments. In the short-term controlled experiments, Sleepy achieves 95.65 percent detection accuracy (DA) and 2.16 percent false negative rate (FNR) on average. In the 60-minute real sleep studies, Sleepy demonstrates strong stability, i.e., 0 percent FNR and 98.22 percent DA. Considering that Sleepy is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for sleep monitoring.

Unmanned Aerial Vehicles (UAVs), popularly known as drones, are foreseen as mobile infrastructures that support communications when a fixed infrastructure is missing in vehicular networks. UAVs can act as message relays between ground vehicles or broadcast alerts in emergency situations. Simulation that involves UAVs, combined with ground vehicles, should support 3D transmission features as the drone is not positioned in a flat surface. Results from real test bed experiments showed that irregular terrains that form hills and mountains can act as obstacles that limit the communications range. In this paper, we propose a simulation framework that runs within the OMNeT++ simulator which exhibits results comparable to the ones obtained in the real test bed, when applied to different scenarios. In the simulation, a measurement of the communications quality between UAVs and cars that considers 3D real-world terrain features which will have an impact on signal attenuation shows that the level of realism has improved when compared to simulation experiments that only consider planar communications.

The ever-evolving mobile applications need more and more computing resources to smooth user experience and sometimes meet delay requirements. Therefore, mobile devices (MDs) are gradually having difficulties to complete all tasks in time due to the limitations of computing power and battery life. To cope with this problem, mobile edge computing (MEC) systems were created to help with task processing for MDs at nearby edge servers. Existing works have been devoted to solving MEC task offloading problems, including those with simple delay constraints, but most of them neglect the coexistence of deadline-constrained and delay- sensitive tasks (i.e., the diverse delay sensitivities of tasks). In this paper, we propose an actor-critic based deep reinforcement learning (ADRL) model that takes the diverse delay sensitivities into account and offloads tasks adaptively to minimize the total penalty caused by deadline misses of deadline-constrained tasks and the lateness of delay-sensitive tasks. We train the ADRL model using a real data set that consists of the diverse delay sensitivities of tasks. Our simulation results show that the proposed solution outperforms several heuristic algorithms in terms of total penalty, and it also retains its performance gains under different system settings.

How can we effectively log a fine-grained sleep record consisting of still postures and in-place motions for the sleep disorder diagnosis without any specialized hardware? Existing sensor-based or vision-based solutions are either obstructive to use or rely on particular devices. This paper introduces SleepGuardian, a Radio Frequency (RF) based sleep monitoring system leveraging only omnipresent WiFi signals to provide a silent (unobtrusive and free of privacy concerns) yet loyal (finegrained and reliable) logging service. The key to SleepGuardian is to model the energy feature of wireless channel as a Gaussian Mixture Model (GMM) to adaptively recognize motions happened during sleep. We prototype SleepGuardian with off-the-shelf WiFi devices and evaluate it in an office. Experimental results over 11 subjects with several artificial and real periods of sleep demonstrate that SleepGuardian is effective since it achieves 100% overall accuracy (ACC), 0% false negative rate (FNR) and 0.64 s mean absolute error (MAE) on average. Considering that SleepGuardian is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for sleep monitoring.

In recent years, the rapid development of cloud computing, networking, and mobile computing have substantially promoted mobile edge computing (MEC). Currently, most of the MEC services can be roughly divided into two categories: computation offloading to accelerate computation and save the energy of mobile devices and data services to shorten the latency between the content providers and the mobile users. Although emerging services such as user-specified transcoding and AR/VR systems require joint optimization of computing resource allocation and data placement, there is little research on it. In this work, we carry out an in-depth study on the interaction of computing resource allocation and data placement in mobile edge computing environments. Based on the analysis of the temporal and spatial characteristics of the two tasks, we propose a joint optimization framework that works with online manner. The proposed method employs hybrid timescales: a coarse-grained timescale to update the data placement and a fine-grained timescale to decide computing resource allocation. The proposed method achieves provable near-optimal performance without buffering users' requirements and does not assume that future trends in user requirements are predictable.

Live streaming applications are becoming increasingly popular recently, and it exposes new technical challenges compared to regular video streaming. High video quality and low latency are two main requirements in live streaming scenarios. A live streaming application needs to make bitrate and target buffer level decisions as well as sets a continuous latency limit value to skip video frames. We formulate the live streaming task as a reinforcement learning problem with discrete-continuous hybrid action spaces, then propose a novel deep reinforcement learning (DRL) algorithm HD3 which can take hybrid actions to solve it. We compare HD3 with several state-of-the-art DRL algorithms on various network environments, and the simulation results show that HD3 can outperform all the other comparison schemes. We emphasize that HD3 generates a single agent which can perform well on different network conditions and video scenes.

In recent years, the fast development of mobile computing has substantially promoted the mobile edge computing (also known as multi-access edge computing, MEC). Placing content in edges is one of the most important uses of MEC for that it can benefit a variety of service and applications such as video streaming and VR/AR. Currently, most of the existing researches are application specified, and the heterogeneities in data allocating devices and content have not been sufficiently explored. Aiming at developing a general optimal data allocating decision algorithm for MEC, in this work, we carry out in-depth study on the interaction of data allocating and fetching in heterogenous edge computing networks, showing the NP-hardness of the optimal decision problem. We then present polynomial algorithms with 1 - 1/e-approximation factor. Our algorithms has reasonable performance guarantee with low computation complexity. We verify the proposed approach with analysis and simulations.

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN)-based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

The conditional privacy-preserving authentication (CPPA) protocol has applications in the construction of secure Internet of Vehicles (IoV) due to its capability to achieve both privacy preservation and authentication simultaneously. We demonstrate the challenge in designing the secure CPPA protocols, e.g., the insecurity of master key, invalidity of pseudo-identity (PID), linkability, etc. Then, we present a certificate-less CPPA (CL-CPPA) protocol to be used in vehicular environments that supports both privacy and security requirements in the IoV system, where the vehicles and trusted authority (TA) does not need to store any certificates for verification and tracking, respectively. We also demonstrate that our proposed CL-CPPA protocol is secure against modification attacks, impersonation attacks, and other existing attacks. A comparative summary shows that our proposed CL-CPPA protocol has lower computation and communication costs in comparison to the state-of-the-art protocols.

| openaire: EC/H2020/815191/EU//PriMO-5G

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

Because of its simplicity and scalability, geographic routing is a popular approach in wireless sensor networks, which can achieve a near-optimal routing path in the networks without holes (i.e., regions without working sensors). With the occurrence of holes, however, geographic routing faces the problems of load imbalance and routing path enlargement. In the literature, several proposals have attempted to fix these issues, but the majority of them considers only the cases when both the source and the destination stay fairly far from the holes. Recently, a few work has been proposed to tackle the problem of routing in the vicinity of routing holes. However, none of them addresses the two problems (i.e., load imbalance and routing path enlargement) concurrently, and none of them can solve the problem of load imbalance thoroughly. In this paper, we introduce a novel approach in dealing with routing in the vicinity of holes, that is the first to target and solve both the load imbalance and path enlargement problems. The theoretical analysis proves that the routing path stretch of our proposed protocol can be controlled to be as small as 1 + ε (for any predefined ε> 0) and the simulation experiments show that our protocol strongly outperforms the existing protocols in terms of load balancing.

In Delay Tolerant Networks, there is no guarantee that a fully connected path between source and destination exists any time. In this context, the limited hop count scheme which allows a message to travel at most k hops is considered as a basic routing scheme. However, there are no significant works offering an efficient method to identify a proper value for k. In this paper, we propose a new approach, named Adaptive Spray (AS-scheme). Instead of identifying a strict value k, it heuristically identifies a real-value coefficient to improve the network performance while eliminating the computational complexity. By leveraging only local knowledge like messages remaining time and nodes meeting rate, AS-scheme is highly scalable. Simulation results reveal that the proposed approach can quickly identify an appropriate coefficient and outperforms Epidemic algorithm in terms of increasing delivery rate, reducing overhead ratio and average latency, especially in dense networks.

Target coverage and connectivity are two main challenging and important issues in wireless sensor networks. The former is for providing sufficient monitoring quality where all points of interest in the network are covered by sensor nodes and the latter is for guaranteeing satisfactory communicating capability where all sensors can connect to at least one sink via relay nodes. In this paper, we focus on minimizing the number of nodes (i.e., sensor nodes and relay nodes) to provide target coverage and connectivity in wireless sensor networks with multiple sinks. We formulate the problem as two sub-problems. The first one (named as TC) is for placing sensor nodes to cover all targets and the second one (named as NC) is for placing relay nodes to connect sensor nodes to the sinks. We then propose a heuristic algorithm for the TC problem that exploits clustering technique. We also propose two heuristic algorithms for the NC problem that base on greedy approach and spanning tree. The experiment results show that our protocols can significantly reduce the number of required nodes in comparison with existing protocols.

After severe natural disasters, being able to communicate is key to effective disaster relief. When survivors are injured and trapped in remote places, for instance, they need to inform rescue workers about their situation in order to get help. The necessary infrastructure can be badly damaged, making the delivery of that information impossible. Even the disaster countermeasures of mobile operators might still leave coverage holes where there is no connection to any cellular network. In this work, we propose the use of Unpiloted Aerial Vehicles (UAVs) as data mules to provide means of communication to survivors in those coverage holes. Having a fleet of UAVs, the generation of suitable flight paths is the first step required, followed by their assignment to the UAVs. This problem has been modeled as an integer linear program. Due to the computational complexity, a heuristic solving algorithm is proposed to solve the formulated problem. The optimal and the heuristic solution both have been implemented and evaluated against each other.

Energy saving in cellular systems is increasingly important due to the ever- deteriorating global warming. Heterogeneous networks (HetNets) can attain energy savings thanks to the lower operational and transmit power consumption of small base stations (BSs). To address inter-cell interference problem yet achieving network energy conservation, green multicell clustering facilitating BS sleeping and coordinated multipoint (CoMP) clustering paves a way toward future green HetNets. To further alleviate the backhaul power consumption induced by content requests from users, caching popular contents at BSs in a shareable manner is regarded as a viable solution. In this paper, we investigate the problem of green multicell clustering in HetNets with shareable caching (RELISH), and show its NP-hardness. By observing that BS sleeping plays a pivoting role in the RELISH problem, we propose the clustering- then-caching strategy to decompose the RELISH problem, and then design the dual- ascending clustering algorithm followed by the zero-replica caching algorithm for solving the sub-problems. The simulation results demonstrate that our proposed solution is effective in reducing total power consumption, and we also show how the power savings vary with system parameters.

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an "all- mmWave" cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the "all- mmWave" cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time- varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of- art baselines, in terms of average queue length and average packet dropping rate.

Video caching is one of the most important research issues in Content-Centric Network (CCN) and greatly affects its overall performance. The computational complexity of state-of-the-art optimal caching schemes is high, due to the arbitrary network topologies. In this paper, the popularity-aware video caching in topology-known CCN is studied. The complex arbitrary network typology is mapped into a virtual cascade network topology and a caching scheme is designed in accordance with the transformed virtual network rather than the original network. This scheme is proved optimal, and is with polynomial computational complexity. Simulations are conducted and the results show that the proposed scheme outperforms the existing schemes.

Access control is an important mechanism in cloud computing. The Key Policy Attribute Based Encryption (KP-ABE) is an important method to implement the access control in cloud service. However, conventional user revocation scheme in KP-ABE costs huge computational overhead. In this paper, we focus on the important user revocation issue in KP-ABE. We introduce several control parameters, including version value, check value and user list. We combine KP-ABE with salt encryption for the implementation. We provide a novel user revocation scheme for KP-ABE to improve the user revocation issue, which can reduce the heavy computational overhead when user being revoked. The performance evaluation shows that the proposed user revocation scheme gives good performance with KP-ABE.

Software-Defined networking (SDN), as a new paradigm, fixes the shortage that traditional network does not support the dynamic, scalable computing and storage needs of more computing environments. SDN, however, also faces security problems such as vulnerable to DDoS attacks. DDoS attacks are well-known and powerful attacks. DDoS detection and DDoS traffic separation for SDN environments are still an open research issue. DDoS attacks in SDN environments will not only bring damage to target server, but also takes exact impact on SDN system. In this paper, we identify a new type DDoS attack, specifically aiming SDN environment, which is harder to be detected. We propose a novel real-time DDoS detection scheme for SDN environment, by using Principal Component Analysis (PCA) scheme to analyze the network status on traffic packets data. We separate the network into different parts, to reduce the total calculation burden. We compare our scheme with sample entropy, showed our scheme achieves better detecting ability for DDoS attacks.

Owing to the explosive growth of Internet traffic, network operators must be able to monitor the entire network situation and efficiently manage their network resources. Traditional network analysis methods that usually work on a single machine are no longer suitable for huge traffic data owing to their poor processing ability. To cope with high speed streaming data, various stream-processing-based big data frameworks, such as Storm, Flink, and Spark Streaming, have been proposed. In this paper, we treat network traffic as a streaming data, and propose an online Internet traffic monitoring framework based on Spark Streaming and Flink, respectively. The framework could be used for real-time TCP performance monitoring and DDoS detection. We conduct typical experiments to compare the performance of Spark Streaming and Flink. The experiments show that our framework performs well for large Internet traffic measurement and monitoring.

Interactive applications that come along with 360- degree (or 360) videos have brought immersive experiences to users thanks to the elevated machine computability. In fact, the provision of such high quality of experience (QoE) hinges on the persistent delivery of 360 videos, potentially consuming an excessive need of network bandwidth. To prevent the delivery of entire 360 videos from adversely affecting QoE, tile-based viewport adaptive streaming that divides 360 video chunks into tiles and conveys streams with differentiated quality levels to viewport and non-viewport areas has been regarded as a promising solution. Existing works have been devoted to the design of 1) viewport prediction (VPP) to predict users' viewport orientation due to head movements, and 2) tile bitrate selection (TBS) to determine tile-based bitrates for viewport and non-viewport areas. Despite the heuristic solutions proposed by the existing works, there is lack of knowledge of whether QoE can be enhanced by learning from historical data. In this paper, we propose the system-Plato, to leverage machine learning to tile-based viewport adaptive streaming for 360 videos. In particular, Plato applies long short term memory (LSTM) model to VPP, and uses part of non-viewport areas to help resist prediction errors. In addition, Plato uses real-world traces to train a TBS agent based on reinforcement learning to determine tile bitrates for both viewport and non-viewport areas. Our simulation results show that Plato outperforms existing schemes in various QoE metrics.

Segment Routed Traffic Engineering is emerging as an important application for network operators to manage resource utilization by using segment routing paths as candidates for route selection. In order to facilitate the network operator demands, a traffic engineering program should be fast and efficient. These two characteristics are very essential since the traffic engineering program must be invoked periodically in short intervals. The segment routing paths can be constructed by concatenating the shortest paths between two nodes such that there is a path from source to destination. We are interested in the problem to find intermediate nodes to construct segment routing paths minimizing the maximum link utilization. However, the existing approaches have the shortcomings that either they require a substantial amount of time to find a solution or they must sacrifice a considerable amount of link utilization. To address these issues, we propose to limit the number of intermediate node candidates by using a bounded stretch constraint relative to the shortest path of the source-destination pair. Then, we evaluate the computation time and link utilization against the existing work. We show that the bounded stretch constraint helps reduce the computation time while a near optimal link utilization can be achieved.

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.

The development of Intelligent Transportation Systems (ITS) aims to enable the Vehicle-to-X (V2X) communication to deal with the road traffic and road safety problems, and to support the comfort applications. Vehicles move fast in Vehicular Ad-hoc Networks (VANETs) and are constrained by the layout of the roads. Furthermore, in urban areas, vehicles are facing the shadowing effects of buildings. These characteristics make the V2X communications in urban VANETs more challenging. According to European Telecommunications Standards Institute (ETSI) standards for ITS, communication between different communication endpoints might be realized by geoBroadcast. But performing geoBroadcast arises the broadcast storm problem that needs to be tackled. In this work, we introduce Unicast-Assisted GeoBroadcast (UAG) that selects several target positions within the geo-region and geoUnicasts a copy of the message from the source vehicle to each target position. UAG makes sure that these copies take different routes towards the geo-region to increase the chance of reaching it. Afterwards, some of the vehicles located in the geo-region are selected to broadcast the message within the geo-region. UAG applies the intersection-based or the road-based approach to select the target positions. Moreover, UAG implements the forwarding-zone breathing based on the road topology of the geo-region and its neighborhood, in order to increase the reachability. We compare our proposed protocol with simple flooding and Urban Geocast based on Adaptive Delay (UGAD), and show that UAG performs better in terms of reachability and scalability.

This paper studies the resource allocation (RA) problem when the in- band device-to-device (D2D) technology is applied to support vehicle-to-vehicle (V2V) communications. Conventional D2D RA normally demands a sufficient level of channel knowledge to reach the optimal performance. But in vehicular communication environments, this would require tremendous signalling overhead and the acquired channel knowledge is easily outdated. Therefore, RA relying only on geographic information is more feasible. To this end, we propose a novel location-partition-based RA scheme. We first divide the cell coverage area and road into small zones, the geographic information of which is stored in a database. Satisfying the requirement that the interference generated by all V2V links to the reused cellular user (CU) is below a certain threshold, an interference matrix that reflects the interference from nodes in cell zones to road zones is established. Three types of power control methods are adopted to maximize the minimum achievable rate of the V2V links. A series of simulations are conducted to verify the performance of our proposed RA solution. The results show that our method can improve the minimum achievable rate compared with conventional location-based RA methods. The impact of different system parameters are also carefully analyzed.

