The growing demand for services and the rapid deployment of virtualized network functions (VNFs) pose significant challenges for achieving low-latency and energy-efficient orchestration in modern edge-core network infrastructures. To address these challenges, this study proposes a Digital Twin (DT)-empowered Deep Reinforcement Learning framework for intelligent VNF migration that jointly minimizes average end-to-end (E2E) delay and energy consumption. By formulating the VNF migration problem as a Markov Decision Process and utilizing the Advantage Actor-Critic model, the proposed framework enables adaptive and real-time migration decisions. A key innovation of the proposed framework is the integration of a DT module composed of a multi-task Variational Autoencoder and a multi-task Long Short-Term Memory network. This combination collectively simulates environment dynamics and generates high-quality synthetic experiences, significantly enhancing training efficiency and accelerating policy convergence. Simulation results demonstrate substantial performance gains, such as significant reductions in both average E2E delay and energy consumption, thereby establishing new benchmarks for intelligent VNF migration in edge-core networks.

The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through efficient computing resource management remains an open challenge. This paper introduces a distributed computing architecture that integrates DTs and Mobile Edge Computing (MEC) within a software-defined vehicular networking framework to enable intelligent, low-latency transportation services. A network aware scalable collaborative task provisioning algorithm is developed to train an autonomous agent, which is evaluated using a realistic connected autonomous vehicle (CAV) traffic simulation. The proposed framework significantly enhances the robustness and scalability of DT operations by reducing synchronization errors to as low as 7% while achieving up to 99.5% utilization of edge computing resources.

Six-generation mobile systems aim to support stringent end-to-end service-level agreements for diverse user applications simultaneously. This paper introduces novel multi-domain computation-aware resource slicing orchestration that jointly manages in-network communications, computation, and caching storage resources for multi-domain networking. It automatically programs wireless access, edge cloud, and regional/central cloud infrastructure to enable wireless-optical network virtualization. Specifically, a mobile virtual network operator's long-term profit maximization problem and two subproblems are formulated to slice wired and wireless infrastructure resources and assign user requests and contents to slices. Accordingly, a reinforcement learning-based slicing with greedy pre-caching is proposed, which automatically allocates in-network resources for dynamic wireless connectivity and supports real-time inferring with minimal user request signaling. Numerical results show that our solutions provide superior performance from both user and infrastructure perspectives, with 20% improved operator profits, 17% enhanced service provisioning rates, and 80% reduced delay when simultaneously serving augmented reality and large language model's quality demands. This innovation exploits a generalized rein-forcement learning approach to minimize the signaling overheads and computation complexity while agilely adapting to practical converged networks, thus benchmarking AI-driven multi-domain network slicing development.

The rapid evolution of Industry 4.0 has led to the emergence of smart factories, where multirobot system autonomously operates to enhance productivity, reduce operational costs, and improve system adaptability. However, maintaining reliable and efficient network operations in these dynamic and complex environments requires advanced automation mechanisms. This study presents a zero-touch network platform that integrates a hierarchical Open Radio Access Network (O-RAN) architecture, enabling the seamless incorporation of advanced machine learning algorithms and dynamic management of communication and computational resources, while ensuring uninterrupted connectivity with multirobot system. Leveraging this adaptability, the platform utilizes federated deep reinforcement learning (FedDRL) to enable distributed decision-making across multiple learning agents, facilitating the adaptive parameter reconfiguration of transmitters (i.e., multirobot system) to optimize long-term system throughput and transmission energy efficiency. Simulation results demonstrate that within the proposed O-RAN-enabled zero-touch network platform, FedDRL achieves a 12% increase in system throughput, a 32% improvement in normalized average transmission energy efficiency, and a 28% reduction in average transmission energy consumption compared to baseline methods such as independent DRL.

The need for prioritized multi-tenant quality of service (QoS) management in emerging mobile edge systems is particularly critical for high-throughput next generation networks. Current traffic engineering tools rely on network administrator driven, complex functions embedded in closed, proprietary infrastructures, which significantly restrict design flexibility, scalability, and adaptability. This study addresses these challenges by proposing a software-defined networking (SDN) based dynamic QoS provisioning scheme, powered by a digital twin (DT) of networks. By separating the control and data planes, the scheme enables automated traffic management through SDN programmability and data-driven decision-making. It incorporates few-shot learning to dynamically identify and prioritize multi-tenant network traffic utilizing flow statistics from SDN. The proposed QoS provisioning mechanism allocates sufficient bandwidth to high-priority flows while optimizing the remaining bandwidth for lower-priority traffic. Performance evaluations show that the model achieves up to 98% accuracy in identifying the priority of previously unseen traffic flows. Hardware-in-the-loop (HiL) simulations further validate the scheme's effectiveness in meeting multi-tenant QoS requirements, offering a robust and scalable solution for traffic prioritization in SDN based edge networks.

