Covert wireless communication ensures both information confidentiality and transmission untraceability, which is increasingly vital for mission-critical extended reality (XR) services. While unmanned aerial vehicles (UAVs) provide mobility and flexible coverage, and intelligent reflecting surfaces (IRSs) enable energy-efficient signal manipulation, their joint use for covert communications has not yet been sufficiently explored. This paper proposes a novel UAV-mounted IRS system for covert communications that passively reflects source signals toward a legitimate receiver while minimizing detection by an adversary warden. In contrast to previous work that treats trajectory design, beamforming, and power control in isolation, the proposed work develops a unified framework based on double deep Q-networks (DDQN) to jointly optimize the UAV trajectory, power allocation, and IRS phase shifts under covert constraints. We analytically derive the optimal detection threshold and the minimum detection error probability, which are dynamically integrated into the learning framework. The optimization problem is formulated as a constrained Markov decision process, which allows the agent to adaptively learn optimal policies in dynamic environments without relying on perfect channel knowledge. Simulation results demonstrate that the proposed framework significantly improves covert rate and energy efficiency compared with the iterative and random benchmark schemes, while also providing insights into the impact of system parameters on performance.

Autonomous underwater vehicles (AUVs) have become a promising solution for data collection in underwater acoustic sensor networks (UASNs), and deep reinforcement learning (DRL) has been widely applied to enhance collection performance. However, our preliminary experiments indicate that existing DRL-based studies still face two critical challenges: 1) Collection blind spots. The sparse collection rewards and the requirement for energy-efficient trajectory planning during data collection jointly restrict AUVs' ability to explore and collect data from all sensor nodes (SNs), ultimately resulting in some SNs remaining uncollected. 2) Collection collisions. Simultaneous data collection by multiple AUVs can lead to packet collisions and collection failures, further decreasing the collection rate. To address these challenges, we propose ahierarchical DRL-basedcollision-freedatacollection scheme (HCDC). Specifically, we leverage a hierarchical DRL framework to decompose the multi-AUV-assisted data collection (MADC) problem into a high-level global target selection (GTS) and a low-level local trajectory planning (LTP) subproblems. For GTS, we design a multi-agent GTS (MA-GTS) algorithm to assign the next target SN for collection to each AUV. The MA-GTS incorporates both global and local rewards to collaboratively optimize the overall energy consumption while avoiding individual penalties. Based on the assigned target SN, a deep deterministic policy gradient-based LTP (DDPG-LTP) algorithm is proposed to conduct AUV trajectory planning, utilizing intrinsic rewards to enhance learning efficiency and eliminate collection blind spots. Furthermore, to avoid packet collisions, we analyze the conditions for collision-free data collection and propose an adaptive back-off slot (ABS) algorithm to schedule AUVs' collection slots. With the collision-free slots, DDPG-LTP dynamically adjusts AUVs' velocities to ensure collision-free collection while reducing energy consumption. Extensive simulation results demonstrate that HCDC can achieve better collection rate and energy efficiency than state-of-the-art schemes.

In this paper, we propose a novel channel access and power control scheme for smart grids in communication networks. The scheme is named digital twin-based memory recall optimization (DMRO), which aims to extract meaningful patterns from noisy network traffic measurements and support more sophisticated decision-making processes for optimizing channel access and power control for smart grids. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, considering the interference and signal-to-interference-plus-noise ratio (SINR) constraints in the wireless environment, we develop a digital twin-based distributed channel access and power control scheme to improve the latency taming and energy utilization efficiency of the phasor measurements units (PMU). We consider both the real-time traffic prediction and the paired optimization scheme on the digital twin side, and utilize memory recall to enhance local model robustness to optimize from a more diverse set of situations by replaying underrepresented experiences. Simulation results demonstrate that the proposed DMRO scheme can achieve high traffic prediction accuracy and improve the latency taming and energy utilization efficiency even increasing industrial channel interference or the number of PMUs.

This paper proposes an innovative approach to improve over-the-air federated learning (OTA-FL) systems by integrating fluid antennas (FAs) at the access point. By exploiting the mobility of FAs, we aim to increase the correlation among the users’ channels, thereby improving the learning performance. We analyze the performance of over-the-air computation and the convergence behavior of the OTA-FL system, highlighting the benefits of FAs. Since the learning performance improves as more devices participate in the FL aggregation, we formulate a non-convex optimization problem that maximizes the number of selected users by jointly optimizing FA positions and the beamforming vector, coupled with a user selection policy subject to a mean-squared error constraint. To address environmental dynamics, we describe the problem as a Markov decision process and develop a long short-term memory (LSTM)-based algorithm for efficient decision-making. Simulation results demonstrate that the proposed FA-assisted OTA-FL framework significantly outperforms conventional setups, achieving higher user selection rates and improved learning performance compared to existing benchmarks.

The proliferation of maritime activities has spurred the emergence of numerous computation-intensive and delay-sensitive marine applications and services. Given the inherent rationality, selfish nature, and limited computational abilities of marine devices, devising effective strategies to incentivize their participation in task processing become a critical challenge. In this article, we investigate the satellite-assisted marine multiaccess edge computing (MEC) and propose a two-tier task offloading scheme through a hybrid Stackelberg-Bargaining game approach to enhance offloading efficiency and maximize the utility of marine devices. Specifically, for the underwater acoustic communication, we consider the scenario where multiple autonomous underwater vehicles (AUVs), managed by maritime autonomous surface ships (MASSs), upload their collected data using nonorthogonal multiple access (NOMA) to optimize channel utilization. For the data transmission above the sea surface, we consider the scenario where a low-Earth orbit satellite (LEOS) functions as a space edge server to provide computing services, and MASS offloads workloads to LEOS through frequency division multiple access (FDMA) to prevent co-channel interference. we define the utility of AUVs, MASSs and LEOSs, and model the offloading process between AUVs and MASSs as a Stackelberg game, while representing the offloading interaction between MASSs and LEOSs as a Bargaining game. Additionally, we propose efficient algorithms to optimize AUV offloading strategies and MASS pricing strategies, while refining the bidding strategies for both MASSs and LEOSs. Simulation results demonstrate that the proposed algorithms significantly outperform benchmark schemes in achieving optimal solutions.

