Search

In this paper, we propose a single-agent Monte Carlo-based reinforced feature selection method, as well as two efficiency improvement strategies, i.e., early stopping strategy and reward-level interactive strategy. Feature selection is one of the most important technologies in data prepossessing, aiming to find the optimal feature subset for a given downstream machine learning task. Enormous research has been done to improve its effectiveness and efficiency. Recently, the multi-agent reinforced feature selection (MARFS) has achieved great success in improving the performance of feature selection. However, MARFS suffers from the heavy burden of computational cost, which greatly limits its application in real-world scenarios. In this paper, we propose an efficient reinforcement feature selection method, which uses one agent to traverse the whole feature set and decides to select or not select each feature one by one. Specifically, we first develop one behavior policy and use it to traverse the feature set and generate training data. And then, we evaluate the target policy based on the training data and improve the target policy by Bellman equation. Besides, we conduct the importance sampling in an incremental way and propose an early stopping strategy to improve the training efficiency by the removal of skew data. In the early stopping strategy, the behavior policy stops traversing with a probability inversely proportional to the importance sampling weight. In addition, we propose a reward-level and training-level interactive strategy to improve the training efficiency via external advice. What's more, we propose an incremental descriptive statistics method to represent the state with low computational cost. Finally, we design extensive experiments on real-world data to demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In the areas after natural disaster strikes, the ground communication network can be failed, due to the damage of the communication infrastructure. However, during or after the natural disasters such as earthquakes or tsunamis, ground vehicles may not enter the affected areas easily to set up mobile base stations. Unmanned Aerial Vehicles (UAVs) can be an alternate to provide emergency coverage for ground nodes (GNs). Therefore, how to determine the best location for UAV to achieve the maximum coverage is a key issue. In this paper, an adaptive UAV deployment scheme is proposed to solve the coverage problem of UAV- aided GNs communication. The objective is to optimize the location of the UAV to cover as many GNs as possible and reduce communication energy consumption. We construct a unique analysis method assisted by the collected ground information to solve this problem. First, we propose an information collection method based on the communication probability of Line-of-Sight (LoS) to guarantee the integrity of ground information acquisition. Then, based on the results of the information collection, a virtual obstacle model is built around each GN. Meanwhile, the UAV’s coverage problem is decomposed from the horizontal and vertical dimensions to simplify the difficulty of solving the problem. Finally, the best location of the UAV and the optimal transmission power of GNs can be obtained through the iterative methods and the power control, respectively. The extensive simulation results demonstrate that the proposed deployment scheme outperforms its counterparts. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology to increase resistance against malicious attacks is a complex problem, especially on 3-dimension (3D) topological deployment. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, the additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the population’s evolution. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. A self-competitive mechanism is used to ensure convergence. We construct a 3D IoT topology that is optimized by AREA. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods. [ABSTRACT FROM AUTHOR]

Occupying the most significant portion of global data traffic, video is being generated in almost every aspect of our life. Because of its huge volume, we are depending much more heavily on machine intelligence based analysis. In the meantime, video coding technology has been continuously improved for better compression efficiency. However, the state-of-the-art video coding standards, such as H.265/HEVC and versatile video coding (VVC), are still designed assuming that the compressed video will be watched by a human later. Such a design is not optimal when the compressed video will be used by computer vision applications. While the human visual system (HVS) is consistently sensitive to the content with high contrast, the impact of pixels on computer vision algorithms is task driven. For example, because of the different categories of objects used to train detection algorithms, the influence of the same image content on those detectors also varies. Therefore, human oriented video coding strategies may not be optimal when the compressed signal is further processed by algorithms, as the encoder is unaware of the task specific information. In this article, taking object detection as an example, we propose a novel video coding strategy for computer vision. By protecting the information according to its importance for an object detector rather than for the human visual system, our proposed method has the potential to achieve a better object detection performance with the same bandwidth. The main contributions of our paper are: 1) the modeling of the relationship between object detection accuracy and bit rate; 2) a back propagation based method to analyze the influence of each pixel on the detection of target objects; 3) an object detection oriented bit allocation and codec control parameter determination scheme; 4) an evaluation metric to compare the impact of video coding strategies on a given object detector over a predefined range of bit rate. Experimental results demonstrate that our proposed algorithm can better preserve the video content vital for object detection than state-of-the-art video coding schemes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the security problem of Time of Arrival (ToA) based localization schemes in a wireless sensor network (WSN) with multiple attackers and this paper focuses on defending against external attacks, especially under cooperative external attackers. The prior scheme for defending against the attacks in the localization scheme often introduce high communication overhead and their security relies on the capability of the attackers. In this paper, we propose a lightweight secure ToA-based localization scheme in a WSN by exploiting the noise feature caused by external distance attacks. In comparison with the prior scheme, our scheme provides lower communication overhead and a higher level of security. We theoretically analyze the performance of the proposed scheme over fading channels and derive the closed-form expressions. We implemented our scheme and conducted extensive performance comparisons through simulations. Our experimental results show that the closed-form expressions for the detection performance perfectly match with their simulation results as we expected. The communication overhead of the proposed scheme is saved by 72.8% than that of the prior scheme for different numbers of anchors and is independent of the times of measurements. [ABSTRACT FROM AUTHOR]