The integration of 5G terrestrial networks with Low Earth Orbit (LEO) satellites has the potential to provide seamless global connectivity and enhanced service quality, particularly in regions with limited terrestrial infrastructure such as rural areas. Furthermore, the incorporation of Multi-connectivity (MC) enables user equipment (UEs) to maintain simultaneous connections with both terrestrial 5G base stations and LEO satellites, improving system reliability. However, the high mobility of LEO satellites and the dynamic behavior of UEs present significant challenges, particularly in handover decision-making which can adversely impact system throughput, and quality of service (QoS). To address these challenges, we propose a novel deep reinforcement learning-based approach that integrates online Random Ensemble Mixture and Dual Experience Replay into a Dueling Double Deep Q-Network architecture. This proposed scheme intelligently optimizes handover decisions in MC-enabled terrestrial-satellite networks, improving decision accuracy in highly dynamic scenarios. Simulation results demonstrate substantial gains in system throughput, reduced system delay, average handover reduction, and increased transmission success probability, setting new performance benchmarks for integrated terrestrial-satellite networks while adhering to diverse QoS requirements.

The distributed and disaggregated architecture of next-generation (NextG) networks, including 6G has sparked growing interest in federated learning (FL) as a strategy for enabling privacy-preserving collaborative network traffic analysis at the edge. However, FL encounters significant challenges due to data heterogeneity driven by diverse data distributions across edge nodes, and the scarcity of labeled data further worsened by the time-intensive process of data labeling. Although a few studies have addressed these challenges in network traffic analysis tasks using Multi-Task Learning (MTL), existing approaches pre-dominantly focus on single-task FL, centralized model solutions and overlook the integration of MTL in NextG networks. To bridge this gap, we propose O-FedMTL, a novel framework that combines FL with MTL to enable cooperative traffic analysis within an Open Radio Access Network (O-RAN) environment in NextG networks. MTL enhances FL by mitigating the issues of data heterogeneity and labeled data scarcity through shared knowledge derived from multiple interconnected traffic analysis tasks, i.e., traffic classification, flow duration analysis, and bandwidth estimation. Additionally, MTL offers significant benefits by reducing energy consumption and computation costs at the edge through the simultaneous processing of these tasks within a single model. Extensive experimental results demonstrate that O-FedMTL achieves the target global accuracy for traffic classification, flow duration analysis, and bandwidth estimation with 20, 12, and 23 fewer global communication rounds, respectively, compared to the baseline federated averaging. Additionally, O-FedMTL reduces computation costs by 43% compared to the baseline-combined.

With the evolution of modern telecommunications, the fronthaul network has become an indispensable component of the infrastructure, particularly in the context of Cloud Ra-dio Access Networks. Nevertheless, fronthaul networks still face significant challenges such as power outages, particularly when a disaster, like an earthquake or severe weather event, occurs. Damage to power supply facilities may cause operational failures while communication is one of the most crucial needs in the disaster area to make rescue operations more effective. Specialized fibers that can deliver electrical power can help mitigate this problem. However, power losses may be extremely large over distances and there is no flexibility after the installation of the fibers. The network topology design that reduces capital and operational costs while satisfying power constraints is an important problem. In this paper, we focus on network topology design in an urban area by taking into account both fiber and power costs. We then propose integer linear programming and fast algorithms based on methods for single-facility location problems. The results demonstrate that multiple approaches help to achieve the optimal design, with our proposed method standing out due to its efficiency in finding feasible solutions, scalability, and ≈656x reduction in execution time.

Integrated terrestrial and satellite networks will allow providing ubiquitous access and global connectivity to remotely deployed battery-activated (machine-to-machine or IoT) sensors or handset devices with messaging/voice capacities via satellite, fostering a series of applications such as intelligent transportation, coastal monitoring, and smart agriculture. In addition, the agile measurement of sensors enables autonomous driving and fast disaster recovery. These applications could generate massive uplink connections in a short period that the network must handle to avoid failures and outages. This paper covers system aspects and refines the 3rd Generation Partnership Project (3GPP)-based solutions to support non-terrestrial networks and make their performance comparable to that of terrestrial networks for providing IoT communications. A general vertical handover framework is introduced for this integrated network to choose the most suitable access technology for a given service. Then, the whole system increases around 100% the successful transmission probability in heavy-loaded scenarios with a slight increment in the number of retransmissions and transmission delays compared to that of single connectivity solutions. Furthermore, we found that by fine-tuning network parameter configurations, the energy consumption and transmission delay can be decreased while providing reliable communications that will benefit small-sized IoT devices with limited resources.

