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Faced with explosive growth of data traffic and shortage of licensed spectrum, licensed-assisted access (LAA) to unlicensed spectrum has been proposed to boost system capacity. To ensure fair coexistence with WiFi systems, listen-before-talk mechanism has been standardized under LAA framework. However, in densely deployed urban networks, the system performance could severely deteriorate due to high collision probability. In this paper, we propose cooperative LAA (CLAA), where multiple LAA small base stations form a cluster and construct a virtual multiuser multiple-input single-output (MISO)/multiple-input and multiple-output (MIMO) system to transmit data cooperatively. CLAA can effectively reduce the number of contending nodes, thereby alleviating transmission collisions. A closed-form expression for the upper bound sum rate of the cluster is derived. Markov analysis is employed to derive the system collision probability and throughput for WiFi and LTE. Our analytical results point to an adequate cluster sizes, where the highest system throughput can be achieved. Extensive simulations confirm the validity of the proposed approach, and demonstrate that CLAA can increase by up to $\text{27}\%$ the overall system throughput, and improve by $\text{30}\%$ in fairness. [ABSTRACT FROM AUTHOR]

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

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

This paper investigates threshold-constrained joint waveform optimization for an integrated sensing and communication (ISAC) system. Unlike existing studies, we employ mutual information (MI) and sum rate (SR) as sensing and communication metrics, respectively, and optimize the waveform under constraints to both metrics simultaneously. This provides significant flexibility in meeting system performance. We formulate three different optimization problems that constrain the radar performance only, the communication performance only, and the ISAC performance, respectively. New techniques are developed to solve the original problems, which are NP-hard and cannot be directly solved by conventional semi-definite programming (SDP) techniques. Novel gradient descent methods are developed to solve the first two problems. For the third non-convex optimization problem, we transform it into a convex problem and solve it via convex toolboxes. We also disclose the connections between three optimizations using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions. [ABSTRACT FROM AUTHOR]

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

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

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

Integrating millimeter Wave (mmWave) and long-term evolution (LTE) sub-6 GHz bands to provide wireless services for high-speed rails (HSRs) is a promising solution to balance the ever-rising service demand and operational cost. In this paper, we optimize the network interface selection of the mmWave and LTE interfaces for HSR communications, to minimize the operational cost while satisfying the service requests of passengers. The network interface selection problem is formulated as a finite-horizon discrete-time Markov decision process (MDP) problem. Based on the file requests and associated deadlines of a passenger and the train schedule, we solve the MDP problem ahead-of-time by using dynamic programming (DP) and achieve optimal interface selection for each waypoint along the railways. Further, by exploiting the subadditivity and superadditivity of the considered problem, we reveal that an optimal switch of interfaces is only triggered at the moment when the remaining undelivered data or the elapsed time reaches their specific thresholds. Based on the thresholds, the optimal interface selection can be directly achieved for every waypoint, hence eliminating the need for DP and substantially reducing the computational complexity. The proposed algorithms are evaluated with practical train schedules. Simulation results show that the proposed algorithms can substantially reduce the operational cost by 35% in our considered simulation setting, as compared to existing alternatives. [ABSTRACT FROM AUTHOR]

It is challenging to realize ultra-reliable and low latency communications (URLLC) under severe shadow fading and imperfect channel state information (CSI). However, reliability can be increased by exploiting space diversity from multiple receive antennas rather than retransmission with limited latency. Massive multi-user multiple-input-multiple-output (MU-MIMO) is studied to enable URLLC with imperfect CSI from least-square channel estimation. The linear minimum error probability (MEP) detector with a given length of pilots (LoP) is derived. Further, the LoP is optimized to minimize the error probability of the uplink with a limited number of channel uses, using the finite blocklength information theory and one-dimensional search methods. Numerical results verify that the proposed linear MEP detection incorporated in massive MU-MIMO improves reliability with limited latency and imperfect CSI. [ABSTRACT FROM AUTHOR]