Random mobility models (RMMs) capture the random mobility patterns of mobile agents, and have been widely used as the modeling framework for the evaluation and design of mobile networks. All existing RMMs in the literature assume independent movements of mobile agents, which does not hold for unmanned aircraft systems (UASs). In particular, UASs must maintain a safe separation distance to avoid collision. In this paper, we propose a new modeling framework of random mobility models equipped with physical sense-and-avoid protocols to capture the flexible, variable, and uncertain movement patterns of UASs subject to separation safety constraints. For the random direction (RD) RMM equipped with a commonly used sense-and-avoid (S&A) protocol, named sense-and-stop (S&S), we provide its statistical properties including stationary location distribution and stationary inter-vehicle distance distribution, using the Markov analysis. This study provides knowledge on the impact of S&A protocols to critical UAS networking statistics. In addition, we define collision probabilities and airspace capacity concepts for UASs based on the inter-vehicle distance distribution, and derive their closed-form expressions. This analytical framework mathematically bridges local autonomy with global airspace capacity, and allows the impact analysis of local autonomy configurations for effective UAS airspace capacity management. [ABSTRACT FROM AUTHOR]

By exploiting the massive data generated from the numerous interconnected machines and control systems, industrial Internet-of-Things (IIoT) provides unprecedented opportunities for facilitating the intelligence and smartness of manufacturing. Timely processing the large-scaled IIoT data by the conventional computation framework, such as Cloud computing, however, is nontrivial due to its costly resource usage, intolerable delay, and unbearable backbone pressures. By leveraging the idle resources of smart objects at the edge, mobile device cloud (MDC) becomes promising for the IIoT data analysis, thanks to the flexible resource provision and nearby task offloading. However, MDC workers are mostly human-carried devices with large scale, high dynamic resource provision, and untruthful behaviors, which pose significant challenges on MDC task allocation. In this article, we propose a blockchain-based decentralized and truthful framework for MDC (BC-MDC). BC-MDC enables the decentralization and prevents dishonesty by incorporating a plasma-based blockchain into the MDC. We design four smart contracts for distributedly managing the worker registration, task posting/allocation, rewarding, and penalizing. Furthermore, MDC task allocation is formulated as a stochastic optimization problem that jointly minimizes the long-term processing cost and risk of task failing. We also design a truthful reward/penalty algorithm that stimulates workers to provide resources and enforce them to keep the promise as well. Collaborated by the extensive simulation tests, we show how our proposed scheme achieves low cost on usage and high truthfulness and outperforms state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art learning adaptive discriminative correlation filters (LADCF) tracking algorithm with a re-detection component based on the support vector machine (SVM) model. The LADCF tracking algorithm localizes the target in each frame, and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to-correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking. [ABSTRACT FROM AUTHOR]

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs. [ABSTRACT FROM AUTHOR]

Understanding the statistical latency between two groups of hosts in a period of time is of great significance to a wide variety of Internet applications and services, such as Service-Level Agreement (SLA) compliance monitoring and Virtual Network Function (VNF) placement. However, direct latency measurement methods are not always applicable to large-scale situations while the existing indirect methods often incur extra deployment costs or security problems. To address this challenge, we design an indirect method based on widely-distributed clients called SSL (Surrogate-based method for large-scale Statistical Latency measurement). SSL estimates the latency between two arbitrary hosts using the measured latencies from several selected clients near one end host, which are called the host's surrogates, to the other end host. To overcome the limited capacity of the volatile clients with unstable CPU, memory, and bandwidth resources, we propose an innovative two-step measurement task assignment mechanism for SSL that can achieve high accuracy measurement results while satisfying the resource constraints simultaneously. Moreover, SSL adopts a sampling technique to reduce the overhead in large-scale measurements, and a resampling technique to determine the confidence interval. Simulation experiments show that SSL can achieve more than 90 percent accuracy in most situations with 10 percent client density and 15 percent sampling rate. [ABSTRACT FROM AUTHOR]

LTE D2D communication, which emerges as a SoLoMo network enabler, has become an essential part of our everyday life and brings benefits to subscribers and telecommunication operators alike. Proximate device discovery, or peer discovery, is an essential part in the D2D technology since the node first needs to find its neighbors before initiating D2D communication. With functions of intelligent discovery, context awareness and interactive exchange, D2D communication facilitates content sharing of a device with others. A plethora of works about device discovery have been carried out in ad hoc networks. However, as a new feature in LTE systems, the special characteristics of proximate device discovery raise several unique challenges. In this article, variant aspects of proximate device discovery are surveyed. We first overview the fundamentals of peer discovery, including basic concepts, application scenarios, associated requirements, along with the current standardization progress. The key challenges and open issues in the design of device discovery signals and device discovery protocols for D2D communication in centralized and distributed situations are respectively surveyed. Possible approaches to address these challenges are discussed. [ABSTRACT FROM AUTHOR]