In the realms of the internet of vehicles (IoV) and intelligent transportation systems (ITS), software defined vehicular networks (SDVN) and edge computing (EC) have emerged as promising technologies for enhancing road traffic efficiency. However, the increasing number of connected autonomous vehicles (CAVs) and EC-based applications presents multi-domain challenges such as inefficient traffic flow due to poor CAV coordination and flow-table overflow in SDVN from increased connectivity and limited ternary content addressable memory (TCAM) capacity. To address these, we focus on a data-driven approach using virtualization technologies like digital twin (DT) to leverage real-time data and simulations. We introduce a DT design and propose two data-driven solutions: a centralized decision support framework to improve traffic efficiency by reducing waiting times at roundabouts and an approach to minimize flow-table overflow and flow re-installation by optimizing flow-entry lifespan in SDVN. Simulation results show the decision support framework reduces average waiting times by 22% compared to human-driven vehicles, even with a CAV penetration rate of 40%. Additionally, the proposed optimization of flow-table space usage demonstrates a 50% reduction in flow-table space requirements, even with 100% penetration of connected vehicles.

C-band enabled Elastic optical networks (EONs) have been one of the most deployed optical network solutions in the world. However, as traffic demands continue to increase, capacity exhaustion is inevitable. There are two major technologies, namely, multiband elastic optical networks (MB-EONs) and space division multiplexed elastic optical networks (SDM-EONs) that can enhance capacity. Each technology offers a tradeoff between better capacity and deployment overhead which directly affects the network performance. Considering the different characteristics of these two technologies, we present our proposed strategy, Progressive Optics Deployment and Integration for Growing Yields (PRODIGY), to gradually migrate the current C-band EONs. PRODIGY uses various proactive measures, inspired by Swiss Cheese Model, to make the network robust for handling network traffic peaks and ensure that the service level agreement is met. We present a detailed comparison of our proposed strategy with customized baseline strategies, and demonstrate the superiority of our proposed approach.

Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication scheme can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.

Smart manufacturing based on artificial intelligence and information communication technology will become the main contributor to the digital economy of the upcoming decades. In order to execute flexible production, smart manufacturing must holistically integrate wireless networking, computing, and automatic control technologies. This article discusses the challenges of this complex system engineering from a wireless networking perspective. Starting from enabling flexible reconfiguration of a smart factory, we discuss existing wireless technology and the trends of wireless networking evolution to facilitate multirobot smart factories. Furthermore, the special sequential decision-making of a multirobot manufacturing system is examined. Social learning can be used to extend the resilience of precision operation in a multirobot system by taking network topology into consideration, which also introduces a new vision for the cybersecurity of smart factories. A summary of highlights of technological opportunities for holistic facilitation of wireless networked multirobot smart factories rounds off this article.

Beamforming is a key technology in communication systems of the fifth generation and beyond. However, traditional optimization-based algorithms are often computationally prohibited from performing in a real-time manner. On the other hand, the performance of existing deep learning (DL)-based algorithms can be further improved. As an alternative, we propose an unsupervised ResNet-inspired beamforming (RI-BF) algorithm in this paper that inherits the advantages of both pure optimization-based and DL-based beamforming for efficiency. In particular, a deep unfolding technique is introduced to reference the optimization process of the gradient ascent beamforming algorithm for the design of our neural network (NN) architecture. Moreover, the proposed RI-BF has three features. First, unlike the existing DL-based beamforming method, which employs a regularization term for the loss function or an output scaling mechanism to satisfy system power constraints, a novel NN architecture is introduced in RI-BF to generate initial beamforming with a promising performance. Second, inspired by the success of residual neural network (ResNet)-based DL models, a deep unfolding module is constructed to mimic the residual block of the ResNet-based model, further improving the performance of RI-BF based on the initial beamforming. Third, the entire RI-BF is trained in an unsupervised manner; as a result, labelling efforts are unnecessary. The simulation results demonstrate that the performance and computational complexity of our RI-BF improves significantly compared to the existing DL-based and optimization-based algorithms.