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

Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms. [ABSTRACT FROM PUBLISHER]

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

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. This paper expands the control domain to a large area with multiple intersections. In particular, a Multi-intersection Vehicular Cooperative Control (MiVeCC) is proposed to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Under the framework, a two-stage reinforcement learning is implemented to obtain the optimal policy for vehicle control. To support RL in the proposed framework, a multi-vehicle state representation method and a safety-oriented value representation method are designed. The former structures the collected vehicle information, and the latter evaluates the traffic condition. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with benchmark methods. [ABSTRACT FROM AUTHOR]

Fixed relay networks will be an integrated component of future International Mobile Telecommunications (IMT)-Advanced. In this paper, we propose a new approach to relay handover and link adaptation. Our approach is built on a new Markov chain model (MCM) that comprehensively characterizes different relay protocols and quantifies their quality-of-service (QoS) measures, such as packet drop rate and latency, and spectral efficiency. Our relay handover and link adaptation scheme combats channel fluctuations while satisfying QoS requirements. It also accommodates multiple relay stations, supports multiplexing and diversity, and copes with mutually related and time-dependent transmissions. Analytical results, which were validated by simulations, show that our scheme can reduce the packet loss by up to three orders of magnitude. It also decreases the packet delay by up to 18% and improves the throughput by up to 10%. [ABSTRACT FROM PUBLISHER]

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

The attribute-based encryption (ABE) has drawn a lot of attention for fine-grained access control in blockchains, especially in blockchain-enabled tampering-resistant Internet-of-Things (IoT) systems. However, its adoption has been severely hindered by the incompatibility between the immutability of typical blockchains and the attribute updates/revocations of ABE. In this article, we propose a new blockchain-based IoT system, which is compatible with the ABE technique, and fine-grained access control is implemented with the attribute update enabled by integrating Chameleon Hash algorithms into the blockchains. We design and implement a new verification scheme over a multilayer blockchain architecture to guarantee the tamper resistance against malicious and abusive tampering. The system can provide an update-oriented access control, where historical on-chain data can only be accessible to new members and inaccessible to the revoked members. This is distinctively different from existing solutions, which are threatened by data leakage toward the revoked members. We also provide analysis and simulations showing that our system outperforms other solutions in terms of overhead, searching complexity, security, and compatibility. [ABSTRACT FROM AUTHOR]

This paper considers that a transmitter superposes secrecy transmissions to a closer receiver with a high security level in the active MIMO-OFDM channel it is using to send data to a farther receiver with a low security level, by using NOMA techniques. A joint optimization of power allocation and beamforming is important to maximize the secrecy rate to the closer receiver, while keeping the requested data rate of the farther receiver unaffected. The optimization problem is challenging because of coupled variables, and has yet to be addressed. We propose to decouple the problem between subcarrier power allocation and beamforming. Given beamforming, we prove the subcarrier power allocation for the two receivers can be equivalently transformed to two convex problems with waterfilling structures and closed-form solutions. Given power allocation, beamforming can be decoupled between subcarriers by distributing the rate requirement of the farther receiver across the subcarriers. The per-subcarrier beamforming is convexified by applying semidefinite relaxation and the Dinkelbach method. Simulations show the proposed approach can outperform its existing alternatives in terms of secrecy rate, and holistic designs of power allocation and beamforming are critical. [ABSTRACT FROM AUTHOR]

In vehicular ad hoc networks (VANET), effective trust establishment with authentication is an important requirement. Trust management among communicating vehicles is significant for secure message transmission; however, very less contributions have been made towards evaluating the trustworthiness of the node. This research work intends to introduce a new trust management system in VANET with two major phases: secured message transmission and node trustability prediction. The security assured message passing is carried out by incorporating the privacy preservation model under the data sanitization process. The key used for the sanitization process is optimally tuned by a new hybrid algorithm termed Sea Lion Explored-Whale Optimization Algorithm, which is the combination of Whale Optimization Algorithm and Sea Lion Optimization Algorithm, respectively. The blockchain technology is assisted to handle the key generated by the nodes. Subsequently, the trustability of the node is evaluated under novel specifics "two-level evaluation process" with a rule-based and machine learning-based evaluation process. Finally, the performance of the proposed model is verified and proved over other conventional methods for certain measures. [ABSTRACT FROM AUTHOR]