As urban traffic condition is diverse and complicated, applying reinforcement learning to reduce traffic congestion becomes one of the hot and promising topics. Especially, how to coordinate the traffic light controllers of multiple intersections is a key challenge for multi-agent reinforcement learning (MARL). Most existing MARL studies are based on traditional $Q$ -learning, but unstable environment leads to poor learning in the complicated and dynamic traffic scenarios. In this paper, we propose a novel multi-agent recurrent deep deterministic policy gradient (MARDDPG) algorithm based on deep deterministic policy gradient (DDPG) algorithm for traffic light control (TLC) in vehiclar networks. Specifically, the centralized learning in each critic network enables each agent to estimate the policies of other agents in the decision-making process and each agent can coordinate with each other, alleviating the problem of poor learning performance caused by environmental instability. The decentralized execution enables each agent to make decisions independently. We share parameters in actor networks to speed up the training process and reduce the memory footprint. The addition of LSTM is beneficial to alleviate the instability of the environment caused by partial observable state. We utilize surveillance cameras and vehicular networks to collect status information for each intersection. Unlike previous work, we have not only considered the vehicle but also considered the pedestrians waiting to pass through the intersection. Moreover, we also set different priorities for buses and ordinary vehicles. The experimental results in a vehicular network show that our method can run stably in various scenarios and coordinate multiple intersections, which significantly reduces vehicle congestion and pedestrian congestion. [ABSTRACT FROM AUTHOR]

In this paper, a light emitting diode grouping based spatial modulation (LGSM) scheme is investigated for the indoor visible light communication (VLC) system for a better symbol error rate (SER) performance with the aid of characteristics of the spatial modulation. In consideration of the high channel correlation property of the visible light communication, the multiple transmitting LEDs are separated into different groups with the purpose of alleviating the channel correlation among each group. Owing to the proposed grouping scheme, the spatial information and channel correlation property can be perfectly utilized to improve the system performance of SER, which implies a higher transmission capability by the proposed LGSM scheme. Meanwhile, the mathematical derivation of SER for the proposed LGSM scheme is also provided by considering the maximal likelihood detection. Both of the simulated and theoretical results have proved that a better SER performance can be obtained via the proposed LGSM scheme compared with the common spatial modulation VLC system. Furthermore, the influence of the elevation angle of the receive photo detector in the LGSM VLC system is also investigated. [ABSTRACT FROM AUTHOR]

As video data are occupying an increasingly more significant portion of global data traffic, video communication has become an indispensable component for most multimedia applications. As an enabling technology of video communication, although video coding is well standardized, the strategy to control video codec is highly customized to applications. The consistency of video quality is being paid more attention in many emerging applications. For example, in video surveillance, the quality of video frames should be stable in order to ensure the performance of machine intelligence algorithm, such as object detection precision. In this paper, we focus on achieving constant objective reconstruction quality in the process of video coding. To achieve certain rate-distortion performance, bit rate and distortion metric are usually modeled as a function of video content and control parameters of codec. For content modeling in existing work, there is still room for improvement, including the design of more efficient content feature, the compensation for assumption about constant RD characteristics among consecutive frames, and the adjustment of Lagrangian multiplier $\lambda $ according to content property. The main contributions of this paper are: 1) a robust content adaptive model for residual bit rate modeling based on content statistics called mean absolute partial transformed difference (MAPTD); 2) a content-related header bit rate modeling; 3) preprocessing scheme for robust content feature estimation at scene change; 4) a distortion model consistent with local content; and 5) content adaptive $\lambda $ determination. The experimental results show that our constant quality control strategy can achieve superior performance compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Faster-than-Nyquist signaling (FTNS) is capable of improving the spectral efficiency (SE) of communication systems. However, for conventional single-carrier FTNS (SC-FTNS) in which only symbol interval is reduced, the increase of SE is very limited due to the presence of inter-symbol interference (ISI) introduced by the FTNS. To deal with this problem, this paper proposes a new time-frequency compressed SC-FTNS (TFC-SC-FTNS) scheme that includes the conventional FTNS as a special case, to improve the SE via two dimensions simultaneously: time dimension by stacking symbols closer; frequency dimension by precoding to make the FTN signal spectrum more compact. Further, an optimization subject to a spectral mask constraint is performed on the precoder to suppress the ISI, according to a mean-square-error criterion, but the optimization problem is non-convex. A nontrivial contribution in the new scheme is that the non-convex problem is transformed into a convex one by a change of variable and an addition of admissibility constraint. Simulation results demonstrate that the proposed scheme significantly outperforms the conventional FTNS in terms of achievable SE or, equivalently, reception performance at a given SE. Further, with larger constellations applied, the gains of the TFC-FTNS increase. [ABSTRACT FROM AUTHOR]