Satellite-terrestrial integrated network (STIN) is a full-scale communication paradigm, which can support joint information processing and seamless service provision by leveraging satellites' wide coverage and terrestrial networks' high capacity. The existing STIN operates with insufficient synergy in transmission scheduling, impacting resource allocation efficiency and transmission delay optimization, particularly in complex transmission scenarios. In this paper, we designDeterministic STIN (DetSTIN), a novel architecture for STIN, along with two algorithms tailored for transmission scheduling to collaboratively optimize resource adaptation and service flow scheduling. Specifically, the DetSTIN enables the smooth interconnection and integration of heterogeneous networks by providing layered deterministic services. Besides, a genetic-based resource adaptation algorithm is designed for fixed-mobile-satellite heterogeneous networks to reduce resource allocation overhead while maintaining the network performance. Furthermore, we propose a deep reinforcement learning-based differentiated scheduling algorithm to solve the routing-queue two-dimensional decision problem to differentially optimize transmission delay of service flows, thus obtaining higher transmission scheduling benefit. By addressing resource adaptation and differentiated scheduling synergistically, the proposed solution achieves reduced resource allocation overhead and increased transmission scheduling benefit, ultimately leading to increased network operation revenue of the DetSTIN. Simulation results demonstrate that the proposed solution delivers effective performance across various flow proportions, and as the number of flows increases, the network operation revenue exhibits a noticeable improvement, compared with benchmark algorithms.

Tile-based streaming has been proposed to address the challenge of high transmission rate demand in 360° virtual reality (VR) video streaming. However, it suffers from network and viewing behavior dynamics (i.e., head movements), while encoded video tiles have various properties in terms of transmission priority, deadline, and reliability requirement. Hence, a supporting transmission protocol is imperative. In this paper, we propose a customized transmission protocol based on Quick UDP Internet Connections (QUIC) which operates over a VR video network slice in the core network. The QUIC protocol is tailored to accommodate the characteristics of tile-based VR video streaming where explicit mapping relations between requested video tiles and QUIC streams are established. Two customized in-network protocol functionalities including packet filtering and caching-based packet retransmission are proposed, to filter out outdated video data due to field-of-view (FoV) prediction errors under viewing behavior dynamics and to achieve efficient packet retransmissions with disparate transmission reliability requirements. A slice-level packet header is designed to support enhanced slice-based VR video transmission with the proposed protocol functionalities. Key transport parameters are determined via theoretical analysis. Simulation results are presented to demonstrate the effectiveness of our proposed transmission protocol in achieving short average video segment downloading time and high average video segment quality.

In this paper, we present a performance analytical model for end-to-end (E2E) service provisioning (i.e., processing or transmission) of layer-encoded video packets over a network slice in the core network. The disparate service reliability requirements of base layer (BL) and enhancement layer (EL) packets are considered in the proposed analytical model for the E2E packet delays, deadline violation probabilities, and throughputs of BL and EL packets. Specifically, a network function virtualization (NFV) node along the routing path of the video streaming slice is split into two consecutive logical nodes, one for packet processing and the other for transmission, based on which a segment-based analysis framework is proposed for E2E service performance modeling. A two-stage queuing model is established to obtain the approximate steady-state probability distribution of queue length at the first node in the first segment, upon which the BL/EL packet delay, deadline violation probability, and throughput at the segment are derived. In addition, the inter-departure time of successive packets departing from the first segment is analyzed based on an approximate M/D/1 system, and the packet departure process at the first segment is approximated as a Poisson process under the assumption of a large packet service rate of the first node. The independence between two consecutive segments is then achieved for analysis tractability, based on which the E2E performance measures are derived. Extensive simulation results demonstrate the accuracy of our proposed performance analytical model and its effectiveness such as in transport parameter determination.

In this paper, we establish a multi-access edge computing (MEC)-enabled sea lane monitoring network (MSLMN) architecture with energy harvesting (EH) to support dynamic ship tracking, accident forensics, and anti-fouling through real-time maritime traffic scene monitoring. Under this architecture, the computation offloading and resource allocation are jointly optimized to maximize the long-term average throughput of MSLMN. Due to the dynamic environment and unavailable future network information, we employ the Lyapunov optimization technique to tackle the optimization problem with large state and action spaces and formulate a stochastic optimization program subject to queue stability and energy consumption constraints. We transform the formulated problem into a deterministic one and decouple the temporal and spatial variables to obtain asymptotically optimal solutions. Under the premise of queue stability, we develop a joint computation offloading and resource allocation (JCORA) algorithm to maximize the long-term average throughput by optimizing task offloading, subchannel allocation, computing resource allocation, and task migration decisions. Simulation results demonstrate the effectiveness of the proposed scheme over existing approaches.

