Sequential recommendation has emerged as a fundamental task in various domains, aiming to predict a user's next interaction based on historical behavior. Recent advances in deep sequence models, particularly Transformer-based architectures and the more recent Mamba, have substantially pushed the boundaries of sequential modeling performance. However, existing methods still face two critical challenges. First, many current approaches overlook the hierarchical structures and high-order dependencies among items, typically restricting representation learning to conventional Euclidean spaces, which limits their capacity to capture complex relational information. Second, although Mamba excels at long-range dependency modeling, its reliance on static Feed-Forward Networks (FFNs) hinders its ability to dynamically adapt to evolving user preferences across diverse contexts. To address these limitations, we propose a Hyperbolic-Enhanced Mixture-of-Experts Mamba recommender (HM2Rec) for sequential recommendation. HM2Rec first encodes user-item relationships through hyperbolic graph convolution to exploit hierarchical structure more effectively. Then, a Variational Graph Auto-Encoder (VGAE) is employed to reconstruct node embeddings, improving structural robustness. To further enhance sequential modeling, we integrate Rotary Positional Encoding (RoPE) into Mamba to better capture relative position dependencies, and replace the FFN with Mixture-of-Expert (MOE) module, enabling dynamic and personalized expert selection for each token. Our extensive experiments on four widely-used public datasets demonstrate that HM2Rec outperforms several advanced baseline models.

Accurate conversion rate (CVR) prediction is critical for recommender systems to capture user conversion intent and increase platform revenues. Traditional CVR models commonly suffer from sample selection bias (SSB) and data sparsity (DS), which has led to the adoption of click-through & conversion rate (CTCVR) multi-task learning frameworks to alleviate these issues. However, existing methods implicitly mislabel some unclicked samples with genuine conversion potential as negatives, thereby exacerbating the false negative sample (FNS) problem. To address this, we propose IdeFN, a multi‑task CVR framework that identifies false negatives in the unclicked space to enable CVR prediction across the entire exposure space and leverages CTR as an auxiliary task for shared‑parameter learning. Specifically, IdeFN consists of two main components, i.e., relaxed partial optimal transport (RPOT) module and sample relabeling mechanism (SRM). The former estimates the soft matching strengths between unclicked samples and positive samples under a relaxed partial optimal transport formulation, establishing corresponding relationships between these samples. The latter adaptively re-labels the unclicked samples according to the derived matching strengths, without relying on static or heuristic thresholds, thus enhancing the reliability of the generated pseudo-labels. Experimental results demonstrate that IdeFN effectively mitigates the FNS problem, achieving substantial improvements in CVR prediction accuracy.

Federated Learning (FL) enables collaborative model training without sharing raw data, but remains highly vulnerable to gradient manipulation and backdoor attacks, particularly under heterogeneous client distributions. Most existing defenses either target a narrow class of attacks, rely on client data, or fail to adapt in heterogeneous settings. We propose SD-CSFL (Synthetic Data-Driven Conformity Scoring for Federated Learning), a unified and privacy-preserving defense algorithm. SD-CSFL leverages a synthetic calibration dataset, independent of client data, to compute entropy-based nonconformity scores that capture irregularities in client updates. An adaptive percentile thresholding mechanism with stratified calibration dynamically distinguishes benign from malicious updates across training rounds. We establish a conformal prediction-based guarantee showing that percentile thresholds bound false positives under arbitrary score distributions. Experiments on CIFAR-10 and Birds-525 demonstrate up to 35% higher detection of gradient manipulation and an 80% reduction in backdoor success rates, outperforming recent defenses in heterogeneous environments. Our implementations and synthetic datasets are available at https://github.com/EbtisaamCS/SD-CSFL

Aerial base stations (ABSs) extend the coverage of internet of things smart devices (ISDs) beyond terrestrial networks; however, ultra-reliable and low-latency communication (URLLC) is constrained by limited battery life and computational resources. To address this, we propose an aerial-terrestrial non-orthogonal multiple access (NOMA) framework that decouples the non-convex problem into feasible sub-problems: (i) optimal clustering via k-means with elbow method and F-test method, alongside a modified pathloss model, (ii) reinforcement learning based ABS placement, and (iii) hybrid deep-learning and fractional transmit power allocation (PA) for power efficiency and fairness. We also derive a closed-form expression for PA among multiplexed devices based on their QoS requirements. Results show that the proposed scheme outperforms benchmark schemes, i.e., the sum-rate for NOMA-DeepFusion-PA [Optimal UAV position] can be increased by 28.5762% than NOMA with a fixed PA method, namely: NOMA-FPA [Optimal UAV position], and 38.3119% higher than orthogonal multiple access (OMA) [Optimal UAV position] for different transmit powers.

This paper addresses the challenging problem of online federated learning (FL) over streaming data in a decentralized communication network. To enable rapid adaptation to new observations, we develop novel non-parametric model-based local training, not deep neural network-based approaches as adopted in most previous studies. In particular, we integrate Gaussian process regression with a Student-t likelihood to improve robustness against data outliers. For global model aggregation, we propose a consensus-based Product of Experts (PoE) algorithm that enables peer-to-peer fusion of non-parametric local models and preserves robustness to outliers. To ensure privacy, we develop a secure aggregation scheme that combines Shamir’s secret sharing (SSS) with public-key encryption. Compared to existing methods, the proposed approach enhances privacy guarantees for learners with limited connectivity in sparse graphs. Theoretical analyses establish robustness, correctness, and privacy properties. Extensive numerical experiments validate the effectiveness of the proposed algorithm.

In recent years, orthogonal time frequency space (OTFS) technique has garnered substantial academic attention as a promising solution for ensuring robust and reliable communication in high-mobility wireless communication environments. In this paper, we present a complex-valued graph neural network (CV-GNN) aided signal detection scheme for OTFS modulation, which can mitigate the channel spreading caused by fractional Doppler shifts. To mitigate inter-carrier interference (ICI) and inter-symbol interference (ISI) induced by fractional Doppler shifts and imperfect channel state information, the proposed detector is able to process the received OTFS signal in the complex plane to acquire the complete phase information of effective channel. Simulation results demonstrate that the proposed method can outperform other state-of-the-art schemes by 1∼4 dB in terms of reliability performance.

With the growing demand for multimedia services, Dynamic Adaptive Streaming over HTTP (DASH) has become a key solution for delivering high-quality video content. In this work, we consider an Edge-DASH scenario and formulate a joint optimization problem that involves four critical aspects: bitrate allocation, user-to-server assignment, caching, and bandwidth allocation. Due to the complexity of the joint problem, we decompose it into sub-problems and address them separately. To solve the resulting sub-problems, we employ deep reinforcement learning, specifically the Deep Deterministic Policy Gradient (DDPG) method, for three of them, and develop a heuristic solution for the fourth. Simulation results demonstrate that our approach enhances performance across multiple metrics, including improved video delivery, reduced buffer underflow and overflow, and more efficient caching, which collectively enable greater utilization of edge resources for streaming. Moreover, we evaluated inference latency across edge and cloud hardware, confirming sub- to few-millisecond performance suitable for real-time deployment. This showcases the benefits of combining learning-based and heuristic techniques to meet the growing demand for adaptive video streaming in edge computing environments.

Test-time adaptation (TTA) offers the potential to enhance model generalizability without relying on training data or retraining processes. However, TTA faces challenges under covariate shift, where discrepancies between the distributions of training and testing phases hinder model performance. This limitation stems from the fact that existing methods usually rely heavily on training data and fail to establish a good connection between the model and the marginal distribution of test data, resulting in reduced generalization ability. To mitigate this issue, we introduce a novel self-supervised framework that integrates latent score matching and pseudo-label refinement into the TTA paradigm to enhance the model's perception of the test data distribution. Our approach, Joint Test-time Adaptation with Refined Pseudo-labels and Latent Score Matching, reinterprets a classifier as a score estimator and trains it using pseudo-label refinement. This enables the model to better align with the test distribution through latent score matching, while simultaneously preserving discriminative performance via pseudo-label refinement. Extensive experiments across diverse architectures and benchmarks demonstrate that TAPS consistently outperforms state-of-the-art methods in terms of generalization performance under various distribution shifts.

As Electric Vehicle (EV) adoption accelerates, the limitations of fixed charging infrastructure, such as spatial inflexibility and peak-time congestion, become more evident. Vehicle-to-Vehicle (V2V) charging allows EV with surplus energy to directly supply others, offering a decentralized complement to conventional infrastructure. Depending on real-time context, energy exchange can occur in either a static mode (while parked) or a dynamic mode (in motion along overlapping routes). Selecting the appropriate mode and coordinating trustworthy peers under uncertain traffic, energy, and spatial conditions poses a significant challenge. This paper presents a hierarchical framework for trust-aware V2V charging coordination under mode uncertainty. At the strategic layer, a fuzzy logic-based trust estimator selects the most reliable charging mode based on factors such as energy gap, location density, and traffic state. At the operational layer, a unified Large Neighborhood Search (LNS) algorithm performs robust EV pairing, using greedy heuristics for static mode and DTW-based trajectory alignment for dynamic mode. Trust-weighted objectives and soft constraint penalties are incorporated to enhance decision robustness and mitigate unreliable matches. Experiments on real-world Helsinki mobility data demonstrate that the framework improves match success rates, reduces waiting times and trajectory deviations, and enhances the overall resilience of V2V energy coordination.

Mobile edge computing (MEC) enables efficient computation offloading for mission-critical applications in resource-constrained vehicles, while reconfigurable intelligent surface (RIS) help address connectivity challenges for vehicles in urban environments with severe signal blockages. Non-orthogonal multiple access (NOMA) is an appealing technique that improves spectral efficiency while mitigating multi-user interference. This work proposes the RIS-assisted NOMA-MEC in vehicular networks, considering dynamic challenges such as heterogeneous vehicle processing capability, time-varying channel from high-mobility and dynamic task workloads. We formulate a system latency minimization problem by jointly optimizing the task offloading ratio, edge server resource allocation and RIS passive beamforming, while satisfying the task deadline and Signal to Interference plus Noise Ratio (SINR) requirements. To overcome the limitations of conventional optimization methods in such dynamic environments, we propose a soft actor critic (SAC)-based deep reinforcement learning (DRL) framework, which dynamically adapts to real-time channel state information (CSI), task workload and vehicle processing capability of all vehicles. Simulation results demonstrate that our approach achieves lower latency performance compared with the Deep Deterministic Policy Gradient (DDPG) baselines. Moreover, the proposed SAC method exhibits robustness and adaptivity to various levels of uncertainty in the CSI.

With the development of Internet of Vehicles (IoVs), data security emerges as a significant challenge, especially regarding data tampering and the spread of false information. While cryptography technologies tackle external security threats, they fall short in addressing internal security threats, such as authorized malicious vehicles tampering with and spreading false information. Consequently, trust management becomes a crucial technology, focusing on the analysis and identification of internal inappropriate behaviors to ensure safe interactions among vehicles. This article explores the effective integration of trust opinions provided by roadside units (RSUs) into trust evaluations in IoVs, ensuring the comprehensiveness and accuracy of trust evaluations. We propose a dual-model consensus-based anti-risk confidence allocation trust management scheme (DCACA) in IoVs. Specifically, DCACA utilizes direct trust, indirect trust, and global trust, to evaluation the trustworthiness of vehicles. Furthermore, to address the potential untrustworthiness of network entities (RSUs and vehicles), DCACA employs a dual-model consensus mechanism operates two processes of reaching consensus, including the real-time collection consensus mechanism (RCCM) and the matrix-based consensus mechanism (MCM). RCCM is based on real-time collected trust opinions, reaching consensus to identify potential malicious trust opinions. MCM utilizes trust opinion matrices to collect trust opinions and achieves consensus through the elements in these matrices, identifying the sources of malicious trust opinions. Additionally, DCACA utilizes an anti-risk confidence allocation mechanism assigns confidence levels based on risk assessments, to mitigate the impact of malicious entities. Extensive experiments demonstrate that our scheme significantly outperforms other baseline schemes, exhibiting high levels of precision, recall, and F-measure.

Random access channel (RACH) procedure is critical to support a multitude of devices transmitting small data payloads while ensuring low-latency access. In 3GPP Release 16, a two-step RACH is proposed to alleviate signaling overhead and access latency. While benefits are noticeable, collisions still persist. In this article, we propose a novel nonorthogonal multiple access (NOMA)-enhanced two-step RACH scheme (NOMA-RACH) that jointly leverages the benefits of access class barring (ACB), two-step RACH, and NOMA random access (NOMA-RA) to further enhance the performance. We conduct a holistic study that accounts for entire access latency. The scheme optimizes NOMA access probabilities, utilizes an adjustable barring mechanism for delay-sensitive devices, and identifies the optimal barring rate for low latency. We develop a Markov chain model to analyze NOMA access and derive the optimal access probabilities and throughput of NOMA blocks. To cope with the practical scenarios with constantly changing user equipment (UE) traffic, we propose a deep contextual multiarmed bandit (DCMAB) model that optimizes the NOMA throughput and dynamically adjusts the barring rate based on the observable channel feedback. Our simulation results demonstrate that the DCMAB model performs better than benchmark schemes and remains close to the optimal latency confirming the effectiveness of our proposed scheme under changing UE traffic.

To address the issues of limited computational capability and constrained battery life faced by users in the Internet of Things, wireless-powered mobile edge computing (MEC) has been proposed as a promising solution. However, the efficiency of its key functions, namely task offloading and energy transfer, can be significantly impaired if the direct links between the access point (AP) and users are obstructed. Inspired by the potentials of active simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) for achieving full-space coverage and mitigating multiplicative fading effects, this paper investigates the incorporation of active STAR-RIS in wireless-powered MEC. To meet the high data rate requirements in future smart environments, we aim to maximize the total number of completed task bits. To address the formulated challenging non-convex problem, a resource allocation and active beamforming algorithm (RAABA) is first proposed for a basic two-user non-orthogonal multiple access (NOMA) scenario, jointly optimizing the energy transfer time, decoding order, transmit power, CPU frequency of users, and beamforming of STAR-RIS. We then extend the RAABA to general multi-user scenarios (RAABAM) by leveraging a matching-theory-based user pairing algorithm. Furthermore, a low-complexity RAABAM (L-RAABAM) is proposed by simplifying the matching process and deriving a closed-form expression for the optimal transmit power of users. Simulation results show that: i) by jointly optimizing multiple highly-coupled variables, our proposed RAABAM and L-RAABAM schemes achieve a higher total number of completed task bits; ii) the active STAR-RIS significantly outperforms passive/active traditional RIS and passive STAR-RIS; iii) the deployment rules for active STAR-RIS differ from those for passive STAR-RIS in wireless-powered MEC, where the optimal deployment location of active STAR-RIS depends on the number of its elements.

In navigating the challenges of real-time semantic communication (SC) codec updates in the 6G-era non-terrestrial network (NTN)-assisted vehicular networks (NTN-VNs), a crucial component of intelligent transportation systems (ITS), this article introduces a novel approach inspired by human society. Facing complexities like 3-dimensional updating, network dynamism, and updating costs, NTN-VNs are treated as social networks. The proposed NTN-VN federated learning (NTN-VN-FL) framework asynchronously addresses challenges such as uplink and downlink SC codec updates, device decentralization, and asynchronous updating. By viewing device behaviors during updating as social behaviors with economic costs, an NTN-VN social management system ensures the proper functioning of the social network in the context of NTN-VN-FL. An economical social behavior selection mechanism, based on the reverse auction game for NTN-VN-FL, minimizes training delay and device energy costs, considering social relationships. The article also presents a two-stage Stackelberg game with the Vickrey auction rule to maximize social welfare in the auction. Simulation results highlight the superiority of NTN-VN-FL over existing potential application algorithms, effectively addressing the unique challenges of SC codec updating in NTN-VN. The efficacy of the social management system and social behavior selection mechanism is demonstrated in achieving optimal outcomes.

In recent years, Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion vehicles, noted for better efficiency, lower operational costs, and reduced carbon emissions. However, with the growing adoption of EVs and limited charging infrastructure, challenges such as charging congestion arise. Traditional plug-in and in-Parking Vehicle-to-Vehicle (V2V) charging modes, constrained by fixed charging locations, lack flexibility and necessitate long charging times. Therefore, this paper introduces a novel in-Motion V2V charging mode, termed V2V (M) mode, allowing an EV as an energy Provider (EV-P) and an EV as an energy Consumer (EV-C) to form a V2V charging Pair (V2V-Pair). Then, the V2V-Pair can transfer energy via wireless V2V charging service while on-the-move. In this paper, the proposed V2V (M) management framework employs a Path Proximity-based V2V Pair matching algorithm and spatio-temporal cooperative path planning, to enhance charging efficiency and reduce charging trip duration. The urban environment simulation results demonstrate marked improvements of the proposed V2V (M) mode. It shorters the charging trip duration and enhances charging service efficiency, offering a viable solution to current EV charging constraints.

With the development of Vehicular Ad-hoc Networks (VANETs), several data security challenges are revealed, such as data hijacking and interception. Although vehicles are authorized, malicious behaviors still be carried out. Security lapses may lead to potential accidents, which emphasizes the importance of laying a solid security foundation for VANETs. Thanks to the base security layer provided by cryptography technologies, security problems can be solved in VANETs to avoid accidents. However, trust management focuses on the analysis and identification of misbehavior, to ensure secure interactions among vehicles, and preserve data integrity against security issues. This paper explores trust assessments that consider the transmission path of message as a novel indicator, to provide a comprehensive and accurate trust assessment. We propose a Multidimensional trust Evidence Fusion and Path-Backtracking mechanism for trust management scheme (MEFPB) in VANETs. MEFPB integrates the multidimensional trust evidence fusion and path-backtracking mechanism. Specifically, MEFPB utilizes the Dempster-Shafer theory to fuse multi-dimensional indicators (direct trust, indirect trust, and transmission path of message) for evaluating the trustworthiness of vehicles. The direct and indirect trust are supplied by the message-sending vehicle and its neighbors (i.e., other vehicles). The transmission path of message is provided by roadside units. Furthermore, the path-backtracking mechanism identifies and traces malicious behaviors based on the transmission path of message. Moreover, extensive experiments demonstrate that our scheme significantly outperforms other baseline schemes, exhibiting a high malicious behavior detection rate within VANETs.

Blockchain-based agricultural IoT systems face key challenges, such as high delay and low-transaction throughput. Existing complicated consensus mechanisms can cause IoT devices work inefficiently due to the limited computing, storage and energy resources. Additionally, many message exchanges can lead to high latency in the consensus process, which hinders the real-time applications of the agricultural IoT. Therefore, we propose Proof-of-Multifactor-Capacity (PoMC), an efficient and secure consensus mechanism for the agricultural IoT. It uses the communication capacity and credibility of a node as the evidence for making consensus. Moreover, a senator node lottery algorithm based on a credit mechanism and a new distributed incentive mechanism are designed to enhance security and motivate nodes to actively maintain the system. This article analyses the performance of PoMC theoretically, including security, latency, and system throughput, and presents a comparison of its asymptotic complexity with some existing consensus mechanisms. The simulation results demonstrate that the average transaction validation latency and average consensus latency of PoMC have decreased by 10% and 23%. In addition, PoMC outperforms SENATE, PoQF and practical Byzantine fault tolerance (PBFT) by 56%, 60% and 64% in terms of the system throughput, respectively.

Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of Quality of Experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure toward communication service providers (CSPs). In the sixth-generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to Intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intent-driven closedloop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in the OpenAirInterface platform and realizing the CL control and management with a near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

Machine learning-based semantic communication is a promising enabler for future-generation wireless network systems such as 6G networks. In practice, effective semantic communication requires online training for unknown content. In highly mobile vehicular networks, however, reliable, and efficient model training becomes significantly challenging. The existing distributed learning approaches are also unable to effectively operate in highly dynamic vehicular semantic communication networks. To address these challenges, we propose a novel mobility-aware split-federated with transfer learning (MSFTL) framework based on vehicle task offloading scenarios in this paper. To enable adaptation to the complex vehicle semantic communication, the proposed framework divides the training of the model into four parts and uses the proposed new splitfederated learning. Furthermore, to improve training efficiency, model accuracy, and the ability to adapt in highly mobile environments, we also present a new transfer learning approach integrated into the proposed framework. Particularly, we propose a high-mobility training resource optimisation mechanism based on a Stackelberg game for MSFTL to further reduce training costs and adapt vehicle mobility scenarios. We also investigate the performance of the proposed schemes through extensive simulations. The results validate the proposed approach and indicate its superiority compared to the conventional learning frameworks for semantic communication in vehicular networks.

High precision train positioning is a crucial component of intelligent transportation systems. Tunnels are commonly encountered in subways and mountainous regions. As part of the communication system infrastructure, Leaky CoaXial (LCX) Cable is widely equipped as antenna in tunnels with many advantages. LCX positioning holds great promise as a technology for rail applications in the upcoming B5G (beyond-5G) and 6G eras. This paper focuses on the LCX positioning methodology and proposes two novel algorithms along with a novel communication-positioning integration signal. Firstly, a novel algorithm called Multiple Slot Distinction (MSD) LCX positioning algorithm is proposed. The algorithm utilizes a generated pseudo spectrum to fully utilize the coupled signals radiated from different slots of LCX. This approach offers higher time resolution compared to traditional methods. To further improve the positioning accuracy to centimeter-level and increase the measuring frequency for fast trains, a novel communication-positioning integration signal is designed. It consists of traditional Positioning Reference Signal (PRS) and a significantly low power Fine Ranging Signal (FRS). FRS is configured to be continuous and superposed onto the cellular signal using Non-Orthogonal Multiple Access (NOMA) principle to minimize its interference to communication. A two-stage LCX positioning method is then executed: At the first stage, the closest slot between the receiver and LCX is estimated by the proposed MSD algorithm using PRS; At the second stage, centimeter-level positioning is achieved by tracking the carrier phase of the continuous FRS. This process is assisted by the closest slot estimation, which helps mitigate interference between neighboring slots and eliminate the integer ambiguities. Simulation results show our proposed LCX position methodology outperforms the existing ones and offer great potentials for future implementations.

The low earth orbit (LEO) satellite-borne edge cloud (SEC) and machine learning (ML) based semantic communication (SemCom) are both enabling technologies for 6G systems facilitating computation offloading. Nevertheless, integrating SemCom into the SEC networks for user computation offloading introduces semantic coder updating requirements as well as additional semantic extraction costs. Offloading user computation in SEC networks via SemCom also results in new functional challenges considering, e.g., latency, energy, and privacy. In this paper, we present a novel SemCom-assisted SEC (SemCom-SEC) framework for computation offloading of resource-limited users. We then propose an adaptive pruning-split federated learning (PSFed) method for updating the semantic coder in SemCom-SEC. We further show that the proposed method guarantees training convergence speed and accuracy. This method also improves the privacy of the semantic coder while reducing training delay and energy consumption. In the case of trained semantic coders in service, for the users processing computational tasks, the main objective is to minimise the users’ delay and energy consumption, subject to sustaining users’ privacy and fairness amongst them. This problem is then formulated as an incomplete information mixed integer nonlinear programming (MINLP) problem. A new computational task processing scheduling (CTPS) mechanism is also proposed based on the Rubinstein bargaining game. Simulation results demonstrate the proposed PSFed and game theoretical CTPS mechanism outperforms the baseline solutions reducing delay and energy consumption while enhancing users’ privacy.

Abstract 3D point cloud models are widely applied in safety-critical scenes, which delivers an urgent need to obtain more solid proofs to verify the robustness of models. Existing verification method for point cloud model is time-expensive and computationally unattainable on large networks. Additionally, they cannot handle the complete PointNet model with joint alignment network that contains multiplication layers, which effectively boosts the performance of 3D models. This motivates us to design a more efficient and general framework to verify various architectures of point cloud models. The key challenges in verifying the large-scale complete PointNet models are addressed as dealing with the cross-non-linearity operations in the multiplication layers and the high computational complexity of high-dimensional point cloud inputs and added layers. Thus, we propose an efficient verification framework, 3DVerifier, to tackle both challenges by adopting a linear relaxation function to bound the multiplication layer and combining forward and backward propagation to compute the certified bounds of the outputs of the point cloud models. Our comprehensive experiments demonstrate that 3DVerifier outperforms existing verification algorithms for 3D models in terms of both efficiency and accuracy. Notably, our approach achieves an orders-of-magnitude improvement in verification efficiency for the large network, and the obtained certified bounds are also significantly tighter than the state-of-the-art verifiers. We release our tool 3DVerifier via https://github.com/TrustAI/3DVerifier for use by the community.

High-dimensional data classification is widely considered as a challenging task in machine learning due to the so-called “curse of dimensionality.” In this article, a novel multilayer jointly evolving and compressing fuzzy neural network (MECFNN) is proposed to learn highly compact multilevel latent representations from high-dimensional data. As a metalevel stacking ensemble system, each layer of MECFNN is based on a single jointly evolving and compressing neural fuzzy inference system (ECNFIS) that self-organizes a set of human-interpretable fuzzy rules from input data in a samplewise manner to perform approximate reasoning. ECNFISs associate a unique compressive projection matrix to each individual fuzzy rule to compress the consequent part into a tighter form, removing redundant information while boosting the diversity within the stacking ensemble. The compressive projection matrices of the cascading ECNFISs are self-updating to minimize the prediction errors via error backpropagation together with the consequent parameters, empowering MECFNN to learn more meaningful, discriminative representations from data at multiple levels of abstraction. An adaptive activation control scheme is further introduced in MECFNN to dynamically exclude less activated fuzzy rules, effectively reducing the computational complexity and fostering generalization. Numerical examples on popular high-dimensional classification problems demonstrate the efficacy of MECFNN.

The requirements for large amounts of data have promoted the rapid emergence of an industry for trading data. However, the current one-to-one trading constraints in the existing data trading schemes lead to low security and low efficiency. To tackle the challenges, a novel one-to-many distributed data trading scheme is proposed based on blockchain, which enables a data seller to sell one piece of data to multiple data buyers simultaneously, saving storage resources and computing resources significantly. Firstly, some new smart contracts are devised for two decentralized applications. Then, attribute-based searchable encryption technology is proposed to establish a data circulation scheme that realizes end-to-end encryption of data and ensures data security and highly efficient access. Finally, an inspection mechanism based on zero-knowledge proof and a pricing strategy based on the Stackelberg game are designed to guarantee fairness in trading and maximize revenue. The experiment results show that, in comparison to one-to-one trading, the high efficiency of this data trading scheme gradually emerges as the number of buyers (n) is greater than 2, and the run time is less than 1/10 of the former when n =35. Furthermore, the pricing strategy can enable buyers and sellers to obtain more revenue when$\text {n} \gt 4$.

Collaborative Intrusion Detection System (CIDS) is an essential technology that enables vehicular ad hoc networks (VANET) to protect against malicious intrusions. CIDS, however, is unable to prevent accidents if an anomalous vehicle is detected. Detecting anomalies and notifying vehicles in the VANET rapidly is thus essential, considering technical challenges such as communication efficiency, vehicle velocity and privacy. In this paper, we propose a novel two-layer privacy-aware trust evaluation CIDS framework, termed 2PT-CIDS, tailored to VANET. In 2PT-CIDS, vehicles and roadside units (RSUs) cooperate efficiently to enhance anomalous vehicle detection and notification. Considering its potential privacy leakage, we then present two types of game-theoretic information incentive mechanisms. In the case of traffic congestion, the privacy-aware incentive mechanism is presented based on the Stackelberg game. A Barycentric Lagrange interpolation (BLI) based algorithm is then proposed to speedy achieve the Nash equilibrium (NE). In the case of traffic smooth, the varying high velocities of vehicles are involved and a noncooperative game-based mechanism is proposed. The optimal NE decision selection is reconstructed as a Markov decision process (MDP) and the NE point is obtained via the designed novel reward-shaping double duelling deep Q network (D3QN) learning algorithm. Simulation results highlight the superiority of 2PT-CIDS over existing CIDS and potential application algorithms for VANET, effectively enhancing anomaly detection and notification considering communication cost and vehicle privacy.

This paper investigates the radio-frequency-energy-harvesting-powered (RF-EH-powered) wireless Industrial Internet of Things (IIoT) networks, where multiple sensor nodes (SNs) are first powered by a wireless power station (WPS), and then collect status updates from the industrial environment and finally transmit the collected data to the monitor with their harvested energy. To enhance the timeliness of data, age of information (AoI) is used as a metric to optimize the system. Particularly, an expected sum AoI (ESA) minimization problem is formulated by optimizing the power adjustment policy for the SNs under multiple practical constraints, including the EH, the minimal signal-to-noise-plus-interference ratio (SINR) and the battery capacity constraints. To solve the non-convex problem with no explicit AoI expression, we transform it into a Markov decision problem (MDP) with continuous state space and action space. Then, inspired by the Soft Actor-Critic (SAC) framework in deep reinforcement learning, a SAC-based age-aware power adjustment (SAPA) method is proposed by modeling the power adjustment as a stochastic strategy. Furthermore, to reduce the communication overhead of SAPA, a multi-agent version of SAPA, i.e., MSAPA, is proposed, with which each SN is able to adjust its transmit power based on its local observations. The communication overhead of SAPA and MSAPA is also analyzed theoretically. Simulation results show that the proposed SAPA and MSAPA converge well with different numbers of SNs. It is also shown that the ESA achieved by the proposed SAPA and MSAPA is lower than that achieved by the baseline methods.

The rapid adoption of IPv6 has increased network access scale while also escalating the threat of Distributed Denial of Service (DDoS) attacks. By the time a DDoS attack is recognized, the overwhelming volume of attack traffic has already made mitigation extremely difficult. Therefore, continuous network monitoring is essential for early warning and defense preparation against DDoS attacks, requiring both sensitive perception of network changes when DDoS occurs and reducing monitoring overhead to adapt to network resource constraints. In this paper, we propose a novel DDoS incident monitoring mechanism that uses macro-level network traffic behavior as a monitoring anchor to detect subtle malicious behavior indicative of the existence of DDoS traffic in the network. This behavior feature can be abstracted from our designed traffic matrix sample by aggregating continuous IPv6 traffic. Compared to IPv4, the fixed-length header of IPv6 allows more efficient packet parsing in preprocessing. As the decision core of monitoring, we construct a lightweight Binary Convolution DDoS Monitoring (BCDM) model, compressed by binarized convolutional filters and hierarchical pooling strategies, which can detect the malicious behavior abstracted from input traffic matrix if DDoS traffic is involved, thereby signaling an ongoing DDoS attack. Experiment on IPv6 replayed CIC-DDoS2019 shows that BCDM, being lightweight in terms of parameter quantity and computational complexity, achieves monitoring accuracies of 90.9%, 96.4%, and 100% when DDoS incident intensities are as low as 6%, 10%, and 15%, respectively, significantly outperforming comparison methods.

Cooperative multi-agent reinforcement learning (c-MARL) is widely applied in safety-critical scenarios, thus the analysis of robustness for c-MARL models is profoundly important. However, robustness certification for c-MARLs has not yet been explored in the community. In this paper, we propose a novel certification method, which is the first work to leverage a scalable approach for c-MARLs to determine actions with guaranteed certified bounds. c-MARL certification poses two key challenges compared to single-agent systems: (i) the accumulated uncertainty as the number of agents increases; (ii) the potential lack of impact when changing the action of a single agent into a global team reward. These challenges prevent us from directly using existing algorithms. Hence, we employ the false discovery rate (FDR) controlling procedure considering the importance of each agent to certify per-state robustness. We further propose a tree-search-based algorithm to find a lower bound of the global reward under the minimal certified perturbation. As our method is general, it can also be applied in a single-agent environment. We empirically show that our certification bounds are much tighter than those of state-of-the-art RL certification solutions. We also evaluate our method on two popular c-MARL algorithms: QMIX and VDN, under two different environments, with two and four agents. The experimental results show that our method can certify the robustness of all c-MARL models in various environments. Our tool CertifyCMARL is available at https://github.com/TrustAI/CertifyCMARL.

Federated Learning (FL) is essential for building global models across distributed environments. However, it is significantly vulnerable to data and model poisoning attacks that can critically compromise the accuracy and reliability of the global model. These vulnerabilities become more pronounced in heterogeneous environments, where clients’ data distributions vary broadly, creating a challenging setting for maintaining model integrity. Furthermore, malicious attacks can exploit this heterogeneity, manipulating the learning process to degrade the model or even induce it to learn incorrect patterns. In response to these challenges, we introduce RFCL, a novel Robust Federated aggregation method that leverages CLustering and cosine similarity to select similar cluster models, effectively defending against data and model poisoning attacks even amidst high data heterogeneity. Our experiments assess RFCL’s performance against various attacker numbers and Non-IID degrees. The findings reveal that RFCL outperforms existing robust aggregation methods and demonstrates the capability to defend against multiple attack types.

Network performance and throughput is affected by network congestion, which is caused by unnecessary bandwidth over-utilisation, expanding transmission delays, and increase in cost. Fog computing has emerged as a promising solution to overcome these shortcomings by provisioning computational resources to the network’s edge. However, selecting suitable fog nodes can pose challenges due to increased latency and high energy consumption, leading to unnecessary bandwidth utilisation. This study proposes a deep learning mechanism called gated graph convolution neural networks (GGCNs) for resource scheduling management in fog computing to improve the average loop delay and the total network usage of the system. Our deep learning mechanism promotes energy-efficient collaborative intelligence among IoT devices while optimising resource utilisation. Reducing energy consumption not only promotes but also enhances sustainability and scalability in IoT networks. Our proposed mechanism shows improved results compared with several benchmark algorithms, such as first come first serve, shortest job first, and particle swarm optimisation. Our results demonstrate that the proposed model will resolve the problem of application placement and present a noticeable reduction in delay and bandwidth. The results can prove to be a standard benchmark in the IoT-Fog computing discipline and used to enhance the quality of service in wide-ranging heterogeneous applications located at distributed locations.

The rapid emergence of 5G technology brings new cybersecurity challenges that hold significant implications for our economy, society, and environment. Among these challenges, ensuring the effectiveness of Intrusion Detection Mechanisms (IDMs) in monitoring networks and detecting 5G-related cyberattacks is of utmost importance. However, optimizing cybersecurity levels and selecting appropriate IDMs remain as critical and ongoing challenges. This work considers multiple pre-deployed distributed Security Agents (SAs) across the network, each capable of running various IDMs, where they differ by their effectiveness in detecting the attacks (referred to as security term) and the consumption of resources (referred to as Quality of Service (QoS) costs). We formulate a joint security and QoS utility function leveraging the Cobb–Douglas production utility function. There are several parameters that impact the joint objective problem, including the set of elasticity parameters, that reflect the importance of the two objectives. We derive an optimal set of elasticity parameters in closed form to identify the balancing point where both objectives have equal utility values. Through comprehensive simulations, we demonstrate that increasing the detection level of SAs enhances the security utility while simultaneously diminishing the QoS utility, as more computational, bandwidth, and monetary resources are utilized for IDM processing. After optimization, our mechanism can strike an effective balance between cybersecurity and QoS overhead while demonstrating the importance of different parameters in the joint problem.

Vehicular ad hoc networks (VANETs) are a special kind of wireless communication network that facilitates vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. This technology exhibits the potential to enhance the safety of roads, efficiency of traffic, and comfort of passengers. However, this can lead to potential safety hazards and security risks, especially in autonomous vehicles that rely heavily on communication with other vehicles and infrastructure. Trust, the precision of data, and the reliability of data transmitted through the communication channel are the major problems in VANET. Cryptography-based solutions have been successful in ensuring the security of data transmission. However, there is still a need for further research to address the issue of fraudulent messages being sent from a legitimate sender. As a result, in this study, we have proposed a methodology for computing vehicle’s reputation and subsequently predicting the trustworthiness of vehicles in networks. The blockchain records the most recent assessment of the vehicle’s credibility. This will allow for greater transparency and trust in the vehicle’s history, as well as reduce the risk of fraud or tampering with the information. The trustworthiness of a vehicle is confirmed not just by the credibility, but also by its network behavior as observed during data transfer. To classify the trust, an ensemble learning model is used. In depth tests are run on the data set to assess the effectiveness of the proposed ensemble learning with feature selection technique. The findings show that the proposed ensemble learning technique achieves a 99.98% accuracy rate, which is notably superior to the accuracy rates of the baseline models.

Multiaccess edge computing (MEC) provides task offloading services to facilitate the integration of idle resources with the network and bring cloud services closer to the end user. By selecting suitable servers and properly managing resources, task offloading can reduce task completion latency while maintaining the Quality of Service (QoS). Prior research, however, has primarily focused on tasks with strict time constraints, ignoring the possibility that tasks with soft constraints may exceed the bound limits and failing to analyze this complex task constraint issue. Furthermore, considering additional constraint features makes convergent optimization algorithms challenging when dealing with such complex and high-dimensional situations. In this article, we propose a new computational offloading decision framework by minimizing the long-term payment of computational tasks with mixed bound constraints. In addition, redundant experiences are gotten rid of before the training of the algorithm. The most advantageous transitions in the experience pool are used for training in order to improve the learning efficiency and convergence speed of the algorithm as well as increase the accuracy of offloading decisions. The findings of our experiments indicate that the method we have presented is capable of achieving fast convergence rates while also reducing sample redundancy.

Benefiting from the powerful data analysis and prediction capabilities of artificial intelligence (AI), the data on the edge is often transferred to the cloud center for centralized training to obtain an accurate model. To resist the risk of privacy leakage due to frequent data transmission between the edge and the cloud, federated learning (FL) is engaged in the edge paradigm, uploading the model updated on the edge server (ES) to the central server for aggregation, instead of transferring data directly. However, the adversarial ES can infer the update of other ESs from the aggregated model and the update may still expose some characteristics of data of other ESs. Besides, there is a certain probability that the entire aggregation is disrupted by the adversarial ESs through uploading a malicious update. In this article, a privacy-preserving FL scheme with robust aggregation in edge computing is proposed, named FL-RAEC. First, the hybrid privacy-preserving mechanism is constructed to preserve the integrity and privacy of the data uploaded by the ESs. For the robust model aggregation, a phased aggregation strategy is proposed. Specifically, anomaly detection based on autoencoder is performed while some ESs are selected for anonymous trust verification at the beginning. In the next stage, via multiple rounds of random verification, the trust score of each ES is assessed to identify the malicious participants. Eventually, FL-RAEC is evaluated in detail, depicting that FL-RAEC has strong robustness and high accuracy under different attacks.

Digital-twin-enabled cloud-centric architecture is a promising evolution trend of sixth generation (6G) network, which brings new opportunities and challenges for mobile video streaming-related services requiring the exponentially increasing traffic demands. Device-to-Device (D2D) communication paradigm is an attractive technique to alleviate the problem. However, the previous research work on D2D built on individuals’ random mobility or position snapshot and cannot guarantee the stable communication flow. In this paper, we leverage the cybertwin as a centric controller and take advantages of crowdsourcing technology to attract mobile users to follow the specified path and share their network resources with other users. The design of the specified path is formulated as a problem of user recruitment optimization with cost constraint, which is a NP-Hard problem. Firstly, we investigate a special case of only one mobile user to offer the network resource and present a pseudo-polynomial time algorithm. Secondly, we present a graph-partition-based approach to solve the more complex case of multiple mobile users. Thirdly, we discuss the least expected budget to achieve the maximum utility in an ideal model. Fourthly, we perform extensive experiments to evaluate and compare the performance with the typical ones in simulated digital-twin-enabled 6G networks.