Sleep is a major event of our daily lives. Its quality constitutes a critical indicator of people's health conditions, both mentally and physically. Existing sleep monitoring systems either are obstructive to use or fail to provide adequate coverage. To overcome these shortages, we propose Sleepy, an adaptive and noninvasive sleep monitoring system leveraging channel response in the commercial WiFi devices. Sleepy needs no calibrations or target-dependent training to recognize posture changes during sleep. To achieve that, a Gaussian Mixture Model (GMM) based foreground extraction method has been designed to adaptively distinguish motions like rollovers (foreground) from background (stationary postures). We prototype Sleepy and evaluate it in two real environments. In the short-term controlled experiments, Sleepy achieves 95.04% detection accuracy and 4.07% false negative rate. In the 60-minute real sleep studies, Sleepy demonstrates strong stability. Considering that Sleepy is compatible with existing WiFi infrastructures, it constitutes a low-cost yet promising solution for sleep monitoring.

Characterizing a virtual network function performance is one of the most challenging issues in softwarized network function deployment due to different configurations leading to a different performance. Since there has been no clear solution on how to configure a software router that provides the minimum average packet latency, we address this problem by proposing a configuration framework for a software router. First, we advance the performance characterization of a software router by conducting a packet latency prediction model based on the Erlang-$k$distribution. Motivated by the analysis of the packet latency distribution, the Erlang-$k$distribution is chosen as a basis of the packet latency prediction model. Our prediction model requires the measurement of only two different configurations, i.e., one and two receiving queues of a network interface card, to predict the average packet latency of all configurations. We cross-verify the accuracy of our prediction model with the measured data. Second, we adopt our prediction model in the configuration selection (CS) algorithm for searching which configuration yields the minimum of average packet latency. The validity of the prediction-based CS algorithm is confirmed by comparing to the measurement-based one. Finally, we present the CS-based measurement system interworking with network function virtualization-management and orchestration for advances in network management.

In spectrum sharing system, an exclusion zone is defined to protect both primary and secondary users from interference. Reducing the size of exclusion zone is critical for efficiently utilizing the fallow spectrum. In this paper, we propose a novel crowdsourcing augmented exclusion zone refinement framework. In our framework, a barter-like exchange model using spectrum access right is employed to incentivize the secondary users (SUs) to participate in the crowdsourcing. We further design a truthful auction mechanism to select the SUs and determine their access time in a computationally efficient way. We perform simulations to validate the proposed mechanism, and compare it with two baseline schemes.

Hybrid analog digital (HDA) architectures have been widely used in communication systems for analog source over discrete-time memoryless Gaussian channels. They employ a linear coding scheme in the analog parts, while considering separately the design of source and channel codes in the digital parts. To the best of our knowledge, none of the previous HDA schemes exploit the advantages of maintaining a joint source and channel coding design in the digital segment. In this paper, we investigate the effect of the analog parts on various outputs of the digital modules in a HDA communication system, and introduce a novel HDA architecture with adaptation for the digital parts. Such adaptation allows our system to exploit the joint effect of the analog components and the channel noise on outputs of the digital components, while simultaneously taking into consideration the unequal distribution of source code outputs. Our simulations illustrate that the proposed HDA system provides robust and graceful performance (on both bandwidth compression and expansion modes) for a wide range of channel conditions.

Accurate estimation of crowd dynamics is difficult, especially when it comes to fine-grained spatial and temporal predictions. A deep understanding of these fine-grained dynamics is crucial during a major disaster, as it guides efficient disaster managements. However, it is particularly challenging as these fine-grained dynamics are mainly caused by high-dimensional individual movement and evacuation. Furthermore, abnormal user behavior during disasters makes the problem of accurate prediction even more acute. Traditional models have difficulties in dealing with these high dimensional patterns caused by disruptive events. For example, the 2016 Kumamoto earthquakes disrupted normal crowd dynamics patterns significantly in the affected regions. We first perform a thorough analysis of a crowd population distribution dataset during Kumamoto earthquakes collected by a major mobile network operator in Japan, which shows strong fine-grained temporal autocorrelation and spatial correlation among geographically neighboring grids. It is also demonstrated that temporal autocorrelation during disasters is more than simple diurnal patterns. Moreover, there are many factors that could potentially influence spatial correlations and affect the dynamics patterns. Then, we illustrate how a spatial-temporal Long-Short-Term-Memory (LSTM) deep neural network could be applied to boost the prediction power. It is shown that the error in terms of Mean Square Error (MSE) is reduced by as much as 55.1-69.4% compared to regressive models such as AR, ARIMA and SVR. Furthermore, LSTM outperforms the aforementioned models significantly even when little training data is available right after the mainshock. Finally, we also show a Region-aware LSTM does not necessarily outperform a regular LSTM.

Designing mobile edge computing (MEC) systems by jointly optimizing communication and computation resources, which can help increase mobile batteries' lifetime and improve quality of experience for computation-intensive and latency-sensitive applications, has received significant interest. In this paper, we consider energy-efficient resource allocation schemes for a multi-user mobile edge computing system with inelastic computation tasks and non-negligible task execution durations. First, we establish a mathematical model to characterize the offloading of a computation task from a mobile to the base station (BS) equipped with MEC servers. This computation-offloading model consists of three stages, i.e., task uploading, task executing, and computation result downloading, and allows parallel transmissions and executions for different tasks. Then, we formulate the weighted sum energy consumption minimization problem to optimally allocate the task operation sequence, the uploading and downloading time durations as well as the starting times for uploading, executing and downloading, which is a challenging mixed discrete- continuous optimization problem and is NP-hard in general. We propose a method to obtain an optimal solution and develop a low-complexity algorithm to obtain a suboptimal solution, by connecting the optimization problem to a three-stage flow-shop scheduling problem and utilizing Johnson's algorithm as well as convex optimization techniques. Finally, numerical results show that the proposed sub-optimal solution outperforms existing comparison schemes.

Edge-caching, which only caches the contents near the users, has low implementation cost and performs comparably with the conventional on-path caching schemes in \textit{Information-Centric Networking} (ICN). However, the independent edge-caching can not capture the content popularity dynamics accurately since each cache node only has local content request information. In addition, the caching information of the cache nodes within the same neighborhood is not efficiently utilized. To solve these issues, we propose a coordinated edge-caching system, where the cache nodes within the same neighborhood can help each other to improve the caching performance. We design a caching decision method for each node, and the decision method takes caching information of edge nodes within the same neighborhood into consideration. We theoretically illustrate the effectiveness of the proposed scheme in typical network scenarios. Simulations are conducted on the real-world network topologies under both stationary and temporal popularity workloads, and the results show the performance of our proposed scheme is superior to the comparison schemes.

To improve the spectrum utilization efficiency, radio propagation model based spectrum database is widely investigated recently. However, it is prone to offer inaccurate and stale spectrum availability since the empirical models do not count for local environment details. One promising solution is to incorporate real- time spectrum measurement into the quasi-static spectrum database. In this paper, we propose a novel fine-grained incentive mechanism for sensing augmented spectrum database. We first present a reverse auction framework, which minimizes the operator's total expenditure subject to the quality requirement of each spot that needs to be augmented. Then we propose a practical incentive mechanism to solve the auction problem, which is proven to be truthful, individual rational and computationally efficient. Simulation results demonstrate that the proposed mechanism could save noticeable expenditure compared to two baseline schemes.

Network disaster recovery is one of the greatest concerns for Mobile Network Operators (MNOs) and first responders during large-scale natural disasters such as earth- quakes. In many recent studies, wireless multi-hop networking has been demonstrated as an effective technique to quickly and efficiently extend the network coverage during disasters. In this paper, we specifically address the network deployment problem by proposing the Population-Aware Relay Placement (PARP) solution, which seeks the efficient deployment of a limited number of relays such that population coverage is maximized in the scenario of network disaster recovery. We provide a graph-based modeling and prove its NP-hardness accordingly. In order to efficiently solve this problem, we propose a heuristic solution, which is constructed in two steps. We first design a simple algorithm based on a disk graph to determine the Steiner locations, which is the biggest challenge in this problem. Then, we formulate the problem as an integer programming problem, which is inspired by the formulation of Prize-Collecting Steiner Tree (PCST). Thus, the integer problem is solved by exploring the similarity of the existing algorithm for PCST. To evaluate the proposed solution extensively, we present numerical results on both real-world and random scenarios, which validate the effectiveness of the proposed solution and show substantial improvement by comparing to the previous one.

SINET5 is a new 100-Gbps-based academic backbone network, which started full-scale operations in April 2016. It uses multi-protocol label switching-transport profile (MPLS-TP) systems and reconfigurable optical add-drop multiplexers (ROADMs) to create a nationwide network and has more than 50 backbone IP routers to provide a wide range of services, such as several virtual private network (VPN) services. It provides end-to-end data communications up to 100 Gbps throughput, minimized-latency, and software-defined networking (SDN)-friendly functions to researchers in every Japanese prefecture. SINET5 is also a platform for dynamic inter-cloud connections and network functions visualization (NFV) services. This paper brief review of the network architecture, and describes new featured services, SDN-oriented layer-2 on-demand VPN services, and NFV functions. Field test results for SINET5 performance are also reported.

In this paper, we target the resource allocation and layer selection problem for the realtime video streaming over highway scenario, by employing Scalable Video Coding (SVC) for the video contents. Especially, we take the freeze-free playback as one of the constraint as well. Since the formulated resource allocation and SVC layer selection problem is NP-hard, we propose the Resource Allocation and Layer Selection with Base layer guarantee (RALSB) algorithm to solve this problem in two phases: the Base layer Guarantee (BG) phase, and the Resource allocation and SVC layer selection (RS) phase. Simulation results show that the proposed RALSB can prevent/reduce the playback freeze in typical scenarios.

Wireless sensor networks (WSNs) are used in many mission-critical applications, such as target tracking on a battlefield, emergency alarms, and disaster detection. In such applications, QoS provisioning in the timeliness domain is indispensable. Moreover, because of the diversity of sensory data, QoS provisioning should support not only one but multiple levels of end-to-end delay constraints. As a result of several characteristics such as the limitations on the energy supply, available storage and computational capacity of the sensor nodes, guaranteeing timely delivery in WSNs is a challenging problem. To overcome these limitations, several lightweight and stateless QoS-based geographic routing protocols have been proposed. The existing protocols work well in networks without routing holes (i.e., regions with no working sensors). However, with the occurrence of routing holes, they suffer from the so-called local minimum phenomenon and traffic congestion around the hole boundary. In this paper, we consider the presence of routing holes and propose a delay-guaranteed geographic routing protocol called DEHA that can support multiple end-to-end delay levels. The main idea is to achieve early awareness of the presence of a routing hole and then to utilize this awareness in determining a routing path that can avoid the hole. Simulation results show that our protocol outperforms the existing protocols in terms of several performance metrics, including packet delivery ratio, energy efficiency, and load balancing.

The occurrence of multiple holes in wireless sensor networks poses many challenges in designing routing protocols. The traditional scheme is forwarding packets along the hole perimeters. However, this scheme leads to two serious problems: data concentration around the hole boundaries and routing path enlargement Recently, several approaches have been proposed to address these two problems, wherein a common idea is to form forbidden areas around the holes from which packets are kept to stay away. However, due to the static nature of the forbidden areas and routing paths, the existing protocols cannot solve these two problems thoroughly. In this paper, we propose a novel protocol for bypassing multiple holes in wireless sensor networks which can balance the traffic over the network while ensuring the constant stretch property of the routing path. Our main idea is to use elastic forbidden areas and dynamic routing paths. The theoretical analysis proves that the routing path stretch of the proposed protocol can be controlled to be as small as 1 + ϵ (for any predefined ϵ > 0), and the simulation experiments show that our protocol strongly outperforms state-of-the-art protocols in terms of load balancing.

Providing the optimal configuration for a software router poses a lot of technical challenges that do not present in the dedicated hardware router. One of them is how to characterize performance varying due to different configurations on commodity hardware. This paper addresses the problem of configuring a software router that provides the minimum of average packet latency. Since changing all combinations of hardware configurations of a software router for searching the optimum is cumbersome, we propose a prediction model to accurately estimate the packet latency of a software router. We first analyze the relationship of the packet latency distribution with the configured and observed parameters. Empirical measurements suggest that the Erlang-k distribution is a reasonable model for estimating the packet latency distribution. Motivated by the parameter relationship analysis, we propose a prediction model for packet latency of a software router based on the Erlang- k distribution. Our prediction model requires measurement of only two different configurations, i.e., one and two Rx queues of a network interface card, to predict the average packet latency of all combinations of configurations. We use the measured data from the testbed experiments and the data of curve fitting method to cross-verify the accuracy of our prediction model. Underlying the prediction model, we propose the optimal configuration selection (OCS) algorithm to justify which configuration yields the minimum of average packet latency. Our prediction model based OCS results in the same optimal configuration with the measured data based ones.

Hybrid digital-analog (HDA) architectures have been widely developed for efficient digital transmission of analog speech, audio or video data. By considering the advantage of both digital and analog components, HDA systems gain better performances than purely analog and digital schemes in a wide range of channel conditions. However, HDA systems described in previous works are mostly designed for continuous-valued sources. In this paper, we address the problem of transmission of discrete sources over noisy channels. In particular, our work focuses on digital speech data in PCM format. We proposed two analog schemes, linear mapping, and non-linear mappings. The linear analog mapping employs an equal error protection scheme while the non-linear mapping takes into account the heterogeneous nature of error values to provide better protection to important values. The experiment results show that our HDA systems provide a better performance on a wide range of channel qualities in comparison with traditional purely digital systems.

We propose a vehicle-to-roadside communication protocol based on a distributed clustering algorithm where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by taking into account multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory-based reward discount is employed to guide each vehicle to select the route which can maximize the whole network performance. We conduct extensive computer simulations to show the performance advantage of the protocol over other approaches.

Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have revealed their potentials in enhancing our living experiences through applications like intrusion detection and sleep monitoring. However, existing solutions still face several critical downsides such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints), and privacy issues (being watched). To overcome such shortages, a radio frequency (RF) based device-free motion detection system (MoSense) is designed via leveraging the attenuation of ubiquitous WiFi signals induced by motions to deliver a reliable and transparent detection service in realtime. The design and implementation of MoSense face two challenges: 1) characterizing stationary states and 2) the noisy subcarriers. For the first challenge, a silence analysis model is proposed to characterize stationary states for distinguishing motions. For the second challenge, we design a distance-based mechanism to select certain subcarriers that better capture the impact of motions from the noisy channel through measuring the similarity between subcarriers. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with other two state-of-the-art systems, i.e., FIMD and FRID, we have shown that MoSense is superior in terms of precision, false negative rate and computational complexity. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.

Interference management is one of the most important issues in heterogeneous cellular networks with multiple macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. Therefore, the adaptive eICIC configuration problem is critical, which adjusts the parameters including the ratio of almost blank subframes (ABS) and the bias of cell range expansion (RE). This problem is challenging especially for the scenario with multiple coexisting network services, since different services have different user scheduling strategies and different evaluation metrics. By using a general service model, we formulate the eICIC configuration problem with multiple coexisting services as a general form consensus problem with regularization and solve the problem by proposing an efficient optimization algorithm based on the alternating direction method of multipliers. In particular, we perform local RE bias adaptation at service layer, local ABS ratio adaptation at BS layer, and coordination among local solutions for a global solution at a network layer. To provide the service scalability, we encapsulate the service details into the local RE bias adaptation subproblem, which is isolated from the other parts of the algorithm, and we also introduce some implementation examples of the subproblem for different services. The extensive simulation results demonstrate the efficiency of the proposed algorithm and verify the convergence property.

Decentralized communication technologies (i.e., ad hoc networks) provide more opportunities for emerging wireless Internet of Things (IoT) due to the flexibility and expandability of distributed architecture. However, the performance degradation of wireless communications with the increase of the number of hops becomes the main obstacle in the development of decentralized wireless IoT systems. The main challenges come from the difficulty in designing a resource and energy efficient multihop communication protocol. Transmission control protocol (TCP), the most frequently used transport layer protocol for achieving reliable end-to-end communications, cannot achieve a satisfactory result in multihop wireless scenarios as it uses end-to-end acknowledgment which could not work well in a lossy scenario. In this paper, we propose a multihop data delivery virtualization approach which uses multiple one-hop reliable transmissions to perform multihop data transmissions. Since the proposed protocol utilizes hop-by-hop acknowledgment instead of end-to-end feedback, the congestion window size at each TCP sender node is not affected by the number of hops between the source node and the destination node. The proposed protocol can provide a significantly higher throughput and shorter transmission time as compared to the end-to-end approach. We conduct real-world experiments as well as computer simulations to show the performance gain from our proposed protocol.

Content Centric Network (CCN) has become a heated research topic recently, as it is proposed as an alternative of the future network. The routers in CCN have the caching abilities and the caching strategies affect the system performance greatly. Each content in CCN is associated with a popularity, which is determined by the corresponding requested times. Popularity-aware caching scheme caches the popular content close to users and can lead to better caching performance in terms of smaller average transmission hops traveled. Content popularity significantly affects the overall system performance, and the content size is not considered during the content level popularity (i.e. item popularity) calculation. In this paper, we study the impact of the item popularity and chunk popularity in CCN, where the chunk popularity is the normalized item popularity considering the content size. Extensive simulations are conducted and the simulation results show the advantages and disadvantages of each scheme. A new popularity calculation method is proposed to perform the tradeoff between the item popularity and chunk popularity.