This paper analyzes the wireless connectivity of an Unmanned Aerial Vehicle (UAV) swarm in the presence of (un)intentional external interference from the ground. Different from existing studies, the swarm UAVs fly independently around a given three-dimensional (3D) location and are all within a 3D spherical space. Closed-form bounds are delivered for the average outage probability of a UAV from its nearest neighbor in the swarm, and the density of the swarm, which allows the swarm to operate uninterruptedly in the presence of the interference. Our analysis involves closed-form approximations of the instantaneous outage probability of a UAV from its nearest neighbor by using the first-order Marcum $Q$ -function and the zero-th order modified Bessel function of the first kind. The analysis also involves applying Jensen's inequality to the instantaneous outage probability to bound the average outage probability and the density of the swarm. Corroborated by simulations, our analysis is accurate, and useful to evaluate the impact of external interference on the connectivity of UAV swarms. Interesting insights are shed on the connectivity and coverage of the UAV swarm. [ABSTRACT FROM AUTHOR]

Two adaptive dedicated channel allocation algorithms, namely dynamic dedicated channel partitioning (${D^2}CP$) and dynamic dedicated channel partitioning with cooperation (${D^2}CP{-}C$), were proposed into improve the system throughput of ultra-dense networks (UDN). However, due to the incorrect use of the geometric–arithmetic mean inequality theorem, the average system throughput could not be guaranteed to be optimal. In this letter, we study the proposed ${D^2}CP$ and ${D^2}CP{-}C$ algorithms in UDN and deduce the average system throughput. Consequently, we prove that the equal resource allocation strategy proposed inis strictly not optimal. [ABSTRACT FROM AUTHOR]

Single-input multiple-output non-orthogonal multiple access (SIMO-NOMA) is a promising technology for the uplink (UL) of the Internet of things (IoT). A rate splitting scheme to guarantee max-min fairness (MMF) of SIMO-NOMA is studied. First, the achievable data rate of the minimum mean square error successive interference cancellation (MMSE-SIC) detection is formulated. An upper bound for the minimum data rate of UL users is derived, and it is proved that the upper bound can be achieved by rate splitting with MMSE-SIC detection. Then, an exhaustive-search rate splitting algorithm to maximize the minimum data rate is designed. The existence of a lower bound for the minimum data rate with two-layer SIMO-NOMA is proved. A low-complexity two-layer rate splitting algorithm is designed with suitable detection order to exceed the lower bound. Numerical results verify that rate splitting SIMO-NOMA has higher minimum data rate and lower transmission latency than single-input multiple-output orthogonal multiple access (SIMO-OMA) and un-layered SIMO-NOMA. [ABSTRACT FROM AUTHOR]

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

With significantly improved efficiency, largescale hybrid antenna arrays with tens to hundreds of antennas have great potential to support millimeter-wave (mmWave) communication for high-speed railway (HSR) applications. The significant beamforming gains rely on fast and accurate estimation of the angle-of-arrival (AoA), but this can be impeded by the high train speed, the cost/energy oriented design of arrays, and the severe attenuation of mmWave signals. This article reviews these challenges, and discusses the limitations of existing AoA estimation techniques under hybrid antenna array settings. The article further reveals a few recent theoretical breakthroughs that can potentially enable fast and reliable estimation, even based on severely attenuated signals. Under a speed setting of 500 km/h, a performance study is carried out to confirm the significant improvements of estimation accuracy and subsequent beamforming gains as the results of the breakthroughs. [ABSTRACT FROM AUTHOR]