Spectrum sensing is a key technique for dynamically detecting available spectrum in cognitive radio networks (CRNs), which can introduce high resource demands such as energy consumption. In this paper, we propose a novel cluster-based cooperative sensing-after-prediction scheme where a learning cluster and a sensing cluster are jointly considered to perform cooperative prediction and sensing efficiently. This enables us to skip the complex physical sensing to reduce the demands when the spectrum availability can be simply predicted using cooperative prediction. Furthermore, the clustering is flexible, in order to meet different performance requirements. We then formulate two optimization problems to minimize the total number of users in the two clusters or to minimize the total energy consumption, to meet different performance requirements, while in both cases guaranteeing the system accuracy requirement and individual energy constraints. To solve the two challenging integer programming problems, the unconstrained problems are mathematically solved first by relaxing the integer variable and fixing the cluster size. Such analytical solutions serve as a foundation for solving the original optimization problems. Then, two low-complexity search algorithms are proposed to achieve the global optimum, as they can obtain the same performance with exhaustive search. Simulation results validate the accuracy of the derived analytical expressions and demonstrate that the total energy consumption and the number of users contributing to learning and sensing can be greatly reduced by applying our optimized clustered sensing-after-prediction scheme.

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimize the training error of the models on historical training data or alternatively to iteratively minimize the intracluster variance of the clusters obtained via online data partitioning. This recognizes the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimizing the training error may potentially lead to overfitting while minimizing the intracluster variance does not necessarily ensure the optimized prototype-based models to attain improved classification outcomes. To achieve better classification performance while avoiding overfitting for zero-order EISs, this article presents a novel multiobjective optimization approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final nondominated set of the resulting optimized models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimization approach in improving the classification performance of zero-order EISs.

Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. As a crucial problem in MEC, service migration needs to decide how to migrate user services for maintaining the Quality-of-Service when users roam between MEC servers with limited coverage and capacity. However, finding an optimal migration policy is intractable due to the dynamic MEC environment and user mobility. Many existing studies make centralized migration decisions based on complete system-level information, which is time-consuming and also lacks desirable scalability. To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information. Specifically, the service migration problem is modeled as a Partially Observable Markov Decision Process (POMDP). To solve the POMDP, we design a new encoder network that combines a Long Short-Term Memory (LSTM) and an embedding matrix for effective extraction of hidden information, and further propose a tailored off-policy actor-critic algorithm for efficient training. The extensive experimental results based on real-world mobility traces demonstrate that this new method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms and can achieve near-optimal results on various MEC scenarios.

The advent of 5G technology introduces new - and potentially undiscovered - cybersecurity challenges, with unforeseen impacts on our economy, society, and environment. Interestingly, Intrusion Detection Mechanisms (IDMs) can provide the necessary network monitoring to ensure - to a big extent - the detection of 5G-related cyberattacks. Yet, how to realize the attack surface of 5G networks with respect to the detected risks, and, consequently, how to optimize the cybersecurity levels of the network, remains an open critical challenge. In respect, this work focuses on deploying multiple distributed Security Agents (SAs) that can run different IDMs over various network components and proposes a cybersecurity mechanism for optimizing the network’s attack surface with respect to the Quality of Service (QoS). The proposed approach relies on a new closed-form utility function to describe the trade-off between cybersecurity and QoS and uses multi-objective optimization to improve the selection of each SA detection level. We demonstrate via simulations that before optimization, an increase in the detection level of SAs brings a direct decrease in QoS as more computational, bandwidth and monetary resources are utilized for IDM processing. Thereby, after optimization, we demonstrate that our mechanism can strike a balance between cybersecurity and QoS while showcasing the impact of the importance of different objectives of the joint optimization.

Non-orthogonal multiple access (NOMA) is a promising radio access technique that enables massive connectivity and increased spectral efficiency. The deployment of aerial base stations (ABSs) as a relay is also an optimistic goal that fairly serves a large number of internet of things (IoT) devices. On one side, ABS-assisted communication leverages effective communication services for secondary IoT devices in smart cities. On the other hand, NOMA allows several IoT devices to concurrently acquire the same frequency-time resource. To this end, weighted sum-rate (WSR) is an essential goal because it allows numerous trade-offs between user fairness and sum-rate efficiency. Therefore, this work aims to investigate the WSR for an integrated aerial terrestrial network subject to cellular power and delay constraints in downlink NOMA. Herein, a theoretical insight-based low-complexity iterative solution is provided for optimal power and blocklength allocation to achieve maximum sum-rate. For this purpose, the mixed-integer non-linear problem is formulated and a low-complexity near-optimal solution is proposed. Numerical results show that the proposed scheme achieves a near-optimal solution and outperforms baseline techniques, i.e., the performance gain of 5.18% over the legacy OMA system for NOMA with two IoT devices per subcarrier.

Abstract Real‐time facial expression recognition is the basis for computers to understand human emotions and detect abnormalities in time. To effectively solve the problems of server overload and privacy information leakage, a real‐time facial expression recognition method based on iterative transfer learning and efficient attention network (EAN) for edge resource‐constrained scenes is proposed in this paper. Firstly, an EAN is designed with its parameter number and computation amount strictly limited by depth separable convolution and local channel attention mechanism. Then, the soft labels of facial expression data were obtained by EAN based on the idea of knowledge distillation, so as to provide more supervision information for the training process. Finally, an iterative transfer learning method of teacher‐student (T‐S) network was proposed; it refines the soft labels of the teacher network and further improves the recognition accuracy of the student network. The tests on the public datasets, FER2013 and RAF‐DB, show that this method can significantly reduce the model complexity and achieve high recognition accuracy. Compared with other advanced methods, the proposed method strikes a good balance between complexity and accuracy, and well meets the real‐time deployment requirements of facial expression recognition technology for edge resource‐constrained scenes.

Double auctions play a pivotal role in stimulating active participation of a large number of users comprising both task requesters and workers in mobile crowdsourcing. However, most existing studies have concentrated on designing offline two-sided auction mechanisms and supporting single-type tasks and fixed auction service models. Such works ignore the need of dynamic services and are unsuitable for large-scale crowdsourcing markets with extremely diverse demands (i.e., types and urgency degrees of tasks required by different requesters) and supplies (i.e., task skills and online durations of different workers). In this article, we consider a practical crowdsourcing application with an on-demand service strategy. Especially, we innovatively design three online service models, namely, online single-bid single-task (OSS), online single-bid multiple-task (OSM), and online multiple-bid multiple-task (OMM) models to accommodate diversified tasks and bidding demands for different users. Furthermore, to effectively allocate tasks and facilitate bidding, we propose a truthful online double auction mechanism for each service model based on the McAfee double auction. By doing so, each user can flexibly select auction service models and corresponding auction mechanisms according to their current interested tasks and online duration. To illustrate this, we present a three-demand example to explain the effectiveness of our on-demand service strategy in realistic crowdsourcing applications. Moreover, we theoretically prove that our mechanisms satisfy truthfulness, individual rationality, budget balance, and consumer sovereignty. Through extensive simulations, we show that our mechanisms can accommodate the various demands of different users and improve social utility, including platform utility and average user utility.

This paper presents a feasibility study of a novel multiple access technique called Multi-Scale Non-Orthogonal Multiple Access (MS-NOMA) for the next generation communication-positioning integration system. Different from the traditional positioning signals which are mostly Time Division Multiple Access with communication signals and are broadcast to all users, MS-NOMA supports continuous positioning waveform and flexible configurations for different positioning users to obtain higher ranging accuracy, lower positioning latency, less resource consumption and better signal coverage. Our major contributions are: Firstly, we present the MS-NOMA waveform and evaluate its performances by theoretical and simulation analyses. The results show it is feasible to use the MS-NOMA waveform to achieve high positioning accuracy and low Bit Error Rate with little resource consumption simultaneously. Secondly, to achieve optimal positioning accuracy and signal coverage, we model the power allocation problem for MS-NOMA as a convex optimization problem satisfying the Quality of Services requirement and other constraints. Then, we propose a novel Communication and Positioning Performances constrained Positioning Power Allocation (CP4A) algorithm which allocates the power of all P-Users iteratively. The theoretical and numerical results show our proposed MS-NOMA waveform with CP4A algorithm has great improvements of ranging/positioning accuracy than traditional Positioning Reference Signal in cellular network.

The ever-increasing popularity of web APIs allows app developers to leverage a set of existing web APIs to achieve their sophisticated objectives. The heavily fragmented distribution of web APIs makes it challenging for an app developer to find appropriate and compatible web APIs. Currently, app developers usually have to manually discover candidate web APIs, verify their compatibility and select appropriate and compatible ones. This process is cumbersome and requires detailed knowledge of web APIs which is often too demanding. It has become a major obstacle to further and broader applications of web APIs. To address this issue, we first propose a web API correlation graph built on extensive data about the compatibility between web APIs. Then, we propose WAR (WebAPIsRecommendation), the first data-driven approach for web APIs recommendation that integrates web API discovery, verification and selection operations based on keywords search over the web API correlation graph. WAR assists app developers without detailed knowledge of web APIs in searching for appropriate and compatible web APIs by typing a few keywords that represent the tasks required to achieve app developers’ objectives. WAR can significantly save app developers’ time and effort in searching for web APIs. We conducted large-scale experiments on 18,478 real-world web APIs and 6,146 real-world apps to demonstrate the usefulness and efficiency of WAR.

In this article, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC first uses all the data samples as microcluster medoids to assign memberships to each other and obtains the membership matrix. Based on this, AFC chooses these data samples that represent local models of data distribution as cluster medoids for initial partition. It then continues to optimize the cluster medoids iteratively to obtain a locally optimal partition as the algorithm output. Moreover, an online extension is introduced to AFC enabling the algorithm to cluster streaming data chunk-by-chunk in a “one pass” manner. Numerical examples based on a variety of benchmark problems demonstrate the efficacy of the AFC algorithm in both offline and online application scenarios, proving the effectiveness and validity of the proposed concept and general principles.

Fog computing-based radio access network (Fog-RAN) leveraging the software-defined networking (SDN) and network function virtualization (NFV) is the most promising solution to offer real-time support for the massive number of connected devices in the industrial Internet of Things (IIoT) networks. However, designing an optimal dynamic radio resource allocation to handle the fluctuating traffic loads is critical. In this article, a novel architectural design of an SDN-based virtual Fog-RAN is proposed, in which we jointly study radio resource allocation and transmit beamforming to improve resource utilization and IIoT users’ satisfaction, by minimizing the network power consumption (NPC) and maximizing the achievable sum-rate (ASR), simultaneously. To this end, we first formulate a mixed-integer nonlinear problem to optimize the physical resource block allocation, the assignment of user equipments, and radio unit, and the downlink transmit beamforming, by considering imperfect channel state information. To solve the ntractable MINLP, we exploit the successive convex approximation approach. Then, we formulate a multiple knapsack problem (MKP) to optimize the assignment between RUs and virtual baseband units, by exploiting the set of active RUs minimized in the previous problem. We solve the formulated MKP by decomposing the dual problems and solving them through the dual descent method. Through performance analysis, we show the proposed approach provides a high users’ satisfaction rate, maximizes the ASR and minimizes the NPC, and provides better savings, in terms of the number of radio and baseband resources utilized, than its counterparts.

Network update enables Software-Defined Networks (SDNs) to optimize the data plane performance. The single update focuses on processing one update event at a time,i.e., updating a set of flows from their initial routes to target routes, but it fails to handle continuously arriving update events in time incurred by high-frequency network changes. On the contrary, the continuous update proposed in “Update Algebra” can handle multiple update events concurrently and respond to the network condition changes at all times. However, “Update Algebra” only guarantees the blackhole-free and loop-free update. The congestion-free property cannot be respected. In this paper, we propose Coeus to achieve the continuous update while maintaining consistency,i.e., ensuring the blackhole-free, loop-free, and congestion-free properties simultaneously. Firstly, we establish the continuous update model based on the update operations in update events. With the update model, we dynamically reconstruct the operation dependency graph (ODG) to capture the relationship between update operations and link utilization variations. Then, we develop a composition algorithm to eliminate redundant operations in update events. To further speed up the update procedure, we present a partition algorithm to split the operation nodes of the ODG into a series of suboperation nodes that can be executed independently. The partition algorithm is proven to be optimal. Finally, extensive evaluations show that Coeus can improve the update speed by at least 179% and reduce redundant operations by at least 52% compared with state-of-the-art approaches when the arrival rate of update events equals three times per second.

Abstract: Security, Trust and Reliability are crucial issues in mobile 5G networks from both hardware and software perspectives. These issues are of significant importance when considering implementations over distributed environments, i.e., corporate Cloud environment over massively virtualized infrastructures as envisioned in the 5G service provision paradigm. The SANCUS1 solution intends providing a modular framework integrating different engines in order to enable next‐generation 5G system networks to perform automated and intelligent analysis of their firmware images at massive scale, as well as the validation of applications and services. SANCUS also proposes a proactive risk assessment of network applications and services by means of maximising the overall system resilience in terms of security, privacy and reliability. This paper presents an overview of the SANCUS architecture in its current release as well as the pilots use cases that will be demonstrated at the end of the project and used for validating the concepts.

Software-defined networking (SDN)-based virtual Fog computing radio access network (Fog-RAN) architecture is known as a potential solution to cope with massive traffic loads in 5G and beyond networks. Dynamic radio and computational resource allocation is needed to handle the fluctuating traffic loads, aiming to reduce power consumption and enhancing user satisfaction. In this paper, we propose a dynamic resource allocation for a SDN-based virtual Fog-RAN. We formulate a mixed-integer non-linear problem to minimize the network power consumption by optimizing the user equipment (UE) - radio units (RUs) association, the physical resource block (PRB) allocation, and the RU power allocation, according to the real-time traffic loads. Then, exploiting the obtained results in the previous problem, we formulate a multiple knapsack problem to optimize the assignment between the active RUs and the virtual baseband units (vBBUs). Finally, we perform a simulation study to analyse the impact of the proposed resource allocation on network power consumption, vBBUs resources savings, and user satisfaction so to validate the performance gains achieved.

In this paper, we study the total effective capacity (EC) of single-cluster non-orthogonal multiple access (NOMA) networks and demonstrate the performance gain of single-cluster NOMA over user-paired NOMA and orthogonal multiple access (OMA). Specifically, the exact closed-form expression and an approximate closed-form expression at high signal-to-noise ratios (SNRs), in terms of the total EC, are derived for single-cluster NOMA networks. The derivations reveal that the total EC at high SNRs only relies on the statistical delay requirement of the strongest user and is independent of the other users' delay requirements. Further, we theoretically analyze the total EC differences between single-cluster NOMA and user-paired NOMA/OMA communications and explore the impact of transmit SNR. Simulation results verify the accuracy of analytical results and further reveal that the single-cluster NOMA network achieves a greater gain in terms of the total EC, compared to the conventional OMA, when the number of users increases.

Positions of primary and secondary users and the distance between them in a cognitive radio network play an important role in the interference caused by the secondary network to the users in the primary network. In this work, we exploit cooperative localization to estimate the distance between the nodes and then use transmission power control for interference avoidance. The distance between the primary and secondary users is estimated using a cooperative model. Next, the transmission power of the secondary user is adjusted so that it does not cause interference to the primary user. The proposed algorithm is verified by simulations. Results show that the primary user achieves considerable gain in the throughput as a result of increase in the signal to interference and noise ratio due to reduction in the interference caused by the secondary network.

This paper investigates the performance of cooperative device-to-device (C-D2D) communications in a cellular network, where the simultaneous wireless information and power transfer (SWIPT) technology is adopted by D2D transmitters (DTs). In this network, DTs can act as relays that consume a portion of energy harvested by a time switching (TS) strategy to satisfy the quality of service (QoS) requirements of cellular users (CUs) with poor channel conditions, in exchange for spectrum resources of CUs for D2D communications. To achieve the sum-throughput maximization of the network while guaranteeing the QoS requirements of both D2D and cellular links, we formulate a novel optimization problem that jointly determines user pairing between DTs and CUs, time allocation for energy harvesting and information transmission, and power allocation at DTs for relaying information and performing D2D communications. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) problem which is computationally prohibitive. To overcome this issue, a two-step policy-based algorithm is proposed to solve the problem in polynomial time. Simulation results validate the convergence of the proposed algorithm and the effectiveness of the joint user pairing and resource allocation scheme for improving network throughput.

Because of its important role in health policy-shaping, population health monitoring (PHM) is considered a fundamental block for public health services. However, traditional public health data collection approaches, such as clinic-visit-based data integration or health surveys, could be very costly and time-consuming. To address this challenge, this article proposes a cost-effective approach called Compressive Population Health (CPH), where a subset of a given area is selected in terms of regions within the area for data collection in the traditional way, while leveraging inherent spatial correlations of neighboring regions to perform data inference for the rest of the area. By alternating selected regions longitudinally, this approach can validate and correct previously assessed spatial correlations. To verify whether the idea of CPH is feasible, we conduct an in-depth study based on spatiotemporal morbidity rates of chronic diseases in more than 500 regions around London for over 10 years. We introduce our CPH approach and present three extensive analytical studies. The first confirms that significant spatiotemporal correlations do exist. In the second study, by deploying multiple state-of-the-art data recovery algorithms, we verify that these spatiotemporal correlations can be leveraged to do data inference accurately using only a small number of samples. Finally, we compare different methods for region selection for traditional data collection and show how such methods can further reduce the overall cost while maintaining high PHM quality.

This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV's trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV's trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush-Kuhn-Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

With the advent of the Internet of Things (IoT) and the increasing popularity of the intelligent transportation system, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data. Analyzing and mining such big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decision making often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and nonparametric model aided by 6G is proposed in this article to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware locality-sensitive hashing (LSH). We design a wide range of experiments based on a real-world big traffic data set to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.

With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing the computational burden when the edge is overloaded. In this article, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Especially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multiobjective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios, respectively, to reveal the advantages of the proposed framework.

A UAV-aided wireless power transfer and data collection network is studied, where it is assumed that when the harvested energy at the sensor node (SN) cannot surpass its circuit activation threshold or the received data rate at UAV falls below a minimal required rate threshold, the information outage occurs. The closed-form expressions of energy outage probability and rate outage probability are derived at first, and then the overall outage probability and coverage performance of the system are analyzed. Based on which, an optimization problem is formulated to minimize the overall outage probability by optimizing UAV's elevation angle and the time splitting (TS) factor. Since the problem is non-convex and has no known solution, an alternating optimization (AO)-based algorithm with Golden-section (GS) based linear search method is designed to find the global optimal solution. In order to explore the maximum coverage area of the UAV for a given tolerable outage probability, another optimization problem is also formulated to maximize the coverage range by optimizing UAV's elevation angle. By using Karush-Kuhn-Tucker (KKT) conditions, the closed-form solution of the optimal elevation angle for maximizing the coverage area is derived. Monte Carlo simulations verify the accuracy of the derived closed-form expression of the overall outage probability and the semi-closed-form expressions of the optimum UAV's elevation angle and TS factor. It shows that there exist a unique optimum elevation angle and the TS factor to achieve the minimum overall outage probability, and significant performance gain can be obtained by using our proposed optimization scheme. The developed theoretical results can be useful to the design of UAV-aided wireless communication systems with wireless power transfer.

Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors' privacy parameters. In this paper, we first present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments.

Localization plays a key role in Internet of Things. This paper proposes a novel intelligent cooperative multisensor localization method called the edge cloud cooperative localization (ECCL) which has the range and angle observations from the neighbor nodes along with the location observations from an absolute coordinate localization system like global positioning system. The edge cloud structure is proposed which employs several distributed Kalman filters in sensor nodes edge and a centralized cooperative fusion unit in the cloud. For a robust fusion, a cooperative redundancy validation method is proposed to detect the outliers. The proposed ECCL scheme has the advantages of both the distributed and centralized localization, which satisfies the needs of high reliability and high accuracy, especially when sensor nodes have limited computational resources. The simulation and experimental results show that our proposed ECCL algorithm outperforms the other schemes both in outlier detection and localization accuracy.