This paper introduces an energy-efficient, software-defined vehicular edge network for the growing intelligent connected transportation system. A joint user-centric virtual cell formation and resource allocation problem is investigated to bring eco-solutions at the edge. This joint problem aims to combat against the power-hungry edge nodes while maintaining assured reliability and data rate. More specifically, by prioritizing the downlink communication of dynamic eco-routing, highly mobile autonomous vehicles are served with multiple low-powered access points (APs) simultaneously for ubiquitous connectivity and guaranteed reliability of the network. The formulated optimization is exceptionally troublesome to solve within a polynomial time, due to its complicated combinatorial structure. Hence, a distributed multi-agent reinforcement learning (D-MARL) algorithm is proposed for eco-vehicular edges, where multiple agents cooperatively learn to receive the best reward. First, the algorithm segments the centralized action space into multiple smaller groups. Based on the model-free distributed Q learner, each edge agent takes its actions from the respective group. Also, in each learning state, a software-defined controller chooses the global best action from individual bests of the distributed agents. Numerical results validate that our learning solution achieves near-optimal performances within a small number of training episodes as compared with existing baselines.

Blind modulation classification has emerged as a promising technology in many military and civilian applications, such as cognitive radios, satellite systems, etc. However, it is very challenging to support this blind mechanism within non-Gaussian noise environments, which recently have been identified in a variety of electromagnetic communication networks. Also, start-of-the-art classification methods are mainly based on neural networks or deep learning, which inevitably induces heavy computation loads and thus cannot proactively learn from wireless data in real time. To address the challenges, this paper introduces a series of low- computation radio frequency analytics, including generalized cyclic spectrum (GCS), principal component analysis (PCA), and support vector machine (SVM), which enables the blind modulation classification under non-Gaussian noise. First, based on raw sensory signals and the designed bounded nonlinear function, GCS is extracted as the radio frequency feature to facilitate discrimination of modulation schemes. This GCS can also effectively suppress the burstiness impact of non-Gaussian noise. Then, PCA method is adopted to optimally reduce the dimensionality of GCS features, and a simple and efficient SVM classifier is employed to identify the exact modulation of received signals. Both Monte Carlo simulations and real- data experiments confirm that the proposed design outperforms existing solutions with higher classification accuracy and robustness, i.e., at least 13\% improvement of recognition accuracy in very low (-2 dB) generalized signal-to-noise ratio scenario.

During the last few years, automatic modulation classification (AMC) has attracted widespread attention in both civilian and military applications. Conventional AMC schemes are primarily developed under Gaussian noise assumptions. However, recent empirical studies show that non-Gaussian noise has emerged in a variety of wireless networked systems. The bursty nature of non-Gaussian noise fundamentally challenges the applicability of the conventional AMC schemes. In order to improve the classification performance under non-Gaussian noise, in this paper, a novel modulation classification method is proposed by using cyclic correntropy spectrum (CCES) and deep residual neural network (ResNet). First, CCES is introduced to effectively suppress non-Gaussian noise through the designated Gaussian kernel. CCES also provides significantly different CCES graphs with respect to different modulation schemes, enabling AMC to directly operate with the graphs without further feature extraction. Next, based on the CCES graphs, an end-to-end deep ResNet-based AMC is developed to recognize the correct modulation by iteratively evaluating the residual information in a cascade of multiple learning layers. Experimental results confirm that the proposed algorithm outperforms existing designs with much higher classification accuracy, i.e., 3 dB less in the required generalized signal to noise ratio for 100% accuracy, in non-Gaussian noise environments.

As a key enabling technology for 5G&B systems, network virtualization allows multiple service providers to simultaneously and independently serve their users via virtualized network slices. However, this innovative technology cannot slice wireless resources without a paradigm shift in existing hardware-based architectures. In this paper, end-to-end network slicing is treated from a perspective of wireless software-defined networking architectures. It jointly optimizes all communication functionalities in both radio access and core networks to ensure optimal data throughput and congestion-free systems. First, based on software- defined cellular architectures, the idea of end- to-end (across access and core network domains) virtualization is introduced with dedicated control units, including high-level controllers and local baseband servers. Next, a stochastic utility-optimal virtualization problem is formulated, which jointly optimizes congestion control, flow routing, and power slicing to maximize the total incoming rates of wireless/wired flows, while satisfying the flow- queue stability and system-level constraints. After transforming the virtualization problem into a tractable form, an iterative network slicing algorithm is proposed that employs a primal-dual Newton method with quadratic convergence and achieves resource-efficient virtualization via control-unit coordination. Numerical results validate the efficacy of our solution, facilitating the 5G&B infrastructure-as-a-service.