The newest generation of video coding standard, high efficiency video coding (HEVC), significantly improves the video compression efficiency by introducing more flexible block partitioning structures and richer coding modes than those of the previous coding standards; however, the encoders suffer from high-computational complexity, which greatly hinders their extensive application. Extensive studies on optimizing the complexity of the HEVC encoders have been conducted. However, most studies do not effectively achieve a trade-off between the rate-distortion (RD) performance loss and complexity during the rate-distortion optimization (RDO). In this paper, we mathematically define the complexity-constrained RDO problem as a constrained optimization problem of subset selection. Next, based on the classification methodology, the derivation process for this optimization problem is simplified to find the adaptive threshold function in the feature space with extremely low complexity. The proposed method is also highly general and is applicable to algorithm design for various coding mode decisions, such as coding unit splitting, prediction unit partitioning and transform unit tree decision, and the global optimum can be achieved. Compared with existing methods, the experimental results show that the proposed method can reduce the coding time by 2–16% with the same RD performance loss and can decrease the BD rate by 0.1–1.2% under the same complexity. In addition, this method is capable of flexibly adjusting the complexity under different rate-distortion complexity trade-off requirements. [ABSTRACT FROM AUTHOR]

Due to the random delay, local maximum and data congestion in vehicular networks, the design of a routing is really a challenging task especially in the urban environment. In this paper, a distributed routing protocol DGGR is proposed, which comprehensively takes into account sparse and dense environments to make routing decisions. As the guidance of routing selection, a road weight evaluation (RWE) algorithm is presented to assess road segments, the novelty of which lies that each road segment is assigned a weight based on two built delay models via exploiting the real-time link property when connected or historic traffic information when disconnected. With the RWE algorithm, the determined routing path can greatly alleviate the risk of local maximum and data congestion. Specially, in view of the large size of a modern city, the road map is divided into a series of Grid Zones (GZs). Based on the position of the destination, the packets can be forwarded among different GZs instead of the whole city map to reduce the computation complexity, where the best path with the lowest delay within each GZ is determined. The backbone link consisting of a series of selected backbone nodes at intersections and within road segments, is built for data forwarding along the determined path, which can further avoid the MAC contentions. Extensive simulations reveal that compared with some classic routing protocols, DGGR performs best in terms of average transmission delay and packet delivery ratio by varying the packet generating speed and density. [ABSTRACT FROM AUTHOR]

This paper investigates the application of non-orthogonal multiple access in millimeter-Wave communications (mmWave-NOMA). Particularly, we consider downlink transmission with a hybrid beamforming structure. A user grouping algorithm is first proposed according to the channel correlations of the users. Whereafter, a joint hybrid beamforming and power allocation problem is formulated to maximize the achievable sum rate, subject to a minimum rate constraint for each user. To solve this non-convex problem with high-dimensional variables, we first obtain the solution of power allocation under arbitrary fixed hybrid beamforming, which is divided into intra-group power allocation and inter-group power allocation. Then, given arbitrary fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming to minimize the inter-group interference. Finally, the analog beamforming problem with the constant-modulus constraint is solved with a proposed boundary-compressed particle swarm optimization algorithm. The simulation results show that the proposed joint approach, including user grouping, hybrid beamforming and power allocation, outperforms the state-of-the-art schemes and the conventional mmWave orthogonal multiple access system in terms of achievable sum rate, and energy efficiency. [ABSTRACT FROM AUTHOR]

We consider the online shortest path routing (SPR) of a network with stochastically time varying link states under potential adversarial attacks. Due to the denial of service (DoS) attacks, the distributions of link states could be stochastic (benign) or adversarial at different temporal and spatial locations. Without any a priori, designing an adaptive and optimal DoS-proof SPR protocol to thwart all possible adversarial attacks is a very challenging issue. In this paper, we present the first such integral solution based on the multi-armed bandit (MAB) theory, where jamming is the adversarial strategy. By introducing a novel control parameter into the exploration phase for each link, a martingale inequality is applied in our formulated combinatorial adversarial MAB framework. The proposed algorithm could automatically detect the specific jammed and un-jammed links within a unified framework. As a result, the adaptive online SPR strategies with near-optimal learning performance in all possible regimes are obtained. Moreover, we propose the accelerated algorithms by multi-path route probing and cooperative learning among multiple sources, and study their implementation issues. Comparing to existing works, our algorithm has the respective 30.3% and 87.1% improvements of network delay for oblivious jamming and adaptive jamming given a typical learning period and a 81.5% improvement of learning duration under a specified network delay on average, while it enjoys almost the same performance without jamming. Lastly, the accelerated algorithms can achieve a maximal of 150.2% improvement in network delay and a 431.3% improvement in learning duration. [ABSTRACT FROM AUTHOR]