Federated learning (FL) has been employed for numerous privacy-sensitive applications, where distributed devices collaboratively train a global model. In industrial Internet of things (IIoT) systems, training latency is the key performance metric as the automated manufacture usually requires timely processing. The existing works increase the number of effective devices to accelerate the training. However, devices in IIoT systems are usually deployed densely; increasing the number of clients can potentially cause serious interference and prolonged training latency. In this article, we propose a resource allocation scheme for FL, namely RaFed. We formulate the problem of reducing training latency as an optimization problem, which is proved to be NP-hard. We propose a heuristic algorithm to select appropriate devices for achieving a good tradeoff between the interference and convergence time. We conduct experiments using an RGB-D dataset in an IIoT system. The results show that RaFed significantly reduces the latency by 29.9%, compared to the state-of-the-art works.

In mobile crowdsourcing (MCS), the platform selects participants to complete location-aware tasks from the recruiters aiming to achieve multiple goals (e.g., profit maximization, energy efficiency, and fairness). However, different MCS systems have different goals and there are possibly conflicting goals even in one MCS system. Therefore, it is crucial to design a participant selection algorithm that applies to different MCS systems to achieve multiple goals. To deal with this issue, we formulate the participant selection problem as a reinforcement learning problem and propose to solve it with a novel method, which we call auxiliary-task based deep reinforcement learning (ADRL). We use transformers to extract representations from the context of the MCS system and a pointer network to deal with the combinatorial optimization problem. To improve the sample efficiency, we adopt an auxiliary-task training process that trains the network to predict the imminent tasks from the recruiters, which facilitates the embedding learning of the deep learning model. Additionally, we release a simulated environment on a specific MCS task, the ride-sharing task, and conduct extensive performance evaluations in this environment. The experimental results demonstrate that ADRL outperforms and improves sample efficiency over other well-recognized baselines in various settings.

To improve data speed and reliability, hybrid wireless networks combine two different Radio Access Technologies (RATs), such as Visible Light Communications (VLC), millimetre wave (mmWave), Wireless Fidelity (WiFi), 4G Long Term Evolution (LTE), etc. The Internet of Radio Light (IoRL) is a cutting-edge system paradigm to combine three RATs for taking advantage the vast VLC and mmWave spectrum with the ubiquitous coverage of WiFi. In this respect, this work introduces a new convex optimisation-based solution method to optimise the three-dimensional (3D) Access Point Assignment (APA) problem of the IoRL system under individual user positioning, priority and minimum Quality-of-Service (QoS) constraints. We use both the IoRL real-world testbed and large-scale Maltab simulations to evaluate that our solution converges in linear time, and attains higher throughput-vs-fairness trade-off than existing efforts.

Network update enables Software-Defined Networks (SDNs) to optimize the data plane performance via southbound APIs. The single update between the initial and the final network states fail to handle high-frequency changes or the burst event during the update procedure in time, leading to prolonged update time and inefficiency. On the contrary, the continuous update can respond to the network condition changes at all times. However, existing work, especially "Update Algebra" can only guarantee blackhole- and loop-free. The congestion-free property cannot be respected during the update procedure. In this paper, we propose Coeus, a continuous network update system while maintaining blackhole-, loop- and congestion-free simultaneously. Firstly, we establish an operation-based continuous update model. Based on this model, we dynamically reconstruct an operation dependency graph to capture unexecuted update operations and the link utilization variations. Subsequently, we develop an operation composition algorithm to eliminate redundant update commands and an operation node partition algorithm to speed up the update procedure. We prove that the partition algorithm is optimal and can guarantee the consistency. Finally, extensive evaluations show that Coeus can improve the makespan by at least 179% compared with state-of-the-art approaches when the arrival rate of update events equals to three times per second.

The analytical model of trilateration localization error expresses the localization error in terms of distance estimation errors. Using the analytical model, we investigate the trilateration localization error, and derive the extreme values of its components and other novel results. We show that these extreme values occur when the distance estimation errors have opposite signs. We further show that better localization accuracy is achieved if all the distance estimation errors are negative compared to when the same magnitudes of distance estimation errors are positive. The results are applicable to all wireless communication systems and networks where trilateration and multilateration may be used for position estimation. This includes global navigation satellite systems, such as the global positioning system, wireless local area networks, wireless sensor networks, internet of things, and other miscellaneous applications. All the results are verified using simulation.

Compared with traditional internal combustion engine vehicles, Electric Vehicles (EVs) have the advantage of eliminating harmful gases in the environment, with great development potential in recent years. However, because the battery capacity of EVs is limited at the current stage, where to charge (to select charging station) and when/whether to charge (order the charging priority of EVs) still limit the large-scale popularity of EVs. In this paper, we develop an Urgency First Charging (UFC) charging scheduling policy, which takes the remaining parking time and charging time of EVs as the standard of charging priority. With this, the CS benefits to the shortest trip duration (summation of travelling time through CS, and charging service time at CS) is selected as optimal solution. We have conducted simulations through Helsinki's traffic scenarios. The results have shown that our proposed CS-Selection scheme effectively improves the charging comfort (in terms of waiting time and trip time) and charging efficiency (in terms of not-fully charged service due to limited parking duration).

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV's trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV's CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV's computing energy can also be greatly reduced.

Data access control in a cloud storage system is regarded as a promising technique for enhanced efficiency and security utilizing a ciphertext-policy attribute-based encryption (CP-ABE) approach. However, due to a large number of data users as well as limited resources and heterogeneity of data devices in Internet of Things (IoT), existing access control schemes for the cloud storage are not effectively applicable to IoT applications. In this article, we construct a new CP-ABE-based storage model for data storing and secure access in a cloud for IoT applications. Our new framework introduces an attribute authority management (AAM) module in the cloud storage system functioned as an agent that provides a user-friendly access control and highly reduces the storage overhead of public keys. Then, we propose a novel secure and efficient multiauthority access control scheme of the cloud storage system for IoT, namely, SEM-ACSIT, which obtains both backward security and forward security when an attribute of a user is revoked. By exploiting encryption outsourcing, simplified key structuring and the AAM module, the computational overhead of a user is immensely decreased. Moreover, a user access control list (UACL) in the cloud server is constructed newly to support authorization access for a specific user. The analysis and simulation results demonstrate that our SEM-ACSIT scheme achieves powerful security with less computational overhead and lower storage costs than the existing schemes.

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack. In this paper, we propose a novel anti-intelligent UAV jamming strategy, in which the ground users can learn the optimal trajectory to elude such jamming. The problem is formulated as a stackelberg dynamic game, where the UAV jammer acts as a leader and the ground users act as followers. First, as the UAV jammer is only aware of the incomplete channel state information (CSI) of the ground users, for the first attempt, we model such leader sub-game as a partially observable Markov decision process (POMDP). Then, we obtain the optimal jamming trajectory via the developed deep recurrent Q-networks (DRQN) in the three-dimension space. Next, for the followers sub-game, we use the Markov decision process (MDP) to model it. Then we obtain the optimal communication trajectory via the developed deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium and derive the closed-form expression for the stackelberg equilibrium in a special case. Moreover, some insightful remarks are obtained and the time complexity of the proposed defense strategy is analyzed. The simulations show that the proposed defense strategy outperforms the benchmark strategies.

LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks.

Despite the thrilling success achieved by existing binary descriptors, most of them are still in the mire of three limitations: 1) vulnerable to the geometric transformations; 2) incapable of preserving the manifold structure when learning binary codes; 3) NO guarantee to find the true match if multiple candidates happen to have the same Hamming distance to a given query. All these together make the binary descriptor less effective, given large-scale visual recognition tasks. In this paper, we propose a novel learning-based feature descriptor, namely Unsupervised Deep Binary Descriptor (UDBD), which learns transformation invariant binary descriptors via projecting the original data and their transformed sets into a joint binary space. Moreover, we involve a ℓ2,1-norm loss term in the binary embedding process to gain simultaneously the robustness against data noises and less probability of mistakenly flipping bits of the binary descriptor, on top of it, a graph constraint is used to preserve the original manifold structure in the binary space. Furthermore, a weak bit mechanism is adopted to find the real match from candidates sharing the same minimum Hamming distance, thus enhancing matching performance. Extensive experimental results on public datasets show the superiority of UDBD in terms of matching and retrieval accuracy over state-of-the-arts.

Trilateration and multilateration are important location estimation techniques used in a diverse range of networks and applications. The system of equations yielded by multilateration can be reduced to simpler linear equations which can be solved to arrive at a closed form analytic solution. Exploiting this solution technique, we develop a novel and unique analytical model for the localization error resulting from trilateration. The analytical model can be used for the analysis of the localization error in all applications wherever multilateration is used for position estimation including internet of things, wireless sensor networks and global navigation satellite system thereby increasing reliability and quality of localization. As an example, we use the analytical model to corroborate the fact that localization error is a function of topology of reference positions in addition to distance estimation errors. The analytical model is verified using simulation experiments.

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack which can learn the optimal attack strategy in complex communication environments. In this paper, we propose an anti-intelligent UAV jamming strategy, in which the mobile users can learn the optimal defense strategy to prevent jamming. Specifically, the UAV jammer acts as a leader and the users act as followers. The problem is formulated as a stackelberg dynamic game, which includes the leader sub-game and the followers sub-game. As the UAV jammer is only aware of the incomplete channel state information (CSI) of the users, we model the leader sub-game as a partially observable Markov decision process (POMDP). The optimal jamming trajectory is obtained via deep recurrent Q-networks (DRQN) in the three-dimension space. For the followers sub-game, we use the Markov decision process (MDP) to model it. Then the optimal communication trajectory can be learned via deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium. The simulations show that the proposed strategy outperforms the benchmark strategies.

In this paper, we investigate the energy-efficient multi-user mobile-edge computing offloading problem in massive MIMO enabled HetNets, where the CPU-cycle frequency of mobile devices, uplink power control, computational task offloading ratio and uplink transmission duration are jointly optimized. The problem is formulated as minimizing the energy consumption of all mobile devices while satisfying the maximum latency requirement. Specifically, to address this non-convex problem, a low-complexity algorithm is proposed relied on alternating optimization, where we address the joint computational task offloading ratio and uplink transmission duration optimization problem and the uplink power control problem iteratively. Besides, the effectiveness and convergence of the proposed iterative algorithm are analytically studied. Numerical results demonstrate that our proposed algorithm consumes less energy compared to local computing and full uploading schemes, and the application of massive MIMO in HetNets helps to reduce energy consumption of mobile devices.

We investigate the robust beamforming design for a simultaneous wireless information and power transfer (SWIPT) enabled system, with the cooperative non-orthogonal multiple access (NOMA) protocol applied. A novel cooperative NOMA scheme is proposed, where a strong user with better channel conditions adopts power splitting (PS) scheme and acts as an energy-harvesting relay to forward the decoded signal to the weak user. The presence of channel uncertainties is considered by introducing the outage-based constraints of signal to interference plus noise ratio (SINR). Specifically, it is assumed that only imperfect channel state information (CSI) is known at the base station (BS), due to the reason that the BS is far away from both users and suffers serious feedback delay. Our aim is to maximize the strong user's data rate, by optimally designing the robust transmit beamforming and PS ratio, while guaranteeing the correct decoding of the weak user. The proposed formulation yields to a challenging nonconvex optimization problem. To solve it, we first approximate the probabilistic constraints with the Bernstein-type inequalities, which can then be globally solved by two-dimensional exhaustive search. To further reduce the complexity, an efficient low-complexity algorithm is proposed with the aid of successive convex approximation (SCA). Numerical results show that the proposed algorithm converges quickly, and the proposed SWIPT-enabled robust cooperative NOMA system achieves better performance than existing protocols.

This paper proposes a new power allocation scheme to jointly optimize energy efficiency (EE) and spectral efficiency (SE) of a point-to-point communication system in which the transmitter is equipped with fixed as well as energy harvesting batteries. Time switching protocol is used such that in each time frame the node either harvests energy or transmits information. Firstly, a multi-objective optimization problem which jointly optimizes EE and SE is formulated. An importance weight parameter is introduced to control the priority level between EE and SE. Secondly, the multi-objective problem is transformed into a single-objective optimization problem by using importance weight, and then solved through fractional programming. Using the Karush-Kuhn-Tucker conditions, the optimum power allocation scheme without input power constraint is developed. The ensuing solution is then generalized for system operation with average input power constraint. Closed-form expressions are derived and tested through simulations. Numerical results results are provided, and show the impact of the harvested power in improving the overall rate of the system. Also investigation is done to analyze the effect of system parameters on the achievable trade-off performance of the energy-harvesting based system.

In this paper, we propose a novel power allocation scheme that maximizes the sum spectral efficiency of the licensee in a dynamic Licensed Shared Access (LSA) system. In particular, our focus is on the time intervals in which the incumbent system is active in the spectrum. We derive an expression for the interference distribution of the licensee, e.g., a mobile network operator, utilizing a spectrum belonging to an airport incumbent under the LSA spectrum sharing. Formulating an optimization problem to maximize the sum spectrum efficiency subject to the interference threshold constraint at the licensee, we then show its convexity, and obtain its optimal solutions. We further investigate the impact of sum rate maximization on the fairness of network resource allocations. Simulation results show a significant gain in the achievable spectrum efficiency, especially during the intervals in which the incumbent system is active in the LSA band. This paper provides quantitative insights on the maximum achievable sum rate in an LSA system in which both the licensee and the incumbent systems are active at the same time.

Software-defined networking (SDN) is a key enabling technology of industrial Internet of Things (IIoT) that provides dynamic reconfiguration to improve data network robustness. In the context of smart grid infrastructure, the strong demand of seamless data transmission during critical events (e.g., failures or natural disturbances) seems to be fundamentally shifting energy attitude toward emerging technology. Therefore, SDN will play a vital role on energy revolution to enable flexible interfacing between smart utility domains and facilitate the integration of mix renewable energy resources to deliver efficient power of sustainable grid. In this regard, we propose a new SDN platform based on IIoT technology to support resiliency by reacting immediately whenever a failure occurs to recover smart grid networks using real-time monitoring techniques. We employ SDN controller to achieve multifunctionality control and optimization challenge by providing operators with real-time data monitoring to manage demand, resources, and increasing system reliability. Data processing will be used to manage resources at local network level by employing SDN switch segment, which is connected to SDN controller through IIoT aggregation node. Furthermore, we address different scenarios to control packet flows between switches on hub-to-hub basis using traffic indicators of the infrastructure layer, in addition to any other data from the application layer. Extensive experimental simulation is conducted to demonstrate the validation of the proposed platform model. The experimental results prove the innovative SDN-based IIoT solutions can improve grid reliability for enhancing smart grid resilience.

The cloud radio access network (C-RAN) emerges as one of the future solutions to handle the ever-growing data traffic, which is beyond the physical resources of current mobile networks. The C-RAN decouples the traffic management operations from the radio access technologies, leading to a new combination of a virtualized network core and a fronthaul architecture. This new resource coordination provides the necessary network control to manage dense Long-Term Evolution (LTE) networks overlaid with femtocells. However, the energy expenditure poses a major challenge for a typical C-RAN that consists of extended virtualized processing units and dense fronthaul data interfaces. In response to the power efficiency requirements and dynamic changes in traffic, this paper proposes C-RAN solutions and algorithms that compute the optimal backup topology and network mapping solution while denying interfacing requests from low-flow or inactive femtocells. A graph-coloring scheme is developed to label new formulated fronthaul clusters of femtocells using power as the performance metric. Additional power savings are obtained through efficient allocations of the virtualized baseband units (BBUs) subject to the arrival rate of active fronthaul interfacing requests. Moreover, the proposed solutions are used to reduce power consumption for virtualized LTE networks operating in the Wi-Fi spectrum band. The virtualized network core use the traffic load variations to determine those femtocells who are unable to transmit to switch them off for additional power savings. The simulation results demonstrate an efficient performance of the given solutions in large-scale network models.

Wireless network virtualization, which decouples the physical supply process and the service provisioning process, can abstract, isolate, and share the physical infrastructure network equipment. This paper studies the resource virtualization in virtual multiple-input multiple-output single-carrier frequency-division-multiple-access uplink systems, where resources are abstracted to hide the complex details of the fading channel and the link rates are virtualized using the statistical method. Furthermore, the virtual link rates are scheduled and instantiated to different slices with customized delay-bounded quality of service (QoS) provisioning. In this scheme, a physical mobile network operator (PMNO) is in charge of the network resource at the physical layer, while virtual mobile network operators (VMNOs) are responsible for the traffic admission and the slice management at the MAC layer. Furthermore, we build up the resource virtualization problem as a cross-layer Stackelberg game, which has the interactive dual processes based on the QoS exponent: top-to-down sub-game of leaders at the MAC layer and down-to-top sub-game of follower at the physical layer. Using the newly designed functions for PMNO and VMNOs, we develop an effective dynamic algorithm with an iterative dual update to meet the optimization targets of PMNO and VMNOs. Simulation results verify the superiority and stability of delay-bounded QoS guaranteed wireless resource virtualization algorithm developed in this paper in terms of convergence, access rate, and delay-outage probability.

By blending the concepts of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiplexing, in this paper, a novel hybrid scheme is conceived for supporting diverse services in future wireless systems. Motivating to maximize energy efficiency (EE), the joint resource management of user clustering (UC) and power allocation is investigated for the downlink hybrid NOMA systems. Under two different power consumption cases, the optimal resource allocation (Opt-RA) algorithm is developed with the help of converting the original mixed integer non-linear programming (MINLP) problem to the tractable decoupled problems. For practical implementation, the heuristic resource allocation (Heur-RA) algorithm is also proposed, and it includes a low-complexity UC algorithm based on the candidate search-and-allocation approach. Our simulation results show that, both the Opt-RA and Heur-RA algorithms achieve significantly higher EE performance than other existing algorithms. Further, the results also prove that, the hybrid NOMA conceived is able to exploit the advantages of NOMA scheme, and is superior to conventional orthogonal multiple access (OMA) in terms of EE, as well as achieving higher flexibility for system configuration than NOMA.

In this paper, we study the robust beamforming design for a simultaneous wireless information and power transfer (SWIPT) enabled system, with cooperative non-orthogonal multiple access (NOMA) protocol applied. A novel cooperative NOMA scheme is proposed, where the strong user with better channel conditions adopts power splitting (PS) scheme and acts as an energy-harvesting relay to transmit information to the weak user. The presence of channel uncertainties is considered and incorporated in our formulations to improve the design robustness and communication reliability. Specifically, only imperfect channel state information is assumed to be available at the base station (BS), due to the reason that the BS is far away from both users and suffers serious feedback delay. To comprehensively address the channel uncertainties, two major design criteria are adopted, which are the outage-based constraint design and the worst-case-based optimization. Then, our aim is to maximize the strong user's data rate, by optimally designing the robust transmit beamforming and PS ratio, while guaranteeing the correct decoding of the weak user. With two different channel uncertainty models respectively incorporated, the proposed formulations yield to challenging nonconvex optimization problems. For the outage-based constrained optimization, we first conservatively approximate the probabilistic constraints with the Bernstein-type inequalities, which are then globally solved by 2-D exhaustive search. To further reduce the complexity, an efficient low-complexity algorithm is then proposed with the aid of successive convex approximation (SCA). For the worst-case-based scenario, we first apply the semidefinite relaxation method to relax the quadratic terms and prove the rank-one optimality. Then the nonconvex max-min optimization problem is readily transformed into convex approximations based on S-procedure and SCA. Simulation results show that for both channel uncertainty models, the proposed algorithms can converge within a few iterations, and the proposed SWIPT-enabled robust cooperative NOMA system achieves better system performance than the existing protocols.