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

This paper analyzes the link capacity between autonomous unmanned aerial vehicles (UAVs) with random three-dimensional (3-D) trajectories. This is distinctively different from existing works typically under the assumption of either two-dimensional (2-D) or deterministic trajectories, and particularly interesting to applications such as surveillance and air combat. The key idea is that we geometrically derive the probability distributions of the UAV-to-UAV (U2U) distances which, by exploiting the Jensen's inequality, can be translated to the closed-form bounds for the capacity between UAVs, and between UAVs and ground stations. Another important aspect is that we extrapolate the idea to dense UAV networks, and analyze the impact of network densification, imperfect channel state information, and interference from ground transmitters on the capacity. Corroborated by simulations, our analysis shows that a U2U link with random 2-D trajectories is superior in terms of capacity due to its short average link distance. It is also revealed that a UAV-to-ground link can incur substantially lower capacity than a U2U link even in the case the 3-D coverage of the UAVs is the same, as the result of its longer average link length. [ABSTRACT FROM AUTHOR]

This article investigates the prominent dilemma between capacity and reliability in heterogeneous ultra-dense distributed networks, and advocates a new measure of effective capacity to quantify the maximum sustainable data rate of a link while preserving the quality of service of the link in such networks. Recent breakthroughs are brought forth in developing the theory of the effective capacity in heterogeneous ultra-dense distributed networks. Potential applications of the effective capacity are demonstrated on the admission control, power control, and resource allocation of such networks, with substantial gains revealed over existing technologies. This new measure is of particular interest to ultra-dense deployment of the emerging 5G wireless networks in the unlicensed spectrum, leveraging the capacity gain brought by the use of the unlicensed band and the stringent reliability sustained by 5G in future heterogeneous network environments. [ABSTRACT FROM AUTHOR]

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

Multiantenna beamforming has potential to improve the efficiency of simultaneous wireless information and power transfer (SWIPT). Existing designs are focused on the downlink of multiple-input-single-output under the assumption of single-antenna users and negligible energy consumption in users’ circuitry, despite the fact that using multiple antennas on the user side can further improve system efficiency. In this paper, novel multiuser collaborative multiple-input multiple-output SWIPT systems are studied under the assumption of nonnegligible circuit energy consumption. Particularly, we convexify and maximize the uplink sum rate of active users, while maintaining the quality of service of their downlink data. The beamformers and durations of both links, and the power splitting factors of individual users are jointly optimized, using semidefinite programming and golden search. Further, the selection of active users is optimized, where all users are assumed to be active in the beginning and those detrimental to the sum-rate maximization are continually deactivated. Evident from simulations, the proposed approaches can eliminate the need for computationally prohibitive combinatorial integer programming at a marginal cost of the sum rate. [ABSTRACT FROM PUBLISHER]

The impact of a finite relay buffer on the throughput of two-way relay is analyzed from a new queueing theoretic point of view. Distinctively from recent Markov model based analyses, the proposed queueing theoretic analysis is able to infer closed-form asymptotic upper bounds for the throughput, shed valuable insights, and point out limitations in the recent analyses. Validated by simulations, our queueing theoretic analysis reveals that the throughput is increasingly insusceptible to the size of the relay buffer, as the buffer enlarges. Moreover, locking the relay in transmitting xored packets can hardly improve the throughput, especially under balanced channel conditions. This is due to the fact that the relay queues stabilize nonempty, and hence, xored packets are forwarded in most cases. [ABSTRACT FROM PUBLISHER]