Broadcasting of real-time video, especially accidents relevant, plays an important role in Vehicle Ad-hoc Networks (VANETs). Multi-view video enables the perception of the targeted scene from multiple angles. By subscribing the real-time multi-view video broadcast, drivers can obtain better knowledge of the highway traffic and road conditions. However, compared with the traditional single view video transmission, the multiview video transmission is more challenging, because of the VANETs' channel variation and a much larger network data rate requirement. The inherent problem thus becomes how to utilize the limited network bandwidth to stream the real-time multi-view video. As far as the authors understand, this problem has not yet been solved in any formal way. This paper focuses on the realtime multi-view broadcast over VANETs and formulates it into an optimization problem with the objective to maximize the average video quality received by users. By exploring the VANET channel characteristic, the correlations inside the multi-view and video popularity, an algorithm named as RMV is proposed to solve the optimization problem. Extensive simulations are conducted and simulation results show that the proposed scheme can outperform the comparison schemes in typical VANET scenarios to a great extent.

Heterogeneous networks (HetNets) are intended to offer wide area coverage and high data rate transmission by deploying small cells besides macrocells. Device-to-device (D2D) communication as an underlay of cellular network enriches local service and offloads base station. In this paper, we target the video transmission demanded by picocell edge users (PEUEs), who suffer from low quality channel due to the inter-cell interference from macrocell and long physical distance from picocell. Moreover, the wireless channels are burst-loss prone for upper layer applications such as video on demand (VoD), which makes the traditional channel coding such as forward error correction (FEC) insufficient. In this paper, we address these issues and propose a cooperative video transmission scheme to improve PEUEs' received video quality by constructing two transmission paths from picocell to each PEUE. The two transmission paths are the direct transmission from pico-eNB (i.e., base station) to PEUE and a relay-assisted path by means of D2D communication for frame recovery, respectively. Reference frame selection and unequal error protection are adopted to further improve the overall performance. Extensive simulations are conducted and results demonstrate that the proposed scheme outperforms state-of-the-art scheme in Config.4b scenarios defined by 3GPP.

Vehicular ad hoc networks (VANETs) have been attracting interest for their potential roles in intelligent transport systems (ITS). In order to enable distributed ITS, there is a need to maintain some information in the vehicular networks without the support of any infrastructure such as road side units. In this paper, we propose a protocol which can store the data in VANETs by transferring data to a new carrier (vehicle) before the current data carrier is moving out of a specified region. For the next data carrier node selection, the protocol employs fuzzy logic to evaluate instant reward by taking into account multiple metrics specifically throughput, vehicle velocity, and bandwidth efficiency. In addition, a reinforcement learning-based algorithm is used to consider the future reward of a decision. We use theoretical analysis and computer simulations to evaluate the proposed protocol.

Device-to-Device (D2D) communication is an important component for 5G networks. Traditional D2D communications are mainly within the same cell. However, due to the reduced cell sizes, and the long communication range among mobile devices, the issues in D2D communication across cells have not yet been well addressed. In this paper, we first introduce an operation protocol to support cross-cell D2D communications underlaying cellular networks, then propose a hypergraph based resource allocation scheme to optimize the sum rate over the shared resource. In a hypergraph, the D2D and cellular users are regarded as vertices, and hyperedges represent mutual interference. For efficient interference coordination, the hypergraph is partitioned into different clusters corresponding to different channels. Simulation results demonstrate that the scheme efficiently leads to a good performance on the sum rate.

Mobile video traffic is exploding and it is particularly challenging to stream video when high density of users are "on the move", e.g., in public transport systems. It becomes increasingly problematic as video traffic is predicted to account for more than 80% of Internet traffic by 2019. This will be exacerbated by factors such as cellular network coverage issues and unstable network throughput due to high speed mobility. By exploiting the predictable public transport mobility patterns, spatio-temporal correlation of user interests and users' video viewing behaviors, we proposed TransFetch which uses intelligent caching on-board the public transport vehicles as well as a novel video chunk placement algorithm. We show through extensive simulations, that TransFetch reduces the system cellular data usage by up to 45% and improves the quality of video streaming by up to 35%. Finally, we demonstrate the practical feasibility of TransFetch by implementing caching units on a Raspberry-Pi and a mobile app on an Android device.

There are two main requirements in dealing with coverage holes in wireless sensor networks (WSNs): locating the hole boundary and finding the locations to deploy new sensors for hole patching. The current protocols on finding the patching locations always require re-running the protocols from scratch many times. This constraint causes the time complexity and energy overhead to increase proportionally to the hole size. In this paper, we propose a lightweight protocol to determine coverage holes in wireless sensor network. Our protocol does not only can determine the exact hole boundary but also approximate the boundary by a simpler shape which can help to speed up the patching location finding process. The simulation experiments show that our protocol can reduce more than 56% of time complexity and save more than 46% of energy overhead in comparison with existing protocols.

We propose a context-aware routing protocol for vehicular ad hoc networks (VANETs). Two types of context information is considered in this paper specifically communication type (broadcast or unicast) and packet size. The proposed protocol constructs route based on virtual clustering which only exchanges beacon messages in one-hop neighborhood area. The packets are forwarded by the cluster heads, and the last 2-hop route is optimized by using a reinforcement learning algorithm which can attain good performance with low overhead. The advantage of the proposed protocol is shown by using computer simulations.

Natural disasters can result in severe damage to communication infrastructure, which leads to further chaos to the damaged area. After the disaster strikes, most of the victims would gather at the evacuation sites for food supplies and other necessities. Having a good communication network is very important to help the victims. In this paper, we aim at recovering the network from the still-alive mobile base stations to the out-of-service evacuation sites by using multi-hop relaying technique. We propose to reconstruct the post-disaster network in a capacity-aware way based on prize collecting Steiner tree. The purpose of the proposed scheme is to achieve high capacity connectivity ratio in a cost efficient way. To provide more accurate evaluation results, we evaluate the proposed scheme by using the real evacuation site and base station data in Tokyo area, and utilizing the big data analysis based post-disaster service availability model.

The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to secondary (or unlicensed) receivers/users on more than one broadcast channels via channel hopping (CH), because a single broadcast channel can be reclaimed by the primary (or licensed) user, leading to broadcast failures. Meanwhile, a secondary receiver needs to synchronize its clock with the BS's clock to avoid broadcast failures caused by the possible clock drift between the CH sequences of the secondary receiver and the BS. In this paper, we propose a CH-based broadcast protocol called SASS, which enables a BS to successfully broadcast to secondary receivers over multiple broadcast channels via channel hopping. Specifically, the CH sequences are constructed on basis of a mathematical construct- the Self-Adaptive Skolem Sequence (SASS). Moreover, each secondary receiver under SASS is able to adaptively synchronize its clock with that of the BS without any information exchanges, regardless of any amount of clock drift.

Given the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface, researchers have shown great interest in analyzing signal fluctuation on this interface for environmental perception. A popular example is the enabling of indoor localization with RF signals. As an alternative to active device-based positioning, device-free passive (DfP) indoor localization has the advantage that the sensed individuals do not require to carry RF sensors. We propose a probabilistic fingerprinting-based technique for DfP indoor localization. Our system adopts CSI readings derived from off-the-shelf WiFi 802.11n wireless cards which can provide fine-grained subchannel measurements in the context of MIMO-OFDM PHY layer parameters. This complex channel information enables accurate localization of non-equipped individuals. Our scheme further boosts the localization efficiency by using principal component analysis (PCA) to identify the most relevant feature vectors. The experimental results demonstrate that our system can achieve an accuracy of over 92% and an error distance smaller than 0.5m. We also investigate the effect of other parameters on the performance of our system, including packet transmission rate, the number of links as well as the number of principle components.

The authors address the problem of determining a replica placement scheme for sensor data in a cache network constructed by a number of cache nodes in edge networks of the Internet, with the purpose of minimising the total costs for data placement and access. They formulate the placement problem as a mixed integer programming (MIP) problem and then propose several heuristic approaches to solve the problem. They first relax the MIP problem to a linear programming (LP) problem and then round the fractional solution values of the LP problem to integers. Next, they employ a Lagrangian relaxation approach to find a solution for each data item without considering node capacity and then rearrange the placement at each node to satisfy the capacity constraint. For comparison, they also propose a greedy approach that determines a replica placement at a node by using the node local information. They show that the former two approaches lead to good performance that is only <8% worst than the theoretical lower bound.

It is particularly challenging to design an efficient routing protocol for vehicular ad hoc networks due to the vehicle movement, limited wireless resources, and lossy feature of the wireless channel. We propose a protocol which uses common backbone vehicles for different traffic flows, as well as employs interflow network coding to encode packets at the backbone vehicles. A reliably connected backbone is selected by taking into account vehicle movement dynamics and link quality between vehicles. By using interflow network coding at the backbone vehicles, the protocol is able to reduce the number of generated packets by 25% in most cases compared with the conventional routing approach. As a result, the proposed protocol can provide a high packet delivery ratio, low overhead, and low delay. We show the effectiveness of the protocol using theoretical analysis and computer simulations.

The “color-plus-depth” format represents a 3D scene using multiple color and depth images captured by an array of closely spaced cameras. Using this format, a novel image as observed from a horizontally shifted virtual viewpoint can be synthesized via depth-image-based rendering (DIBR), using neighboring camera-captured viewpoint images as reference. In this paper, using the same popularized color-plus-depth representation, we propose to construct, in addition, novel images as observed from virtual viewpoints closer to the 3D scene, enabling a new dimension of view navigation. To construct this new image type, we first perform a new DIBR pixel-mapping for z-dimensional camera movement. We then identify expansion holes-a new kind of missing pixels unique in z-dimensional DIBR-mapped images-using a depth layering procedure. To fill expansion holes we formulate a patch-based maximum a posteriori problem, where the patches are appropriately spaced using diamond tiling. Leveraging on recent advances in graph signal processing, we define a graph-signal smoothness prior to regularize the inverse problem. Finally, we design a fast iterative reweighted least square algorithm to solve the posed problem efficiently. Experimental results show that our z-dimensional synthesized images outperform images rendered by a naı̈ve modification of VSRS 3.5 by up to 4.01 dB.

Abstract We propose a novel vertical handoff scheme with the support of the software‐defined networking technique for heterogeneous wireless networks. The proposed scheme solves two important issues in vertical handoff:network selectionandhandoff timing. In this paper, the network selection is formulated as a 0‐1 integer programming problem, which maximizes the sum of channel capacities that handoff users can obtain from their new access points. After the network selection process is finished, a user will wait for a time period. Only if the new access point is consistently more appropriate than the current access point during this time period, will the user transfer its inter‐network connection to the new access point. Our proposed scheme ensures that a user will transfer to the most appropriate access point at the most appropriate time. Comprehensive simulation has been conducted. It is shown that the proposed scheme reduces the number of vertical handoffs, maximizes the total throughput, and user served ratio significantly. Copyright © 2016 John Wiley & Sons, Ltd.

Development of cloud computing and mobile wireless technologies has given rise to mobile cloud computing (MCC). The limitations of battery capacity and computing capability of mobile devices can be alleviated by offloading some tasks from mobile devices to the cloud. In this paper, we focus on the computation offloading problem in mobile cloud computing. In particular, for a given set of computational components which constitute a mobile application, we attempt to decide which components should be offloaded to the cloud such that the application can be completed at the minimal execution cost. We formulate the mobile computation offloading problem as an optimization problem. Then we propose two optimal offloading algorithms to solve the problem. The efficiency of the proposed algorithms is evaluated using numerical experiments.

In the software defined networks (SDNs), the Open-Flow protocol is typically used as the southbound API in manipulating OpenFlow switches. However, the OpenFlow control messages are in a low abstraction level. Therefore, even a single application-level operation requires many OpenFlow messages, which consume the bandwidth of the control network and reduce the SDN's scalability. One potential solution is to use high level domain specific northbound APIs in the control network. In this paper, we explore the possibility of adopting this solution by implementing and evaluating a new SDN framework, Ryuo. In Ryuo, we introduce Local Service, which runs directly on each SDN switch (hardware/software). In operations, Local Service provides northbound APIs to the SDN applications while it can use different southbound APIs for different switches. Ryuo eliminates unnecessary control messages, hence it decreases the volume of control traffic. Our evaluation of Ryuo on Mininet with example applications shows that Ryuo reduces the volume of control traffic at least 50% compared to the standard OpenFlow, and up to 40% compared to the local controller approach. We also evaluate the performance of running Local Services directly on physical switches. The results show that we can achieve lower event handling latency in large networks, but with the trade-off of a lower event handling throughput due to the computing power limitation on physical switches. In summary, we have shown that using high level northbound API in the control network can make the control network more efficient, and leads to better scalability.

Dynamic spectrum reallocation, under which the spectrum owners temporarily share the underutilized spectrum to secondary users for economic profit, is an important approach to improve the spectrum utilization ratio. Auction is believed to be a natural marketing tool to incentivize the spectrum owners, and thus redistribute the idle spectrum efficiently. Extensive researches have been done in the problem of truthful spectrum auction, in which the bidders bid based on their true valuations of the spectrum. The true valuation of the individual bidder, however, is a private information which should be protected against exposure. In this paper, we propose a privacy preserving truthful spectrum auction scheme by utilizing homomorphic encryption. The proposed scheme reveals the group bids but hides the users' bids even from the auctioneer. The evaluation results show that the proposed scheme achieves good spectrum utilization efficiency with low communication and computation overheads.

Network virtualization (NV) is a promising solution for higher resource utilization, improved system performance, and lower investment capitals for network operators. Spectrum sharing is an important issue for NV in the wireless networks. Meanwhile, the scheme of Almost Blank Subframe (ABS) causes new challenge for NV in the heterogeneous cellular networks (HetNet). This paper aims at investigating the joint optimization problem of spectrum sharing and ABS adaptation, and the optimization target is represented through general utility functions of logical virtual operators (LVOs). We formulate the problem, and decouple it into two subproblems. We propose a dynamic programming based algorithm for the spectrum sharing subproblem, and an alternating direction method of multipliers (ADMM) based algorithm for the ABS adaptation subproblem. The simulation results demonstrate the efficiency of the proposed algorithm.

In cognitive radio (CR) networks, “TTR”, a.k.a. time-to-rendezvous, is one of the most important metrics for evaluating the performance of a channel hopping (CH) rendezvous protocol, and it characterizes the rendezvous delay when two CRs perform channel hopping. There exists a trade-off of optimizing the average or maximum TTR in the CH rendezvous protocol design. On one hand, the random CH protocol leads to the best “average” TTR without ensuring a finite “maximum” TTR (two CRs may never rendezvous in the worst case), or a high rendezvous diversity (multiple rendezvous channels). On the other hand, many sequence-based CH protocols ensure a finite maximum TTR (upper bound of TTR) and a high rendezvous diversity, while they inevitably yield a larger average TTR. In this paper, we strike a balance in the average-maximum TTR trade-off for CR rendezvous by leveraging the advantages of both random and sequence-based CH protocols. Inspired by the neighbor discovery problem, we establish a design framework of creating a wake-up schedule whereby every CR follows the sequence-based (or random) CH protocol in the awake (or asleep) mode. Analytical and simulation results show that the hybrid CH protocols under this framework are able to achieve a greatly improved average TTR as well as a low upper-bound of TTR, without sacrificing the rendezvous diversity.

This paper presents the analysis of outage probability for dual-hop orthogonal frequency division multiplexing (OFDM) relay system with ordered subcarrier mapping (OSM) in Nakagami-m fading. Accurate closed-form expressions are derived for the outage probability while considering a fixed gain amplify-and-forward (AF) relay system. Two special cases of Nakagami-m fading, i.e., Rayleigh (m = 1) and one-sided Gaussian (m = 1/2) are, specifically, focused with capacity-enhancement subcarrier pairing scheme and their performance is analyzed for balanced links, i.e., when SNR of each hop is same, as well as for unbalanced links, i.e., when the SNR of second hop is one-half of the first hop SNR, scenarios. The later which is known as the worst case of fading is, especially, focused for various analysis of the system. The variation of outage probability is shown for different values of fading severity (m) and number of resolvable paths (L) parameters. Numerical results validate analytical work in Nakagami-m fading channel model.

Existing multi-hop routing protocols for vehicular ad hoc networks (VANETs) do not consider the packet payload size and some important MAC layer issues for the route selection. In this paper, we first solve the performance anomaly problem by providing the same transmission time for different nodes which have different channel qualities using an adaptive frame aggregation mechanism. Next, we propose a packet size-aware routing protocol where a communication route is determined by taking into account the payload size of data packets. We consider multiple metrics for the route selection specifically vehicle mobility, frame aggregation efficiency, and link quality. The proposed protocol is evaluated using real-world experiments as well as computer simulations.

Interference management is one of the most important issues in the heterogeneous cellular networks (HetNet) with macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. The adaptive eICIC configuration problem is studied in this paper to adjust the parameters including the ratio of Almost Blank Subframes (ABS) and the bias of cell range expansion (RE). We formulate the problem as a general form consensus problem with regularization, and solve the problem by providing an efficient distributed optimization framework. Our algorithm is based on the alternating direction method of multipliers (ADMM) in which the solutions to local subproblems on each macro cell and pico cell are coordinated to find a solution to the global problem for the whole network. We also propose the dynamic programming based algorithms to solve the local subproblems on macro cell or pico cell. The simulation results demonstrate the efficiency of the proposed algorithm compared with existing approaches, and verify the convergence properties of the proposed algorithm.

This paper presents a new analytical model to analyze the performance of a probabilistic caching scheme with various cache replacement policies in content-centric networks. The cache replacement policies include Random Replacement (RR), First In First Out (FIFO), and Least Recently Used (LRU). This analytical model is based on Markov chains under Independent Reference Model (IRM) and Zero Download Delay (ZDD) assumption. A closed-form expression of the stationary distribution of cache state is derived and is used to compute the hit rates of caching systems. Moreover, we use this model to establish several important properties of the probabilistic caching scheme as well as the guidelines on effectively using it. Results of computer simulations show that the proposed analytical solution can model the probabilistic caching scheme very accurately.