Localization is vital and fundamental for underwater acoustic sensor networks (UASNs), as it provides location information for UASNs to achieve various practical underwater tasks. Most existing localization methods assume small-scale scenarios without battery energy constraints, making it inapplicable to large-scale UASNs. In large-scale UASNs, localization suffers from the challenges of excessive energy consumption and large localization error because of harsh underwater conditions like node mobility and huge ranging errors. To this end, we propose an efficient localization scheme with velocity prediction (LSVP) to solve the above challenges for large-scale UASNs. LSVP considers node mobility, ranging errors, and energy balance in a unified framework, which is applicable to realistic and scalable UASNs. Specifically, we first design a Doppler-assisted velocity prediction (DVP) algorithm to decrease energy consumption, which can solve the excessive communications caused by node mobility under ocean currents. Then, a acrlong CIL algorithm is proposed to decrease the localization error, which can reduce location uncertainty and error propagation caused by ranging errors. Extensive simulation results indicate that LSVP can achieve accurate velocity prediction and high precision localization for large-scale UASNs.

The complex topography of urban canyons with many reflectors and scatterers makes it challenging to detect low-altitude, smaller, and slow-speed targets. In this paper, we present a novel multipath-assisted passive detection scheme based on the global navigation satellite system signals in urban canyons. We first propose an information-level target detection scheme, where a binary hypothesis test is conducted according to variation in the received signal given the presence or absence of targets in the environment. To take usage of multipath components (MPCs) in the proposed scheme, we introduce virtual anchors to model reflected signals’ propagation paths. We also introduce the reconfigurable intelligent surface to artificially improve the reflective environment and enhance the quality of received MPCs. The detection performance indicators are analyzed theoretically. Simulation results show that the proposed schemes respectively reach 90% and 94% detection probability at a signal-to-noise ratio of 5 dB. The RIS-based method outperforms the multipath-assisted method when the RIS error is less than 0.41 m.

In this paper, we investigate dense short packet forwarding for clustering-based massive Internet-of-Things (mIoT). The objective is to support the data forwarding with minimal communication overhead while satisfying the differentiated latency constraints from the transport layer perspective. To this end, we propose a dense packet assembling scheme, named ShuttleBus, for forwarding devices in mIoT to achieve effective data merging. The assembling scheme is designed based on the stream multiplexing mechanism of the Quick UDP Internet Connection (QUIC) protocol. With ShuttleBus, the payload data sent from IoT devices are extracted as independent frames belonging to different data streams. The ShuttleBus can bundle data frames from multiple streams into a single packet while ensuring data integrity of these streams. Furthermore, we develop a resilient packing mechanism in packet assembling to merge data received from IoT devices within a cluster. In addition, a latency-oriented scheduling mechanism for backlogged QUIC data is established to guarantee satisfactory delivery of diverse transmission tasks. To accommodate the dynamic network environment, we tailor a learning-based algorithm to determine the optimal packet assembling time adaptively. We evaluate the performance of ShuttleBus under various network load conditions. Both analytical and experimental results demonstrate that the proposed scheme significantly reduces communication overhead and enhances data delivery performance under stringent latency constraints.

In this paper, we present a three-layer (i.e., device, field, and factory layers) deterministic federated learning (FL) framework, named DetFed, which accelerates collaborative learning process for ultra-reliable and low-latency industrial Internet of Things (IoT) via integrating 6G-oriented Time-sensitive Networks (TSN). Utilizing dispersive local data, industrial IoT devices distributively train a deep neural network (DNN) model, and the updated model parameters are aggregated at their associated field servers every round or at a centralized factory server every a few rounds. Aiming at optimizing the learning accuracy of FL without affecting the co-transmission of burst traffic (e.g., safety-critical traffic), an integrated TSN is considered to establish connections among the three layers, where a cyclic queuing and forwarding mechanism is deployed in each switch to support deterministic model parameter transmission with microsecond-level delay and near-zero packet loss requirements. To improve the FL performance, we formulate a multi-objective stochastic optimization problem to simultaneously maximize the scheduling success ratio and learning accuracy while satisfying the deterministic requirements of delay, jitter, and packet loss. Since the objective function is implicit and the available time slots of the considered TSN in each FL round are temporally correlated, the problem is difficult to solve in real time. Therefore, we transform the problem into a Markov decision process formulation and propose a dynamic resource scheduling algorithm, based on deep reinforcement learning, to make optimal resource scheduling decisions while adapting to device heterogeneity and network dynamics. Experimental results based on real-world dataset demonstrate that the proposed DetFed significantly accelerates FL convergence and improves learning accuracy as compared to state-of-the-art benchmarks.

Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.

Driven by the ever-increasing computing capabilities of mobile devices, the next-generation wireless networks are evolving toward a distributed networking and computing platform, which enables in-network computing and unified resource/service provisioning. The evolution leads to a growing research interest in wireless computing networks that operate under both the high dynamics of the wireless environment and the resource heterogeneity, which complicates resource allocation, scheduling among network flows, and overall optimization. In this paper, we aim to study a low-complexity efficient solution to jointly allocate both networking resources (e.g., links to forward packets between connected computing nodes) and computing resources (e.g., computing power at each node for packet processing). We formulate a novel network utility maximization problem under computing and networking resource constraints and develop an enhanced backpressure-based dynamic scheduling and routing algorithm. Finally, we verify the effectiveness of the algorithm with extensive simulations.

In this paper, we investigate joint caching and computing resource reservation for supporting location-aware augmented reality (AR) applications in an edge-assisted two-tier radio access network. We aim at minimizing the caching and computing resource consumption while satisfying the AR service delay requirement. Specifically, to capture the spatio-temporal AR service dynamics, the resource consumption minimization problem is formulated as a long-term stochastic optimization problem. Due to the time-varying service demands and tightly coupled multi-resource reservation decisions, we propose a novel resource reservation algorithm based on the Lyapunov optimization technique to solve the problem. We first transform the original long-term problem into multiple one-shot optimization problems, each of which is then solved by our designed iterative algorithm in an online manner. Simulation results demonstrate that the proposed algorithm can significantly reduce the overall resource consumption compared to benchmark algorithms.