• We reveal the intrinsic relation between the unknown frequency-domain representation of general band-limited graph signals and the graph Fourier transform basis. • A new analytical expression is derived to determine the graph Fourier transform basis of observed band-limited signals. • Given the graph Fourier transform basis and the statistical knowledge of the graph Laplacian, the estimation of the eigenvalues of the graph Laplacian is convex and efficiently solved using the alternating direction method of multipliers. A graph provides an effective means to represent the statistical dependence or similarity among signals observed at different vertices. A critical challenge is to excavate graphs underlying observed signals, because of non-convex problem structure and associated high computational requirements. This paper presents a new graph learning technique that is able to efficiently infer the graph structure underlying observed graph signals. The key idea is that we reveal the intrinsic relation between the frequency-domain representation of general band-limited graph signals, and the graph Fourier transform (GFT) basis. Accordingly, we derive a new closed-form analytic expression for the GFT basis, which depends deterministically on the observed signals (as opposed to being solved numerically and approximately in the literature). Given the GFT basis, the estimation of the graph Laplacian, more explicitly, its eigenvalues, is convex and efficiently solved using the alternating direction method of multipliers (ADMM). Simulations based on synthetic data and experiments based on public weather and brain signal datasets show that the new technique outperforms the state of the art in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Comparison studies on timer-based distributed cooperative retransmission protocols are challenging, given a variety of backoff techniques. We propose a new unified model, which can characterize a wide range of cooperative retransmission protocols. The key idea is a new trellis diagram that extrapolates the retransmission probabilities in each timeslot to the entire cooperative process. Following the trellis, performance metrics, such as success rate and collision intensity, can be derived in a structured manner. The new trellis model, coupled with Markov techniques, can be also extended to analyze the distributed binary exponential backoff processes of cooperative retransmissions. Confirmed by simulations, the proposed trellis model accurately reveals the impact of the relays' relative locations and density on different protocols. Our model also has the potential to be used as a management tool to adaptively configure protocol parameters. [ABSTRACT FROM PUBLISHER]

Detection of Denial-of-Service (DoS) attacks has attracted researchers since 1990s. A variety of detection systems has been proposed to achieve this task. Unlike the existing approaches based on machine learning and statistical analysis, the proposed system treats traffic records as images and detection of DoS attacks as a computer vision problem. A multivariate correlation analysis approach is introduced to accurately depict network traffic records and to convert the records into their respective images. The images of network traffic records are used as the observed objects of our proposed DoS attack detection system, which is developed based on a widely used dissimilarity measure, namely Earth Mover’s Distance (EMD). EMD takes cross-bin matching into account and provides a more accurate evaluation on the dissimilarity between distributions than some other well-known dissimilarity measures, such as Minkowski-form distance Lp and X^2 statistics. These unique merits facilitate our proposed system with effective detection capabilities. To evaluate the proposed EMD-based detection system, ten-fold cross-validations are conducted using KDD Cup 99 dataset and ISCX 2012 IDS Evaluation dataset. The results presented in the system evaluation section illustrate that our detection system can detect unknown DoS attacks and achieves 99.95 percent detection accuracy on KDD Cup 99 dataset and 90.12 percent detection accuracy on ISCX 2012 IDS evaluation dataset with processing capability of approximately 59,000 traffic records per second. [ABSTRACT FROM PUBLISHER]

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

Abstract: This paper summarizes the results of an experimental study on cotton stalk (CS) combustion in a circulating fluidized bed. The mixing and fluidizing characteristics of binary mixture of CS with 10–100mm in length and alumina bed material with a certain size distribution in a cold test facility were studied. The results show that CS by itself cannot fluidize, and adding inert bed material can improve the fluidization condition. CS can mix well with alumina at fluidization number N =3–7. As N is more than 7, there will exist a little more segregation. The study concerning combustion characteristics of pure CS was performed on a circulating fluidized bed with a heat input of 0.5MW. The effects of fluidizing velocity, secondary air flow and gas flow to the loop seal on the bed temperature profiles were investigated. Although there is a little more segregation at N higher than 7 in the cold tests, the hot experimental results indicate that slight segregation has little effect on the steady combustion of the dense region. In this study, the concentrations of major gaseous pollutants (CO, SO2 and NO) in flue (stack) gas were measured. [Copyright &y& Elsevier]