This paper proposes a novel design of cooperative non-orthogonal layered multicast multiple access in a heterogeneous network, where the information is encoded into the messages of high priority (HP) and low priority (LP). Two types of multicast users coexist in the network: 1) regular users (RUs), which are located far away from the base station (BS) and expect to decode only the HP message (due to the weak channels), and 2) advanced users (AUs), which are located close to the BS and expect to decode both HP and LP messages. To improve the reliability of layered multicast, we consider that the successful AUs (those AUs who successfully decode the HP and LP messages) serve as potential relays to assist other AUs/RUs. Based on this idea, two novel cooperation strategies are proposed for different cases of channel information availability. For each proposed strategy, we derive closed-form exact outage probabilities of AUs and RUs and then further analyze their diversity orders. Moreover, considering that the layered multicast is outage-constrained, we theoretically evaluate the energy consumption of both strategies and demonstrate their energy saving gains over the direct non-orthogonal multiple access for layered multicast. Finally, our theoretical analysis is verified by numerical results, and the advantages of the proposed strategies are also demonstrated.

This paper proposes a deep hashing framework, namely Unsupervised Deep Video Hashing (UDVH), for largescale video similarity search with the aim to learn compact yet effective binary codes. Our UDVH produces the hash codes in a self-taught manner by jointly integrating discriminative video representation with optimal code learning, where an efficient alternating approach is adopted to optimize the objective function. The key differences from most existing video hashing methods lie in 1) UDVH is an unsupervised hashing method that generates hash codes by cooperatively utilizing feature clustering and a specifically-designed binarization with the original neighborhood structure preserved in the binary space; 2) a specific rotation is developed and applied onto video features such that the variance of each dimension can be balanced, thus facilitating the subsequent quantization step. Extensive experiments performed on three popular video datasets show that UDVH is overwhelmingly better than the state-of-the-arts in terms of various evaluation metrics, which makes it practical in real-world applications.

In recent years, with the development of mobile devices, the location based services (LBSs) have become more and more prevailing and most applications installed on these devices call for location information. Yet, the untrusted LBS provider can collect this location information, which may potentially threaten users' location privacy. In view of this challenge, we propose a two-tier schema for the privacy preservation based on k-anonymity principle meanwhile reducing the cost for privacy protection. Concretely, we divide the users into groups in order to maximize the privacy level and in each group one proxy is selected to generate dummy locations and share the returned results from LBS provider; then, on each group, an auction mechanism is proposed to determine the payment of each user to the proxy as the compensation, which satisfies budget balance and incentive compatibility. To evaluate the performance of the proposed schema, a simulated experiment is conducted.

It is anticipated that a considerably higher network capacity will be achieved by the fifth generation (5G) small cell networks incorporated with the millimeter wave (mm-wave) technology. However, the mm-wave signals are more sensitive to blockages than signals in lower frequency bands, which highlight the effect of anisotropic path loss in network coverage. According to the fractal characteristics of cellular coverage, a multi-directional path loss model is proposed for the 5G small cell networks, where different directions are subject to different path loss exponents. Furthermore, the coverage probability, association probability, and the handoff probability are derived for the 5G fractal small cell networks based on the proposed multi-directional path loss model. The numerical results indicate that the coverage probability with the multi-directional path loss model is less than that with the isotropic path loss model, and the association probability with long link distance, e.g., 150m, increases obviously with the increase of the effect of anisotropic path loss in 5G fractal small cell networks. Moreover, it is observed that the anisotropic propagation environment is having a profound impact on the handoff performance. Meanwhile, we could conclude that the resulting heavy handoff overhead is emerging as a new challenge for 5G fractal small cell networks.

The development of modellings and analytical tools to structurise and study the allocation of resources through noble user competitions become essential, especially considering the increased degree of heterogeneity in application and service demands that will be cornerstone in future communication systems. Stochastic asymmetric Blotto games appear promising to modelling such problems, and devising their Nash equilibrium (NE) strategies by anticipating the potential outcomes of user competitions. In this regard, this paper approaches the generic energy efficiency problem with a new stochastic asymmetric Blotto game paradigm to enable the derivation of joint optimal bandwidth and transmit power allocations by setting multiple users to compete in multiple auction-like contests for their individual resource demands. The proposed modelling innovates by abstracting the notion of fairness from centrally-imposed to distributed-competitive, where each user's pay-off probability is expressed as quantitative bidding metric, so as, all users' actions can be interdependent, i.e., each user attains its utility given the allocations of other users, which eliminates the chance of low-valued carriers not being claimed by any user, and, in principle, enables the full utilisation of wireless resources. We also contribute by resolving the allocation problem with low complexity using new mathematical techniques based on Charnes-Cooper transformation, which eliminate the additional coefficients and multipliers that typically appear during optimisation analysis, and derive the joint optimal strategy as a set of linear single-variable functions for each user. We prove that our strategy converges towards a unique, monotonous and scalable NE, and examine its optimality, positivity and feasibility properties in detail. Simulation comparisons with relevant studies confirm the superiority of our approach in terms of higher energy efficiency performance, fairness index and quality-of-service provision.

A tractable stochastic geometry model is proposed to characterize the performance of novel point-to-multipoint (P2MP) assisted backhaul networks with millimeter-wave (mm-wave) capability. The novel performance analysis is studied based on the general backhaul network (GBN) and the simplified backhaul network (SBN) models. To analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability of the backhaul networks, a range of the exact- and closed-form expressions are derived for both the GBN and SBN models. With the aid of the tractable model, the optimal power control algorithm is proposed for maximizing the trade-off between energy-efficiency (EE) and area spectral-efficiency (ASE) for the mm-wave backhaul networks. The analytical results of the SINR coverage probability are validated, and they match those obtained from Monte-Carlo experiments. The numerical results of the ASE performance demonstrate the significant effectiveness of our P2MP architecture over the traditional point-to-point setup. Moreover, our P2MP mm-wave backhaul networks are able to achieve dramatically higher rate performance than that obtained by the ultra-high-frequency networks. Furthermore, to achieve optimal EE and ASE tradeoff, the mm-wave backhaul networks should be designed to limit the link distances and line-of-sight interferences while optimizing the transmission power.

In this paper, a novel approach is introduced to study the achievable delay-guaranteed secrecy rate, by introducing the concept of the effective secrecy rate (ESR). This study focuses on the downlink of a non-orthogonal multiple access (NOMA) network with one base station, multiple single-antenna NOMA users and an eavesdropper. Two possible eavesdropping scenarios are considered: 1) an internal, unknown, eavesdropper in a purely antagonistic network; and 2) an external eavesdropper in a network with trustworthy peers. For a purely antagonistic network with an internal eavesdropper, the only receiver with a guaranteed positive ESR is the one with the highest channel gain. A closed-form expression is obtained for the ESR at high signal-to-noise ratio (SNR) values, showing that the strongest user’s ESR in the high SNR regime approaches a constant value irrespective of the power coefficients. Furthermore, it is shown the strongest user can achieve higher ESR if it has a distinctive advantage in terms of channel gain with respect to the second strongest user. For a trustworthy NOMA network with an external eavesdropper, a lower bound and an upper bound on the ESR are proposed and investigated for an arbitrary legitimate user. For the lower bound, a closed-form expression is derived in the high SNR regime. For the upper bound, the analysis shows that if the external eavesdropper cannot attain any channel state information (CSI), the legitimate NOMA user at high SNRs can always achieve positive ESR, and the value of it depends on the power coefficients. Simulation results numerically validate the accuracy of the derived closed-form expressions and verify the analytical results given in the theorems and lemmas.

In this paper, we investigate secure communication for an untrusted relay network, in which a source equipped with NS antennas communicates to M users with the help of an untrusted relay. To protect the data confidentially while concurrently relying on the untrusted relays, joint antenna and user selection scheme has been proposed with the aid of cooperative jamming. Furthermore, the impacts of both the number of antennas and the number of users on system performance are studied. Both the secrecy outage probability (SOP) and ergodic secrecy rate (ESR) are derived in closed form, and the secrecy diversity order of the considered networks is proved to be min(NS, M/2). Compared with the traditional single antenna and single source-destination scenario, both SOP and ESR can achieve a great improvement owing to the mutual effects of antenna diversity and user diversity. In addition, numerical results are conducted to demonstrate the validity of the proposed scheme.

In this paper, we investigate energy efficiency for uplink LoRa, which is one of the most promising and widely deployed low-power wide-area (LPWA) networks. In the considered networks, we explore user scheduling, spreading factor (SF) assignment, and power allocation jointly. A nonconvex optimization problem for maximizing the system energy efficiency is formulated, with targeted SNR requirement and power range as constraints for each LoRa user. To solve this problem, we first propose a low-complexity suboptimal algorithm, which includes energy-efficient user scheduling and heuristic SF assignment for scheduled users based on matching theory. Then a novel power allocation based on Charnes-Cooper transformation is proposed to transform the fractional objective into the convex form to maximize the system energy efficiency. Simulation results show that the proposed algorithms achieve near-optimal performance in terms of energy efficiency.

This paper proposes a novel design of cooperative non-orthogonal multiple access (NOMA) for layered multicast, where the information is encoded into the messages of high-priority (HP) and low-priority (LP). Two types of multicast users coexist in the system: 1) regular users (RUs), which locate far away from the base-station (BS) and demand only the HP message; 2) advanced users (AUs), which locate close to the BS and demand both HP and LP messages. To improve the reliability of layered multicast, an opportunistic cooperative NOMA multicast strategy is proposed, in which one successful AU is selected to forward both HP and LP messages. For the proposed strategy, we derive closed-form exact outage probabilities of AUs and RUs. By further carrying out the asymptotic analysis, the achieved diversity orders are shown to be not less than the number of AUs, i.e., full diversity is achieved. Finally, numerical results verify the theoretical analysis and demonstrate the superiority of the proposed strategy.

State-of-the-art inter-datacenter WANs rely on software defined networking (SDN) to orchestrate their data transmission. Optimization requires frequent network update operations to switch forwarding tables. When scheduling inter-datacenter WANs, the utility of services should be respected. Yet, existing network update approaches do not respect network utility and could result in performance degradation during the network update procedure. Further, the update causes not only performance degradation, but also the degradation period is unnecessarily prolonged. In this paper we propose Hermes, a utility-aware network update system. We aim to find a rate limiting scheme for update which maximizes the sum of service utility, while ensuring the congestion-free property during the update. We propose an optimization framework for the maximum utility network update problem (MUP). MUP is NP-hard and a series of algorithms are developed to solve it. Extensive simulation and testbed experiments with a prototype demonstrate that Hermes can increase the total utility by 80% compared to state-of-the-art. At the same time, it reduces the total update time and control overhead by 40% and 55%, respectively.

Vehicular ad hoc networks (VANETs) have a potential to promote vehicular telematics and infotainment applications, where a key and challenging issue is the design of robust and efficient vehicular content transmissions to combat the lossy inter-vehicle links. In this paper, we focus on the robust optimization of content transmissions over cooperative VANETs. We first derive a stochastic model for estimation of time-varying inter-vehicle distance, which is dependent of the vehicle real-time kinematics and the distribution of the initial space headway. With this model, we analytically formulate the transient inter-vehicle connectivity assuming Nakagami fading channels for the physical (PHY) layer. We also model the contention nature of the medium access control (MAC) layer, on which we are based to evaluate the throughput achieved by each vehicle equipped with dedicated short-range communication (DSRC). Combining these models, we derive a closed-formed expression for the upper bound of the probability of failure in intact-content transmissions. Based upon this theoretical bound, we develop a robust optimization model for assigning content data traffic among different cooperative transmission paths, where the objective is to minimize the maximum likelihood of unsuccessful content transmissions over the cooperative VANET. We mathematically transform the optimization model to another equivalent form, such that it can be practically deployed. Finally, we validate our theoretical development with extensive simulations. Numerical results are also provided to confirm the power of cooperation in boosting the VANET performance as well as demonstrate the advantage of the proposed robust optimization in terms of content data reception reliability.

Physical-layer security has drawn ever-increasing attention in the next generation wireless communications. In this paper, we focus on studying the secure communication in an HPN-to-devices (HTD) network, in which a new type of MAC spoofing attack is considered. To detect the malicious attack, we propose a novel algorithm, namely, eigenvalue test using random matrix theory (ETRMT) algorithm, which needs no prior information about the channel. In particular, when the number of samples is finite at the receiver or the number of devices is large, the sampled signal is the biased estimation of the actual signal, which inspires us to use the random matrix theory to analyze the spoofing attack detection. The closed-form expressions of the detection probability, the false alarm probability, and the Neyman-Pearson threshold are derived based on eigenvalue distribution of the spiked population model. In addition, taking the channel time-varying into consideration, we provide an adaptive threshold tracking method by using Bayesian forecasting. Finally, the simulations are conducted to validate our proposed method and some insightful conclusions are obtained.

This paper studies the secure transmission for downlink multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system in which imperfect channel state information (CSI) of the eavesdropper links is considered. We propose the novel robust beamforming strategies for the direct transmission NOMA (DT NOMA) and cooperative jamming NOMA (CJ NOMA) with a helper. We formulate our problem as the worst-case sum power minimization subject to secrecy rate constraint. The semidefinite relaxation (SDR) method is firstly applied to relax the quadratic terms and rank-one optimality is proved. Then an iterative algorithm based on successive convex approximation (SCA) is proposed to transform the nonconvex problem into convex approximations. Simulation results show that both the proposed NOMA schemes outperform the orthogonal multiple scheme, and CJ NOMA scheme can achieve much better system performance gain than DT NOMA scheme.

Non-orthogonal multiple access (NOMA) and multiple input multiple output (MIMO) have been widely regarded as two key techniques envisioned for 5G network. In this paper, we apply the core principle of antenna selection (AS) to multi-relay cooperative network, and propose a mechanism that has merits of both MIMO and NOMA. Then, in view of performance and complexity tradeoffs, a suboptimal joint selection scheme with reduced complexity is further presented. Furthermore, to characterize the achievable rates of proposed scheme, the lower and upper bounds of ergodic sum rate at high SNR are derived in closed form. The analytical results are valid for the evaluations for various antenna and relay configurations. Finally, through comprehensive simulations and comparisons, we demonstrate that the proposed scheme can outperform the conventional MIMO scheme or other relevant NOMA schemes in terms of ergodic sum rate and outage performance.

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoT-driven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems.

The increasing adoption of the Internet of Things has led to the need for systems with higher spectral and energy efficiency (EE) in order to enable communication. Larger data rate demands had led researchers to look at millimeter wave (mmWave) bands to boost network rates. This paper investigates the downlink performance of a three-tier heterogeneous network that consists of sub-6 GHz macrocells overlaid with small cells operating on both the mmWave and sub-6 GHz bands. A model is developed using tools from stochastic geometry to analyze the coverage, rate, area spectral efficiency, and EE of such a network. Various deployment strategies and their impacts on the considered metrics are studied. Simulation results are used to verify the validity of the proposed model.

Vehicular ad hoc networks (VANETs) can help reduce traffic accidents through broadcasting emergency messages among vehicles in advance. However, it is a great challenge to timely deliver the emergency messages to the right vehicles which are interested in them. Some protocols require to collect nearby real-time information before broadcasting a message, which may result in an increased delivery latency. In this paper, we proposed an improved position-based protocol to disseminate emergency messages among a large scale vehicle networks. Specifically, defined by the proposed protocol, messages are only broadcasted along their regions of interest, and a rebroadcast of a message depends on the information including in the message it has received. The simulation results demonstrate that the proposed protocol can reduce unnecessary rebroadcasts considerably, and the collisions of broadcast can be effectively mitigated.

We present a bio-inspired unicast routing protocol for vehicular ad hoc networks which uses the cellular attractor selection mechanism to select next hops. The proposed unicast routing protocol based on attractor selecting (URAS) is an opportunistic routing protocol, which is able to change itself adaptively to the complex and dynamic environment by routing feedback packets. We further employ a multiattribute decision-making strategy, the technique for order preference by similarity to an ideal solution, to reduce the number of redundant candidates for next-hop selection, so as to enhance the performance of attractor selection mechanism. Once the routing path is found, URAS maintains the current path or finds another better path adaptively based on the performance of current path, that is, it can self-evolution until the best routing path is found. Our simulation study compares the proposed solution with the stateof-the-art schemes, and shows the robustness and effectiveness of the proposed routing protocol and the significant performance improvement, in terms of packet delivery, end-to-end delay, and congestion, over the conventional method.

The recent advances in microelectronics and communications have led to the development of large-scale Internet of Things (IoT) networks, where tremendous sensory data is generated and needs to be processed. To support realtime processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node (EN) deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of ENs. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of ENs and improves the throughput between IoT and ENs.

Global navigation satellite system (GNSS) is not suitable for the dense urban or indoor environments as the satellite signals are very weak. Meanwhile, positioning is an important application of the fifth-generation (5G) communication system. GNSS/5G integrated positioning system becomes a promising research topic with the development of 5G standard. This paper focuses on the integrated methodology of GNSS and device to device (D2D) measurements in 5G communication system. We analyze the characteristics of this type of integrated system and propose a high-efficiency D2D positioning measure protocol, named crossover multiple-way ranging, which consumes less communication resources. Then, to deal with the high-dimensional state space in the integrated system, a state dimension reduction method is proposed to overcome the particle degeneracy problem of particle filter which is used to fusion GNSS and 5G D2D measurements. Three integrated algorithms in different scenarios have been proposed: the first one is the integrated algorithm when the range measurements can be measured directly. The second one is the integrated algorithm with unknown time skew and offset of each mobile terminal. The third one is the integrated algorithm in GNSS-denied environment which is prevalent in urban and indoor applications. The simulation and experimental results show that our proposed integrated methodology outperforms the nonintegrated one.

In this paper, we concentrate on the robust multi-objective optimization (MOO) for the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in device-to-device (D2D) communications underlaying heterogeneous networks (HetNets). Different from traditional resource optimization, we focus on finding robust Pareto optimal solutions for spectrum allocation and power coordination in D2D communications underlaying HetNets with the consideration of interference channel uncertainties. The problem is formulated as an uncertain MOO problem to maximize EE and SE of cellular users (CUs) simultaneously while guaranteeing the minimum rate requirements of both CUs and D2D pairs. With the aid of ε-constraint method and strict robustness, we propose a general framework to transform the uncertain MOO problem into a deterministic single-objective optimization problem. As exponential computational complexity is required to solve this highly non-convex problem, the power coordination and the spectrum allocation problems are solved separately, and an effective two-stage iterative algorithm is developed. Finally, simulation results validate that our proposed robust scheme converges fast and significantly outperforms the non-robust scheme in terms of the effective EE-SE tradeoff and the quality of service satisfying probability of D2D pairs.

This paper focuses on the performance evaluation of an energy harvesting (EH) equipped dual-hop relaying system for which the end-to-end signal-to-noise ratio (SNR) and the overall system throughput are analyzed. The transmitter and relay nodes are equipped with both fixed and EH batteries. The source for harvesting at the transmitter is the solar energy, and at the relay node, the interference energy in the radio frequency is the harvesting source. Time switching scheme is used at the relay to switch between EH and decoding information. Harvest-use approach is implemented, and we investigate the effects of the harvesting energy in enhancing the performance of the relaying system by deriving estimated closed-form expressions for the cumulative distribution function of each link's individual SNR and of the end-to-end SNR. The analytical expression for the ergodic capacity is also derived. These expressions are validated through Monte-Carlo simulations. It is also shown that with the additional EH at the transmitter (source and relay), a significant improvement in the system throughput can be achieved when fixed batteries are running on low powers.

Extensive deployment of heterogeneous small cells in cellular networks results in ultra-dense small-cell networks (USNs). The USNs have been established as one of the vital networking architectures in the 5G to expand system capacity and augment network coverage. However, intensive deployment of cells results in a complex interference problem. In this paper, we propose a distributed multi-domain interference management scheme among cooperative small cells. The proposed scheme mitigates the interference while optimizing the overall network utility. In addition, we jointly investigate OFDMA scheduling, TDMA scheduling, interference alignment (IA), and power control. We model small cells' coordination behavior as an overlapping coalition formation game. In this game, each base station can make an autonomous decision and participate in more than one coalition to perform IA and suppress intra-coalition interference. To achieve this goal, we propose a distributed joint interference management (JIM) algorithm. The proposed algorithm allows each small-cell base station to self-organize and interact into a stable overlapping coalition structure and reduce interference gradually from multi-domain, thus achieving an optimal tradeoff between costs and benefits. Compared with existing approaches, the proposed JIM algorithm provides appreciable performance improvement in terms of total throughput, which is demonstrated by simulation results.