With the development of 4G/5G technology and smart devices, more and more users begin to play games via their mobile devices. As a promising way to enable users to play any games, cloud gaming is proposed to stream game scene rendered remotely in the cloud with the format of video. However, it faces major challenges in terms of long delay and high network bandwidth. To this end, a novel framework named EdgeGame is proposed to improve the cloud gaming experience by leveraging resources in the edge. Compared to existing cloud gaming systems, EdgeGame offloads the computation-intensive rendering to the network edge instead, which can reduce network delay and bandwidth consumption greatly. Moreover, EdgeGame introduces deep reinforcement learning in the edge to adjust the video bitrates adaptively to accommodate the network dynamics. Finally, we implemented a prototype system and compared it with an existing cloud gaming system. The experiments show that EdgeGame can reduce the average network delay by 50 percent and improve user's QoE by 20 percent. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the combination of non-orthogonal multiple access and millimeter-wave communications (mmWave-NOMA). A downlink cellular system is considered, where an analog phased array is equipped at both the base station and users. A joint Tx-Rx beamforming and power allocation problem is formulated to maximize the achievable sum rate (ASR) subject to a minimum rate constraint for each user. As the problem is non-convex, we propose a sub-optimal solution with three stages. In the first stage, the optimal power allocation with a closed form is obtained for an arbitrary fixed Tx-Rx beamforming. In the second stage, the optimal Rx beamforming with a closed form is designed for an arbitrary fixed Tx beamforming. In the third stage, the original joint Tx-Rx beamforming and power allocation problem is reduced to a Tx beamforming problem by using the previous results, and a boundary-compressed particle swarm optimization (BC-PSO) algorithm is proposed to obtain a sub-optimal solution. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results show that the proposed sub-optimal solution can achieve a significantly better performance in terms of ASR compared with those of the state-of-the-art schemes and the conventional mmWave orthogonal multiple access (mmWave-OMA) system. [ABSTRACT FROM AUTHOR]

The integration of non-orthogonal multiple access in millimeter-Wave communications (mm Wave-NOMA) can significantly improve the spectrum efficiency and increase the number of users in the fifth-generation (5G) mobile communication and beyond. In this paper, we consider a downlink mm Wave-NOMA cellular system, where the base station is mounted with an analog beamforming phased array, and multiple users are served in the same time-frequency resource block. To guarantee user fairness, we formulate joint beamforming and power allocation problem to maximize the minimal achievable rate among the users, i.e., we adopt the max–min fairness. As the problem is difficult to solve due to the non-convex formulation and high dimension of the optimization variables, we propose a sub-optimal solution, which makes use of the spatial sparsity in the angle domain of the mm Wave channel. In the solution, the closed-form optimal power allocation is obtained first, which reduces the joint optimization problem into an equivalent beamforming problem. Then, an appropriate beamforming vector is designed. The simulation results show that the proposed solution can achieve a near-upper-bound performance in terms of achievable rate, which is significantly better than that of the conventional mm Wave orthogonal multiple access (mm Wave-OMA) system. [ABSTRACT FROM AUTHOR]

The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model communication paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model communication through a set of experiments. Moreover, the interoperability in deep learning model communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have been the popular targets for cyberattacks these days. One type of network topology for WSNs, the scale-free topology, can effectively withstand random attacks in which the nodes in the topology are randomly selected as targets. However, it is fragile to malicious attacks in which the nodes with high node degrees are selected as targets. Thus, how to improve the robustness of the scale-free topology against malicious attacks becomes a critical issue. To tackle this problem, this paper proposes a Robustness Optimization scheme with multi-population Co-evolution for scale-free wireless sensor networKS (ROCKS) to improve the robustness of the scale-free topology. We build initial scale-free topologies according to the characteristics of WSNs in the real-world environment. Then, we apply our ROCKS with novel crossover operator and mutation operator to optimize the robustness of the scale-free topologies constructed for WSNs. For a scale-free WSNs topology, our proposed algorithm keeps the initial degree of each node unchanged such that the optimized topology remains scale-free. Based on a well-known metric for the robustness against malicious attacks, our experiment results show that ROCKS roughly doubles the robustness of initial scale-free WSNs, and outperforms two existing algorithms by about 16% when the network size is large. [ABSTRACT FROM AUTHOR]

Welcome to the IEEE JSAC special issue onAirborne Communication Networks. The goal of this special issue is to disseminate the contributions in the field of airborne communication networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the combination of two key enabling technologies for the fifth generation wireless mobile communication, namely millimeter-wave (mm-wave) communications and non-orthogonal multiple access (NOMA). In particular, we consider a typical two-user uplink mm-wave-NOMA system, where the base station equips an analog beamforming structure with a single radio-frequency chain and serves two NOMA users. An optimization problem is formulated to maximize the achievable sum rate of the two users while ensuring a minimal rate constraint for each user. The problem turns to be a joint power control and beamforming problem, i.e., we need to find the beamforming vectors to steer to the two users simultaneously subject to an analog beamforming structure, and meanwhile control appropriate power on them. As direct search for the optimal solution of the non-convex problem is too complicated, we propose decomposing the original problem into two sub-problems that are relatively easy to solve: one is a power control and beam gain allocation problem, and the other is an analog beamforming problem under a constant-modulus constraint. The rationale of the proposed solution is verified by extensive simulations, and the performance evaluation results show that the proposed sub-optimal solution achieves a close-to-bound uplink sum-rate performance. [ABSTRACT FROM AUTHOR]