In this article, we propose an integrated satellite–terrestrial network (ISTN) architecture to support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite networks serve as a complement to terrestrial networks by providing additional communication resources, backhaul capacities, and seamless coverage. Under this architecture, we investigate how to jointly make offloading link selection and bandwidth allocation decisions for BSs and IoT users. Considering the differentiated decision-making time granularities, we formulate a two-timescale stochastic optimization problem to minimize the overall task offloading delay. To accommodate the two-timescale network dynamics and characterize state–action relations, we establish a hierarchical Markov decision process (H-MDP) framework with two separate agents tackling two-timescale network management decisions, and two evolved MDP-based subproblems are formulated accordingly. To efficiently solve the subproblems, we further develop a hybrid proximal policy optimization (H-PPO)-based algorithm. Specifically, a hybrid actor–critic architecture is designed to deal with the mixed discrete and continuous actions. In addition, an action mask layer and an action shaping function are designed to sample feasible task offloading decisions from the time-variant action set. Extensive simulation results have validated the superiority of the proposed ISTN architecture and the H-PPO-based algorithm, especially, in scenarios with scarce spectrum resources and heavy traffic loads.

In this paper, we propose a massive access control scheme in machine-type communications (MTC) aided smart grid, which prioritizes and clusters the devices based on the latency requirement and the distance between devices. The considered use case features a single cell in the massive connectivity smart grid and a large number of devices with different priority types. For the delay-sensitive devices, a dynamic random access channel (RACH) resource allocation scheme is proposed, where a back-off mechanism is used to defer the access requests of delay-tolerance devices. In addition, we propose a cluster-based congestion control algorithm, which clusters the devices to establish local collaboration. Simulation results show the proposed scheme reduces the average blocking probability, while significantly reducing access delay for delay-sensitive devices compared with state-of-the-art access control methods.

In this article, we propose a multilayer Ka/Q-band satellite–terrestrial integrated network for the Internet of Remote Things (IoRT) to achieve a high transmission rate with communication robustness in dynamic network environments. Under this architecture, we investigate how to jointly manage the offloading path selection and resource allocation to offload computation-intensive and delay-sensitive tasks in the IoRT. Considering continuous low earth orbit (LEO) satellite movements and Markovian rainfall changes, the computation offloading problem is described as a Markov decision process (MDP) formulation with the objective of maximizing the number of offloaded tasks with satisfied delay requirements and minimizing the power consumption of the LEO satellites. A deep reinforcement learning (DRL) approach is leveraged to make optimal decisions by taking account of dynamic queues of IoRT devices, channel conditions that vary with rainfall intensities and satellite positions, and computing capabilities of ground stations. Extensive simulations are conducted to validate the effectiveness and superiority of our proposed scheme.

In this article, a two-level soft-slicing scheme is proposed for 5G-and-beyond radio access networks to support ultrareliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services with delay/reliability and throughput requirements, respectively. At the network level, we first determine the number of radio resources required for eMBB services and analyze the delay violation probability for URLLC services. Then, an integer nonlinear program is formulated for the network-level resource preallocation. Since the formulated problem is NP-complete, a low-complexity heuristic algorithm is proposed to obtain near-optimal solutions. Given the preallocated resources at each gNodeB (gNB), a gNB-level resource scheduling scheme is designed to enable real-time resource sharing among URLLC services considering the reliability and delay requirements. Simulation results show that the proposed soft-slicing scheme meets stringent quality-of-service requirements for both URLLC and eMBB services and achieves high resource utilization efficiency when compared with conventional hard resource slicing schemes.

In this paper, we investigate a computing task offloading problem in a cloud-based autonomous vehicular network (C-AVN), from the perspective of long-term network wide computation load balancing. To capture the task computation load dynamics over time, we describe the problem as an Markov decision process (MDP) with constraints. Specifically, the objective is to minimize the expectation of a long-term total cost for imbalanced base station (BS) computation load and task offloading decision switching, with per-slot computation capacity and offloading latency constraints. To deal with the unknown state transition probability and large state-action spaces, a multi-agent deep Q-learning (MA-DQL) module is designed, in which all the agents cooperatively learn a joint optimal task offloading policy by training individual deep Q-network (DQN) parameters based on local observations. To stabilize the learning performance, a fingerprint-based method is adopted to describe the observation of each agent by including an abstraction of every other agent’s updated state and policy. Simulation results show the effectiveness of the proposed task offloading framework in achieving long-term computation load balancing with controlled offloading switching times and per-slot QoS guarantee.

Through network function virtualization (NFV), virtual network functions (VNFs) can be mapped onto substrate networks as service function chains (SFCs) to provide customized services with guaranteed Quality of Service (QoS). In this article, we solve a multi-SFC embedding problem by a game-theoretical approach considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. Specifically, each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all NFV nodes. Due to processing-resource sharing, additional delay is incurred and incorporated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We prove that the formulated resource-constrained multi-SFC embedding game (RC-MSEG) is an exact potential game admitting at least one pure Nash equilibrium (NE) and has the finite improvement property (FIP). Two iterative algorithms are developed, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE. Simulations are conducted to demonstrate the effectiveness of the proposed game-theoretical approach.