We propose a routing protocol for drive-thru Internet access in delay tolerant vehicular networks. The contribution of this paper is two-folds. First, we propose a new approach which utilizes the concept of delay tolerant network (DTN) to supplement the conventional communication approach. Second, we propose an algorithm to schedule DTN transmissions in order to maximize the system throughput. The proposed protocol is able to attain higher TCP throughput than the conventional approach by providing more efficient wireless resource utilization. We use theoretical analysis and computer simulations to evaluate the proposed protocol.

This paper investigates the secure information transfer issue for cognitive radio networks that have multiple non-altruistic primary users, secondary users and eavesdroppers. The design objective is to improve the secrecy rates of the primary users, and create the transmission opportunities for the secondary users. To achieve this goal, we propose to incentivize the non-altruistic users to cooperate by a barter-like exchange. Specifically, the primary users leverage the assist of the secondary users in the form of cooperative transmitting or friendly jamming, and in return, yield certain licensed spectrum accessing time to the aided secondary users. We propose a truthful Double Auction mechanism for Secure Information transfer in cognitive radio networks, namely DASI, to jointly formulate the cooperator/jammer assignment and the corresponding resource allocation problems. We prove that DASI preserves nice economic properties that are critical for the auction design, including truthfulness, individual rationality and budget balance. We also evaluate DASI in terms of aggregated throughput and spectrum utilization ratio by simulations.

This paper addresses the spectrum leasing issue in cognitive radio networks by exploiting the primary user's cooperative ARQ (automatic repeated-request). To incentivize the otherwise non-cooperative users, we propose a novel trading model to foster the cooperation in the context of cooperative retransmitting. By formulating the network as a Stackelberg game, we maximize the utilities of both primary and secondary users in terms of transmission rates and revenues. We analyze the existence of the unique Nash equilibrium of the game, and give the optimal solutions with corresponding constraints. Numerical results demonstrate the efficiency of the proposed framework, under which the performance of the whole system could be substantially improved.

Designing an efficient multi-hop broadcast protocol is very important for the realization of collision avoidance systems and other many interesting applications in vehicular ad hoc networks (VANETs). Existing protocols are optimized for a specific scenario, and are not capable of working in various scenarios. Therefore, designing an intelligent protocol which can tune itself in relation to the change of network environment is particularly important. In this paper, we propose a broadcast protocol which is able to make forwarding decision based on a self-learning mechanism. The protocol employs a fuzzy logic-based relay node selection approach to take into account multiple metrics for the forwarding algorithm. The parameters used for the fuzzy logic are tuned online using a reinforcement learning approach. Transfer learning is used to transfer knowledge to new arriving vehicles (agents) in order to shorten the convergence time. The combination of reinforcement learning, transfer learning and fuzzy logic can provide an intelligent solution for broadcasting in VANETs. We conduct computer simulations to evaluate the proposed protocol.

Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, multi-user computation partitioning problem (MCPP), which considers the partitioning of multiple users’ computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namelySearchAdjust, to solve MCPP. We demonstrate through benchmarks thatSearchAdjustoutperforms both the single user partitioning approaches and classical job scheduling approaches by 10 percent on average in terms of application delay. Based onSearchAdjust, we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces.

In vehicular ad hoc networks (VANETs), due to the limited radio propagation range of wireless devices, many safety applications require a multihop broadcast protocol to disseminate traffic warning information. However, providing an efficient multi-hop forwarding of broadcast messages has been a challenging problem due to vehicle movement, limited wireless resources, and unstable signal strength. In this paper we propose a broadcast protocol that can provide a low message overhead and a high packet dissemination ratio. The proposed scheme uses a fuzzy logic algorithm to choose the next hop relay nodes and uses network coding to improve the packet dissemination ratio without increasing the message overhead. By using the fuzzy logic algorithm, the protocol can choose the best relay node by taking intervehicle distance, vehicle velocity, and link quality into account. Network coding is used to improve the packet reception ratio by utilizing the broadcast nature of wireless channels. We show the effectiveness of the proposed scheme by using both theoretical analysis and computer simulations.

Multihop data dissemination in vehicular ad hoc networks (VANETs) is very important for the realization of collision avoidance systems and many other interesting applications. However, designing an efficient data dissemination protocol for VANETs has been a challenging issue due to vehicle movements, limited wireless resources, and the lossy characteristics of wireless communication. In this paper, we propose a protocol that can provide a lightweight and reliable solution for data dissemination in VANETs. The protocol employs dynamically generated backbone vehicles to disseminate broadcast packets to reduce the MAC-layer contention time at each node while maintaining a high packet dissemination ratio by taking into account vehicle movement dynamics and the link quality between vehicles for the backbone selection. The protocol also uses network coding to reduce the protocol overhead and to improve the packet reception probability as compared with conventional approaches. We use theoretical analysis and computer simulations to show the advantage of the proposed protocol over other existing alternatives.

Scalable video coding (SVC) is attractive technology for multicasting video to users with different available transmission capacities. In this paper, we investigate the joint optimization of resource allocation and user association problems for SVC multicast over heterogeous cellular networks (HetNet) employing the schemes of cell range expansion (RE) and almost blank subframe (ABS). We solve the joint optimization problem by decoupling it into two problems, namely, resource allocation (RA) subproblem and user association (UA) master problem. For the RA subproblem, we propose a dynamic programming based algorithm to optimally set the transmission profile. For the UA master problem, we propose a similarity-based negotiation protocol (SBNP) based algorithm to obtain the Pareto-optimal range expansion bias. The simulation results demonstrate the efficiency of these algorithms.

In vehicular ad hoc networks (VANETs), multi-hop broadcast communications are required for many applications including driver assistance systems. However, providing a low end-to-end latency has been very challenging. In this paper, we propose a joint MAC network layer multi-hop broadcast protocol. For the network layer, the proposed protocol reduces the number of sender nodes by using common forwarder nodes for different traffic flows. At the MAC layer, the proposed protocol uses a Q-Learning algorithm to adjust the contention window size. By interacting with the environment, the protocol can find the best contention window size to transmit data packets and therefore can provide a high packet dissemination ratio and low end-to-end delay for various scenarios. The simulation results demonstrate the advantage of the proposed protocol over other existing alternatives.

Packet delivery ratio and end-to-end delay are the two most important metrics for vehicular ad hoc network applications. In this paper, we propose a MAC layer protocol which can provide a high packet delivery ratio, low end-to-end delay, and high fairness for various scenarios. The proposed protocol uses a Q-Learning algorithm to adjust the contention window size in order to provide an efficient channel access scheme for various network situations. The simulation results demonstrate the advantage of the proposed protocol over other alternatives.

In wireless sensor networks (WSNs), the multi-hop MAC protocol combines duty cycling radio and forwarding packet via multiple hops (i.e., multi-hop forwarding) to achieve a good balance between energy and latency efficiency. The multi-hop MAC protocol has been proven to outperform other traditional duty cycling protocols, which allow forwarding a packet at most one hop in an operational cycle. Among state-of-the-art multi-hop protocols, M AC2, which additionally leverages packet concatenation technique, achieves the best performance in normal environments (i.e., without considering noisy). The concatenation technique, which lets several small packets be concatenated into a bigger one before sending out at a node, effectively saves control overhead and shortens delivery latency in WSNs. However, the packet concatenation may downgrade the performance of the network in the noisy environment since the cost for retransmission is high. In this paper, we investigate the negative effect of the noisy level of environment on the efficiency of M AC2. We observe that in the noisy environment, M AC2still keeps the reasonable performances, which are better than the one without concatenation (i.e., the demand-wake up MAC DW-MAC). The simulation results using ns-2 confirm our observations.

Emerging information-centric networking architectures drives a wider focus of content caching mechanisms. Most recent efforts have been attempting for server and user friendliness, i.e., reduction in content-server load and content-delivery time. Those solutions require the prior knowledge of cache state and content popularity exchanged among routers. Challenging tasks are pushed to routers since their explicit coordination is required. Instead of burdening routers, but still keeping the effectiveness of in-network caching, we move the explicit coordination task from routers to a cache orchestrator where the offline optimal caching policy is computed while leaving the simplest cache decision to the routers. By means of the network function virtualization, this paper proposes a cache orchestration lifecycle including three parts, i.e., name hit caching (NHC) policy, pending time history and network cache orchestration. By exploiting NHC policy, a router simply caches the content whose name matches its assigned content name set. Evaluation through simulations demonstrates that NHC policy achieves the lowest content-server load and content downloading time and the highest cache hit ratios in all routers comparing with leaving copy everywhere, probability caching and content-popularity-based cache orchestration policies.

This paper presents the analysis of BER and ergodic capacity for dual-hop orthogonal frequency division multiplexing (OFDM) Amplify-and-forward (AF) relay system with subcarrier mapping (SCM) in Nakagami-m fading channel for m ≤ 1. The performance of capacity-enhancement optimal pairing scheme, i.e., Best-to-Best (BTB) SCM scheme is analyzed for the two special cases of Nakagami-m fading, i.e., one sided Gaussian fading and Rayleigh fading. The exact and approximate closed-form expressions for the moment generating function (MGF) of end-to-end SNR are derived. The upper bound for ergodic capacity is also derived. The simulation results validate the analysis in Nakagami-m fading channel.

WiFi-Direct, a Wi-Fi standard, enables devices to connect easily with each other without requiring a wireless access point and communicate at typical WiFi speeds for file transfer, Internet connectivity, etc. It is widely used in many applications such as the traffic local sharing. The loss model of WiFi-direct link can facilitate the theoretical analysis when designing or optimizing the systems hence it is an important issue to be investigated. But as far as we understand, no formal work has been done to analyse the WiFi-Direct channels. In this paper, we set up the experiments connecting two mobile devices using WiFi-Direct and analyzed the performance of the loss models in literature. A new model based on Gilbert-Elliot model was proposed thereafter and evaluated with likelihood criterion. The new model turned out to outperform others in the evaluation.

This paper introduces a cross-layer implementation, named WiPoMu, that aims to improve efficiency and resilience of the traditional WiFi model. WiPoMu is developed based on three key technologies: Wireless virtualization, Policy routing, and Multipath TCP (MPTCP). Specifically, WiPoMu adopts the wireless virtualization to create multiple virtual WiFi interfaces on a single physical one, hence WiPoMu enables concurrent connections with different access points (APs). In addition, WiPoMu leverages the policy routing and MPTCP in order to efficiently direct and schedule traffic flows over multiple virtual interfaces. On the other hand, WiPoMu is transparent to users since it requires no modification in the physical and application layers. We have conducted evaluations to validate the efficiency of WiPoMu on an indoor testbed and a real home network. The evaluation results show that a WiFi client equipped WiPoMu is able to establish multiple active paths to Internet across different APs. Besides that, WiPoMus is resilient to path failure by achieving seamless handover between the active paths. Furthermore, WiPoMu improves up to 300% aggregated throughput comparing to the traditional WiFi model using TCP in the testbed.

This paper deals with the retransmission protocol design for cognitive radio networks by exploiting the primary hybrid ARQ. In contrast with previous work that focuses on cancellation based retransmissions, we propose a novel cooperative coding based retransmission protocol for cognitive radio networks. The design objective is to improve the throughput of the primary user and create the transmission opportunity for the secondary user. By exploiting the primary retransmission appropriately, the knowledge on primary packet which is required by the cooperative coded retransmission can be obtained without any non-causal assumption. Performances on the proposed protocol are analyzed mathematically, and verified by numerical results.

In vehicular ad hoc networks (VANETs), multi-hop broadcast protocols are required for many applications including driver assistance systems. However, providing a low end-to-end latency has been very challenging. In this paper, we propose a protocol which can provide a fast channel access for broadcast traffic flows in VANETs. The protocol introduces new control messages to reserve time slots for the forwarder nodes and eliminate the hidden terminal problem. By providing a contention-free multi-hop forwarding scheme based on reliably generated forwarding backbone, the protocol can provide low end-to-end delay and high packet dissemination ratio. The theoretical analysis and simulation results demonstrate the advantage of the proposed protocol over other existing alternatives.

This paper presents an optimal cooperative routing protocol (OCRP) for Content Centric Networking (CCN) aiming to improve the in-network cache utilization. The objective of OCRP is to selectively aggregate the multiple flows of interest messages onto the same path. This improves the cache utilization while mitigating the cache contention in the Content Store (CS) of CCN routers on the routing path. The optimal routing path is obtained by binary linear optimization under threes constraints: flow conservation constraint, cache contention mitigating constraint, and path length constraint. Our simulation results of OCRP show the reduction in the server load and round-trip hop distance in comparison to those of the shortest path routing and our previously proposed cooperative routing schemes.

The Content-Centric Networking (CCN) architecture exploits a universal caching strategy whose inefficiency has been confirmed by research communities. Various caching schemes have been proposed to overcome some drawbacks of the universal caching strategy but they come with additional complexity and overheads. Besides those sophisticated caching schemes, there is a probabilistic caching scheme that is more efficient than the universal caching strategy and adds a modest complexity to a network. The probabilistic caching scheme was treated as a benchmark and the insights into its behavior have never been studied despite its promising performance and feasibility in practical use. In this paper we study the probabilistic caching scheme by means of computer simulation to explore the behavior of the probabilistic caching scheme when it works with various cache replacement policies. The simulation results show the different behavioral characteristics of the probabilistic caching scheme as a function of the cache replacement policy.

This paper investigates the secure information transfer issue for cognitive radio networks by exploiting the spectrum leasing technique. The design objective is to improve the secrecy rate of the primary user, and meanwhile, create the transmission opportunities for the secondary users. To achieve this goal, we consider a system model where the primary user harnesses the assist of the secondary users in the form of cooperative transmitting. And in return, the primary user provides certain transmission opportunities over licensed spectrum for the cooperating secondary users. We propose an auction-based spectrum leasing scheme to jointly formulate the optimal cooperator selection and resource allocation problems. By analyzing and solving the dominant strategy equilibrium for the proposed scheme, we present reliable predictions for the system behavior and the achievable performances. Simulation results reveal that the proposed scheme could provide substantial gains for both the primary user and the cooperating secondary user.

This paper investigates the joint optimization problem of user scheduling, user association, and resource partition in heterogeneous cellular networks (HetNet) with a general concave utility function used as the performance metric. We formulate the joint optimization problem, and decouple the problem into three sub problems. After proving the sub problems belong to the set of problems that maximizes a monotone sub modular set function with mastoid constraint, we solve them by the proposed greedy based algorithms with theoretical approximation factors. Extensive simulation results demonstrate the efficiency of the proposed algorithms in terms of system utility. In addition, we evaluate some assumptions and results in the related work to show their impacts and correctness.

Next-generation wireless networks integrate multiple wireless access technologies to provide seamless wireless connectivity for mobile nodes (MNs). When MNs move in wireless heterogeneous networks, they may suffer from the great degradation of received signal strength (RSS) and further quality of services (QoS), if randomly selecting an access point (AP). We address vertical handoff with game theory to enable MNs to trigger the handoff and select an appropriate network from multiple wireless access technologies. On the other hand, the existing vertical handoff schemes lack of jointly considering the behaviors of MNs and APs for approaching the reality. To solve this problem, we propose a repeated game based scheme for vertical handoff. Each sub-game is formulated as a non-cooperative strategic game between a MN and an AP in which the Nash equilibrium is the solution of each strategic game. The proposed repeated game is to optimize the utility functions of the whole network by finding an equilibrium point. We perform the performance analysis, which shows that proposed scheme can achieve better bandwidth utilization and throughput of the network compared to the AP random selection scheme.

Video is playing a more and more important role in future Vehicular Ad-hoc Network (VANET) communication. It is a feasible medium for information sharing and entertainment (infotainment) with its high capacity, consistency and influence on human beings. This paper introduces the SVC coding scheme to VANET video streaming. We propose an Optimal Scheduling Algorithm (OSA) for video streaming in highway VANET scenarios using scalable video coding (SVC). We conduct extensive simulations to evaluate the performance of OSA. Simulation results show that OSA outperforms all the competing schemes over variant density and velocity highway VANET.

Heterogeneous networks (HetNets) are intended to offer wide area coverage and high data rate transmission by deploying small cells besides macrocells. One way to improve the system throughput is intercell interference coordination. The coordination methods in literature use the system throughput as the only selection criteria without considering each user's data rate requirement and investigate the intercell interference only during the period when small cell's edge users are served. In this paper, we propose an algorithm to maximize the users' data rate satisfaction ratio in HetNet leveraging on the two existing intercell interference coordination schemes ABS and CoMP. Intensive simulations are conducted and the results demonstrate that our proposed scheme achieves considerable gains over competing schemes in terms of the data rate satisfaction ratio and the system capacity in Config.4b scenarios defined by 3GPP.

Most work in vehicular ad hoc network focuses on an efficient mechanism to deliver data. These mechanisms need a beacon to exchange all necessary information between neighbors. Although a beacon is a small packet that is periodically broadcast to maintain accuracy, too much information can cause a bulky beacon. This leads to a contention problem due to limited resources in wireless networks. Many approaches are proposed to avoid the problem by reducing the frequency of beacon broadcasting. However, bulky beacons still exist. In this paper, we propose a solution using a Bloom filter to create fixed-size beacons. A single Bloom filter can replace all variable size data structures. Our solution reduces the complexity of a connected dominating set algorithm from O(n^5) to O(n). The evaluation indicates that our proposed solution can significantly reduce the beacon overhead without decreasing the protocol performance.