In this paper we consider the time-varying characteristics of practical wireless networks and propose a joint dynamic rate allocation and transmission scheduling optimization scheme for scalable video multirate multicast based on opportunistic routing (OR) and network coding. With OR the decision of optimal routes for scalable video coding layered streaming is integrated into the joint optimization formulation. The network throughput is also increased by taking advantage of the broadcast nature of the wireless shared medium and by network coding operations in intermediate nodes. To maximize the overall video reception quality among all destinations the proposed scheme can jointly optimize the video reception rate the associated routes to different destinations and the time fraction scheduling of transmitter sets that are concurrently transmitting in the shared wireless medium. By using dual decomposition and primal-dual update approach we develop a cross-layer algorithm in a fully distributed manner. Simulation results demonstrate significant network multicast throughput improvement and adaptation to dynamic network changes relative to existing optimization schemes. [ABSTRACT FROM PUBLISHER]

The backpressure scheduling scheme has been applied in Internet of Things, which can control the network congestion effectively and increase the network throughput. However, in large-scale Emergency Internet of Things (EIoT), emergency packets may exist because of the urgent events or situations. The traditional backpressure scheduling scheme will explore all the possible routes between the source and destination nodes that cause a superfluous long path for packets. Therefore, the end-to-end delay increases and the real-time performance of emergency packets cannot be guaranteed. To address this shortcoming, this paper proposes EABS, an event-aware backpressure scheduling scheme for EIoT. A backpressure queue model with emergency packets is first devised based on the analysis of the arrival process of different packets. Meanwhile, EABS combines the shortest path with backpressure scheme in the process of next-hop node selecting. The emergency packets are forwarded in the shortest path and avoid the network congestion according to the queue backlog difference. The extensive experiment results verify that EABS can reduce the average end-to-end delay and increase the average forwarding percentage. For the emergency packets, the real-time performance is guaranteed. Moreover, we compare EABS with two existing backpressure scheduling schemes, showing that EABS outperforms both of them. [ABSTRACT FROM PUBLISHER]

Interference has been a key challenge to wireless networks. As a fundamental transmission unit, the Gaussian interference channel (GIC) provides much insight on understanding the optimal transmission policy and the transmission limit over wireless networks. This paper investigates the multiple-layer power allocation of GIC that maximizes the system sum-rate. First, we derive the optimal signal-layer power allocation and the corresponding sum-rate in closed form for all cases of weak interference GIC based on the rate splitting scheme. Theoretical result indicates that: 1) In low power and asymmetric power regimes, the rate splitting scheme degrades to the pure public or private message transmission at transmitters and simple successive decoding process is efficient enough at receivers and 2) the signal-layer sum-rate is not concave for weak interference GIC and a frequency division scheme brings strict positive sum-rate gain for some power constraints. Second, we specify the relationship between the optimal bandwidth-power allocation of the frequency division scheme and the concave envelope of signal-layer sum-rate in the subband-layer. Finally, considering general GIC with time-varying flat fading, we present an optimal channel-state-layer power allocation associated with rate splitting and frequency division. Numerical results demonstrate that the comprehensive power allocation over the signal-layer, the frequency subbands, and the channel states can largely increase the sum-rate of weak interference GIC in most scenarios. [ABSTRACT FROM PUBLISHER]

Due to the recent proliferation of cyber-attacks, improving the robustness of wireless sensor networks (WSNs), so that they can withstand node failures has become a critical issue. Scale-free WSNs are important, because they tolerate random attacks very well; however, they can be vulnerable to malicious attacks, which particularly target certain important nodes. To address this shortcoming, this paper first presents a new modeling strategy to generate scale-free network topologies, which considers the constraints in WSNs, such as the communication range and the threshold on the maximum node degree. Then, ROSE, a novel robustness enhancing algorithm for scale-free WSNs, is proposed. Given a scale-free topology, ROSE exploits the position and degree information of nodes to rearrange the edges to resemble an onion-like structure, which has been proven to be robust against malicious attacks. Meanwhile, ROSE keeps the degree of each node in the topology unchanged such that the resulting topology remains scale-free. The extensive experimental results verify that our new modeling strategy indeed generates scale-free network topologies for WSNs, and ROSE can significantly improve the robustness of the network topologies generated by our modeling strategy. Moreover, we compare ROSE with two existing robustness enhancing algorithms, showing that ROSE outperforms both. [ABSTRACT FROM PUBLISHER]