In this paper, we study the achievable link-layer rate, namely, effective capacity (EC), under the per-user statistical delay quality-of-service (QoS) requirements, for a downlink non-orthogonal multiple access (NOMA) network with M users. Specifically, the M users are assumed to be divided into multiple NOMA pairs. Conventional orthogonal multiple access (OMA) then is applied for inter-NOMA-pairs multiple access. Focusing on the total link-layer rate for a downlink M-user network, we prove that OMA outperforms NOMA when the transmit signal-to-noise ratio (SNR) is small. On the contrary, simulation results show that NOMA prevails over OMA at high values of SNR. Aware of the importance of a two-user NOMA network, we also theoretically investigate the impact of the transmit SNR and the delay QoS requirement on the individual EC performance and the total link-layer rate for a two-user network. Specifically, for delay-constrained and delay-unconstrained users, we prove that for the user with the stronger channel condition in a two-user network, NOMA prevails over OMA when the transmit SNR is large. On the other hand, for the user with the weaker channel condition in a two-user network, it is proved that NOMA outperforms OMA when the transmit SNR is small. Furthermore, for the user with the weaker channel condition, the individual EC in NOMA is limited to a maximum value, even if the transmit SNR goes to infinity. To confirm these insightful conclusions, the closed-form expressions for the individual EC in a two-user network, by applying NOMA or OMA, are derived for both users and then confirmed using Monte Carlo simulations.

Cyber-physical Internet of things system (CPIoTS), as an evolution of Internet of things (IoT), plays a significant role in industrial area to support the interoperability and interaction of various machines (e.g., sensors, actuators, and controllers) by providing seamless connectivity with low bandwidth requirement. The fifth generation (5G) is a key enabling technology to revolutionize the future of industrial CPIoTS. In this paper, a communication framework based on 5G is presented to support the deployment of CPIoTS with a central controller. Based on this framework, multiple sensors and actuators can establish communication links with the central controller in full-duplex mode. To accommodate the signal data in the available channel band, the resource allocation problem is formulated as a mixed integer nonconvex programming problem, aiming to maximize the sum energy efficiency of CPIoTS. By introducing the transformation, we decompose the resource allocation problem into power allocation and channel allocation. Moreover, we consider an energy-efficient power allocation algorithm based on game theory and Dinkelbach's algorithm. Finally, to reduce the computational complexity, the channel allocation is modeled as a three-dimensional matching problem, and solved by iterative Hungarian method with virtual devices (IHM-VD). A comparison is performed with well-known existing algorithms to demonstrate the performance of the proposed one. The simulation results validate the efficiency of our proposed model, which significantly outperforms other benchmark algorithms in terms of meeting the energy efficiency and the QoS requirements.

In this paper, an indoor 3-dimensional (3-D) hybrid visible light communication (VLC)-radio frequency (RF) system with spatially random terminals is considered. Specifically, VLC and RF communications are employed over downlink and uplink, respectively. Meanwhile, the devices, like sensor nodes, are designed to harvest the energy from the light emitted by the light-emitting diode over the downlink, which is used for the transmissions over the uplink. The light energy harvesting model is proposed after introducing the line of sight propagation model for VLC. Then, the outage performance for orthogonal multiple access (OMA) scheme over the uplink has been studied by using stochastic geometry theory, while considering all devices are spatially random distributed in the 3-D room and all uplinks follow independent/correlated Rician fading. Finally, the analytical expressions for the outage probability with OMA scheme are derived and verified through Monte Carlo simulations.

Recently, there has been a surge of increased interest in using physical layer security to safeguard the fifth generation (5G) mobile networks. In this paper, we study the problem of secure communications in untrusted relay networks. A new nonorthogonal relaying protocol is proposed, aiming at maximizing the secrecy rate. Specifically, we allow the source and relay to transmit signals simultaneously over non-orthogonal channels, and apply successive interference cancellation at the destination to separate the multiplexed signals. We also propose two transmit antenna selection schemes to further improve security. To evaluate the secrecy performance, closed-form expressions for ergodic secrecy rate (ESR) and ESR scaling law are derived. The results show that the proposed non-orthogonal relaying protocol yields a considerable ESR improvement than conventional orthogonal relaying, despite the presence of co-channel interference. It is also revealed that the ESR scales with the average signal-to-noise ratio γ̅ and the number of available transmit antennas Ntaccording to log (√γ̅ log Nt), thus achieving superior secrecy performance in the untrusted relay networks.

Many vehicular ad hoc applications rely on vehicular broadcasting-based multi-hop routing to disseminate messages. In this work, we study the question of vehicular broadcasting-based routing. In particular, by modelling the vehicular message dissemination with a limited-time epidemic dynamics, we propose an online self-adaptive beaconing method to dynamically learn the optimal beaconing policy for vehicular broadcasting with consideration of varying opportunistic contacts between vehicles. The vehicular broadcasting incorporated within the proposed method can ensure message delivery with low dissemination delay and routing cost. Both theoretical analysis and simulation results are provided to exhibit the robustness and effectiveness of the proposed solution and the significantly performance with respect to the conventional solution.

At present, the earth surface stress monitoring mainly relies on the ground drilling method. Affected by test conditions and cost, the large-scale and regional stress measurement cannot be realized by traditional methods in the actual measurement. As the ground information at the large scale can be observation by satellite in all-weather condition, it is possible for detecting the stress information via the satellite remote sensing. In this paper, the experimental studies on the microwave and thermal infrared spectrum of loaded rock were carried out. Based on the experimental results, the possibility for detecting the earth surface stress condition via remote sensing was discussed. This study demonstrates the potential ability to use satellite remote sensing to observe ground stress condition and earthquakes.

Software Defined Networking (SDN) is the key technology for combining networking and Cloud solutions to provide novel applications. SDN offers a number of advantages as the existing resources can be virtualized and orchestrated to provide new services to the end users. Such a technology should be accompanied by powerful mechanisms that ensure the end-to-end quality of service at high levels, thus, enabling support for complex applications that satisfy end users needs. In this paper, we propose an intelligent mechanism that agglomerates the benefits of SDNs with real-time “Big Data” forecasting analytics. The proposed mechanism, as part of the SDN controller, supports predictive intelligence by monitoring a set of network performance parameters, forecasting their future values, and deriving indications on potential service quality violations. By treating the performance measurements as time-series, our mechanism employs a novel ensemble forecasting methodology to estimate their future values. Such predictions are fed to a Type-2 Fuzzy Logic system to deliver, in real-time, decisions related to service quality violations. Such decisions proactively assist the SDN controller for providing the best possible orchestration of the virtualized resources. We evaluate the proposed mechanism w.r.t. precision and recall metrics over synthetic data.

Millimeter wave (mmWave) links have the potential to offer high data rates and capacity needed in fifth generation (5G) networks, however they have very high penetration and path loss. A solution to this problem is to bring the base station closer to the end-user through heterogeneous networks (HetNets). HetNets could be designed to allow users to connect to different base stations (BSs) in the uplink and downlink. This phenomenon is known as downlink-uplink decoupling (DUDe). This paper explores the effect of DUDe in a three tier HetNet deployed in two different real-world environments. Our simulation results show that DUDe can provide improvements with regard to increasing the system coverage and data rates while the extent of improvement depends on the different environments that the system is deployed in.

In this paper we propose a novel evolutionary detection algorithm for large-scale multiple-input multiple-output (MIMO) systems, utilizing the concepts of quantum bit and quantum rotation gate in quantum computing. Specifically, we consider the detection of BPSK and 4-QAM signals, and the uncertainty on the information bits at the receiver is modeled as a sequence of quantum bits, which is referred to as a quantum particle. The proposed algorithm begins with a population of such particles, each initialized randomly. Then by the aid of quantum rotation gate along with a fitness function, a simple mechanism is proposed to allow all quantum particles to evolve in a guided manner towards a potentially optimal area and finally converges. It is shown by simulations that the proposed algorithm can achieve near-optimal performance.

The existing cellular networks are being modified under the umbrella of fifth generation (5G) networks to provide high data rates with optimum coverage. Current cellular systems operating in ultra high frequency (UHF) bands suffer from severe bandwidth congestion hence 5G enabling technologies such as millimeter wave (mmWave) networks focus on significantly higher data rates. In this paper, we explore the impact of co-existance of massive Multiple-Input Multiple-Output (MIMO) that provides large array gains and mmWave small cells on coverage. We investigate the downlink performance in terms of coverage and rate of a three tier network where a massive MIMO macro base stations (MBSs) are overlaid with small cells operating at sub-6GHz and mmWave frequency bands. Based on the existing stochastic models, we investigate user association, coverage probability and data rate of the network. Numerical results clearly show that massive MIMO enabled MBSs alongside mmWave small cells enhance the performance of heterogeneous networks (HetNets) significantly.

Large-scale and heterogeneity of the Internet of Things (IoT) sensing networks introduce a big challenge to device connectivity. There exist some isolated nodes in randomly deployed IoT sensing networks running on a tree-typed topology due to limitations of some network parameters, which reduces network connectivity. In this paper, a connectivity enhancement scheme for the sensing networks of the IoT is proposed based on link transformation. Under constraints of network depth and the number of child nodes, we boost capability of an in-network node to connect more isolated nodes by reducing its or ancestors' depth. Furthermore, three-level node shifting is utilized to take full advantage of network locality, thus highly improving ability of a potential parent node to accept connection request of an isolated node. Finally, when failing to reduce depth of a node and disabling to shift out a child node of a parent node, the scheme exploits node swapping to improve present link status, thus enabling further some isolated nodes to join into the sensing networks. Our simulation results show that the proposed scheme can raise proportion of joined nodes and effectively enhance connectivity of the sensing networks in the IoT.

To maximize the economic benefits, a cloud service provider needs to recommend its services to as many users as possible based on the historical user-service quality data. However, when a cloud platform (e.g., Amazon) intends to make a service recommendation decision, considering only its own user-service quality data is insufficient, because a cloud user may invoke services from multiple distributed cloud platforms (e.g., Amazon and IBM). In this situation, it is promising for Amazon to collaborate with other cloud platforms (e.g., IBM) to utilize the integrated data for the service recommendation to improve the recommendation accuracy. However, two challenges are present in the above-mentioned collaboration process, where we attempt to use multi-source data for the service recommendation. First, protecting users' privacy is challenging when IBM releases its own data to Amazon. Second, the recommendation efficiency and scalability are often low when the user-service quality data of Amazon and IBM update frequently. Considering these challenges, a privacy-preserving and scalable service recommendation approach based on distributed locality-sensitive hashing, i.e., SerRecdistri-LSH, is proposed in this paper to handle the service recommendation in a distributed cloud environment. Extensive experiments on the WS-DREAM data set validate the feasibility of our approach in terms of service recommendation accuracy, scalability, and privacy preservation.

Software-defined networks (SDNs) introduce interesting new opportunities in how network routes can be defined, verified, and changed over time. Despite the logically-centralized perspective offered, however, an SDN still needs to be considered a distributed system: rule updates communicated from the controller to the individual switches traverse an asynchronous network and may arrive out-of-order. This can lead to (temporary or permanent) inconsistencies and triggered much research over the last years. We, in this paper, initiate the study of algorithms for consistent network updates in “timed SDNs”-SDNs in which individual node updates can be scheduled at specific times. While technology enabling tightly synchronized SDNs is emerging, the resulting algorithmic problems have not been studied yet. This paper presents, implements and evaluates Chronus, a system which provides provably congestion- and loop-free network updates, while avoiding the flow table space headroom required by existing two-phase update approaches. We formulate the minimum update time problem as an optimization program and propose two polynomial-time algorithms which lie at the heart of Chronus: a decision algorithm to check feasibility and a greedy algorithm to find a good update sequence. Extensive experiments on Mininet and numerical simulations show that Chronus can substantially reduce transient congestion and save over 60% of the rules compared with the state of the art.

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive multiple-input-multiple-output-enabled heterogenous networks while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously. With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared with other algorithms.

Non-orthogonal multiple access (NOMA) is emerging as a promising, yet challenging, multiple access technology to improve spectrum utilization for the fifth generation (5G) wireless networks. In this paper, the application of NOMA to multicast cognitive radio networks (termed as MCR-NOMA) is investigated. A dynamic cooperative MCR-NOMA scheme is proposed, where the multicast secondary users serve as relays to improve the performance of both primary and secondary networks. Based on the available channel state information (CSI), three different secondary user scheduling strategies for the cooperative MCR-NOMA scheme are presented. To evaluate the system performance, we derive the closed-form expressions of the outage probability and diversity order for both networks. Furthermore, we introduce a new metric, referred to as mutual outage probability to characterize the cooperation benefit compared to non-cooperative MCR-NOMA scheme. Simulation results demonstrate significant performance gains are obtained for both networks, thanks to the use of our proposed cooperative MCR-NOMA scheme. It is also demonstrated that higher spatial diversity order can be achieved by opportunistically utilizing the CSI available for the secondary user scheduling.

Ultra-dense networks can further improve the spectrum efficiency (SE) and the energy efficiency (EE). However, the interference avoidance and the green design are becoming more complex due to the intrinsic densification and scalability. It is known that the much denser small cells are deployed, the more cooperation opportunities exist among them. In this paper, we characterize the cooperative behaviors in the Nash bargaining cooperative game-theoretic framework, where we maximize the EE performance with a certain sacrifice of SE performance. We first analyze the relationship between the EE and the SE, based on which we formulate the Nash-product EE maximization problem. We achieve the closed-form sub-optimal SE equilibria to maximize the EE performance with and without the minimum SE constraints. We finally propose a CE2MG algorithm, and numerical results verify the improved EE and fairness of the presented CE2MG algorithm compared with the non-cooperative scheme.

In this paper, a dual-hop decode-to-forward cooperative system is considered where multiple relays are with finite energy storage and can harvest energy from the destination. In our analysis, the relays are spatially randomly located with invoking stochastic geometry. In an effort to improve spectral efficiency, an optimal source-relay link scheme is employed. Assuming Nakagami-m fading, two different scenarios are considered: 1) the single-antenna source with perfect channel state information (CSI) and 2) the multiple-antenna source with transmit antenna selection and imperfect CSI. In both scenarios, the destination node is equipped with a single transmit antenna to forward power via frequency radio signal to the relay candidates. For improving the system performance, multiple antennas at the destination are considered to process the multiple copies of the received signal from the best relay. For characterizing the performance of the proposed scenarios, exact closed-form analytical expressions for the outage probability are derived. To obtain further insights, we carry out diversity gain analysis by adopting asymptotic relative diversity. We also derive the exact closed-form analytical expression for the system throughput. Finally, simulation results are presented to corroborate the proposed analysis and to show that: 1) the system performance is improved by enlarging the area of the circle and the density of the relays and 2) the energy storage size has impacts on the performance of considered networks, which determines the maximal transmit power at relays.

In this paper, the total system effective capacity (EC) maximization problem for the uplink transmission, in a multi-user multi-carrier orthogonal frequency division multiple access system, is formulated as a combinatorial integer programming problem, subject to each user's link-layer energy efficiency (EE) requirement as well as the individual's average transmission power limit. To solve this challenging problem, we first decouple it into a frequency provisioning problem and an independent multi-carrier link-layer EE-EC tradeoff problem for each user. In order to obtain the subcarrier assignment solution, a low-complexity heuristic algorithm is proposed, which not only offers close-to-optimal solutions, while serving as many users as possible, but also has a complexity linearly relating to the size of the problem. After obtaining the subcarrier assignment matrix, the multi-carrier link-layer EE-EC tradeoff problem for each user is formulated and solved by using Karush-Kuhn-Tucker conditions. The per-user optimal power allocation strategy, which is across both frequency and time domains, is then derived. Further, we theoretically investigate the impact of the circuit power and the EE requirement factor on each user's EE level and optimal average power value. The low-complexity heuristic algorithm is then simulated to compare with the traditional exhaustive algorithm and a fair-exhaustive algorithm. Simulation results confirm our proofs and design intentions, and further show the effects of delay quality-of-service exponent, the total number of users, and the number of subcarriers on the system tradeoff performance.

Nowadays, people can enjoy a rich real-time sensing cognition of what they are interested in anytime and anywhere by leveraging powerful mobile devices such as smartphones. As a key support for the propagation of these richer live media contents, cellular-based access technologies play a vital role to provide reliable and ubiquitous Internet access to mobile devices. However, these limited wireless network channel conditions vary and fluctuate depending on weather, building shields, congestion, etc., which degrade the quality of live video streaming dramatically. To address this challenge, we propose to use crowdsourcing brokerage in future networks which can improve each mobile user's bandwidth condition and reduce the fluctuation of network condition. Further, to serve mobile users better in this crowdsourcing style, we study the brokerage scheduling problem which aims at maximizing the user's quality of experience satisfaction degree cost effectively. Both offline and online algorithms are proposed to solve this problem. The results of extensive evaluations demonstrate that by leveraging crowdsourcing technique, our solution can cost-effectively guarantee a higher quality view experience.

This paper proposes a novel joint resource allocation algorithm combining dynamic user grouping, multi-cell cooperation, and resource block (RB) allocation for single carrier-frequency division multiple access uplink in multi-cell virtual MIMO systems. We first develop the dynamic multi-cell user grouping criteria using minimum mean square error equalization and adaptive modulation with bit error rate (BER) constraint. Then, we formulate and solve a new throughput maximization problem whose resource allocation includes cell selection, dynamic user grouping, and RB pattern assignment. Furthermore, to reduce the computational complexity significantly, especially in the case of large numbers of users and RBs, we present an efficient iterative Hungarian algorithm based on user and resource partitions to solve the problem by decomposing the large scale problem into a series of small scale sub-problems, which can obtain close-to-optimal solution with much lower complexity. The simulation results show that our proposed joint resource allocation algorithm with dynamic multi-cell user grouping scheme achieves better system throughput with BER guarantee than fixed user grouping algorithm and other proposed schemes in the literature.

This paper investigates the allocation of resources, including subcarriers and spreading codes, as well as intercell interference (ICI) mitigation for multicell downlink multicarrier direct-sequence code division multiple-access systems, which aim to maximize the system's spectral efficiency (SE). The analytical benchmark scheme for resource allocation and ICI mitigation is derived by solving or closely solving a series of mixed integer non-convex optimization problems. Based on the optimization objectives the same as the benchmark scheme, we propose a novel distributed resource allocation assisted by ICI mitigation scheme referred to as resource allocation assisted by ICI mitigation (RAIM), which requires very low implementation complexity and demands little backhaul resource. Our RAIM algorithm is a fully distributed algorithm, which consists of the subcarrier allocation (SA) algorithm named RAIM-SA, spreading code allocation (CA) algorithm called RAIM-CA and the ICI mitigation algorithm termed RAIM-IM. The advantages of the RAIM are that its CA only requires limited binary ICI information of intracell channels, and it is able to make mitigation decisions without any knowledge of ICI information. Our simulation results show that the proposed RAIM scheme, with very low complexity required, achieves significantly better SE performance than other existing schemes, and its performance is very close to that obtained by the benchmark scheme.

This paper designs a novel non-orthogonal multiple access (NOMA) unicast-multicast system, where a number of unicast users (those who require different messages) and a group of multicast users (those who require identical message) share the same time/space/frequency resource. For the designed NOMA unicast-multicast system, an efficient two-phase cooperation strategy is proposed to improve the reliability of all users. In the first phase, the base station (BS) broadcasts a superposed message consisting of all users' information. In the second phase, a multicast user is selected to forward the information intended by unsuccessfully decoded unicast and/or multicast users. Moreover, the multicast user selection is investigated under two different power allocation (PA) approaches: 1) fixed PA (FPA), in which the PA coefficients for both the phases are predetermined, and 2) dynamic PA (DPA), in which the PA coefficients for the first phase are predetermined, while the PA coefficients for the second phase are dynamically determined based on instantaneous channel information. Under the FPA approach, a best user selection (BUS) scheme (called F-BUS) is proposed to minimize the outage probability. Under the DPA approach, the local optimal PA coefficients for the second phase are derived in closed form first. Based on the derived PA coefficients, a BUS scheme (called D-BUS) is then proposed for outage probability minimization. To verify the reliability of the proposed cooperation strategy with employing the BUS schemes, we theoretically analyze the outage probability as well as diversity orders. It is shown that the proposed cooperation strategy achieves diversity orders equal to the number of multicast users, indicating that the inherent diversity orders offered by the multicast users are fully exploited. Finally, simulation results are presented to validate the theoretical results and demonstrate the advantages of the proposed cooperation strategy and the BUS schemes.