Multi-hop data dissemination in vehicular ad hoc networks (VANETs) is very important for the realization of collision avoidance systems and other many interesting applications. However, designing an efficient data dissemination protocol in VANETs has been a challenging issue due to vehicle movements, limited wireless resources and lossy characteristics of wireless communication. In this paper, we propose a protocol which employs intra-flow and inter-flow network coding to reduce the protocol overhead as compared to traditional protocols. The protocol also can improve the packet reception probability at the receiver nodes by using the network coding. Therefore, the protocol can provide a lightweight and reliable solution for data dissemination in VANETs. We use theoretical analysis and computer simulations to show the advantage of the proposed protocol over other existing alternatives.

Many routing protocols in vehicular ad hoc networks (VANETs) utilize location information to find a route to the destination. However, tracking the location information of other nodes is very challenging in highly dynamic VANETs. We propose a location service which can provide location information with low overhead and low delay. The location service periodically disseminates the location of each vehicle to 3-hop distance for every second with very low overhead. By eliminating location errors by taking account of the velocity of a vehicle, the proposed protocol can provide accurate position information. The location service also provides a lightweight location query mechanism for longer distance destination nodes. We show the effectiveness of the protocol by using theoretical analysis and computer simulations.

Many researchers have recently proposed using LTE to support traffic safety and transportation efficiency applications in vehicular ad hoc networks (VANETs). The deviceto- device (D2D) communication technique underlay LTE network has been considered as an effective way to potentially conduct vehicle-to- vehicle (V2V) communications. Since the vehicular communication environment has different characteristics compared with conventional cellular systems, efficient resource allocation strategies for D2D communications in VANETs are demanded. In this paper, we propose two such algorithms aiming at minimizing the consumption of cellular radio resources. We divide the vehicles on a road into multiple clusters, each of which utilizes a single cellular user's resource. One of the proposed algorithms is constructed based on the dynamic programming technique to search the optimal clustering solution. The other seeks to solve the resource allocation problem with low computing complexity. The performances of the algorithms are exhibited through simulations under practical traffic environments.

Device-to-device (D2D) communication underlaying a cellular infrastructure has been proposed as a means of facilitating rich local services and offloading the base station traffic. However, D2D communication presents a challenge in radio resource management due to the potential interference it may cause to the cellular network. In this paper, the joint optimization problem of D2D mode selection, modulation and coding schemes (MCSs) assignment, radio resources and power allocation is formulated to minimize the overall power consumption under minimum required rate guarantee. The problem is decoupled into two sub-problems which are solved by Lagrangian relaxation and tabu search methods, respectively. Simulation results show its performance superiority over other schemes, especially in the scenarios with high required rate and limited resources.

We address the route optimization problem for a nested mobile IPv6 NEtwork MObility (NEMO) environment. We propose an adaptive scheme which can optimize the routing process of the data communication, and minimize the end-to-end delay. The adaptive scheme consists of two sub-schemes: mobility-transparency sub-scheme and time-saving sub-scheme. The mobility-transparency sub-scheme performs well for the high mobility scenarios, while the time-saving sub-scheme performs well for the low mobility and large communication traffic scenarios. A threshold is used to determine which sub-scheme should be applied for the current situation. Theoretical analysis and simulation results demonstrate that the proposed scheme can reduce the end-to-end delay of data communication for nested mobile IPv6 NEMO environment significantly.

We consider the detection of activities from non-cooperating individuals with features obtained on the radio frequency channel. Since environmental changes impact the transmission channel between devices, the detection of this alteration can be used to classify environmental situations. We identify relevant features to detect activities of non-actively transmitting subjects. In particular, we distinguish with high accuracy an empty environment or a walking, lying, crawling or standing person, in case-studies of an active, device-free activity recognition system with software defined radios. We distinguish between two cases in which the transmitter is either under the control of the system or ambient. For activity detection the application of one-stage and two-stage classifiers is considered. Apart from the discrimination of the above activities, we can show that a detected activity can also be localized simultaneously within an area of less than 1 meter radius.

Using texture and depth maps of a single reference viewpoint, depth-image-based rendering (DIBR) can synthesize a novel viewpoint image by translating texture pixels of the reference view to a virtual view, where synthesized pixel locations are derived from the associated depth pixel values. When the virtual viewpoint is located much closer to the 3D scene than the reference view (camera movement in the z-dimension), objects closer to the camera will increase in size in the virtual view faster than objects further away. A large increase in object size means that a patch of pixels sampled from an object surface in the reference view will be scattered to a larger spatial area, resulting in expansion holes. In this paper, we investigate the problem of identification and filling of expansion holes. We first propose a method based on depth histogram to identify missing or erroneously translated pixels as expansion holes. We then propose two techniques to fill in expansion holes with different computation complexity: i) linear interpolation, and ii) graph-based interpolation with a sparsity prior. Experimental results show that proper identification and filling of expansion holes can dramatically outperform inpainting procedure employed in VSRS 3.5 (up to 4.25dB).

Visual saliency is a probabilistic estimate of how likely a given spatial area in an image or video is to attract human visual attention relative to other areas. Bottom-up saliency models aggregate low-level image features like luminance and color contrast, flicker, 2D motion, etc. to construct a plausible saliency map. In this paper, we introduce 3D motion (object movements towards or away from the observer) into bottom-up video saliency modeling. Given availability of per-pixel depth maps, we first propose a novel algorithm to estimate 3D motion vectors (3DMVs) for arbitrarily shaped sub-blocks in texture-plus-depth videos. We then derive two feature channels from 3DMVs to be incorporated into a widely accepted bottom-up saliency model. Experiments on subjective quality of Region-of-Interest (ROI) based video coding show that our enriched saliency model with 3DMV channels is more accurate in estimating human visual attention.

In this paper, we study the network Lifetime Maximization problem in Mobile healthcare sensor systems (LMM). For the healthecare system, we consider a dynamic scenario where users are mobile at their own wills and periodically report their personal health information (PHI) to a static sink, e.g. a powerful server, for further processing and distributing. The objective is to optimize the network lifetime by flow scheduling. The major difficulty lies in the time-dependent network topologies. Therefore, we propose a novel temporal-spatial network modeling method by extending current model with time dimension. Based on this model, we show that if the movement of users are known in advance (i.e. offline case), the problem can be optimally solved in polynomial time by a linear programming. However, the online LMM problem is much more difficult to tackle, since we prove that there exists no online algorithm with a constant performance ratio to the offline optimal algorithm in terms of the network lifetime. We further design simulations to show the performance gap between online and offline LMM. Considering the user mobility within a given scenario follows some certain patterns, we show the potential improvements of using a prediction-based method. This investigation provides certain insights on designing efficient online algorithms for the LMM problem.

The current coding standard employs differential coding to reduce the coding rate. Decoding error may propagate in the following frames after a loss-unrecoverable frame. Since most of the current electronic devices are implemented with multiple interfaces, such as the 3G and WiFi, one way to solve the error propagation problem is to use cooperative strategies, which leverage on the ‘uncorrelatedness’ of the clients' channels (different channels may experience different channel losses). The cooperative strategies helping to repair the primary network's (base station to client) losses via the neighboring clients' packets sharing (client to client) through the second network interface and optimally allocating the modulation and coding schemes to the limited network resource can improve the clients' received quality massively. The inherent problem is how to solve the resource allocation problem with the cooperative strategies. In this paper, we jointly consider the resource allocation and the cooperative repair for the video streaming. We formulate the problem into the optimization problem, i.e. maximizing the clients' received video quality, and apply the structured network coding to further improve the overall performance. Simulation results show that the proposed approach outperforms the other competing schemes by at least 1.5dB in term of the received video quality in typical network scenarios.

A crucial challenge in the implementation of cooperative protocol is how to assign relay nodes properly. In this paper, we address the relay assignment issue in the wireless networks where the source nodes, destination nodes and relay nodes are randomly distributed in a large area. Without any kind of central controller, we try to solve the relay assignment problem in a distributed manner with the local information. Based on this network model, we developed a Distributed Genetic Algorithm (DGA) based scheme to pursue a better system capacity compared with other distributed algorithms. We also presented a brief discussion and analysis on the DGA algorithm. At last, extensive simulations show that the DGA outperforms other distributed algorithms with obvious improvement of the system capacity and converges fast.

Due to a mismatch between downloading and caching content, the network may not gain significant benefit from the sophisticated in-network caching of information-centric networking (ICN) architectures by using a basic caching mechanism. This paper aims to seek an effective caching decision policy to improve the content dissemination in ICN. We propose PopCache-a caching decision policy with respect to the content popularity-that allows an individual ICN router to cache content more or less in accordance with the popularity characteristic of the content. We propose an analytical model to evaluate the performance of different caching decision policies in terms of the server-hit rate and expected round-trip time. The analysis confirmed by simulation results shows that PopCache yields the lowest expected round-trip time compared with three benchmark caching decision policies, i.e., the always, fixed probability and path-capacity-based probability, and PopCache provides the server-hit rate comparable to the lowest ones.

A typical Forwarding Information Based (FIB) construction in the Content Centric Networking (CCN) architecture relies on the name prefix dissemination following the shortest path manner. However, routing based on the shortest path may not fully exploit the benefits of forwarding and data planes of the CCN architecture since different content requester routers may use disjoint paths to forward their interest packets, even though these packets aim at the same content. To exploit this opportunity, we propose a cooperative routing protocol for CCN, which focuses on a FIB reconstruction based on the content retrieval statistics to improve the in-network caching utilization. A binary linear optimization problem is formulated for calculating the optimal path for the cooperative routing. The simulation results show an improvement in the server load and round-trip time provided by the cooperative routing scheme compared with that of the conventional shortest path routing scheme.

We consider the recognition of activities from passive entities by analysing radio-frequency (RF)-channel fluctuation. In particular, we focus on the recognition of activities by active Software-defined-radio (SDR)-based Device-free Activity Recognition (DFAR) systems and investigate the localisation of activities performed, the generalisation of features for alternative environments and the distinction between walking speeds. Furthermore, we conduct case studies for Received Signal Strength (RSS)-based active and continuous signal-based passive systems to exploit the accuracy decrease in these related cases. All systems are compared to an accelerometer-based recognition system.

As one of the key technical challenges in long term evolution (LTE)-Advanced heterogeneous networks (HetNets) is intercell interference coordination, many different schemes, such as coordinated multipoint (CoMP) and almost blank subframe (ABS) schemes, have been proposed in this field. Different cells are pushed to cooperate to eliminate intercell interference in CoMP coordinated beamforming, but performance is heavily dependent on the quality of channels. ABS schemes allow highpower cells to transmit almost blank subframes, and avoid interference with low-power cell edge users, which will in turn result in reduced high-power cell throughput as cost. An adaptive ABS-CoMP scheme in an LTE-Advanced HetNet is proposed, which combines both the ABS and CoMP coordinated beamforming schemes to eliminate intercell interference, and adaptive transmits signals in different ways. It is assumed to improve system throughput and mitigate signaling overhead and computational complexity. The results from simulations demonstrated that the proposed scheme outperformed the ABS and CoMP schemes, and achieved considerable system throughput gains in Config.4b scenarios defined by 3GPP.

Device-to-device (D2D) communications as underlays of cellular networks facilitate diverse local services and reduce base station traffic. However, D2D communication may cause interference with the primary cellular network. To avoid this problem, the network should flexibly allocate its resources and select a proper mode for users. Here, we formulate a joint mode selection and resource allocation problem to maximize the system throughput with a minimum required rate guarantee. A mode selection and resource allocation scheme based on particle swarm optimization (PSO-MSRA) is proposed in which solutions are mapped onto particles and a fitness function embodies the constraints in a penalty function. Simulation results show its superiority over other schemes in terms of throughput and minimum required rate guarantee.

It is increasingly popular that there are multiple accessible access points (APs) surrounding a WiFi client. In an ideal case, the client would simultaneously connects to all the APs and maximize the connections' utilization, for example, aggregating the bandwidth of APs' backhauls, load balancing among the connections, etc. In this paper, we present WM (Wireless virtualization with Multipath TCP) a cross-layer approach that aims to improve performance of mobile WiFi users. The demonstration shows that aWiFi client equipped WM can keep multiple concurrent connections to APs by using wireless virtualization. Moreover, WM enhances the aggregated bandwidth and achieves seamless handover by adopting Multipath TCP.

In interactive multiview video streaming (IMVS), a user observes one view at a time, but can periodically switch to a desired neighboring captured view as the video is played back in time. Previous IMVS works focus on efficient compression techniques that facilitate interactive view switching. In this paper, in addition to the loss-resilient aspect during network streaming we address how to design efficient coding tools and optimize frame structure for transmission to facilitate view switching and contain error propagation in differentially coded video due to packet losses. We first design a new unified distributed source coding (uDSC) frame-a new coding tool that simultaneously offers view switching and loss-resilient capabilities-for periodic insertion into the multiview frame structure. After inserting uDSC-frames into the coding structure, we schedule packets for network transmission in a rate-distortion optimal manner for both wireless multicast and wired unicast streaming scenarios. For wireless multicast over a Gilbert-Elliott loss model, frames in a group of pictures are packetized and reordered, so that uDSC frames are correctly decoded with high probability, mitigating error propagation. For wired unicast, we use a Markov decision process to optimize packet transmission to minimize expected distortion given a bandwidth constraint. Experimental results show that systems that insert uDSC frames and optimize packet transmission can outperform other competing coding schemes by up to 2.8 and 11.6 dB in wireless multicast and wired unicast streaming scenarios, respectively.

Eye gaze is now used as a content adaptation trigger in interactive media applications, such as customized advertisement in video, and bit allocation in streaming video based on region-of-interest (ROI). The reaction time of a gaze-based networked system, however, is lower-bounded by the network round trip time (RTT). Furthermore, only low-sampling-rate gaze data is available when commonly available webcam is employed for gaze tracking. To realize responsive adaptation of media content even under non-negligible RTT and using common low-cost webcams, we propose a Hidden Markov Model (HMM) based gaze-prediction system that utilizes the visual saliency of the content being viewed. Specifically, our HMM has two states corresponding to two of human's intrinsic gaze behavioral movements, and its model parameters are derived offline via analysis of each video's visual saliency maps. Due to the strong prior of likely gaze locations offered by saliency information, accurate runtime gaze prediction is possible even under large RTT and using common webcam. We demonstrate the applicability of our low-cost gaze prediction system by focusing on ROI-based bit allocation for networked video streaming. To reduce transmission rate of a video stream without degrading viewer's perceived visual quality, we allocate more bits to encode the viewer's current spatial ROI, while devoting fewer bits in other spatial regions. The challenge lies in overcoming the delay between the time a viewer's ROI is detected by gaze tracking, to the time the effected video is encoded, delivered and displayed at the viewer's terminal. To this end, we use our proposed low-cost gaze prediction system to predict future eye gaze locations, so that optimized bit allocation can be performed for future frames. Through extensive subjective testing, we show that bit-rate can be reduced by up to 29% without noticeable visual quality degradation when RTT is as high as 200 ms.

This paper exploits sink mobility to prolong the network lifetime in wireless sensor networks where the information delay caused by moving the sink should be bounded. Due to the combinational complexity of this problem, most previous proposals focus on heuristics and provable optimal algorithms remain unknown. In this paper, we build a unified framework for analyzing this joint sink mobility, routing, delay, and so on. We discuss the induced subproblems and present efficient solutions for them. Then, we generalize these solutions and propose a polynomial-time optimal algorithm for the origin problem. In simulations, we show the benefits of involving a mobile sink and the impact of network parameters (e.g., the number of sensors, the delay bound, etc.) on the network lifetime. Furthermore, we study the effects of different trajectories of the sink and provide important insights for designing mobility schemes in real-world mobile WNNs.

ABSTRACT This paper deals with the partial target coverage problem in wireless sensor networks under a novel coverage model. The most commonly used method in previous literature on the target coverage problem is to divide continuous time into discrete slots of different lengths, each of which is dominated by a subset of sensors while setting all the other sensors into the sleep state to save energy. This method, however, suffers from shortcomings such as high computational complexity and no performance bound. We showed that the partial target coverage problem can be optimally solved in polynomial time. First, we built a linear programming formulation, which considers the total time that a sensor spends on covering targets, in order to obtain a lifetime upper bound. Based on the information derived in previous formulation, we developed a sensor assignment algorithm to seek an optimal schedule meeting the lifetime upper bound. A formal proof of optimality was provided. We compared the proposed algorithm with the well‐known column generation algorithm and showed that the proposed algorithm significantly improves performance in terms of computational time. Experiments were conducted to study the impact of different network parameters on the network lifetime, and their results led us to several interesting insights. Copyright © 2011 John Wiley & Sons, Ltd.

By transmitting texture and depth videos from two adjacent captured viewpoints, a client can synthesize via depth-image-based rendering (DIBR) any intermediate virtual view of the scene, determined by the dynamic movement of the client's head. In so doing, depth perception of the 3D scene will be created through motion parallax. Due to the stringent playback deadline of interactive free viewpoint video, burst packet losses in the texture and depth video streams caused by transmission over unreliable channels are difficult to overcome and can severely degrade the synthesized view quality at the client. We propose a multiple description coding (MDC) of free viewpoint video in texture-plus-depth format that will be transmitted on two disjoint network paths. Specifically, we encode even frames of the left view and odd frames of the right view separately as one description and transmit it on path one. Similarly, we encode odd frames of the left view and even frames of the right view as the second description and transmit it on path two. Appropriate quantization parameters (QP) are selected for each description, such that its data rate matches optimally the available transmission bandwidth on each of the two paths. If the receiver receives one description but not the other due to burst loss on one of the paths, it can still partially reconstruct the missing frames in the loss-corrupted description using a computationally efficient DIBR-based recovery scheme that we design. Extensive experimental results show that our MDC streaming system can outperform the traditional single-path single-description transmission scheme by up to 7dB in Peak Signal-to-Noise Ratio (PSNR) of the synthesized intermediate view at the receiving client.