Spreading a large file consisting of many packets over a mobile network is challenging due to the short meeting duration for each transmission. Moreover, two typical causes of inefficient file spreading are duplicate packet reception at the destination nodes and excessive overhead exchanges. We propose to employ fountain codes at the source node to jointly addresses the three issues: 1) each coded packet can be small enough to fit into the meeting duration; 2) duplicate packet reception is significantly reduced since each coded packet is innovative; and 3) overhead is greatly saved by using file-level ACK instead of packet-level ACK. We conduct performance analysis in terms of the source-to-destination file delay and source-to-destination file spreading time in both non-relaying and relaying scenarios. While packet duplication can be eliminated in the former scenario, there is still a non-trivial duplication probability if relaying is allowed. Therefore, we propose a fountain-coded two-hop relaying (FTTR) protocol to further reduce the packet duplication ratio so that the spreading performance does not degrade with network size. The file spreading time and packet duplication ratio of FTTR are derived in closed form and verified through simulations. [ABSTRACT FROM PUBLISHER]

With the tremendous development of data science, using unstructured documents to analyze marketing dynamics is attracting a great deal of attention. In this letter, we propose an iterative scheme to extract the new words, which is often a bottleneck for Chinese natural language processing (NLP) in financial markets analysis. In contrast to existing static features, the key novelty is the proposed dynamic features that characterize the similarity of context patterns. Via iteration, distinguishable seed context patterns are extracted. Tested on a 203 MB corpus, 19 291 words representing emerging industries, entities, projects, and products were extracted with a precision of 89.8% and recall of 88.9%, which outperforms most competitor methods. [ABSTRACT FROM PUBLISHER]

Decode and forward (DF) and compress and forward (CF) are two efficient cooperation strategies for relay networks. The combination of the DF and CF strategies is promising to improve the achievable rate of cooperative communication systems. To thoroughly aggregate the advantage of the DF and CF strategies, we put forward a joint power allocation and strategy selection (PASS) scheme for the half-duplex relay channel. The PASS scheme is shown to achieve a higher rate compared with a regular hybrid DF/CF scheme. The rate gain obtained in the static relay channel results from actively adjusting and optimizing the relay power, followed by selecting the better strategy between DF and CF. In particular, when the relay receives and transmits in sequential time slots, we characterize the positive rate improvement area of the PASS scheme analytically and give a near-optimal setting for approaching the best rate performance. The PASS scheme is extended to fading relay channels. We show that the rate increment obtained from the PASS scheme is further magnified by employing an advanced relay power allocation technique over channel states. The corresponding optimal power allocation is established based on the concavity of the rate achieved by the PASS scheme in static relay channel. Numerical results are presented to validate the analysis. [ABSTRACT FROM PUBLISHER]

Thanks to the rapid development of information technology, the size of a wireless network is becoming larger and larger, which makes spectrum resources more precious than ever before. To improve the efficiency of spectrum utilization, game theory has been applied to study efficient spectrum sharing for a long time. However, the scale of wireless networks in existing studies is relatively small. In this paper, we introduce a novel game called aggregative game and use it to model spectrum sharing in large-scale, heterogeneous, and dynamic networks. Meanwhile, the massive usage of the spectrum leads to easier divulgence of privacy of spectrum users, which calls for privacy and truthfulness guarantees. In a large decentralized scenario, each user has no priori about other users’ channel access decisions, which forms an incomplete information game. A “weak mediator,” e.g., the base station or licensed spectrum regulator, is introduced and it turns the incomplete spectrum sharing game into a complete one. This is essential in reaching a Nash equilibrium (NE). By utilizing past channel access experience, we propose an online learning algorithm to improve the utility of each user. We show that the learning algorithm achieves an NE over time and provides no regret guarantee for each user. Specifically, our mechanism admits an approximate ex-post NE, and is joint differentially private and incentive-compatible. Efficiency of the approximate NE is evaluated, and innovative scaling law results are disclosed. We also provide simulation results to verify our analysis. [ABSTRACT FROM AUTHOR]

The connectivity of moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) that critically influences the performance of data transmission. Due to lack of in-depth analysis of real-world vehicular mobility traces, we do not understand the connectivity in realistic large-scale urban scenarios. Specifically, the mechanism of how the mobility of networked vehicles impacts the network connectivity remains unknown. In this paper, we aim to unveil the underlying relationship between the mobility and connectivity of VANETs. To achieve this goal, we employ some key topology metrics, including component speed and component size, to characterize mobility and connectivity. In our investigation of a large-scale real-world urban mobility trace data set, we discover, to our surprise, that there exists a dichotomy in the relationship between component speed and size. This dichotomy indicates that mobility destroys the connectivity with a power-law decline when the component speed is larger than a threshold; otherwise, it has no apparent impact on connectivity. Based on this observation, we propose a mathematical model to characterize this relationship, which agrees well with empirical results. Our findings thus offer a comprehensive understanding of the relationship between mobility and connectivity in urban vehicular scenarios, and based on this, helpful guidelines can be provided in the design and analysis of VANETs. [ABSTRACT FROM AUTHOR]