As a key enabling technology for 5G&B systems, massive multi-input multi-output (MIMO) allows the system capacity to be theoretically increased by simply installing additional antennas to remote radio heads (RRHs). However, this innovative technology cannot support higher data capacity without accurate channel state information and interference handling, especially for multi-cell scenarios. In this paper, the dynamic macro- diversity (i.e., network) massive MIMO is treated from the perspective of software-defined cellular architecture. The so-called software-defined massive MIMO is introduced, which dynamically coordinates highly-deployed RRHs equipped with massive antennas so that the maximum spectral efficiency is achieved. First, the software- defined cellular architecture is presented, where distributed massive antenna systems with centralized control and time-division duplexing massive MIMO are investigated. Next, in this considered architecture, a rigid analysis of achievable ergodic user sum-rates is given for macro-diversity massive MIMO schemes. An optimization framework of software-defined massive MIMO is further proposed that optimizes RRH clustering pattern and RRH-user associations while satisfying system-level constraints. To address the NP-complete problem of the optimal framework design, an iterative, global search algorithm is developed that exploits genetic algorithms and yields satisfactory solutions in only few rounds. Performance evaluation validates the efficacy of our solution which facilitates universal frequency reuse for 5G&B wireless networks.

Supporting on-line and adaptive traffic engineering in software-defined networks entails the fast, robust control message forwarding from software-defined switches to the controller(s). In-band control using the existing infrastructure is cost-efficient, but imposes a substantial barrier to timely transmissions of control messages. Also, due to the limited computational capability of a single controller, only the use of multiple controllers is practically viable for large-scaled networks. Therefore, in this paper, the optimal software-defined network planning is investigated with multi-controllers. First, the network planning problem is formulated as a nonlinear multi-objective optimization, which aims to simultaneously minimize the number of controllers and the control traffic delay for each switch. This planning problem is then partitioned into two sub-problems, i.e., multi-controller placement and control traffic balancing, which are respectively solved by the proposed fast-convergent algorithms. Furthermore, an adaptive feedback control mechanism is proposed to iteratively work out the two sub-problems and enable the dynamic network replanning, subject to the time-varying traffic volume and network topology. Simulations validate the adaptivity of our control scheme, which significantly reduces delay with maximum throughput for control flows, brings minimal impact to normal data flows, and requires the minimum controllers.

In-situ monitoring of oil reservoirs is crucial for determining the sweet spot of oil and natural gas reserves. Wireless sensor nodes are a promising technology to collect data from oil reservoirs in real time, such nodes are not sufficient for transmitting the required data within a power budget because of limitations caused by the very small size of sensors and the environment. To overcome limitations caused by harsh environmental conditions and power constraints, this paper proposes an accurate energy model framework of a linear oil sensor network topology that gives feasible sensors' transmission rates and sensor network topology while always guarantees enough energy. Since the magnetic induction communication channels is employed, we first examine the non-flat MI fading channels to obtain the accurate received signal qualities. Then, we design MI-based modulation and error control coding schemes and evaluate the energy consumption for MI transmissions to determine optimal sensors' transmissions rate and number of sensors while sensors' packet error rate and energy constraints are satisfied at the same time. we confirm the accuracy of our model via theoretical and simulation evaluations.

As a key enabling technology for 5G cellular systems, wireless virtualization allows multiple service providers to simultaneously and independently serve their users via virtualized network slices. However, differently from wired network virtualization that has been studied for many years, the research of wireless resource slicing is still at a very early stage. In this paper, based on the proposed 5G software-defined systems, novel wireless infrastructure-as-a-service (WIaaS) is introduced, which enables mobile virtual network operators to provide distinguished services to their subscribed users while sharing a common physical infrastructure. Specifically, through software-defined networking and fine-grained base station designs, a throughput-efficient resource allocation is proposed, by which, at the same time, (1) the data-rate requirements of traffic flows in virtual networks are fulfilled, (2) the isolation among applications and deployed protocols in networks is guarded, and (3) the global resource utilization is maximized. Simulations confirm that the proposed solution outperforms state-of-the-art schemes with greater system throughput and fairness support. Moreover, the performance improvement becomes significant when transmitted data has real-time requirements or the flow density and diversity are increased. Thus, WIaaS facilitates wireless resource slicing upon software-defined architectures and has opened a new research area of virtualization in next-generation cellular systems.