In wireless sensor networks (WSNs), MAC protocols utilize duty cycling to extend the network lifetime. Receiver-Initiated MAC (RI-MAC) is an asynchronous duty cycling protocol, which schedules transmissions based on receivers. By letting a receiver initiate the rendezvous for the transmissions between senders and the receiver, the protocol achieves good latency performance at the cost of high energy consumption at the senders. On the other hand, different types of traffic are very popular in WSNs. The MAC protocols are necessarily to be equipped QoS (quality of service) mechanisms to handle with the variation of traffic. In this paper, we propose AQ-MAC; a new asynchronous MAC protocol with QoS awareness. AQ-MAC adopts the receiver-initiated manner and provides QoS service per packet following the priority imposed. When a node has an incoming packet with a high priority, it immediately turns on the radio in order to wait for a transmission initiated by a receiver. Otherwise, the node keeps a low priority packet in a queue and sends out in a burst until a high priority packet comes or after a timeout value. In addition, AQ-MAC utilizes a packet concatenation scheme to improve the energy efficiency by reducing control overhead. We have evaluated AQ-MAC in multiple scenarios using ns-2. The results show that AQ-MAC adapts well with different types of traffic as well as achieves good performance in terms of energy efficiency and latency.

The resource allocation problem to support scalable-video multicast for wireless relay networks is complex due to the existence of the relay station. In this paper, we consider the resource allocation for SVC multicast over two-hop wireless relay networks to maximize the total system utility of all users where the system utility can be a general non-negative, non-decreasing function. We model the problem in three-layer structure (choice elements, action elements, and user elements) to cope with the joint dependency and overlapping phenomena. We formulate the problem as Cascaded Coverage Problem (CCP) and propose a greedy algorithm with polynomial time complexity. Simulation results show that our algorithm keeps good performance as compared with the optimal result. We also evaluate the influence of different user distribution types and the number of relay stations.

We introduce a passive system to recognise environmental situations. Differing from other RF-based approaches, our system has the advantage of neither installing a transmitter generating the signal nor equipping the monitored entities with any active component. When activities are performed, it consecutively samples ambient RF-signals, derived from a non-cooperating FM-radio source. Since changes in an environment impact the propagation of radio waves, this data implicitly contains information to distinguish environmental situations. We experimentally demonstrate the distinction of the situations 'empty room', 'opened door' and 'walking person' with an average accuracy of over 90%.

This paper exploits sink mobility to prolong the network lifetime in wireless sensor networks (WSNs) where the information delay caused by moving the sink should be bounded. We build a unified framework for analyzing this joint sink mobility and routing problem. We offer a mathematical modeling that is general and captures diversified issues, e.g. sink mobility, routing, delay, etc. We discuss the induced subproblems and present efficient solutions for them. Then, we generalize these solutions and propose a polynomial-time optimal algorithm for the origin problem. In simulations, we show the benefits of involving a mobile sink. We also show that the impact of the delay bound on the network lifetime.

Because of differential coding used in standard video compression algorithms to exploit temporal correlation in adjacent frames for coding gain a frame lost in network will cause error propagation in subsequent frames at the decoder Previously proposed distributed source coding (DSC) frames can be periodically inserted to halt this error propagation by overcoming the uncertainty at encoder of which frames will be correctly received at decoder without resorting to large intra-coded I-frames In the case of interactive multiview video streaming (IMVS) where a user watches one of M available captured views at a time but can periodically select and switch to a neighboring view the encoder must encode multiview video to enable this view-switching interactivity without knowing the exact view trajectories taken by viewers at stream time In this paper we propose a unified DSC frame construction for IMVS so that the encoder can overcome both types of uncertainty in a coding-efficient manner; ie halt error propagation in differentially coded multiview video and facilitate periodic interactive view-switching at the same time Having the additional unified DSC frames we design a multiview frame structure to maximize the expected number of correctly decoded frames at decoder for a given bandwidth constraint We develop a fast algorithm to find locally optimal structure parameters and packetization and packet reordering strategies for transmission Experimental results show that our optimized frame structures using unified DSC frames outperform naïve structures using I- and P-frames only by up to 49% in fraction of correctly decoded frames under typical network condition.

This paper proposes a novel approach of cross-layer resource allocation based on Quality of Experience (QoE) for wireless video transmission. We model and formulate the cross-layer resource allocation problem in accordance with the relationship between transmission bit rate and QoE. Our objective is to ameliorate the system QoE level while guaranteeing the fairness for users. The video quality fluctuation that affects the system QoE is considered as well. The proposed algorithm designs an adaptive resource allocation approach depending on limited network resources. We use a downlink LTE system as a simulation example of a high speed downlink access system. The simulation results demonstrate that the proposed method considerably and agreeably improves system QoE more than other algorithms.

Future 4G cellular networks are featured with high data rates and improved coverage, which will enable real-time video multicast and broadcast services. Scalable video coding with different modulation and coding schemes (MCSs) applied to different video layers is very appropriate for wireless multicast services because it can provide different video quality to different users according to their channel conditions and light-weight feedback on how many packets they have received. It is important to choose an appropriate MCS for each layer, decide how many parity packets in one layer should be transmitted, and determine the resources allocated to multiple video sessions to apply scalable video coding to wireless multicast streaming. This paper proposes an optimal algorithm that finds the optimal total system utility of all users where the utility can be a generic nonnegative, non-decreasing function of the received rate. The results from simulations revealed that our algorithm offer significant improvements to video quality over an optimal algorithm without feedback from users and a naïve algorithm especially in scenarios with multiple video session and limited resources.

Video multicast over wireless networks has its own challenges when facing the heterogeneity of networks and end-user capabilities, along with packet losses. Scalable video coding using different modulation and coding schemes (MCSs) applied to different video layers can provide different video qualities to different users according to their channel conditions. A layered hybrid NC/ARQ scheme is proposed in this paper to handle packet losses together with a structure network coding (SNC) technique to encode the SVC stream. It is important to choose an appropriate MCS for each layer, decide how many SNC packets in one layer should be transmitted, and determine the resources allocated to multiple video sessions to apply scalable video coding to wireless multicast streaming. We prove that such a resource allocation problem is NP-hard and propose an optimal algorithm with a pseudo-polynomial run time under a reasonable assumption. We also discuss the phenomenon of unexpected packet losses caused degradation of the layered hybrid NC/ARQ schemes in a high packet loss ratio environment, and propose a solution for overcoming such a problem. Our algorithm can attain the optimal transmission configuration for maximizing the expected utility for all receivers. The results from simulations revealed that our algorithm offers significant improvements to the video quality over an optimal algorithm without needing feedback from the receivers and an algorithm using a layered hybrid FEC/ARQ scheme, and it has the outstanding ability to combat unexpected packet losses.

Typical wireless sensor networks have restricted resources on memory capacity, computing/processing power, and energy supply. However, wireless sensor network has been increasingly applied to various applications and security in wireless sensor networks has become an essential and challenging issue. There is a wide range of security attacks in wireless sensor networks. A node replication attack is a type of attacks in wireless sensor networks. An attacker can be easily disguised in a wireless sensor network and acts as an intermediate node to intercept data packets. The attacker could be a clone node in the network by capturing nodes' ID, the pair-wise key etc. The base station may not be able to distinguish between good nodes and malicious nodes. In this paper, we present a new method, named Area-Based Clustering Detection (ABCD) method, for detecting node replication attacks. Our simulation results show that the proposed ABCD method can achieve high successful detection rate while decrease the communication overheads when compared with the Line-Selected Multicast (LSM) method previously proposed in the literature. The proposed ABCD method can also maintain the network lifetime and decrease the number of stored messages when compared with a centralized approach.

The ability of a person to perceive image details falls precipitously with larger angle away from his visual focus. At any given bitrate, perceived visual quality can be improved by employing region-of-interest (ROI) coding, where higher encoding quality is judiciously applied only to regions close to a viewer's focal point. Straight-forward matching of viewer's focal point with ROI coding using a live encoder, however, is computation-intensive. In this paper, we propose a system that supports ROI coding without the need of a live encoder. The system is based on dynamic switching between two pre-encoded streams of the same content: one at high quality (HQ), and the other at mixed quality (MQ), where quality of a spatial region depends on its pre-computed visual saliency values. Distributed source coding (DSC) frames are periodically inserted to facilitate switching. Using a Hidden Markov Model (HMM) to model a viewer's temporal gaze movement, MQ stream is pre-encoded based on ROI coding to minimize the expected streaming rate, while keeping the probability of a viewer observing low quality (LQ) spatial regions below an application-specific ϵ. At stream time, the viewer's gaze locations are collected and transmitted to server for intelligent stream switching. In particular, server employs MQ stream only if: i) viewer's tracked gaze location falls inside the high-saliency regions, and ii) the probability that a viewer's gaze point will soon move outside high-saliency regions, computed using tracked gaze data and updated saliency values, is below ϵ. Experiments showed that video streaming rate can be reduced by up to 44%, and subjective quality is noticeably better than a competing scheme at the same rate where the entire video is encoded using equal quantization.

In wireless mesh networks (WMN), most routing algorithms apply broadcasting at some stage of the path discovery process. They thereby consume large chunks of the network throughput. Intelligent rebroadcast algorithms aim to reduce this overhead by calculating the usefulness of a rebroadcast and the likelihood of collisions. Unfortunately, this introduces latency and breaks the rebroadcast chain, resulting in reduced reachability. In this paper we present our Social-aware Routing Protocol with Parallel Collision Guidance Broadcasting for WMN (SCG). It reduces rebroadcasting without a loss in reachability and without a significant increase in latency. Our claims are validated through simulations comparing our algorithm with existing protocols.

We introduce a novel activity recognition method based on the RF-signal originated from ambient FM radio source. For the purpose of classifying activities, we utilise a two stage approach which can initially distinguish between coarse-grained activities, then make further fine-grained recognition. Additionally, a study on features is conducted to investigate the most suitable combination to achieve the highest accuracy on the detection of activities. By comparing to a one stage classification process, the experimental results demonstrate the advantage of our designed approach.

We exploit activity recognition from RF-channels. Exceeding current studies, we discuss an implicit recognition scheme to compute context classifications with a network of wireless nodes. In particular, we propose a networked adhoc classification scheme that utilises the RF-features on the wireless channel among nodes as implicit inputs. Furthermore, we discuss the possibility to execute mathematical operations during transmission on the wireless channel. We present a data encoding which can be utilised to implicitly add, multiply, subtract or divide values during simultaneous transmission. In a simulation, we demonstrate the computation with a set of values by these implicit operations during transmission.

This paper addresses the relay assignment problem in a wireless cooperative network. We propose a distributed algorithm which does not require global information, hence is more practical in a real network environment. It is based on the distributed auction structure that can achieve the optimal network capacity as centralized ones but with much lower complexity and higher scalability. We give formal proofs for optimality and convergence of this algorithm, and then we analyze its time complexity and further improve it by reducing the iterations in cost of little capacity degradation. Extensive simulations also show huge computation reduction on proposed algorithms than centralized ones with comparable capacity performance.

This study addresses the resource allocation problem for multi-user MIMO systems with consideration of real-time services. Specifically, we focused on the delay constraints modeling in this paper, since it is a fundamental QoS requirement for all real-time services. To simultaneously meet the delay constraints of all users, we first modeled this resource allocation problem with specific delay restrictions as a bargaining game. Based on the Nash bargaining solution, we derived the system utility function that achieves delay constraints in a long term. Then, we formulated the resource allocation problem as an optimization problem through the weighted sum rate maximization, which can be solved by a modified iterative water-filling algorithm. Simulation results show that our proposed resource allocation algorithm not only achieves delay requirements for different realtime services, but also obtains the Pareto optimal efficiency with acceptable complexity.

As a promising way in surveillance applications, wireless sensor networks (WSNs) often encounter the target coverage (TC) problem, i.e., scheduling energy-limited sensors to monitor physical targets to prolong the network lifetime. Due to the complexity of the problem (scheduling in time domain), previous proposals mainly focus on heuristics and provable optimal algorithms remain unknown. In this paper, we fill in the research blank by providing several theoretical results. First, we present a mathematical formulation and several investigations of the problem in time domain. Such time-related results provide fundamental understandings of the problem and serve as a basis. Second, we offer an upper bound on the network lifetime derived from the time-dependant formulation. The bound, which is solvable in polynomial-time, serves as a performance benchmark. Third, we verify the set cover-based method, which is widely used by previous studies, via a transformation of the problem from time to space domain. Lastly, we offer a specialized nonlinear column generation (CG) based approach to solve the problem in space domain optimally. Simulation results show that not only the bound is effective, but also the CG-based approach offers significant improvement on the network lifetime over a brutal search algorithm and a state-of-art heuristic.

Abstract A mobile ad hoc network (MANET) is a self‐organized and adaptive wireless network formed by dynamically gathering mobile nodes. Since the topology of the network is constantly changing, the issue of routing packets and energy conservation become challenging tasks. In this paper, we propose a cross‐layer design that jointly considers routing and topology control taking mobility and interference into account for MANETs. We called the proposed protocol as Mobility‐aware Routing and Interference‐aware Topology control (MRIT) protocol. The main objective of the proposed protocol is to increase the network lifetime, reduce energy consumption, and find stable end‐to‐end routes for MANETs. We evaluate the performance of the proposed protocol by comprehensively simulating a set of random MANET environments. The results show that the proposed protocol reduces energy consumption rate, end‐to‐end delay, interference while preserving throughput and network connectivity. Copyright © 2010 John Wiley & Sons, Ltd.

Video multicast over broadband wireless networks suffers from packet losses induced by fading wireless channels and user heterogeneity in channel conditions within a multicast group. A promising solution to these problems is the use of layered hybrid FEC/ARQ for scalable video multicast. However, how to allocate the radio resources to multiple video layers and how to address the cross-layer combination of application layer hybrid FEC/ARQ and physical layer MCS (modulation and coding schemes) for each video layer, is not a trivial issue. We prove that this problem is NP-hard and propose an optimal Error-resilient Video Multicast (ERVM) framework in infrastructure-based broadband wireless networks. To avoid user heterogeneity and feedback implosion, we use a weighted designated user group to send light-weight feedback messages. The ERVM algorithm is based on the dynamic programming method with a pseudo-polynomial run time, and can get the optimal transmission configuration to maximize the expected utility for the designated user group, which is a probabilistic function of the received video rate. Simulation results show that our ERVM algorithm offers significant improvements over the conventional single-layer FEC/ARQ scheme and the layered FEC scheme. Furthermore, our weighted designated user group with light-weight and accurate feedbacks is better than other user feedback schemes.

Sink scheduling is shown to be a promising scheme in wireless sensor networks. However, previous approaches on this topic suffer from poor performance due to lack of joint considerations. Therefore, in this paper, we aim to fill in the research blank. First, we develop a novel notation Placement Pattern (PP) to bound time-varying routes with placement of sinks. This bounding technique transforms time-related constraints into pattern-based ones and allows us to formulate this optimization in pattern domain. If there is only one sink, we develop a polynomial time algorithm to solve it optimally. If there are multiple sinks, we develop a column generation based approach to solve it efficiently. Simulations not only demonstrate the efficiency of proposed algorithms but also substantiate the importance of sink mobility for energy-constrained sensor networks.

Video multicast over Wireless Wide Area Networks (WWAN) is difficult because of unavoidable packet losses and impracticality of retransmission on a per packet, per client basis, due to the known NAK implosion problem. Recent approach exploits clients' cooperation for packet recovery, so that a peer group's received WWAN packets are shared using a secondary network like Wireless Local Area Network (WLAN). For multiview video multicast, where a client can switch views interactively by subscribing to different WWAN multicast channels streaming different views, two new difficulties arise. First, system must provide timely view-switching mechanism, so that client can switch to a desired view quickly for correct decoding and display. Second, it is difficult for system to leverage neighboring peers for cooperative loss recovery, since neighbors are more likely to be subscribing to different views than a loss-stricken peer. In this paper, we use Distributed Source Coding (DSC), a new compression tool in video coding, to solve both problems. Each DSC frame is encoded with a set of predictor frames, and correct decoding only requires one of the predictors in the set to be available at decoder. Periodic insertion of DSC frames into video streams then enables a peer to switch from view v to v' at the DSC frame boundary, assuming DSC frame of view v' was encoded using a frame in view v as one predictor. For the same assumption, a neighbor watching view v can help a peer watching view v' evade error propagation resulting from earlier losses and resume decoding at the DSC boundary. Experiments show that optimized usage of DSC frames in a coding structure, where unequal error protection is enabled to decrease the probability of decoding failure earlier in a group of pictures, outperforms a structure using I-frames instead for view switching by up to 11 dB in video quality in typical WWAN network loss environment.

With the advent of eye gaze tracking technology, eye gaze is increasingly being used as a media interaction trigger in a variety of applications, such as eye typing, video content customization, and network video streaming based on region-of-interest (ROI). The reaction time of a gaze-based networked system, however, is in practice lower-bounded by the round trip time (RTT) of today's networks, which can be large. To improve the efficacy of gaze-based networked systems, in the paper we propose a Hidden Markov Model (HMM)-based gaze prediction strategy to predict future gaze locations to lower end-to-end reaction delay. We first design an HMM with three states corresponding to human's three major types of intrinsic eye movements. HMM parameters are obtained offline on a per-video basis during training phase. During testing phase, a window of noisy gaze observations are collected in real-time as input to a forward algorithm, which computes the most likely HMM state. Given the deduced HMM state, linear prediction is used to predict gaze location RTT seconds into the future. We demonstrate the applicability of our gaze prediction strategy by focusing on ROI-based bit allocation for network video streaming. To reduce transmission rate of a video stream without degrading viewer's perceived visual quality, we allocate more bits to encode the viewer's current spatial ROI, while devoting fewer bits in other spatial regions. The challenge lies in overcoming the delay between the time a viewer's ROI is detected by gaze tracking, to the time the effected video is encoded, delivered and displayed at the viewer's terminal. To this end, we use our proposed gaze-prediction strategy to predict future eye gaze locations, so that optimized bit allocation can be performed for future frames. Our experiments show that bit rate can be reduced by 21% without noticeable visual quality degradation when end-to-end network delay is as high as 200ms.