To efficiently allocate heterogeneous resources for customized services, in this article, we propose a network virtualization (NV)-based network architecture in cybertwin-enabled 6G core networks. In particular, we investigate how to optimize the virtual network (VN) topology (which consists of several virtual nodes and a set of intermediate virtual links) and determine the resultant VN embedding in a joint way over a cybertwin-enabled substrate network. To this end, we formulate an optimization problem whose objective is to minimize the embedding cost, while ensuring that the end-to-end (E2E) packet delay requirements are satisfied. The queueing network theory is utilized to evaluate each service’s E2E packet delay, which is a function of the resources assigned to the virtual nodes and virtual links for the embedded VN. We reveal that the problem under consideration is formally a mixed-integer nonlinear program (MINLP) and propose an improved brute-force search algorithm to find its optimal solutions. To enhance the algorithm’s scalability and reduce the computational complexity, we further propose an adaptively weighted heuristic algorithm to obtain near-optimal solutions to the problem for large-scale networks. Simulations are conducted to show that the proposed algorithms can effectively improve network performance compared to other benchmark algorithms.

Next-generation core networks are expected to achieve service-oriented traffic management for diversified Quality-of-Service (QoS) provisioning based on software-defined networking (SDN) and network function virtualization (NFV). In this article, a learning-based transmission protocol customized for Video-on-Demand (VoD) streaming services is proposed for a Cybertwin-enabled next-generation core network, which provides caching-based congestion control and throughput enhancement functionalities at the edge of the core network based on traffic prediction. The per-slot traffic load of a VoD streaming service at an ingress edge node is predicted based on the autoregressive integrated moving average (ARIMA) model. To balance the tradeoff between network congestion and throughput enhancement, a multiarmed bandit (MAB) problem is formulated to maximize the expected overall network performance in a long run, by capturing the relationship between transmission control actions and QoS provisioning. A comprehensive transmission protocol operation framework is also presented with in-network congestion control and throughput enhancement modules. Simulation results are presented to validate the efficacy of the proposed protocol in terms of packet delay, goodput ratio, throughput, and resource utilization.

The paradigm of network function virtualization (NFV) with the support of software defined networking (SDN) emerges as a promising approach for customizing network services in fifth generation (5G) networks. In this paper, a multicast service orchestration framework is presented, where joint traffic routing and virtual network function (NF) placement are studied for accommodating multicast services over an NFV-enabled physical substrate network. First, we investigate a joint routing and NF placement problem for a single multicast request accommodated over a physical substrate network, with both single-path and multipath traffic routing. The joint problem is formulated as a mixed integer linear programming (MILP) problem to minimize the function and link provisioning costs, under the physical network resource constraints, flow conservation constraints, and NF placement rules; Second, we develop an MILP formulation that jointly handles the static embedding of multiple service requests over the physical substrate network, where we determine the optimal combination of multiple services for embedding and their joint routing and placement configurations, such that the aggregate throughput of the physical substrate is maximized, while the function and link provisioning costs are minimized. Since the presented problem formulations are NP-hard, low complexity heuristic algorithms are proposed to find an efficient solution for both single-path and multipath routing scenarios. Simulation results are presented to demonstrate the effectiveness and accuracy of the proposed heuristic algorithms.

Internet-of-Vehicles (IoV) connects vehicles, sensors, pedestrians, mobile devices, and the Internet with advanced communication and networking technologies, which can enhance road safety, improve road traffic management, and support immerse user experience. However, the increasing number of vehicles and other IoV devices, high vehicle mobility, and diverse service requirements render the operation and management of IoV intractable. Software-defined networking (SDN) and network function virtualization (NFV) technologies offer potential solutions to achieve flexible and automated network management, global network optimization, and efficient network resource orchestration with cost-effectiveness and are envisioned as a key enabler to future IoV. In this article, we provide an overview of SDN/NFV-enabled IoV, in which SDN/NFV technologies are leveraged to enhance the performance of IoV and enable diverse IoV scenarios and applications. In particular, the IoV and SDN/NFV technologies are first introduced. Then, the state-of-the-art research works are surveyed comprehensively, which is categorized into topics according to the role that the SDN/NFV technologies play in IoV, i.e., enhancing the performance of data communication, computing, and caching, respectively. Some open research issues are discussed for future directions.

Software defined networking (SDN) and network function virtualization (NFV) are key enabling technologies in fifth generation (5G) communication networks for embedding service-level customized network slices in a network infrastructure, based on statistical resource demands to satisfy long-term quality of service (QoS) requirements. However, traffic loads in different slices are subject to changes over time, resulting in challenges for consistent QoS provisioning. In this paper, a dynamic flow migration problem for embedded services is studied, to meet end-to-end (E2E) delay requirements with time-varying traffic. A multi-objective mixed integer optimization problem is formulated, addressing the trade-off between load balancing and reconfiguration overhead. The problem is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. It is proved that there is no optimality gap between the two problems; hence, we can obtain the optimum of the original problem by solving the MIQCP problem with some post-processing. To reduce time complexity, a heuristic algorithm based on redistribution of hop delay bounds is proposed to find an efficient solution. Numerical results are presented to demonstrate the aforementioned trade-off, the benefit from flow migration in terms of E2E delay guarantee, as well as the effectiveness and efficiency of the heuristic solution.

In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing communication data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among base stations (BSs), allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Through linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, the optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.

In this paper, we propose a low-complexity user selection algorithm for an uplink multiuser transmission in millimeter wave (mmWave) WLAN. We first formulate the user selection problem, taking hybrid beamforming (HBF), an NP-hard problem, into consideration. We then develop a three-step HBF algorithm that incorporates user selection. Specifically, users can be selected based on semi-orthogonality instead of collecting perfect channel state information (CSI) from all potential users. We optimize the digital beamforming to mitigate residual interference among the selected users. Furthermore, we provide analytical validation for the proposed user selection algorithm and study the impact of angle correlation, analog beam pattern, and beamwidth on the achievable rate of the selected users. Extensive simulations validate the performance of the proposed overall HBF algorithm when compared with existing solutions.