Millimeter-wave communication is one of the enabling technologies to meet high data-rate requirements of 5G wireless systems. Millimeter-wave systems due large available bandwidth enable gigabit-per-second data rates for line-of-sight (LOS) transmissions in short distances. However, for non-line-of-sight (NLOS) transmissions, millimeter-wave systems suffers performance degradation because the received signal strengths at user equipments (UEs) are not satisfactory. In this paper, the NLOS problem in millimeter-wave systems is treated from SoftAir (a wireless software-defined networking architecture) perspective. In particular, a so-called dynamic base station (BS) formation is introduced, which adaptively coordinates BSs and their multiple antennas to always satisfy UEs' quality-of-service (QoS) requirements in NLOS cases. First, the architecture for software-defined millimeter-wave system is introduced, where remote radio heads (RRHs) coordination is explained and millimeter-wave channel model between RRHs and UEs is analyzed. A ubiquitous millimeter-wave coverage problem is formulated, which jointly optimizes RRH-UE associations and beamforming weights of RRHs to maximize the UE sum-rate while guaranteeing QoS and system-level constraints. After proving the np-hardness of the coverage optimization problem with non-convex constraints, an iterative algorithm is developed for dynamic BS formation that achieves ubiquitous coverage with high data rates in LOS and NLOS cases. Through successive convex approximations, the proposed dynamic BS formation algorithm transforms the original mixed-integer nonlinear programming into a mixed-integer second-order cone programming, which is efficiently solved by convex tools. Simulations validate the efficacy of our solution that completely solves NLOS problem by facilitating ubiquitous coverage in 5G millimeter-wave systems.

Wireless underground sensor networks enable many applications, such as mine and tunnel disaster prevention, oil upstream monitoring, earthquake prediction and landslide detection, and intelligent farming and irrigation among many others. Most applications are location-dependent, so they require precise sensor positions. However, classical localization solutions based on the propagation properties of electromagnetic waves do not function well in underground environments. This paper proposes a magnetic induction (MI)-based localization that accurately and efficiently locates randomly deployed sensors in underground environments by leveraging the multipath fading free nature of MI signals. Specifically, the MI-based localization framework is first proposed based on underground MI channel modeling with additive white Gaussian noise, the designated error function, and semidefinite programming relaxation. Next, this paper proposes a two-step positioning mechanism for obtaining fast and accurate localization results by: first, developing the fast-initial positioning through an alternating direction augmented Lagrangian method for rough sensor locations within a short processing time, and then proposing fine-grained positioning for performing powerful search for optimal location estimations via the conjugate gradient algorithm. Simulations confirm that our solution yields accurate sensor locations with both low and high noise and reveals the fundamental impact of underground environments on the localization performance.

Heavy-tailed (HT) traffic (e.g., the Internet and multimedia traffic) fundamentally challenges the validity of classic scheduling algorithms, designed under conventional light-tailed (LT) assumptions. To address such a challenge, this paper investigates the impact of HT traffic on delay-based maximum weight scheduling (DMWS) algorithms, which have been proven to be throughput-optimal with enhanced delay performance under the LT traffic assumption. First, it is proven that the DMWS policy is not throughput-optimal anymore in the presence of hybrid LT and HT traffic by inducing unbounded queuing delay for LT traffic. Then, to solve the unbounded delay problem, a delay-based maximum power-weight scheduling (DMPWS) policy is proposed that makes scheduling decisions based on queuing delay raised to a certain power. It is shown by the fluid model analysis that DMPWS is throughput-optimal with respect to moment stability by admitting the largest set of traffic rates supportable by the network, while guaranteeing bounded queuing delay for LT traffic. Moreover, a variant of the DMPWS algorithm, namely the IU-DMPWS policy, is proposed, which operates with infrequent queue state updates. It is also shown that compared with DMPWS, the IU-DMPWS policy preserves the throughput optimality with much less signaling overhead, thus expediting its practical implementation.

Scheduling is one of the most important resource allocation for networked systems. Conventional scheduling policies are primarily developed under light-tailed (LT) traffic assumptions. However, recent empirical studies show that heavy-tailed (HT) traffic flows have emerged in a variety of networked systems, such as cellular networks, the Internet, and data centers. The highly bursty nature of HT traffic fundamentally challenges the applicability of the conventional scheduling policies. This paper aims to develop novel throughput-optimal scheduling algorithms under hybrid HT and LT traffic flows, where classic optimal policies (e.g., maximum-weight/backpressure schemes), developed under LT assumption, are not throughput-optimal anymore. To counter this problem, a delay-based maximum-weight scheduling policy with the last-in first-out (LIFO) service discipline, namely LIFO-DMWS, is proposed with the proved throughput optimality under hybrid HT and LT traffic. The throughput optimality of LIFO-DMWS gives that a networked system can support the largest set of incoming traffic flows, while guaranteeing bounded queueing delay to each queue, no matter the queue has HT or LT traffic arrival. Specifically, by exploiting asymptotic queueing analysis, LIFO-DMWS is proved to achieve throughout optimality without requiring any knowledge of traffic statistic information (e.g., the tailness or burstiness of traffic flows). Simulation results validate the derived theories and confirm that LIFO-DMWS achieves bounded delay for all flows under challenging HT environments.