Error resilient video broadcast over Wireless Wide Area Networks (WWAN) remains difficult due to unavoidable packet losses (a result of the underlying unreliable and time-varying transmission medium) and unavailability of per-packet, per-user retransmissions (stemming from the well-known NAK implosion problem). Previous cooperative solutions for multi-homed devices listening to the same video broadcast call for local recovery via packet sharing: assuming peers are physically located more than one transmission wavelength apart, channels to the streaming source are statistically independent, and peers can exchange different subsets of received packets with neighbors via a secondary network like ad hoc Wireless Local Area Net work (WLAN) to alleviate individual WWAN packet losses. While it is known that using structured network coding (SNC) to encode received packets before peer exchange can further improve packet repair performance, the decisions of who should send repair packets encoded in what SNC types at available transmission opportunities were not optimized in any formal way. In this paper, we propose a distributed decision making strategy based on Markov decision process (MDP), so that each peer can make locally optimal transmission decisions based on observations eavesdropped on the WLAN channel. Our proposed MDP is both computationally scalable and peer-adaptive, so that state transition probabilities in MDP can be appropriately estimated based on observed aggregate behavior of other peers. Experiments show that decisions made using our proposed MDP outperformed decisions made by a random scheme by at least 4dB in PSNR in received video quality.

Femtocell access points are inexpensive, plug and play home base stations designed to extend radio coverage and increase capacity within indoor environments. Their inherent uncoordinated and overlaid deployment however, means existing radio resource management (RRM) techniques are often ineffectual. Recent advances in dynamic RRM have emphasised the need for more efficient resource management strategies. While centralised resource management offers improved coordination and operator control giving better interference management, it is not scalable for increasing nodes. Distributed management techniques in contrast, do afford scaled deployment, but at higher node densities incur performance degradation in both system throughput and link-quality because of poor coordination. The level of spectrum sharing mandated by macro-femto deployment also impacts on system throughput and is scenario dependant. This paper presents a new hybrid resource management algorithm( HRMA) for down-links in orthogonal frequency division multiple access-based systems, with the model analysed for a range of macro-femto deployment scenarios. HRMA employs a dynamic fractional frequency reuse scheme for macro-cell deployment with frequency reuse defined for femto users depending on their location by making certain frequencies locally available based on macro-femto tier information sharing and efficient localised spectrum utilisation. Quantitative performance results confirm the efficacy of the HRMA strategy for various key system metrics including interference minimisation, outage probability and throughput.

A forest fire has long been a severe threat to the forest resources and human life. The threat could effectively be mitigated by timely and accurate detection. In this paper, we propose a novel accurate forest fire detection system using Wireless Sensor Networks (WSNs). In the proposed system, the detection accuracy is increased by applying the multi-criteria detection that an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network which fuses sensing data corresponding to multiple attributes of a forest fire into an alarm decision. Due to the utilization of the artificial neural network, the proposed system enjoys low overhead and the self-learning capability. Furthermore, we have developed a prototype consisting TelosB sensor nodes and carried out extensive experiments to study the performance of the proposed system. We have also developed a solar battery in order to persistently power the unattended sensor node deployed in the forest.

Directional antennas in wireless ad hoc networks (WANETs) offer great potential to reduce the radio interference, and improve the communication throughput. Using directional antennas, however, introduces a new problem in the wireless media access control (MAC), deafness, which may cause severe performance degradation. To solve the deafness problem, in this paper, we propose a novel CRDMAC protocol by using a sub-transmission channel and RTR (Ready To Receive) packets, which modifies the IEEE 802.11 distributed coordinated function. The sub-channel avoids collisions to other ongoing transmission and the RTR packets notify the neighbors that the mutual transmission has finished. The proposed MAC protocol decreases the binary exponential back off time of the waiting nodes. We evaluate our protocol through simulations. Simulation results show that the proposed protocol outperforms the existing DMAC (directional MAC) protocol and the CRCM (Circular RTS and CTS MAC) protocol in terms of throughput and packet drop rate.

Error resilient video multicast over Wireless Wide Area Networks (WWAN) is difficult because of unavoidable packet losses and impracticality of retransmission on a per packet, per client basis due to the well-known NAK implosion problem. In response, Cooperative Peer-to-peer Repair (CPR) calls for multi-homed devices listening to the same video multicast to locally exchange received WWAN packets via a secondary network like ad hoc Wireless Local Area Network (WLAN) to alleviate individual WWAN packet losses. When videos of the interested 3D scene are captured by multiple closely spaced cameras, each video can be encoded into a separate video stream and transmitted on its own WWAN multicast channel. Clients can then switch observation viewpoints periodically by simply re-subscribing to different WWAN multicast channels- a scenario called interactive multiview video streaming (IMVS). IMVS complicates the CPR WWAN loss recovery process, however, since neighbors of a loss-stricken peer can now be watching different views. In this paper, we optimize the decision process for individual peers during CPR for recovery of multiview video content in IMVS. In particular, for each available transmission opportunity, a peer decides-using Markov decision process as a mathematical formalism-whether to transmit, and if so, how the CPR packet should be encoded using structured network coding (SNC). A loss-stricken peer can then either recover using received CPR packets of the same view, or using packets of two adjacent views and subsequent view interpolation via image-based rendering. Experiments show that decisions made using our proposed MDP outperforms decisions made by a random scheme by at least 1.8dB in PSNR in received video quality in typical network scenario.

Achieving low latency and high energy efficiency are critical requirements in many of sensor network applications. Combining the duty cycling mechanism and multi-hop transmission is one of the solutions to this problem. Multi-hop protocols such as demand wakeup MAC (DW-MAC) schedule data transmissions during the sleep period. In so doing, a data packet can traverse multiple hops within a single operational cycle. By introducing a scheduling parameter, called proportional mapping function, DW-MAC can provide better performance in terms of both latency and energy efficiency, while avoiding data collision. In this paper, we derive the minimum value Rminof the scheduling function by numerically analyzing the delivery latency. Using that value, a DW-MAC-like protocol not only guarantees no collisions at the intended receivers but also achieves the minimum latency in the multi- hop scenario. In addition, we also show the upper and lower bounds of the delay. We evaluated the accuracy of our findings by running ns-2 simulations in multiple scenarios.

We address the optimal sink scheduling problem in wireless sensor networks (WSNs). The problem is inherently difficult since sink scheduling and data routing are tightly coupled. Previous approaches either have questionable performance due to no joint considerations, or are based on relaxed constraints. Our aim is to fill in this blank in the research. First, by discretizing continuous time, we develop a novel bound technique to connect time-varying routes with the placement of sinks. This bounding technique transforms time-related constraints into pattern-based ones and allows us to mathematically formulate this optimization in a pattern-based way. The complexity of directly solving this optimization is intractable; therefore, on the basis of column generation (CG), a computationally efficient algorithm is developed to reduce the complexity by decomposing the problem into sub-problems and iteratively solving them to approach optimality. Simulations demonstrate the efficiency of the algorithm and substantiate the importance of sink mobility in energy-constrained sensor networks.

MIMO, OFDMA and cooperative relaying are the key technologies in future wireless communication systems. However, under the usage of these technologies, resource allocation becomes a more crucial and challenging task. In multi-user MIMO-OFDMA relay-enhanced cellular networks, we formulate the optimal instantaneous resource allocation problem including user group selection, path selection, power allocation, and subchannel scheduling to maximize system capacity. We first propose a low-complex resource allocation algorithm named `CP-CP' under constant uniform power allocation and then use a water-filling method named `CP-AP' to allocate power among transmitting antennas. Moreover, we solve the original optimization problem efficiently by using the Jensen's inequality and propose a modified iterative water-filling algorithm named `AP-CP'. Based on `AP-CP', the `AP-AP' algorithm is proposed to allocate power adaptively not only among subchannels but also among multiple transmitting. Finally, we compare the performance of the four schemes. Our results show that allocating power among subchannels is more effective than among transmitting antennas if the average signal-to-noise radio of users is low, and vice versa. Furthermore, the `AP-AP' algorithm achieves the highest throughout especially for users near the cell edge.

With a formulated game of non-cooperative internet service providers (ISPs), this paper proposes a new framework for apportioning ISP's responsibility in an end-to-end quality of service (QoS) request. The strategy is based on Path-Classification scheme under Nash equilibrium (PC-Nash), which is obtained by classifying paths according to the quantized QoS level. Optimal QoS-level selection of individual ISP is then captured by the Nash equilibrium. To facilitate the game solution searching, a loss network model is derived for the call acceptance probabilities and the expected utility values. Solutions provided by PC-Nash are compared with three conventional policies, i.e. most-effort (ME), least-effort (LE) and equal-distribution (ED). The reported results show the conformity of call acceptance probabilities between mathematical analysis and discrete-event simulations. Furthermore, with the utility functions of practical service models, ME and LE are found to provide comparable utilities to PC-Nash with respect to peer and retail/wholesale service models, respectively, for a network with the same path quality. However, for networks with different path qualities, PC-Nash outperforms all the conventional policies significantly. From this evidence, PC-Nash is thus expected to be useful in QoS provisioning of practical inter-domain networks.

A novel two-dimensional differential demodulation (2-D DD) algorithm, which exploits both the time and frequency dimensions by using the orthogonal frequency division multiplexing (OFDM) frame structure, was investigated for 64-differential amplitude and phase shift keying (DAPSK) modulated OFDM signals. Without changing the transmitter, the proposed algorithm based on graph attempts to search for a more confident bypass, probably containing multiple DD operations, to demodulate one 64-DAPSK symbol rather than conventional direct DD. In order to carry out the optimal path searching process, the soft decision metrics of the amplitude ratio and phase difference were derived by making use of a Gaussian approximation. Simulation results show that the proposed algorithm can obtain substantial performance gains with reasonable computational complexity for uncoded AWGN and frequency selective fading channels. In addition, it is advantageous to withstand the Doppler effect, which is hence suitable for high mobile (e.g., in a car or train) broadband wireless access (BWA) systems.

The OTA performance of Frequency Selective Scheduling (WiMAX Band AMC mode) is compared with that of Frequency Diverse Scheduling (WiMAX PUSC mode) as MS velocity is increased for Mobile WiMAX 802.16e. Frequency Selective Scheduling is shown to outperform Frequency Diverse Scheduling for velocities less than 15km/h and demonstrates upto 50% gain in throughput over the latter. The practical implications of this margin are: that pedestrian MSs in urban deployments may leverage the benefits of fast fading for performance gains without risk. And scheduler implementations can benefit from opportunistic switching between the two schemes given appropriate differentiating inputs.

Although fair bandwidth allocation (FBA) can be achieved in optical burst switching (OBS) networks, the actual transmission rate of the flow not only depends on the fairly allocated rate but also its burst loss probability, which tends to increase with larger hop counts due to the high-loss characteristic of OBS. The previous proposals provide FBA with distance fairness provisioning in various ways, e.g., providing distance fairness only when traffic in the network is over the link capacity. This paper introduces a rate and distance fairness preemption (RDFP) scheme to achieve max-min FBA, to isolate services, to provide protection among flows, and to ensure distance fairness for traffic transmitted under the max-min rate. Overload traffic in RDFP is not provided distance fairness and it has the lowest preemptive priority. Therefore, the well-behaved flows do not suffer from the misbehavior of flows. Simulation results demonstrate the efficiency of RDFP.

By exploiting multiple network interfaces on one device, e.g., Wireless Wide Area Network (WWAN) and Wireless Local Area Network (WLAN), peers receiving different subsets of WWAN broadcast/multicast packets can perform Cooperative Peer-to-peer Repair (CPR) by exchanging received WWAN packets with their local WLAN peers. This effectively improves the transmission success from a WWAN broadcast/multicast source to a CPR collective. In this paper, we propose a novel joint source/channel bit allocation scheme for WWAN scalable video multicast that leverages the CPR paradigm. One key observation is that given a peer can successfully receive a packet either from theWWAN channel directly, or via a CPR neighbor using ad-hoc WLAN connections, more bits can be redistributed from channel to source coding out of a fixed WWAN bit budget to further minimize individual node's expected visual distortion. In our proposal, groups of peers requiring different video resolutions are assigned to the same multicast group, and we perform one WWAN resource allocation and subsequent CPR over heterogeneous peers of different resolutions together. Our simulations show that our joint multicast group optimization can improve video quality by up to 2.84 dB, compared to a scheme where both WWAN resource allocation and WLAN CPR are separately performed for heterogeneous peers.

Recently, sink deployment, in the form of deploying the sink among different sites so as to leverage traffic burden, is shown to be a promising scheme to save energy and prolong network lifetime in wireless sensor networks. For this paradigm, the choice of sink sites plays a critical role in the overall system performance. In this paper, we address the optimal deployment problem for the sink in wireless sensor networks, where routing issues are naturally involved. The major contribution of this paper is the development of an efficient grid-based algorithm to solve this problem. By dividing the continuous search space into a limited number of so-called communication intersections, computational complexity has been significantly reduced. A formal proof of optimality for this algorithm is given and several interesting properties have been revealed by theoretic analysis as well as experimental results.

Sensor network MAC protocols use a duty cycling mechanism and a multi-hop transmission to create a good trade-off between their energy efficiency and latency. The nodes in duty cycling multi-hop MAC protocols such as RMAC periodically sleep/listen during the operational cycle to reduce their energy consumption by idle listening; and the listening period is usually long in order to support packets in reaching a multi-hop destination in a single cycle (multi-hop transmission). The traffic network in the wireless sensor network is otherwise very scarce, and keeping the radio on during such long listening periods when there is no data transmission in the network wastes energy. In addition, these protocols incur a large amount of control overhead, which is one of the main energy wasted sources. This paper proposes an adaptive low overhead MAC protocol we call AM-MAC (Adaptive Multi-hop MAC). It can reduce the wastage caused by long listening period and minimizes the control overhead. The nodes in AM-MAC use an adaptive method, and thus they can adjust the duration of the listening period according to the traffic load. To reduce the control overhead, a single packet has more than one role. During a multi-hop transmission, the new control packet replaces the RTS/CTS pair, and one DATA packet can play both DATA/ACK roles. An extended evaluation of AM-MAC has been conducted through simulation, against RMAC. The results illustrate that AM-MAC significantly reduces the energy consumption and notably lessens the end-to-end latency.

Distributed antenna systems (DAS) reduce the access distance for users in areas away from the cell center, thereby, enhancing the transmission capability for those users. Recent studies have shown that antenna selection transmission (AST) is preferable to maximum ratio transmission (MRT) in a multicell DAS environment, owing to its interference reduction. However, it is obvious that users still suffer from strong interference at the cell edge when applying universal frequency reuse in DAS. We propose an efficient cooperative beamforming transmission (CBT) algorithm, whose basic idea is taking the distributed transmitting antennas within a cell and receiving antennas including desired users and adjacent cells' interfered users, as a distributed multiple-input multiple-output (D-MIMO) channel, by which CBT mitigates the interference through joint precoding. We analyze the proposed algorithm mathematically and compare it with other existing algorithms. A Monte-Carlo simulation is also carried out to verify the derivation. Both the analysis and simulation results show that the CBT algorithm outperforms both AST and MRT especially near the cell edge.

This paper deals with the partial target coverage (PTC) problem in wireless sensor networks with the objective of optimizing network lifetime. We first build a linear programming formulation, which takes total time a sensor spends on covering some targets into consideration, in order to obtain a lifetime upper bound. Then, based on the information of this formulation, we develop a sensor assignment algorithm to seek an optimal time table meeting the lifetime upper bound. A formal proof of optimality is given. We compare the proposed algorithm with a state-of-the-art algorithm: column generation approach and show that the proposed algorithm significantly outperforms in terms of computational time. Experiments have been conducted to study the effect of network parameters on network lifetime and interesting insights have been offered.

Vehicular wireless communication should be able to provide vehicles with reliable and efficient data transmissions for various applications, especially safety applications. We present a dedicated multi- channel MAC protocol that has an adaptive broadcasting mechanism, specifically designed to provide collision-free and delay-bounded transmissions for safety applications under various traffic conditions. Besides defining the implementation of this protocol, we conducted simulation evaluations showing that it has promising performance on critical statistics.

The deployment of relay stations in OFDMA cellular networks is a promising solution to provide ubiquitous high-data-rate coverage. However, it makes the resource allocation a more crucial and challenging task. In OFDMA relay-enhanced cellular networks, we formulate the optimal instantaneous resource allocation problem including path selection, power allocation and subchannel scheduling to achieve the proportional fairness in the long term. We first propose a low-complex resource allocation algorithm named 'VF w PF' under the constant uniform power allocation, and then use a void filling method to make full use of the wasted resources caused by the unbalanced data rates of the two hops in a relaying path. We further use a dual decomposition approach to solve the original optimization problem efficiently in its Lagrangian dual domain, and propose a modified iterative water-filling algorithm named 'PA w PF'. Simulation results show that our resource allocation algorithms improve the throughput for cell-edge users, and achieve a tradeoff between system throughput maximization and fairness among users. Moreover, compared with the constant power allocation, the optimal power allocation can not gain much in system throughput but can significantly improve the throughput for cell-edge users and also the fairness.