Millimeter wave (mmWave) technology is considered as a key enabler for fifth generation (5G) networks to achieve higher data rates with low transmission power by offloading the users with low signal-to-noise-ratios. Millimeter wave networks operating at E and W frequency bands have available bandwidth of 1 GHz or more to provide higher data rates whereas their propagation characteristics differ greatly from the conventional Ultra High Frequency (UHF) networks operating at sub 6 GHz frequency band. The purpose of this paper is to investigate the performance in terms of coverage and rate, of hybrid cellular networks where base stations (BSs) operating at mmWave and sub 6 GHz bands coexist in suburban environment such as a university campus. The actual building locations within a suburban university campus are modeled as blockages and the analysis is carried out for different densities of UHF and mmWave BSs for different densities of outdoor users. Our analysis also highlight the fact that mmWave cellular networks are predominantly noise-limited due to larger available bandwidth in comparison to the interference limited conventional UHF networks. Extensive simulation results demonstrate the effectiveness of dense deployment of mmWave BSs to achieve better coverage and rate probabilities in comparison to the stand alone UHF network.

To simulate the actual satellite observation situation of crust rock on the land surface, the thermal infrared spectrum observation experiment of loaded quartz sandstone was carried out in the cold sky background. In order to analysis the mechanism of radiation variation due to stress, the spectrum observation comparison experiment of the sample in the heating process is conducted as well. The experimental results indicate that the spectral radiance variation features in the loading process is different from that in the heating process. The trend of the two variation curves are opposite in the range of 8.0-9.7μm, which is corresponding to the wave range of reststrahlen features (RF) of the spectral emissivity. The factors affecting the radiance variation are analyzed. It can be concluded that the radiance increase in the compressed loading process is caused by both the temperature and the emissivity change. The results indicate that it is probable to establish a relationship between the emissivity and stress and provide an experimental basis for the observation of the stress by means of the infrared remote sensing.

For the past few years, 5G heterogeneous networks (HetNets) have gain phenomenal attention in the wireless industry. In this paper, we propose a hierarchical game theoretical framework for the optimal resource allocation on the uplink of a heterogeneous network with femtocells overlaid on the edge of a macrocell. In the first game, the femtocell access points (FAPs) play a non- cooperative game to choose their access policy between open and closed in order to maximize the rate of their home subscribers. The second game of the algorithm allows macrocell user equipments (MUEs) to decide their connectivity between the FAPs and the macrocell base station (MBS) with the goal of maximizing their rates and the overall network performance; thereby, distributing intelligence and control to the users. The FAPs and the MUEs are the players of two different games that strategically decide their policies in an ordered fashion. Simulation results show that this hierarchical game approach with network- assisted user-centric design offers a significant improvement in terms of the performance of HetNets relative to an closed and only network-centric access policy schemes.

Millimeter wave (mmWave) technology integrated with heterogeneous networks (HetNets) has emerged as a new wave to overcome the thirst for higher data rates and severe shortage of spectrum. In this paper, we consider the uplink of a hybrid HetNet with femtocells overlaid on a macrocell, and formulate a two layer game theoretic framework to maximise the energy efficiency (EE) while optimising the network resources. The outer layer allows each femtocell access point (FAP) to maximise the data rate of its users by selecting the frequency band either from the sub-6 GHz and the mmWave. The solution to this non-cooperative game can be obtained by using pure strategy Nash equilibrium. The inner layer ensures the energy efficient user association method subject to the minimum rate and maximum transmission power constraints by using dual de-composition approach. Simulation results show that the proposed hybrid HetNet scheme exploiting the mmWave frequency band improves the sum-rate and EE in comparison to the scenario where all the networks operate at sub-6 GHz frequency band. The performance can further be enhanced by incorporating the power control mechanism.

This study considers the problem of beamforming optimisation in an amplify‐and‐forward relaying cooperative network, in which the relay node harvests the energy from the radio‐frequency signal. Based on the self‐energy recycling relay protocol, the authors study the beamforming optimisation problem. The formulated problem aims to maximise the achievable rate subject to the available transmitted power at the relay node. The authors develop a semidefinite programming (SDP) relaxation method to solve the proposed problem. They also use SDP and the full search to solve the beamforming optimisation based on a time‐switching relaying protocol as a benchmark. The simulation results are presented to verify that the self‐energy recycling protocol achieves a significant rate gain compared with the time‐switching relaying protocol and the power‐splitting relaying protocol.

The growing demand for online distribution of high quality and high throughput content is dominating today's Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to quality of experience (QoE) fluctuations on delivered content, and unfairness between end users, while new network protocols, technologies, and architectures, such as software defined networking (SDN), are being developed for the future Internet. The programmability, flexibility, and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFairHA, which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact, and cost efficiency) to achieve user-level fairness in video distribution. UFairHAhas also been implemented in a purpose-built SDN testbed using open technologies, including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks.

Modern applications involve green communication technologies motivating well optimization in the power-limited regime. In comparison with most of the existing related work that assumes perfect channel state information (CSI) is always available, which is unfortunately not true in reality, this paper focuses on an optimal energy-efficient solution for resource allocation in multiuser orthogonal frequency division multiple access networks in the presence of imperfect CSI and data outage conditions. In particular, in view that wireless channel conditions, circuit power consumptions, and users' quality-of-service (QoS) requirements are heterogeneous in nature, we enable attractive tuning options by letting energy efficiency optimization objective to assign weights to each allocation link. In addition, we interpret the effects of data outage due to imperfect CSI using a profound insight on the monotonicity of noncentral chi-squared inverse distribution function, which reveals that our design complies with expected physics and mechanics of conventional energy efficiency approach and that it can be successfully degenerated to the energy-efficiency model with perfect CSI. Furthermore, we formulate a mixed combinatorial problem toward maximizing the energy efficiency subject to a minimum QoS requirement, channel interference, and transmitting power constraints. The problem is transformed into an equivalent quasi-concave problem with respect to power, and concave problem with respect to the subcarrier indexing coefficients using the concept of subcarrier time sharing. We optimize through a simple and versatile methodology, which uses standard-Lagrangian optimization technique to obtain joint dynamic subcarrier and adaptive power allocations by means of final formulas. We also examine key properties of the introduced optimal solution in terms of implementation convergence and complexity, level of optimality, and impact of imperfect CSI coefficients and circuit power on network performance. The simulation results demonstrate the effectiveness of our allocation scheme for achieving higher energy efficiency performance with the guaranteed QoS support and lower complexity than the existing approaches especially when perfect CSI is not available.

Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In vehicular ad hoc networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the ant colony optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented evolving graph (VoEG) model to perform plausibility checks on the routing control messages exchanged among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service.

In many applications of industrial wireless sensor networks, sensor nodes need to determine their own geographic position coordinates so that the collected data can be ascribed to the location from where it were gathered. We propose a novel intelligent localization algorithm that uses variable range beacon signals generated by varying the transmission power of beacon nodes. The algorithm does not use any additional hardware resources for ranging and estimates position using only radio connectivity by passively listening to the beacon signals. The algorithm is distributed, so each sensor node determines its own position and communication overhead is avoided. As the beacon nodes do not always transmit at maximum power and no transmission power is used by unknown sensor nodes for localization, the proposed algorithm is energy efficient. It also provides control over localization granularity. Simulation results show that the algorithm provides good accuracy under varying radio conditions.

In resource constrained wireless systems, achieving higher spectral efficiency (SE) and energy efficiency (EE), and greater rate fairness are conflicting objectives. Here, a general framework is presented to analyze the tradeoff among these three performance metrics in cooperative OFDMA systems with decode-and-forward relaying, where subcarrier pairing and allocation, relay selection, choice of transmission strategy, and power allocation are jointly considered. In our analytical framework, rate fairness is represented utilizing the α-fairness model, and the resource allocation problem is formulated as a multi-objective optimization problem. We then propose a cross-layer resource allocation algorithm across application and physical layers, and further devise a heuristic algorithm to tackle the computational complexity issue. The SE-EE tradeoff is characterized as a Pareto optimal set, and the efficiency and fairness tradeoff is investigated through the price of fairness. Simulations indicate that higher fairness results in a worse SE-EE tradeoff. It is also shown imposing fairness helps to reduce the outage probability. For a fixed number of relays, by increasing circuit power, the performance of SE-EE tradeoff is degraded. Interestingly, by increasing the number of relays, although the total circuit power is increased, the SE-EE tradeoff is not necessarily degraded. This is thanks to the extra degree of freedom provided in relay selection.

A joint optimization problem of link-layer energy efficiency (EE) and effective capacity (EC) in a Nakagami-m fading channel under a delay-outage probability constraint and an average transmit power constraint is considered and investigated in this paper. First, a normalized multi-objective optimization problem (MOP) is formulated and transformed into a single-objective optimization problem (SOP), by applying the weighted sum method. The formulated SOP is then proved to be continuously differentiable and strictly quasiconvex in the optimum average input power, which turns out to be a cup shape curve. Furthermore, the weighted quasiconvex tradeoff problem is solved by first using Charnes-Cooper transformation and then applying Karush-Kuhn-Tucker (KKT) conditions. The proposed optimal power allocation, which includes the optimal strategy for the link-layer EE-maximization problem and the EC-maximization problem as extreme cases, is proved to be sufficient for the Pareto optimal set of the original EE-EC MOP. Moreover, we prove that the optimum average power level monotonically decreases with the importance weight, but strictly increases with the normalization factor, the circuit power and the power amplifier efficiency. Simulation results confirm the analytical derivations and further show the effects of fading severeness and transmission power limit on the tradeoff performance.

Abstract In the fifth generation (5G) wireless communication systems, a majority of the traffic demands are contributed by various multimedia applications. To support the future 5G multimedia communication systems, the massive multiple‐input multiple‐output (MIMO) technique is recognized as a key enabler because of its high spectral efficiency. The massive antennas and radio frequency chains not only improve the implementation cost of 5G wireless communication systems but also result in an intense mutual coupling effect among antennas because of the limited space for deploying antennas. To reduce the cost, an optimal equivalent precoding matrix with the minimum number of radio frequency chains is proposed for 5G multimedia massive MIMO communication systems considering the mutual coupling effect. Moreover, an upper bound of the effective capacity is derived for 5G multimedia massive MIMO communication systems. Two antennas that receive diversity gain models are built and analyzed. The impacts of the antenna spacing, the number of antennas, the quality‐of‐service (QoS) statistical exponent, and the number of independent incident directions on the effective capacity of 5G multimedia massive MIMO communication systems are analyzed. Comparing with the conventional zero‐forcing precoding matrix, simulation results demonstrate that the proposed optimal equivalent precoding matrix can achieve a higher achievable rate for 5G multimedia massive MIMO communication systems. Copyright © 2016 John Wiley & Sons, Ltd.

We propose a novel inter-cell power allocation for multi-carrier cognitive cellular networks. The proposed scheme incorporates the network-wide primary service communication activity into sub-channel power allocation. To model the primary service activity we define sub-channel activity index (SAI). SAI is then evaluated through a simple yet efficient collaborative spectrum monitoring scheme with very low signaling overhead. Corresponding to a secondary user transmission over a sub-channel, a utility function is defined which is a decreasing function of SAI, and an increasing function of the sub-channel achievable rate. Optimal power allocation is then formulated to maximize the total secondary base station (SBS) utility, subject to SBS transmit power, and primary system collision probability constraints. The sub-optimal solutions to the non-convex optimization are then obtained utilizing dual decomposition method. Comparing with a cognitive cellular network with no signalling among the SBSs, where SBS adopts equal sub-channel power allocation, simulation results indicate a significant gain on the achievable rate. We further compare the rate performance with an ideal system in which perfect interference channel state, and spectrum sensing information are available at the SBS and a combination of underlay and overlay access techniques are adopted. Comparing to the ideal system, the proposed method requires significantly lower signaling overhead while its rate performance closely follows the ideal access.

Green Small Cell Networks aim at achieving high rates and low powers by offloading users with low signal-to-noise-ratios from macrocell to the pico base station. In this work, we propose to jointly optimise energy efficiency (EE) and spectrum efficiency (SE) such that the network providers can dynamically tune the trade-off parameter for different design requirements. This paper formulates the EE-SE trade-off as a multi-objective optimisation problem (MOP) in the uplink of multi-user two-tier Orthogonal Frequency Division Multiplexing Heterogeneous Networks. Using the weighted sum method, the MOP can be transformed into a single-objective optimisation problem (SOP). The proposed EE and SE trade-off optimisation problem is strictly quasi-concave. Hence, using Dual Decomposition approach, we derive the unique optimal solution. Numerical results demonstrate the effectiveness of the proposed approach and illustrate the fundamental tradeoff between EE and SE for different tradeoff parameters such as maximum transmission power and circuit power.

This paper proposes a new power allocation technique to jointly optimize link-layer energy efficiency (EE) and effective capacity (EC) of a Rayleigh flat-fading channel with delay-outage probability constraints. Specifically, EE is formulated as the ratio of EC to the sum of transmission power and rate-independent circuit power consumption. A multi-objective optimization problem (MOP) to jointly maximize EE and EC is then formulated. By introducing importance weight into the MOP, we can flexibly change the priority level of EE and EC, and convert the MOP into a single-objective optimization problem (SOP) which can be solved using fractional programming. At first, for a given importance weight and a target delay-outage probability, the optimum average transmission power level to maximize the SOP is found. Then, the optimal power allocation strategy is derived based on the obtained average input power level. Simulation results confirm the analytical derivations and further show the effects of circuit power, importance weight, and transmission power constraint limit on the achievable tradeoff performance.

To satisfy the massive wireless traffic transmission generated by multimedia applications, the massive multi-input-multi-output (MIMO) wireless system has emerged as a possible solution for future 5G wireless communication systems. However, the mutual coupling effect of massive MIMO systems has a negative effect potential on the wireless capacity. In this paper, the receive diversity gain is first defined and analyzed for massive MIMO wireless systems. Furthermore, we propose an effective capacity with the mutual coupling effect and the quality of service (QoS) statistical exponent constraint for multimedia massive MIMO wireless systems. Based on numerical results, the effective capacity approaches the effective capacity with the antenna spacing of d = 0.5λ when the antenna spacing is increased in the massive MIMO antenna array.

HTTP adaptive streaming (HAS) technology is becoming a popular vehicle for online video delivery. HAS applications often compete for network resources without any coordination between each other in a shared network. This leads to quality of experience (QoE) fluctuations and unfairness between end users. This paper introduces a user-level fairness model (UF) which exploits video quality, switching impact and cost efficiency as the fairness metrics to achieve user-level fairness in resource allocation. Experimental results demonstrate how this model is a foundation to orchestrate the resource consumption of HAS streams.

This paper presents the three-fold energy, rate and delay tradeoff in mobile multimedia fading channels. In particular, we propose a rate-efficient power allocation strategy for delay-outage limited applications with constraints on energy-per-bit consumption of the system. For this purpose, at a target delay-outage probability, the link-layer energy efficiency, referred to as effective-EE, is measured by the ratio of effective capacity (EC) and the total expenditure power, including the transmission power and the circuit power. At first, the maximum effective-EE of the channel at a target delay-outage probability is found. Then, the optimal power allocation strategy is obtained to maximize EC subject to an effective-EE constraint with the limit set at a certain ratio of the maximum achievable effective-EE of the channel. We then investigate the effect of the circuit power level on the maximum EC. Further, to set a guideline on how to choose the effective-EE limit, we obtain the transmit power level at which the rate of increasing EC (as a function of transmit power) matches a scaled rate of losing effective-EE. Analytical results show that a considerable EC-gain can be achieved with a small sacrifice in effective-EE from its maximum value. This gain increases considerably as the delay constraint becomes tight.

This paper presents a delay-QoS-driven spectrum and energy efficiency optimization transmission technique. In particular, considering the cross-layer effective capacity (EC) model in Rayleigh fading channels, a spectrum- and energy-efficient power allocation strategy is proposed when maximum spectrum efficiency is achieved under minimum energy efficiency (EE) requirement. For this purpose, at a target delay-outage probability, the spectrum efficiency is measured by the EC. Further, the EE is formulated as the ratio of the EC to the total expenditure power. At first, the maximum achievable EE of the link at the target delay-outage probability is found. Then, the optimal power allocation strategy is obtained to maximize the EC subject to a minimum EE constraint set at a certain ratio of the maximum achievable EE. We prove that the optimization problem is a concave maximization problem and develop the global optimal solution. The analytical results show that a considerable EC-gain can be achieved with a small sacrifice in EE. This gain increases considerably as the delay constraint becomes tight.

This paper proposes joint user association and energy-efficient resource allocation in the uplink of multi-user two-tier Orthogonal Frequency Division Multiplexing (OFDM) Heterogeneous Networks (HetNets) subject to user's maximum transmission power and minimum-rate constraints. The proposed scheme aims at achieving high rates at low powers satisfying the user's quality-of-service (QoS) constraints (in terms of minimumrate requirements) by offloading the users with low signal to noise ratio (SNR) from macrocell to the pico base station (BS). A channel-to-noise-ratio (CNR)-based rate proportional resource allocation approach is proposed to transform the minimumrate constraint into a minimum required transmission power constraint on each subcarrier. The single-user single-carrier and multi-user multi-carrier energy efficiency (EE) maximization problems are then solved under maximum and minimum power constraints using Karush-Kuhn-Tucker (KKT) conditions. The impact of users' maximum transmission power and minimumrate requirements on EE and throughput are investigated through illustrative results. The rate-proportional approach is evaluated against the equal rate allocation approach for different user associations and various numbers of users, maximum transmission power, and circuit powers. Significant gains in EE can be achieved for the HetNets if the path loss based user association is combined with the proposed CNR rate proportional mechanism.

Vehicular ad hoc networks (VANETs) are a special form of networks which enable the communications among vehicles on roads with no need of fixed infrastructure. The special characteristics of VANETs like high mobility and frequent changes of network topology create challenging technical issues, which need to be resolved in order to deploy these networks effectively. Routing reliability is one of the most critical issues where, the established route should be the most reliable one among all other routes to the destination. In this paper, we propose a new reliability-based routing scheme for VANETs in order to facilitate Quality of Service (QoS) support in the routing process. The link reliability is defined as the probability that an active link remains available for a certain time interval. The location and velocity information of vehicles are used to calculate link reliability accurately. We demonstrate that the proposed scheme improves significantly the performance of the standard Ad hoc On-demand Distance Vector (AODV) routing protocol.

As the challenges associated with delivering reliable electrical power in the world are increasing, the mixture of 'fast response time' and 'distributed data sources' may cause scalability issues with the traditional approach that mostly uses centralized communication infrastructure. Due to tremendous demand growing in the future, the centralized approach will become less efficient. Therefore, this paper presents multicast-based Publisher/Subscriber communication models based on IEC-61850 standard for the smart grids. Models are proposed with regards to digital substations indicating the use of the emerging digital communication standards offered by IEC-61850 over typical Wireless Local Area Network. Simulation models are developed using the OPNET simulator. Therefore, the dynamic performance issues of the smart grid could be studied conveniently during the planning stage in advance of field trials.