Machine-to-machine (M2M) communication enables many applications such as smart grid, vehicular safety, and health care among many others. To achieve ubiquitous data transportation among objects and the surrounding environment, deploying spectrum sharing M2M communications with existing wireless networks is a must. A general large-scale cognitive M2M network (CM2MN), adopting cognitive radio technology, consists of multiradio systems, the primary system (PS), and secondary system(s) with tremendous cooperative cognitive machines, under heterogeneous wireless architecture. For these CM2MNs, due to dynamic spectrum access (DSA) nature, there exists possibly unidirectional opportunistic wireless fading links and thus traditional flow control mechanisms at link level do not fit anymore. Furthermore, effective end-to-end quality-of-service (QoS) control is still required to provide a reliable transportation for such multihop CM2M communications. Facing the above challenges, we propose a novel statistical QoS control mechanism through cooperative relaying, realizing virtual multiple-input and multiple-output (MIMO) communicationsat session level. In particular, a probabilistic network coded routing algorithm and the statistical QoS guarantee are first proposed to coordinate and cooperate tremendous machines. Next, based on the proposed guarantee and routing algorithm, the statistical QoS control mechanism is designed to enable MIMO communications for the session traffic. Specifically,the diversity modeis used to deal with PS’s opportunistic nature and wireless fading, andthe spatial multiplexing modeis employed to obtain the maximum end-to-end throughput. Simulation results confirm that under our control solution, the great improvements of end-to-end delay violation probability are obtained, thus practically facilitating network coded multipath routing in large CM2MNs.

Deploying spectrum sharing machine-to-machine (M2M) communications with the existing wireless networks achieves ubiquitous data transportation among objects and the surrounding environment to benefit our daily life. However, the lack of schemes to completely characterize M2M network topology, to efficiently share radio resource, and to provide quality-of-service (QoS) guarantee regarding end-to-end delay creates challenges to practically facilitate M2M communications. Via mathematical derivations, the network connectivity, degree distribution, and average distance are provided for large M2M networks. To achieve reliable communications upon such M2M networks, inspired by cognitive radio technology and cooperative communications, acognitive and opportunistic relay(COR) scheme is proposed. Specifically, machines with the proposed COR autonomously sense the primary systems’ spectrum usage so as to mitigate detractive interference and adopt opportunistic forwarder selection for lower link delay of packet transmissions. Furthermore, by analytical deriving the effective capacity of the COR over connected M2M networks, the throughput under statistical QoS guarantee and the corresponding delay violation probability are proposed to specify the QoS guarantee capability of the networks and thus suggest the conditions of dependable end-to-end transmissions. Simulation results confirm that the proposed COR effectively achieves the delay guarantee performance, to yield a novel framework for facilitating reliable M2M communications in large machine networks.

Software-Defined Networking (SDN) has been recognized as the next-generation networking paradigm. It is a fast-evolving technology that decouples the network control plane from the data forwarding plane. A logically centralized controller is responsible for all the control decisions and communication among the forwarding elements. However, current traffic engineering techniques and state-of-the-art routing algorithms do not effectively use the merits of SDNs, such as global centralized visibility, control and data plane decoupling, network management simplification and portability. In this paper, a multi-tenancy management framework is proposed to fulfill the quality-of-services (QoSs) requirements through tenant isolation, prioritization and flow allocation. First, a network virtualization algorithm is provided to isolate and prioritize tenants from different clients. Second, a novel routing scheme, called QoS-aware Virtualization-enabled Routing (QVR), is presented. It combines the proposed virtualization technique and a QoS-aware framework to enable flow allocation with respect to different tenant applications. Simulation results confirm that the proposed QVR algorithm surpasses the conventional algorithms with less traffic congestion and packet delay. This facilitates reliable and efficient data transportation in generalized SDNs. Therefore, it yields to service performance improvement for numerous applications and enhancement of client isolation.

Wireless underground sensor networks (WUSNs) enable many applications such as underground pipeline monitoring, power grid maintenance, mine disaster prevention, and oil upstream monitoring among many others. While the classical electromagnetic waves do not work well in WUSNs, the magnetic induction (MI) propagation technique provides constant channel conditions via small size of antenna coils in the underground environments. In this paper, instead of adopting currently layered protocols approach, a distributed cross-layer protocol design is proposed for MI-based WUSNs. First, a detailed overview is given for different communication functionalities from physical to network layers as well as the QoS requirements of applications. Utilizing the interactions of different layer functionalities, a distributed environment-aware protocol, called DEAP, is then developed to satisfy statistical QoS guarantees and achieve both optimal energy savings and throughput gain concurrently. Simulations confirm that the proposed cross-layer protocol achieves significant energy savings, high throughput efficiency and dependable MI communication for WUSNs.