To facilitate the deployment of private industrial Internet-of-Things (IoT), applying long-term-evolution (LTE) over unlicensed spectrum (LTE-U) is a promising technology, which can deal with the licensed spectrum scarcity problem and the stringent quality-of-service (QoS) requirement via centralized control. In this paper, we investigate the computing offloading problem for LTE-U-enabled IoT, where computing tasks on an IoT device are either executed locally or offloaded to the edge server on an LTE-U base station. Considering a constrained edge computing cost (e.g., operation power consumption) for offloaded tasks, the task scheduling problem is formulated as a constrained Markov decision process (CMDP) to maximize the long-term average reward, which integrates both task completion profit and task completion delay. In order to address the uncertainty of task arrivals and channel availability, a constrained deep Q-learning-based task scheduling algorithm with provable convergence is proposed, where an adaptive reward function can appropriately bound the average edge computing cost. Extensive simulation results show that the proposed scheme considerably enhances the system performance.

In order to tackle the spectrum scarcity problem and enhance the spectrum efficiency, deploying LTE in unlicensed band (LTE-U) is an emerging technology for supporting massive connections in future networks. By taking into account of the coexistence between the LTE-U cellular user equipments (CUEs) and the legacy Wi-Fi stations (STAs) in the unlicensed band, a partial non-orthogonal multiple access (NOMA)-based scheme is proposed in this paper. By dividing all UEs into two groups and making the Wi-Fi STA as the UE with the weakest channel gain in its group, we can exploit the multiplexing gain of NOMA by introducing no extra modification to Wi-Fi STAs. Accordingly, a fairness-oriented resource allocation framework is formulated as a max-min problem to jointly optimize the inter-group time occupancy ratio and the intra-group power allocation when the guaranteed bit rate (GBR) requirements for each UE are considered. A modified two-dimensional bisection algorithm is proposed to search the optimal time occupancy ratio and the max-min rate in this coexisting network. Numerical results validate the effectiveness of the partial NOMA scheme and outperform the traditional orthogonal multiple access method, in terms of both efficiency and robustness.

Wireless Body Area Networks (WBANs) have been developed rapidly with the increasing popularity of wireless network and wearable technologies. The inherent characteristics of convenience and efficiency for health monitoring facilitate the depth and width of WBAN applications. However, the inter-WBAN interference problem affects the network performance in intensive WBAN scenarios, degrading reliability and increasing latency of health data. In this paper, we propose a Two-Tier Multi-channel Medium Access Control (2TM-MAC) protocol with interference mitigation for reliable health monitoring. Specially, the 2TM-MAC establishes an inter-WBAN interference matrix for every WBAN to show the mutual interference among coexisting WBANs. We design a multi-channel selection algorithm at the first tier to select different numbers of channels for each WBAN to avoid inter-WBAN interference and collisions. At the second tier, the hub of each WBAN schedules the available channels assigned from the first tier to sensor nodes according to their traffic requirements, mitigating the intra-WBAN interference as well. 2TM-MAC protocol enhances the reliability of emergency data and service experience in healthcare applications. Simulation results show the 2TM-MAC protocol significantly improves the network throughput and decreases the average packet delay compared with IEEE 802.15.6 for densely deployed coexisting WBANs scenarios.

In this paper, a comprehensive software-defined networking (SDN) based transmission protocol (SDTP) is presented for fifth generation (5G) communication networks, where an SDN controller gathers network state information from the physical network to improve data transmission efficiency between end hosts, with in-path packet retransmission. In the SDTP, we first develop a new two-way handshake mechanism for connection establishment between a pair of end host. With the aid of SDN control module, signaling exchanges for establishing E2E connections are migrated to the control plane to improve resource utilization in the data plane. A new SDTP packet header format is designed to support efficient data transmission with in-path packet caching and packet retransmission. Based on the new data packet format, a novel in-path receiver-based packet loss detection and caching-based packet retransmission scheme is proposed to achieve in-path fast recovery of lost packets. Extensive simulation results are presented to validate the effectiveness of the proposed protocol in terms of low connection establishment delay and low end-to-end packet transmission delay.

Service-oriented virtual network deployment is based on statistical resource demands of different services, while data traffic from each service fluctuates over time. In this paper, a delay-aware flow migration problem for embedded services is studied to meet end-to-end (E2E) delay requirement with time-varying traffic. A non-convex multi-objective mixed integer optimization problem is formulated, addressing the trade-off between maximum load balancing and minimum reconfiguration overhead due to flow migrations, under processing and transmission resource constraints and QoS requirement constraints. Since the original problem is non-solvable in optimization solvers due to unsupported types of quadratic constraints, it is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. The optimality gap between the two problems is proved to be zero, so we can obtain the optimum of the original problem through solving the MIQCP problem with some post-processing. Numerical results are presented to demonstrate the aforementioned trade-off, as well as the benefit from flow migration in terms of E2E delay performance guarantee.

Massive machine-type communication (mMTC) is a new focus of services in fifth generation communication networks. The associated stringent delay requirement of end-to-end (E2E) service deliveries poses technical challenges. In this paper, we propose a joint random access and data transmission protocol for mMTC to guarantee E2E service quality of different traffic types. First, we develop a priority-queueing-based access class barring (ACB) model and a novel effective capacity is derived. Then, we model the priority-queueing-based ACB policy as a noncooperative game, where utility is defined as the difference between effective capacity and access penalty price. We prove the existence and uniqueness of Nash equilibrium (NE) of the noncooperative game, which is also a submodular utility maximization problem and can be solved by a greedy updating algorithm with convergence to the unique NE. To further improve the efficiency, we present a price-update algorithm, which converges to a local optimum. Simulations demonstrate the performance of the derived effective capacity and the effectiveness of the proposed algorithms.