This paper proposes a novel dynamic network selection mechanism in cooperative heterogeneous wireless networks considering both network and user perspective. This approach adopts a suitably defined utility function, which at the same time takes into account the users' importance for the considered attributes (i.e. offered bit rate, coverage prediction, preferred interface and price being charged) and the quality offered for these attributes by the available networks. The strength of this approach is its ability to allow users to dynamically change their preferences achieving better Quality of Service (QoS) whereas the network operator can also vary their controlling parameters (e.g. network re-configuration and network adjustment) dynamically to improve their benefits. The dynamics of network selection in cooperative wireless networks is modeled using an evolutionary game theory where an evolutionary equilibrium is sought as a solution to this game. The performance of the proposed dynamic network-selection algorithm is investigated and evaluated by using simulations.

This paper proposes a new Nash bargaining solution (NBS) based cooperative game-theoretic scheduling framework for joint channel and power allocation in orthogonal frequency division multiple access cognitive radio (CR) systems. Our objectives are to maximize the overall throughput of the CR system with the protection of primary users' transmission, while guaranteeing each CR user's minimum rate requirement and the proportional fairness and efficient power distribution among CR users. Using time-sharing variable transformation, we introduce a novel method that involves Lambert-W function properties and obtain closed-form analytical solutions. A low-complexity algorithm is also developed which does not require iterative processes as usual to search the optimal solution numerically. Simulation results demonstrate that our optimal policies outperform the existing maximal rate, fixed assignment and max-min fairness, while achieving the 99.985% in average of the optimal capacity.

This paper proposes to utilise Evolutionary Algorithm (EA) to jointly estimate the Time of Arrival, Direction of Arrival, and amplitude of impinging waves in a mobile radio environment. The problem is presented as the joint Maximum Likelihood (ML) estimation of the channel parameters where typically, the high dimensional non-linear cost function is deemed to be too computationally expensive to be solved directly. Simulation results show that the proposed method is extremely robust to initialisation errors and low SNR environments, while at the same time it is also computationally more efficient than popular iterative ML methods i.e. the Space-Alternating Generalised Expectation-maximisation (SAGE) algorithm.

This paper proposes a novel game-theoretic cross-layer design for orthogonal frequency division multiple access (OFDMA) wireless networks, which operates optimal subcarrier, power and rate allocation. Based on the Nash bargaining solution (NBS) and coalitions, the proposed scheme not only maximizes the system's effective data rate but also supports proportional fairness among the users by considering the heterogeneity of their requirements, as well as the rate outage due to imperfect channel state information (CSI) available at the transmitter (CSIT). The simulation results confirm that the proposed scheme achieves an optimum tradeoff between effective data rate and proportional fairness, while it guarantees the quality of service (QoS) requirements, and outperforms the existing solutions in terms of power consumption, resilience to CSIT errors and stability.

Contention-based forwarding (CBF) has proven to achieve a good performance in routing in mobile and vehicular ad hoc networks. CBF's main advantage is that it does not require the knowledge about the local neighborhood, eliminating, therefore, the periodic beacon exchange that causes degradation in the network performance, especially when the density of the neighborhood is high. On the other hand, the main issue that the contention-based forwarding faces is packet duplications, which mainly occurs due to the hidden node problem. This paper introduces a suppression algorithm for contention-based forwarding to eliminate packet duplications, called Sender-based Suppression and referred to as SS-CBF. In addition to the idea of dividing the radio range of the sending node into Reuleaux triangle zones, SS-CBF assigns a time delay to every zone, which aggregates to the timer value, set by the nodes, to form the time required for deferring. Moreover, the Sending node is involved in the suppression process in order to prevent unwanted nodes from transmission. SS-CBF is simulated using OPNET 14.5 modeler and evaluated against area-based suppression scheme in terms of network overhead and effective throughput.

The use of Broadband Wireless Access (BWA) technology is increasing due to the use of Internet and multimedia applications with strict requirements of end-to-end delay and jitter, through wireless devices. The IEEE 802.16 standard, which defines the physical (PHY) and the medium access control (MAC) layers, is one of the BWA standards. Its MAC layer is centralized basis, where the Base Station (BS) is responsible for assigning the needed bandwidth for each Subscriber Station (SS), which requests bandwidth competing between all of them. The standard defines a contention resolution process to resolve the potential occurrence of collisions during the requesting process. In this paper, we propose to modify the contention resolution process to improve the network performance, including end-to-end delay and throughput.

In this paper we investigate the potential benefits of a selective delayed channel access algorithm (SDCA) for the future IEEE 802.11n based high-throughput networks. The proposed solution aims to resolve the poor channel utilization and the low efficiency that EDCA's high priority stations adhere due to shorter waiting times and consequently to the network's degrading overall end performance. The algorithm functions at the MAC level where it delays the packets from being transmitted by postponing the channel access request, based on their traffic characteristics. As a result, the flow's average aggregate size increases and consequently so is the channel efficiency. However, in some situations we notice that further deferring has a negative impact with TCP applications, thus we further introduce a traffic awareness feature that allows the algorithm to distinguish which flows are using the TCP protocol and override any additional MAC delay. We validate through various simulations that SDCA improves throughput significantly and maximizes channel utilization.

This paper proposes a novel cross-layer scheduling scheme for a single-cell orthogonal frequency division multiple access (OFDMA) wireless system with partial channel state information (CSI) at transmitter (CSIT) and heterogeneous user delay requirements. Previous research efforts on OFDMA resource allocation are typically based on the availability of perfect CSI or imperfect CSI but with small error variance. Either case consists to typify a non tangible system as the potential facts of channel feedback delay or large channel estimation errors have not been considered. Thus, to attain a more realistic resolution our cross-layer design determines optimal subcarrier and power allocation policies based on partial CSIT and individual user's quality of service (QoS) requirements. The simulation results show that the proposed cross-layer scheduler can maximize the system's throughput and at the same time satisfy heterogeneous delay requirements of various users with significant low power consumption.

In this paper we propose a novel model for the capacity analysis on the reservation-based random multiple access system, which can be applied to the medium access control protocol of the emerging WiMax technology. In such a wireless broadband access system, in order to support QoS, the channel time is divided into consecutive frames, where each frame consists of some consequent mini-slots for the transmission of requests, used for the bandwidth reservation, and consequent slots for the actual data packet transmission. Three main outcomes are obtained: first, the upper and lower bounds of the capacity are derived for the considered system. Second, we found through the mathematical analysis that the transmission rate of reservation-based multiple access protocol is maximized, when the ratio between the number of mini-slots and that of the slots per frame is equal to the reciprocal of the random multiple access algorithm's transmission rate. Third, in the case of WiMax networks with a large number of subscribers, our analysis takes into account both the capacity and the mean packet delay criteria and suggests to keep such a ratio constant and independent of application-level data traffic arrival rate.

We propose a novel Minimum Distance guided Genetic Algorithm (MDGA) for Multi-User Detection (MUD) in a synchronous Multi-Carrier Code Division Multiple Access (MC-CDMA) broadband wireless system. In contrast to conventional GAs, our MDGA exploits adequately the output from a bank of Matched Filters as guidance. It starts with a balanced ratio of exploration and exploitation which is maintained throughout the process. A novel replacement strategy is proposed which increases dramatically the convergence rate as compared to the conventional GAs. This allows us to use the simplest form of genetic operators to gain significant reduction in computational complexity as well as near-optimum results. The simulation results demonstrate that our scheme achieves 99.54% and 50+% reduction in computational complexity as compared to the MUD schemes using exhaustive search and conventional GA respectively.

A novel scheme using spatial data stream multiplexing (SDSM) in the upcoming multiple-input multiple-output (MIMO)-based IEEE 802.11n physical layer is proposed. It is shown that with SDSM, the same data rate can be achieved by using less number of transmit and receive antennas and therefore this scheme can reduce the number of antennas which results in reducing mutual coupling effects, hardware costs and implementation complexities. The maximum data rates that can be achieved using a 2×2 MIMO system is 270 Mbps and for a 4×4 MIMO system is 540 Mbps. The same data rates can be achieved using the SDSM technique which reduces the 2×2 MIMO system to 1×1 SISO system and the 4×4 MIMO system to a 2×2 MIMO system.

Reliability and timely information delivery are mostly concerned in vehicular ad hoc networks (VANETs). In this paper, we propose a link Delay and Reliability constrained QoS routing algorithm (DeReQ) for multimedia communications in VANETs. A new link reliability mathematical model which considers not only the impact of the link duration but also the traffic density is designed. The aim of the DeReQ algorithm is to find a route which is not only reliable but also compliant with delay requirements. We evaluate the performance of DeReQ algorithm through simulations and our simulation results demonstrate that significant performance improvement can be achieved by the combined DeReQ and AODV protocol in comparison to the original AODV protocol, an QoS-extended AODV protocol (AAC), and the location-based routing protocol (LBM).

Abstract A radio frequency identification (RFID) system is a special kind of sensor network to identify an object or a person using radio frequency transmission. A typical RFID system includes transponders (tags) and interrogators (readers): tags are attached to objects/persons, and readers communicate with the tags in their transmission ranges via radio signals. RFID systems have been gaining more and more popularity in areas such as supply chain management, automated identification systems, and any place requiring identifications of products or people. RFID technology is better than barcode in many ways, and may totally replace barcode in the future if certain technologies can be achieved such as low cost and protection of personal privacy. This paper provides a technology survey of RFID systems and various RFID applications. We also discuss five critical research issues: cost control, energy efficiency, privacy issue, multiple readers' interference, and security issue. Copyright © 2006 John Wiley & Sons, Ltd.

IEEE 802.16 protocols for metropolitan broadband wireless access systems have been standardized recently. According to the standard, a subscriber station can deliver bandwidth request messages to a base station by numerous methods. This paper provides both the simulation and analytical models for the investigation of specified random access method, which is compared with centralized polling and station- grouping mechanisms. Based on the assumptions of Bernoulli request arrival process and ideal channel conditions, the mean delay of a request transmission is evaluated for varying number of transmission opportunities and different arrival rates.

We consider the medium access control (MAC) layer for very high-speed wireless LANs, which is designed to support rich multimedia applications such as high-definition television. In such networks, the physical (PHY) layer data rate is proposed to exceed 216 Mbps. The legacy MAC layer, however, greatly restricts the performance improvement due to its overhead. It has been shown that MAC utilizes less than 20% of the transportation ability provided by the PHY layer. To mitigate this inefficiency, we propose an aggregation with fragment retransmission (AFR) scheme, which supports transmissions of very large frames and partial retransmissions in the case of errors. Aggregation allows for increased performance despite per-transmission overhead while partial retransmission alleviates the risk of losing the entire frame. Extensive simulations show that AFR fundamentally outperforms the legacy MAC protocol. It is particularly effective for applications with high data rates and large packet sizes such as HDTV and high-rate UDP traffic. For applications with very low data rates and small packet sizes such as voice over IP, AFR performs slightly better.

In this paper, we propose a novel contention-based protocol called backoff counter reservation and classifying stations for the IEEE 802.11 distributed coordination function (DCF). In the proposed scheme, each station has three states: idle, reserved, and contentious. A station is in the idle state if it has no frame ready to transmit. A station is in the reserved state if it has a frame ready to transmit and this frame's backoff counter has been successfully announced through the previous successfully transmitted frame so that other stations know this information. A station is in the contentious state if it has a frame ready to transmit, but this frame's backoff counter has not been successfully announced to other stations. All the stations in the idle state, the reserved state, and the contentious state form an idle group, a reserved group, and a contentious group, respectively. Two backoff schemes are proposed in the BCR-CS protocol based on the number of stations in the contentious group including the optimal pseudo-p-persistent scheme. The proposed schemes are compared with the DCF and the enhanced collision avoidance (ECA) scheme in the literature. Extensive simulations and some analytical analysts are carried out. Our results show that all proposed schemes outperform both the DCF and the ECA, and the BCR-CS with optimal pseudo-p-persistent scheme is the best scheme among the four schemes

Abstract A Block Transmission and Acknowledgement (BTA) scheme, also called Block ACK, has been proposed in the IEEE 802.11e wireless local area networks (WLAN) specification to improve efficiency of the medium access control layer. The idea of the BTA scheme is to transmit multiple data frames followed by only one acknowledgement frame in a transmission block. In this paper, we present a theoretical model to evaluate the saturation throughput for the BTA scheme under error channel conditions in the ad hoc mode, validated with simulations. We show some advantages of BTA over the legacy MAC, and analyze how to select a proper number of frames for each transmission block. Results show that BTA is particularly effective in very high‐speed wireless networks, and it is important that the number of frames in each block is negotiated before transmissions to provide better efficiency. Copyright © 2006 John Wiley & Sons, Ltd.

A block ACK (BTA) scheme has been proposed in IEEE 802.11e to improve medium access control (MAC) layer performance. It is also a promising technique for next-generation high-speed wireless LANs (WLANs) such as IEEE 802.11n. We present a theoretical model to evaluate MAC saturation throughput of this scheme. This model takes into account the effects of both collisions and transmission errors in a noisy channel. The accuracy of this model is validated by NS-2 simulations.

In this paper, we propose and analyze a new system architecture for video multicast over Internet, namely, the sender-adaptive and receiver-driven layered multicast (SARLM). In SARLM, the sender of a video source splits the video data coded by a scalable codec and a channel codec into multiple data streams, each of which corresponds to a separate multicast group. The sender can adjust the way in which the video sequence is split dynamically based on the receivers' network parameters collected through feedback. Meanwhile, a receiver can estimate available bandwidth based on a modified packet-pair technique and choose to reassemble and playback the video sequence for a given quality level by dynamically subscribing a given part or all of the data streams according to its network conditions. To optimize the sender's adaptation strategy, we introduce a quality-space (Q-Space) model to describe and analyze the mathematical relationship between the sending rate of different SARLM layers and the video quality received by a given receiver identified by its network characteristics including available bandwidth and packet loss ratio. Our simulation results demonstrate that, under the same network topology and condition, the SARLM architecture can achieve higher network throughput and better video qualities on the receiver side than the existing approaches.

Abstract It is well known that the medium access control (MAC) layer is the main bottleneck for the IEEE 802.11 wireless LANs. Much work has been done on performance analysis of the 802.11 MAC. However, most of them assume that the wireless channel is error free. In this paper, we investigate the saturation throughput performance achieved at the MAC layer, in both congested and error‐prone channels. We provide a simple and accurate analytical model to calculate the MAC throughput. The model is validated through extensive simulation results. Our results show that channel errors have a significant impact on the system performance. Copyright © 2005 John Wiley & Sons, Ltd.

The emerging widespread use of real-time multimedia applications over wireless networks makes the support of quality of service (QoS) a key problem. In this paper, we focus on QoS support mechanisms for IEEE 802.11 wireless ad-hoc networks. First, we review limitations of the upcoming IEEE 802.11e enhanced DCF (EDCF) and other enhanced MAC schemes that have been proposed to support QoS for 802.11 ad-hoc networks. Then, we describe a new scheme called adaptive fair EDCF that extends EDCF, by increasing the contention window during deferring periods when the channel is busy, and by using an adaptive fast backoff mechanism when the channel is idle. Our scheme computes an adaptive backoff threshold for each priority level by taking into account the channel load. The new scheme significantly improves the quality of multimedia applications. Moreover, it increases the overall throughput obtained both in medium and high load cases. Simulution results show that our new scheme outperforms EDCF and other enhanced schemes. Finally, we show that the adaptive fair EDCF scheme achieves a high degree of fairness among applications of the same priority level.

Abstract Quality‐of‐service (QoS) is a key problem of today's IP networks. Many frameworks (IntServ, DiffServ, MPLS etc.) have been proposed to provide service differentiation in the Internet. At the same time, the Internet is becoming more and more heterogeneous due to the recent explosion of wireless networks. In wireless environments, bandwidth is scarce and channel conditions are time‐varying and sometimes highly lossy. Many previous research works show that what works well in a wired network cannot be directly applied in the wireless environment. Although IEEE 802.11 wireless LAN (WLAN) is the most widely used IEEE 802.11 wireless LAN (WLAN) standard today, it cannot provide QoS support for the increasing number of multimedia applications. Thus, a large number of 802.11 QoS enhancement schemes have been proposed, each one focusing on a particular mode. This paper summarizes all these schemes and presents a survey of current research activities. First, we analyze the QoS limitations of IEEE 802.11 wireless MAC layers. Then, different QoS enhancement techniques proposed for 802.11 WLAN are described and classified along with their advantages/drawbacks. Finally, the upcoming IEEE 802.11e QoS enhancement standard is introduced and studied in detail. Copyright © 2004 John Wiley & Sons, Ltd.

Combining differential Alamouti space-time block code (DASTBC) with orthogonal frequency-division multiple access (OFDMA), this paper introduces a multiuser/multirate transmission scheme, which allows full-rate and full-diversity noncoherent communications using two transmit antennas over frequency-selective fading channels. Compared with the existing differential space-time coded OFDM designs, our scheme imposes 10 restrictions on signal constellations, and thus can improve the spectral efficiency by exploiting efficient modulation techniques such as QAM, APSK etc. The main principles of our design are s follows: OFDMA eliminates multiuser interference, and converts multiuser environments to single-user ones; Space-time coding achieves performance improvement by exploiting space diversity available with multiple antennas, no matter whether channel state information is known to the receiver. System performance is evaluated both analytically and with simulations.

The IEEE 802.11 medium access control (MAC) protocol provides a contention-based distributed channel access mechanism for mobile stations to share the wireless medium, which may introduce a lot of collisions in case of overloaded active stations. Slow contention window (CW) decrease scheme is a simple and efficient solution for this problem. In this paper, we use an analytical model to compare the slow CW decrease scheme to the IEEE 802.11 MAC protocol. Several parameters are investigated such as the number of stations, the initial CW size, the decrease factor value, the maximum backoff stage and the coexistence with the RequestToSend and ClearToSend (RTS/CTS) mechanism. The results show that the slow CW decrease scheme can efficiently improve the throughput of IEEE 802.11, and that the throughput gain is higher when the decrease factor is larger. Moreover, the initial CW size and maximum backoff stage also affect the performance of slow CW decrease scheme.

This paper describes an adaptive service differentiation scheme for QoS enhancement in IEEE 802.11 wireless ad-hoc networks. Our approach, called adaptive enhanced distributed coordination function (AEDCF), is derived from the new EDCF introduced in the upcoming IEEE 802.11e standard. Our scheme aims to share the transmission channel efficiently. Relative priorities are provisioned by adjusting the size of the contention window (CW) of each traffic class taking into account both applications requirements and network conditions. We evaluate through simulations the performance of AEDCF and compare it with the EDCF scheme proposed in the 802.11e. Results show that AEDCF outperforms the basic EDCF, especially at high traffic load conditions. Indeed, our scheme increases the medium utilization ratio and reduces for more than 50% the collision rate. While achieving delay differentiation, the overall goodput obtained is up to 25% higher than EDCF. Moreover, the complexity of AEDCF remains similar to the EDCF scheme, enabling the design of cheap implementations.

Abstract—In this paper, we propose and analyze a new system architecture for video multicast over Internet, namely, the sender-adaptive and receiver-driven layered multicast (SARLM). In SARLM, the sender of a video source splits the video data coded by a scalable codec and a channel codec into multiple data streams, each of which corresponds to a separate multicast group. The sender can adjust the way in which the video sequence is split dynamically based on the receivers ’ network parameters collected through feedback. Meanwhile, a receiver can estimate available bandwidth based on a modified packet-pair technique and choose to reassemble and playback the video sequence for a given quality level by dynamically subscribing a given part or all of the data streams according to its network conditions. To optimize the sender’s adaptation strategy, we introduce a quality-space (Q-Space) model to describe and analyze the mathematical relationship between the sending rate of different SARLM layers and the video quality received by a given receiver identified by its network characteristics including available bandwidth and packet loss ratio. Our simulation results demonstrate that, under the same network topology and condition, the SARLM architecture can achieve higher network throughput and better video qualities on the receiver side than the existing approaches. Index Terms—Automatic repeat request (ARQ), bandwidth estimation, forward error correction (FEC), feedback implosion, layered multicast, receiver driven, scalable video, sender adaptive, streaming. I.