Preemption is one of the most effective ways to achieve fair bandwidth allocation in OBS networks. Preemption allows ingress edge switches to transmit the traffic of flows over their fairly allocated bandwidth but core switches drop over-used traffic when there is contention. This paper proposes a r

The target coverage problem is one of the most fundamental challenges in wireless sensor networks. Due to the complexity of the problem (time-dependent network topology and coverage constraints), previous studies have mainly focused on heuristic algorithms and the theoretical bound remains unknown. In this paper, we aim to fill in this gap by providing fundamental results. First, we investigate the properties of a problem in time domain via an example topology and build a novel transformation to connect a problem in the time domain with a corresponding problem in the space domain while maintaining the same network lifetime. Based on this transformation, we mathematically formulate the problem and build a column-generation based algorithm, which decomposes the original formulation into two sub-formulations and iteratively solves them in a way that approaches the optimal solution. We prove that the network lifetime that can be guaranteed by the proposed algorithm is at least (1-¿) of the optimum, where ¿ can be made arbitrarily small depending on the required precision.

This paper presents the novel resource allocation scheme for multiclass services in the downlink OFDMA system, in which the frame-based transmission with multiple subchannels and multiple time slots is used. As system utilization and fairness are necessary, the scheme is based on proportional fairness (PF) utility function. Two main service classes are considered. One is the guaranteed service class. The other is the non-guaranteed service class. The scheme guarantees the minimum bit rate for the former class. The PF-based optimization problem with minimum bit rate requirement is formed and solved by using the Lagrange multiplier method with relaxed constraints. The novel PF-based utility function is derived and used for this class. The non-guaranteed class is served after the guaranteed class by using the conventional PF utility function. Extensive simulation with user mobility and finite backlog is conducted. The results show that the proposed scheme is able to satisfy the minimum rate requirement for the guaranteed class with moderate system utilization. Furthermore, within the same class, the scheme can provide very high throughput fairness.

This paper describes the implementation and evaluation of layer-1 bandwidth-on-demand (BoD) capabilities in the Japanese academic backbone network, called SINET3. The network has a nationwide GMPLS-based layer-1 platform and provides reservation-based and signaling-based BoD services. The overall architecture for providing BoD services including its capabilities, user interface, path calculation, and interface to drive the layer-1 platform are described. Actual examples of BoD services and evaluations of the path setup/release time in the network are also presented.

In this paper, we propose a novel downlink scheduling scheme for a multiuser OFDMA/TDD network. Resources in one OFDMA frame are defined as a number of chunks that are composed of frequency and time. Time multiplexing is allowed by using time slots. With multichannel multiuser diversity, our scheme aims to satisfy the required minimum bit rate, maximize system utilization, and provide throughput fairly among users. We formulate the optimization problem based upon a multichannel proportional fairness with minimum bit rate guarantee. The solution is considered by using the Lagrange multipliers with relaxed constraints. Then, our fast cross-layer approach integrates the requirement to the solution and schedules packets with finitely backlogged queue consideration. Simulation with user mobility concern is conducted and results show that the proposed scheme can satisfy minimum bit rate requirement, as well as can provide high throughput fairness.

A wireless ad hoc network (WANET) is a collection of wireless terminals that communicate with each other without predetermined topology. Since WANET devices are power-limited, network protocols should be designed to prolong the battery lifetime of these devices. In this paper, we propose a cross-layer integration approach for power efficient routing protocol. The proposed cross-layer integration between power control in link layer and routing protocol in network layer aims to maximize the network lifetime. We implement our proposed protocol as an extension to AODV routing protocol. We evaluated the proposed protocol by comprehensively simulating a set of random WANET environments. We simulated six different metrics comparing our proposed protocol with AODV protocol. The results showed that the proposed protocol maximizes the network lifetime, reduces the end-to-end delay, and saves the total energy consumption while achieving the throughput requirement.

Target coverage problem in wireless sensor networks remains a challenge. Due to nonlinear nature, previous work has mainly focused on heuristic algorithms, which remain difficult to characterize and have no performance guarantee. To solve the problem, this paper offers two important contributions. The first contribution is to have two lifetime upper bounds, which could be used to justify performance of previously proposed heuristic algorithms. One upper bound is based on the relaxation and reformulation technique while the other is derived by relaxing coverage constraints. We study the interesting connection between those two bounds and thus endow them with physical meanings. The second contribution is proposing a column generation based (CG) approach. The objective is to find an optimal schedule, defined as a time table specifying from what time up to what time which sensor watches which targets while the maximum lifetime has been obtained. We also offer an in-depth theoretic analysis as well as several novel techniques to further optimize the approach. Numerical results not only demonstrate that the lifetime upper bounds are very tight, but also verify that the proposed CG based approach constantly yields the optimal or near optimal solution.

Orthogonal frequency division multiple access (OFDMA) is the access technique adopted in the new generation wireless networks such as WiMAX. In this paper, we consider resource allocation of OFDMA in time division duplex (TDD) mode in which the new transmission frame with multiple time slots is popped up on every pre-specified period. Frame resource is divided into chunks that are composed of a group of subcarriers with equal and constant time duration. Our centralized resource allocation scheme aims to provide the guaranteed service to users by converting the required service into the network cost. The users whose network costs are too high are not guaranteed in order not to waste the precious bandwidth. We formulate the optimization problem with the objective of minimizing the total cost. Furthermore, a heuristic method is proposed to schedule users' data into the downlink subframe by exploiting multiuser multichannel diversity to guarantee the service and to utilize system's bandwidth wisely. Intensive simulation shows that our scheme provides satisfied throughput, low packet drop rate, and low queuing delay. Moreover, the results also show that the scheme is fair to users in both throughput and service time.

The cooperative selection diversity (CSD) scheme which dynamically selects the best transmission scheme between decode-and-forward relaying and direct transmission outperforms the other complex cooperative diversity schemes in terms of throughput and implementation complexity. In this paper, we formulate the optimal resource allocation problem with a fairness constraint in the downlink of OFDMA relay-enhanced system when CSD based transmissions are considered. The problem is linearized into a binary integer programming problem by using a constant power allocation. To solve the linear programming problem, we proposed a heuristic joint path selection and subchannel allocation algorithm. Moreover, a void filling algorithm is designed to make full use of the resources. Simulation results demonstrate that our path selection rule based on the end-to- end achievable data rate and the void filling algorithm achieve more system throughput especially when the proportion of the two zones used in the two-slot relaying pattern is far away from the optimal value. The heuristic resource allocation algorithm provides a suboptimal solution to gain a tradeoff between system throughput maximization and fairness.

This paper describes the network design and configurations of the new Japanese academic infrastructure, called SINET3, which provides a rich variety of network services to more than 700 universities and research institutions. Since the start of full-scale operations in June 2007, the network has expanded its services to include multi-layer transfer services (IP, Ethernet, and layer-1), enriched virtual private network services (L3VPN, L2VPN, VPLS, and L1VPN), enhanced QoS services (packet-based and circuit-based), and brand-new layer-1 bandwidth-on-demand (BoD) services. This paper explains how the network provides these various network services on a single network platform by effectively configuring leading-edge networking components, such as high-performance IP routers, layer- 1 switches, and a BoD server. Evaluations of the network design and configurations confirmed that the networking functions were effectively coordinated. The procedures and techniques related to the configuration validation that covered all phases of the network design and construction are also presented.

Abstract Wireless sensor networks have emerged recently as an effective way of monitoring remote or inhospitable physical targets, which usually have different quality of service (QoS) constraints, i.e., different targets may need different sensing quality in terms of the number of transducers, sampling rate, etc. In this paper, we address the problem of optimizing network lifetime while capturing those diversified QoS coverage constraints in such surveillance sensor networks. We show that this problem belongs to NP‐complete class. We define a subset of sensors meeting QoS requirements as acoverage pattern, and if the full set of coverage patterns is given, we can mathematically formulate the problem. Directly solving this formulation however is difficult since number of coverage patterns may be exponential to number of sensors and targets. Hence, a column generation (CG)‐based approach is proposed to decompose the original formulation into two subproblems and solve them iteratively. Here a column corresponds to a feasible coverage pattern, and the idea is to find a column with steepest ascent in lifetime, based on which we iteratively search for the maximum lifetime solution. An initial feasible set of patterns is generated through a novel random selection algorithm (RSA), in order to launch our approach. Experimental data demonstrate that the proposed CG‐based approach is an efficient solution, even in a harsh environment. Simulation results also reveal the impact of different network parameters on network lifetime, giving certain guidance on designing and maintaining such surveillance sensor networks. Copyright © 2009 John Wiley & Sons, Ltd.

Dynamic and flexible composition of higher-level network services, such as security, QoS, or adaptive services are required by future network applications. However, the development of such extensible applications makes them rather complex. In addition, many old applications, which do not support such services, would stick to be used. To solve these problems, we propose a generic and multi-platform infrastructure called FreeNA1 that extends existing applications by transparently incorporating the services to them. FreeNA offers abstract interfaces such that users can insert the services into each packet flow based on a configuration file. In this paper, we describe the design and implementation of FreeNA including a functionality comparison with relevant systems, and our performance evaluation results. The result shows that FreeNA offers finer configurability, composability, and usability and can be used widely than other similar systems. We also show that overhead of transparent service insertion is about 1-2% at a maximum compared to a method of inserting such services into applications directly.

Traffic volume anomalies refer to apparent abrupt changes in time series of traffic volume, which can be propagate through the network. Detecting and tracing anomalies is a critical and difficult task for network operators. In this paper, we first propose a traffic decomposition method, which decomposes the traffic into three components: trend component, autoregressive (AR) component, and noise component. A traffic volume anomaly is detected when the AR component is out of prediction band for multiple links simultaneously. Then, the anomaly is traced using the projection of the detection result matrices for the observed links which are selected by a shortest-path-first algorithm. Finally we validate our detection and tracing method by using traffic data of the third-generation Science Information Network (SINET3) and show the detected and traced results.

In wireless sensor networks (WSNs), a duty-cycling scheme is typically applied to the medium access control (MAC) protocol to reduce energy consumption due to idle listening. However, this scheme introduces huge end-to-end latency and still suffers from a large control packet overhead. We propose a new MAC protocol with low latency and low control overhead for WSNs use (the LCO-MAC). In our protocol, a DATA packet can be transmitted through multiple hops in a single duty cycle to shorten end-to-end latency. To reduce energy consumption caused by control packet overhead, we force one packet to play more than one role. In the initial transmission period, a control packet acts as an RTS (request to send) for a downstream node and a CTS (clear to send) for an upstream node. In the actual data transmission period, a DATA packet keeps its original DATA role for the downstream node, but also plays an ACK (acknowledgment) role for the upstream node. Our simulation using ns-2 has shown that LCO-MAC enables a notable improvement in energy efficiency and decreases end-to-end latency compared to those of RMAC.

We propose a packet scheduling scheme called OFDMA frame-based proportional fairness (OFPF) for a frame- based OFDMA wireless system. OFPF aims to maximize system throughput as well as maintain fairness between users. We define the scheduling resources as slots of fixed duration per OFDMA subchannel. Furthermore, we assume a realistic traffic pattern in which the user queues are not always backlogged. We suggest a simple method to update the average data rate in the PF ratio. In addition, we introduce an uncomplicated approach to schedule user data using matrix-based calculation. The simulation shows that our scheme improves performance in terms of throughput, delay, and fairness.

The key issue we address in this paper concerns how the available bandwidth within the downlink of a multiple-input multiple-output (MIMO) cellular network should be shared between competing users to achieve both fairness and high system throughput. We propose a high performance packet scheduling framework, called MIMO packet-based proportional fairness (MP-PF). The scheduler is work conserving and based on a novel proportional fairness concept that takes the packet length, users' backlogs, and users' guarantees into consideration. Furthermore, we gain an efficient packet-based framework in the MAC layer. Intensive simulation studies that took into consideration the traffic characteristics and the mobility of the users were used to demonstrate the superior performance of our scheduling framework for both the average delay of users and the time/service fairness. We also propose two useful equations for the time/service comparison of MIMO schedulers.

OFDMA-based relay-enhanced cellular systems have been proposed as a promising solution for the next-generation wireless communications. The deployment of relay stations (RS) in cellular networks makes resource scheduling a more crucial and challenging task. In this paper, a novel centralized scheduling scheme called centralized scheduling with void filling (CS-VF) is proposed for two-hop relay-enhanced networks. In CS-VF, the remaining slots in the RS-subframe are filled with packets destined to users who directly communicate with the base station (BS). Moreover, based on our CS-VF scheduling scheme, four representative single-hop scheduling algorithms: round-robin, max C/I, max-min fairness, and proportional fairness, are extended to multihop scenarios. Simulation results indicate that when compared with the existing centralized scheduling scheme, which does not consider void filling, our proposed CS-VF scheme is more adaptable to different traffic distributions caused by dynamic network topology and user mobility. And it enhances not only the system throughput but also the fairness among users.

A preemption scheme, called Resource Consumption Based Preemption (RCBP), is proposed in order to reduce unfairness in an OBS network. In RCBP, the network operator can select any parameters to represent the cost of network resource consumption, and this makes the RCBP flexible to solve complicated fairness problems. We evaluate the performance of the RCBP in reducing unfairness among bursts with different numbers of hops between source and destination in a simulated ring network. The numerical results show that RCBP can enhance fairness in the OBS network.

In this paper, we address the problem of cross layer scheduling for the downlinks of multiple-input multiple-output (MIMO) cellular systems. Previously proposed MIMO schedulers have problems such as ignoring traffic arrival process or complexity. We focus on designing an adaptive fair scheduling algorithm to traffic load changes. We propose a load adaptive multi-output fair queueing (LA-MO-FQ) scheduler, which is based on a fair queueing algorithm with mechanisms for rate selection, compensation of lagging users, and virtual time system. Since some of the scheduler’s system parameters are sensitive to the traffic load, it dynamically adjusts them in a way with low complexity so the system performs better. Furthermore, we also propose in this paper some formulae for the time and service fairness comparisons of MIMO schedulers. Intensive simulation studies considering the mobility of users and the traffic characteristics demonstrate the good performance of LA-MO-FQ. Finally, we compare both the performance and fairness of our scheduler with some famous existing schedulers.

This paper describes brand-new layer-1 bandwidth on demand (BoD) services implemented in the new Japanese academic backbone network, called SINET3. SINET3 is an advanced converged network that provides multi-layer transfer, enriched VPN, enhanced QoS, and layer-1 BoD services. The layer-1 BoD services are dynamic layer-1 resource allocation services directly triggered by users and artfully achieved on the multi-service platform by using a layer-1 BoD server. This paper first explains how the network accommodates a wide variety of network services by effectively combining leading-edge technologies. The paper next describes the overall mechanism for the dynamic layer-1 path setup on the multi-service platform and details the functions of the BoD server in many aspects. The designs focus on the tangible achievement of these services over a nationwide network composed of 75 layer-1 switches and 12 IP/MPLS routers.

Mobile ad hoc networking allows nodes to form temporary networks and communicate with each other possibly via multiple hops. By using a special node called the gateway, an ad hoc network can be connected to the Internet so that packets generated in the ad hoc network can be relayed to the Internet and vice versa. However, a problem may arise in which the network bandwidth is not used fairly among the nodes. That is, nodes near the gateway may overuse the bandwidth while nodes far away from the gateway scarcely share the bandwidth. Furthermore, if the destination node is located in the same ad hoc domain as the source, existing route selection methods result in high overhead or long routes. Addressing these problems, we propose an efficient tree construction algorithm that constructs a tree structure rooted at the gateway in order to improve the fairness of the bandwidth usage. We also devise a rerouting algorithm that dynamically finds new and better routes for the internal traffic within the same ad hoc domain.

Quality of Service (QoS) in Layer 3 is essential for satisfying various types of Internet-application requirements by making the best use of limited bandwidth. A large number of such applications still use TCP/IP in order to connect various types of computer nodes. This paper investigates QoS performance in a network equipment testbed. We examine the major Class of Service (CoS) functions provided by the Juniper T320 router, and measure their performance. In addition to fundamental analysis of the QoS behavior, we show the impact of QoS operations on a parallel system distributed in multi-domain networks as a practical case study of grid environments.

The antennas array in multiple-input multiple-output (MIMO)-based systems can be used to increase the data rate. Besides, many fair scheduling algorithms have been proposed for wireless networks with single antenna, single rate or multiple rates. However, a multi-output fair packet scheduler using rate selection mechanism and compensation scheme in wireless networks has not yet been designed. Therefore, we propose a multi-output fair queueing (MO-FQ) scheduler based on virtual time system which takes into account the above mentioned issues. Intensive simulation studies, considering traffic characteristics and the mobility of users, demonstrate the efficiency and adaptability to the environment of our scheduler.

A sensor network consists of a large number of distributed wireless sensors which are equipped with low power wireless transceivers. Network lifetime, scalability, and load balancing are important requirements for many data gathering sensor network applications. Network clustering is an effective approach for achieving these goals wherein sensors are grouped into multiple clusters. Multihop data transmission may provide efficient energy conservation further. A sensor in a cluster may work as a cluster head for data gathering, aggregation, and report to the base station (BS). These tasks can also be performed by distinct sensors in the same cluster. In this paper, we clarify some characteristics of multi-hop data transmission. Furthermore, we propose a new data gathering approach for single-hop transmission wherein both the data gathering and the aggregation are performed by the same sensor in a cluster but the report to the BS may be done by a different sensor. The simulation results show that the proposed algorithm achieves better performance than other existing algorithms.