In this paper, an analytical end-to-end (E2E) packet delay modeling is established for multiple traffic flows traversing an embedded virtual network function (VNF) chain in fifth generation communication networks. The dominant-resource generalized processing sharing is employed to allocate both computing and transmission resources among flows at each network function virtualization (NFV) node to achieve dominant-resource fair allocation and high resource utilization. A tandem queueing model is developed to characterize packets of multiple flows passing through an NFV node and its outgoing transmission link. For analysis tractability, we decouple packet processing (and transmission) of different flows in the modeling and determine average packet processing and transmission rates of each flow as approximated service rates. An M/D/1 queueing model is developed to calculate packet delay for each flow at the first NFV node. Based on the analysis of packet interarrival time at the subsequent NFV node, we adopt an M/D/1 queueing model as an approximation to evaluate the average packet delay for each flow at each subsequent NFV node. The queueing model is proved to achieve more accurate delay evaluation than that using a G/D/1 queueing model. Packet transmission delay on each embedded virtual link between consecutive NFV nodes is also derived for E2E delay calculation. Extensive simulation results demonstrate the accuracy of our proposed E2E packet delay modeling, upon which delay-aware VNF chain embedding can be achieved.

The software defined networking (SDN) enabled network function virtualization (NFV) architecture emerges as a cost-effective solution for service customization in fifth generation (5G) networks. In this paper, a joint traffic routing and virtual network function (VNF) placement problem is studied for a multicast service request accommodated over a physical substrate network, where the multipath traffic routing is considered between embedded VNFs. The joint problem is formulated as a mixed integer linear programming (MILP) problem to minimize the provisioning cost of both VNFs and links, under the physical network resource constraints, flow conservation constraints, and VNF placement rules. Since the problem is NP-hard, low complexity heuristic algorithms, with the consideration of both the single-path and multipath routing cases, are proposed to determine an efficient solution. Simulation results are presented to demonstrate the effectiveness and accuracy of the proposed heuristic algorithms especially for a large-size network.

To facilitate the private deployment of industrial Internet-of-Things (IoT), applying LTE in unlicensed spectrum (LTE-U) is a promising approach, which both tackles the problem of lacking licensed spectrum and leverages an LTE protocol to meet stringent quality-of- service (QoS) requirements via centralized control. In this paper, we investigate the computing offloading problem in an LTE-U-enabled network, where the task on an IoT device is carried out either locally or is offloaded to the LTE-U base station (BS). The offloading policy is formulated as an optimization problem to maximize the long term discounted reward, considering both task completion profit and the task completion delay. Due to the stochastic task arrival process at each device and the Wi-Fi's contention-based random access, we reformulate the computing offloading problem into a Q-learning problem and solve it by a deep learning network-based approximation method. Simulation results show that the proposed scheme considerably enhances the system performance.

Network function virtualization (NFV) is one of the enabling technologies for fifth generation (5G) networks. How to allocate physical resources to customized network services both fairly and efficiently remains a challenging research issue in NFV. This paper proposes a two-stage approach to jointly optimize the chaining and embedding of virtual network functions (VNFs), to obtain feasible composition and embedding results with low complexity, while the average embedding cost is minimized and the total revenue is increased. In the first stage, the VNF chaining order is optimized based on the location and functionality of substrate nodes, and the ratio of outgoing data rate over incoming data rate for each required VNF. In the second stage, we allocate the physical resources based on the preliminary VNF ordering under the resource capacity constraints. A node splitting mechanism is also employed to improve the resource allocation fairness and increase the service acceptance ratio for the substrate network. Simulation results are presented to validate the feasibility and effectiveness of the proposed approach.

Wireless Body Area Network (WBAN) technology jumps into popularity owing to its real-time ability and high reliability in health monitoring. The accompanying interference problem must be highly concerned in coexisting densely deployed WBANs since the inter-WBAN interference results in high delay and low reliability data transmissions, especially with the movement of human body. In the paper, we analyze the dynamic interference with human mobility in multiple coexisting WBANs with the consideration of different distances between inter-WBANs and varying number of coexisting WBANs. Moreover, we investigate the influence of inter- WBAN interference on the performance of normalized throughput and average access delay of different traffic types. The results show that the interference generated by mobile neighbour WBANs extremely decreases the throughput of the target WBAN and increases the average packet delay 1.76 times of emergency data compared with the target WBAN without interference. The dynamic interference analysis provides insights on the practical WBAN management and interference mitigation protocol design, especially for the deeply deployed coexisting WBAN scenarios.

Intelligent wireless body area networks (WBANs) have entered into an incredible explosive popularization stage. WBAN technologies facilitate real-time and reliable health monitoring in e-healthcare and creative applications in other fields. However, due to the limited space and medical resources, deeply deployed WBANs are suffering severe interference problems. The interference affects the reliability and timeliness of data transmissions, and the impacts of interference become more serious in mobile WBANs because of the uncertainty of human movement. In this paper, we analyze the dynamic interference taking human mobility into consideration. The dynamic interference is investigated in different situations for WBANs coexistence. To guarantee the performance of different traffic types, a health critical index is proposed to ensure the transmission privilege of emergency data for intra- and inter-WBANs. Furthermore, the performance of the target WBAN, i.e., normalized throughput and average access delay, under different interference intensity are evaluated using a developed three-dimensional Markov chain model. Extensive numerical results show that the interference generated by mobile neighbor WBANs results in 70% throughput decrease for general medical data and doubles the packet delay experienced by the target WBAN for emergency data compared with single WBAN. The evaluation results greatly benefit the network design and management as well as the interference mitigation protocols design.