Broadband mobile communication systems experience fading over a wide frequency band, which induces severe intersymbol interference and drastic channel gain fluctuation, resulting in a time–frequency doubly selective fading channel gain. Most existing works on fading channel modeling assume wide-sense stationarity with respect to (w.r.t.) time and uncorrelated scattering w.r.t. delay. However, due to the time-varying movement of mobile terminals, fading is usually nonstationary w.r.t. time, i.e., the Doppler spectrum is nonstationary. In addition, electromagnetic signals reflected/scattered from the same clustter/object are correlated and may induce nonstationarity w.r.t. delay due to time-varying cluster environments, e.g., wind causes the movement of leaves, resulting in time-varying scattering of leaves. To model nonstationary broadband mobile fading channels, this paper introduces a two-dimensional evolutionary spectrum (2-D ES) approach, which is compatible with the power spectral density of a stationary process. Based on the 2-D ES, we also propose an estimation method to estimate the 2-D ES parameters from a trace (sample path) of a fading process. Furthermore, we show how to set up a simulator to generate a 2-D stationary or nonstationary fading process according to our developed 2-D ES framework. Using the 2-D ES approach, we analyze a class of Nakagami- $m$ channels which is nonstationary in both the time and frequency domains. Two examples are used to show the accuracy of our developed 2-D ES-based channel modeling approach. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) communications in 60 GHz band have become a hot topic in wireless communications. New medium access control (MAC) protocols are needed because of the fundamental differences between mmWave communications and existing other communication systems. In mmWave wireless personal area networks, the channel transmission rates of links vary significantly because of the difference in the distance between nodes, the accuracy of beamforming, and the existence of obstructions. Owning to the directivity of mmWave links, spatial reuse should be exploited to improve network capacity. In this paper, we develop a channel transmission rate aware directional MAC protocol, termed RDMAC, in which both the multirate capability of links and spatial reuse are exploited to improve network performance. RDMAC has two stages. The first stage measures the channel transmission rates of links, and a heuristic algorithm is proposed to compute near-optimal measurement schedules with respect to the total number of measurements. The second stage accommodates the traffic demand of links, and a heuristic transmission scheduling algorithm is proposed to compute near-optimal transmission schedules with respect to the total transmission time. Simulations under various traffic modes show that compared with existing protocols, RDMAC has lower network latency, higher network throughput, and also a good fairness performance. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Widespread deployment of the Internet of Things (IoT) has changed network services in which people, processes, data, and things connect each other. According to Cisco, 500 billion devices are expected to be connected to the Internet by 2030, where IoT is the network of these connected devices. Currently, most advanced IoT devices are equipped with visual sensors, making up the so-called visual IoT (V-IoT). By means of advanced performance capabilities of 5G and AI infrastructures, V-IoT technologies are widely used in autonomous cars, immersive video, smart cities and more. [ABSTRACT FROM AUTHOR]

Knowledge of end-to-end network distances is essential to many service-oriented applications such as distributed content delivery and overlay network multicast, in which the clients have the flexibility to select their servers from among a set of available ones based on network distance. However, due to the high cost of global measurements in large-scale networks, it is infeasible to actively probe end-to-end network distances for all pairs. In order to address this issue, network distance prediction has been proposed by measuring a few pairs and then predicting the other ones without direct measurements, or splicing the path segments between each pair via observation. It is considered important to improve network performance, and enables service- oriented applications over large-scale networks. In this article, we first illustrate the basic ideas behind network distance prediction, and then categorize the current research work based on different criteria. We illustrate how different protocols work, and discuss their merits and drawbacks. Finally, we summarize our findings, and point out potential issues and future directions for further research. [ABSTRACT FROM PUBLISHER]

To address the problem of segmenting an image into sizeable homogeneous regions, this paper proposes an efficient agglomerative algorithm on the basis of modularity optimization. Given an oversegmented image that consists of many small regions, our algorithm automatically merges those neighboring regions that produce the largest increase in modularity index. When the modularity of the segmented image is maximized, the algorithm stops merging and produces the final segmented image. To preserve the repetitive patterns in a homogeneous region, we propose a feature on the basis of the histogram of states of image gradients and use it together with the color feature to characterize the similarity of two regions. By constructing the similarity matrix in an adaptive manner, the oversegmentation problem can be effectively avoided. Our algorithm is tested on the publicly available Berkeley Segmentation Data Set as well as the semantic segmentation data set and compared with other popular algorithms. Experimental results have demonstrated that our algorithm produces sizable segmentation, preserves repetitive patterns with appealing time complexity, and achieves object-level segmentation to some extent. [ABSTRACT FROM AUTHOR]

This paper investigates collaborative task execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative task execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative task execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local execution and the remote execution, the collaborative task execution can significantly save the energy consumption on the mobile device, prolonging its battery life. [ABSTRACT FROM PUBLISHER]