Cloud based machine-to-machine (M2M) communications have emerged to achieve ubiquitous and autonomous data transportation for future daily life in the cyber-physical world. In light of the need of network characterizations, we analyze the connected M2M network in the machine swarm of geometric random graph topology, including degree distribution, network diameter, and average distance (i.e., hops). Without the need of end-to-end information to escape catastrophic complexity, information dissemination appears an effective way in machine swarm. To fully understand practical data transportation, G/G/1 queuing network model is exploited to obtain average end-to-end delay and maximum achievable system throughput. Furthermore, as real applications may require dependable networking performance across the swarm, quality of service (QoS) along with large network diameter creates a new intellectual challenge. We extend the concept of small-world network to form shortcuts among data aggregators as infrastructure-swarm two-tier heterogeneous network architecture, then leverage the statistical concept of network control instead of precise network optimization, to innovatively achieve QoS guarantees. Simulation results further confirm the proposed heterogeneous network architecture to effectively control delay guarantees in a statistical way and to facilitate a new design paradigm in reliable M2M communications.

To alleviate the spectrum shortage for sensor networks with tremendous sensors, cognitive radio technology enabling multi-hop opportunistic networking and concurrent transmissions overlaying the primary system suggests an attractive facilitation of large-scale wireless sensor networks (WSNs) and machine-to-machine communications. However, subsequent significant end-to-end delay in large WSN can prohibit practical applications. Leveraging the nature of traffic in sensor networks, we develop in-network computation to reduce requisite transmissions and to accommodate more concurrent transmissions under a given spectrum. Specifically, distributed source coding and broadcasting in wireless communication are exploited to build the computational framework and the achievable network capacity is examined. Furthermore, a greedy networking algorithm is adopted to justify significant improvement on end-to-end delay and further statistical QoS guarantee, while yielding considerable system throughput gain for practical deployment of WSNs. Performance evaluations confirm that we successfully demonstrate communication efficiency from in-network computations and facilitate a new paradigm for spectrum efficient cognitive radio networks, which shall be applicable in general multi-hop wireless networks and spectrum-sharing WSNs.

To potentially alleviate the spectrum shortage for sensor networks of tremendous number of nodes, cognitive radio technology, and thus multi-hop opportunistic and concurrent transmissions overlaying with the primary system suggest an attractive facilitation of large-scale sensor networks. However, it is shown to result in significant end-to-end delay to prohibit practical applications. Noting the nature of traffic in sensor networks, with the aid of distributed source coding and broadcasting in wireless communication, we develop in-network computation to reduce requisite transmissions and to accommodate more concurrent transmissions within given spectrum. Without end-to-end table to significantly save control signaling, a greedy networking algorithm schedules traffic among cooperative relay paths and achieves great delay reduction under various communication scenarios. Such in-network computation further suggests a new design paradigm of communication-computation tradeoff in multi-hop cognitive sensor networks and thus machine-to-machine communications.

Cloud-based machine-to-machine communications emerge to facilitate services through linkage between cyber and physical worlds. In addition to great challenges in a large network of machine/sensor swarm, effective network architecture involving interconnection of wireless infrastructure and multi-hop ad hoc networking in the machine swarm remains open. Inspired by the small-world phenomenon in social networks, we may establish a short-cut path under a heterogeneous network architecture through wireless infrastructure and cloud, by connecting to data aggregators or access points in the machine swarm, such that end-to-end delay can be significantly reduced. Our mathematical analysis on network diameter and average delay, along with verifications by simulations, demonstrate spectral and energy efficiency of our proposed heterogeneous network architecture in large machine-to-machine communication networks.

Cognitive radio (CR) emerges as a key technology to enhance spectrum efficiency and thus creates opportunistic transmissions over links. Supporting the routing function on top of numerous opportunistic links is a must to route packets in a general cognitive radio network (CRN) consisting of multi-radio systems. However, there lacks complete understanding of these highly dynamic available links and a reliable end-to-end transportation mechanism over CRN. Aspiring to meet this need, we propose novel spectrum aware opportunistic routing (SAOR) algorithm suited for the CRN under wireless fading channels. With innovative establishment of the spectrum map from local sensing information and the derivation of the routing metric for opportunistic links known as opportunistic link transmission (OLT), the opportunistic path metrics, and the CR node metrics, the promising SAOR employs a cooperative scheme to enable multi-path transmissions and maintains the statistical QoS guaranteed throughput for practical applications. Numerical results confirm that SAOR enjoys less delay with guaranteed throughput, not only in CRN, but also in general wireless network.