Next generation (5G) wireless networks are expected to accommodate proliferation of connected devices and multimedia services. To support multimedia services in an agile, cost-effective, and flexible way, network virtualization is a potential solution. This paper investigates service- oriented network virtualization for 5G wireless networks, to efficiently allocate heterogeneous resources to accommodate multimedia services. Specifically, we study joint resource allocation for virtual network requests (VNRs) and online embedding the resultant VNRs in core networks (CNs). With the deployment of multiple traffic aggregation points (TAPs) in radio access networks (RANs), the end-to- end traffic from heterogeneous access technologies can be aggregated and then grouped based on their destinations. Queueing models are developed in determining the minimal capacity required at each core network element. Virtual network embedding (VNE) in the core network is further proposed to achieve efficient physical resource sharing in CNs. Simulation results validate the VNE process in core networks based on the optimized capacities.

Platooning has been identified as a promising vehicular traffic management strategy to improve road capacity, energy efficiency, and on-road safety in intelligent transportation systems (ITS). Inter-vehicle communications within a platoon and among multiple platoons can assist platoon control by maintaining a constant inter-vehicle distance, which in turn enhances road safety. An efficient method of sharing inter-vehicle information successfully and timely is critical to many platooning applications. In this paper, a resource allocation (RA) approach is proposed to support inter-vehicle communications underlaying cellular network for a multiplatooning (a chain of platoons) scenario. By applying the evolved multimedia broadcast multicast services (eMBMS) in the Evolved Node B (eNB), the transmission delay for intra-platoon and inter-platoon communications can be reduced. Then, using the proposed subchannel allocation and power control schemes, the number of required subchannels and the transmission powers of each vehicle and the eNB can be minimized. Numerical results show that the proposed approach outperforms the candidate RA scheme in terms of transmission delay, especially in a multiplatooning scenario with a large number of vehicles.

In this paper, we propose a distributed and adaptive hybrid medium access control (DAH-MAC) scheme for a single-hop Internet of Things (IoT)-enabled mobile ad hoc network supporting voice and data services. A hybrid superframe structure is designed to accommodate packet transmissions from a varying number of mobile nodes generating either delay-sensitive voice traffic or best-effort data traffic. Within each superframe, voice nodes with packets to transmit access the channel in a contention-free period (CFP) using distributed time division multiple access, while data nodes contend for channel access in a contention period (CP) using truncated carrier sense multiple access with collision avoidance. In the CFP, by adaptively allocating time slots according to instantaneous voice traffic load, the MAC exploits voice traffic multiplexing to increase the voice capacity. In the CP, a throughput optimization framework is proposed for the DAH-MAC, which maximizes the aggregate data throughput by adjusting the optimal contention window size according to voice and data traffic load variations. Numerical results show that the proposed MAC scheme outperforms existing quality-of-service-aware MAC schemes for voice and data traffic in the presence of heterogeneous traffic load dynamics.

In this paper, a distributed token-based adaptive medium access control (TA-MAC) scheme is proposed for a two-hop Internet of Things (IoT)-enabled mobile ad hoc network. In the TA-MAC, nodes are partitioned into different one-hop node groups, and a time division multiple access (TDMA)-based superframe structure is proposed to allocate different TDMA time durations to different node groups to overcome the hidden terminal problem. A probabilistic token passing scheme is devised to distributedly allocate time slots to nodes in each group for packet transmissions, forming different token rings. The distributed time slot allocation is adaptive to variations of the number of nodes in each token ring due to node movement. To optimize the medium access control (MAC) design, performance analytical models are presented in closed-form functions of both MAC parameters and network traffic load. Then, an average end-to-end delay minimization framework is established to derive the optimal MAC parameters under a certain network load condition. Analytical and simulation results demonstrate that, by adapting the MAC parameters to the varying network condition, the TA-MAC achieves consistently minimal average end-to-end delay, bounded delay for local transmissions, and high aggregate throughput. Further, the performance comparison with other MAC schemes shows the scalability of the proposed MAC in an IoT-based two-hop environment with an increasing number of nodes.

Cognitive radio sensor network (CRSN) has emerged as a promising solution to address the spectrum scarcity problem in traditional sensor networks, by enabling sensor nodes to opportunistically access licensed spectrum. To protect the transmission of primary users and enhance spectrum utilization, collaborative spectrum sensing is generally adopted for improving spectrum sensing accuracy. However, as sensor nodes may be compromised by adversaries, these nodes can send false sensing reports to mislead the spectrum sensing decision, making CRSNs vulnerable to spectrum sensing data falsification (SSDF) attacks. Meanwhile, since the energy consumption of spectrum sensing is considerable for energy-limited sensor nodes, SSDF attack countermeasures should be carefully devised with the consideration of energy efficiency. To this end, we propose a secure and energy-efficient collaborative spectrum sensing scheme to resist SSDF attacks and enhance the energy efficiency in CRSNs. Specifically, we theoretically analyze the impacts of two types of attacks, i.e., independent and collaborative SSDF attacks, on the accuracy of collaborative spectrum sensing in a probabilistic way. To maximize the energy efficiency of spectrum sensing, we calculate the minimum number of sensor nodes needed for spectrum sensing to guarantee the desired accuracy of sensing results. Moreover, a trust evaluation scheme, named FastDtec, is developed to evaluate the spectrum sensing behaviors and fast identify compromised nodes. Finally, a secure and energy-efficient collaborative spectrum sensing scheme is proposed to further improve the energy efficiency of collaborative spectrum sensing, by adaptively isolating the identified compromised nodes from spectrum sensing. Extensive simulation results demonstrate that our proposed scheme can resist SSDF attacks and significantly improve the energy efficiency of collaborative spectrum sensing.