Without network slicing supports, traditional Fronthaul architectures struggle to meet the demanding requirements of 5G networks, such as the ultra-low latency and high bit rate specified by the enhanced common public radio interface (eCPRI). In this paper, we design a novel Fronthaul architecture that leverages FlexEthernet (FlexE) over elastic optical network (EON) to enable Fronthaul slicing meeting 5G Fronthaul requirements. Our Fronthaul design is optimized by an integer linear programming (ILP) model, named eFFP, that minimizes the total cost of ownership (TCO). While eFFP meets the strict Fronthaul requirements by provisioning network resources based on worst-case traffic load, it tends to overestimate required bit rate as a result of the inherent uncertainty and variability in real-world traffic. To tackle this challenge, we introduce uFFP, a stochastic Fronthaul provisioning strategy tailored to accommodate uncertain traffic demands and mitigate expenditure wastage. Relying on historical data, uFFP assesses statistical characteristics of traffic patterns to better estimate Fronthaul bit rate. Subsequently, we employ chance-constrained optimization to reformulate the uFFP problem, which is approximately solved using a convex relaxation approach known as uFFPA, and optimally solved using a deep reinforcement learning (DRL) approach called uFFPL. Simulation results demonstrate that our proposed solutions achieve significant cost savings, reducing TCO by 39.79% compared to the baseline.

Network slicing, a key technology for 6G communications, allows diverse services to coexist on a shared physical infrastructure by allocating different resources to virtual networks (VNs, or equivalently, “network slices”) mapped over the shared infrastructure. However, it presents challenges in terms of failure survivability, as the failure of one physical element can lead to the failure of multiple VNs mapped to it, making survivability of ultra-reliable services against multiple failures a crucial research topic. In this study, we investigate the Survivable Virtual Network Mapping (SVNM) problem, focusing on double-link failures. SVNM against double-link failures can be guaranteed by enforcing appropriate SVNM constraints (e.g., any double-link failure cannot disconnect any virtual node from other virtual nodes in the same VN), but this approach requires excessive redundant capacity deployment. To address this issue, we propose a novel technique called SVNM with Inter-VN Capacity Sharing (SINC), which allows capacity sharing across different VNs to improve survivability against double-link failures with efficient spare capacity utilization. Since SINC may fail to reconnect some VNs due to insufficient spare capacity, we propose combining it with a spare slice (a VN fully dedicated to enhancing survivability) to create an advanced version, SINC+, which improves survivability by reconnecting VNs with additional spare capacity. We then formulate both SINC and SINC+ through Integer Linear Programming (ILP) models, which can provide optimal solutions. Moreover, to address the computational limitations of the ILP formulation, we developed scalable heuristic algorithms applicable to both SINC and SINC+ with a small optimality gap. Our numerical results show that VN availability using SINC improves by up to 9.48% over SVNM, with the same total link resource consumption (TLRC). Furthermore, SINC+ ensures VN survivability against all potential double-link failures, and the additional TLRC can be reduced to less than 1% in presence of a high number of VNs or large nodal connectivity, underscoring the sustainability of our proposed solutions.

In spectrum pooling, which is a well-known technique of spectrum sharing, the initial licensed spectrum of each Mobile Network Operator (MNO) is partitioned into reserved and shared spectrum. The reserved spectrum is for the personal use of an MNO, and the shared spectrum of all MNOs constitutes a spectrum pool that can be flexibly utilized by MNOs that require extra spectrum. Nevertheless, the spectrum pool management problem substantially impacts the spectrum efficiency among these MNOs. In this paper, we formulate this problem as a non-linear programming problem that strives to maximize the average binary scale satisfaction (BSS) of MNOs. To achieve this objective, we introduce an event-driven deep reinforcement learning-based spectrum management scheme, termed EDRL-SMS. This approach adopts a spectrum pool manager (SPM) to efficiently supervise the spectrum pool to reach long-term optimization of network performance. The SPM smartly allocates spectrum resources by fully utilizing a DRL approach, Deep Deterministic Policy Gradient, for each stochastic arrival spectrum request event. The simulation results show that the average BSS of MNOs of the proposed EDRL-SMS significantly outperform our previous work, Bankruptcy Game-based Resource Allocation (BGRA), greedy, random, and without sharing schemes.

As smart cities evolve, rising computational demands strain infrastructures. Offloading tasks to edge cloud data centers offers potential but faces challenges like high latency, energy use, and data leakage, especially in dense urban areas. This paper presents a low-latency, energy-efficient digital twin (DT) architecture tailored for smart cities, integrating edge computing (EC) and multiple s (IRS) to enhance communication. Dynamic voltage and frequency scaling (DVFS) technology is considered for user devices to reduce energy consumption. To mitigate the risk of user privacy leakage during task offloading, we address sensitive user location data that may be exposed by proposing a perturbed sliding task queue (PSTQ) algorithm based on differential privacy (DP), and demonstrate the effectiveness of the algorithm. To optimize task processing time and energy efficiency, we decompose the complex problem using block coordinate descent and propose an intelligent scheduling for energy sustainability (ISES) algorithm based on Karush-Kuhn-Tucker conditions and deep reinforcement learning (DRL). Experimental results demonstrate that our proposed architecture and algorithms achieve over 90% improvement in key optimization objectives, alleviating the computational pressure on existing devices while significantly enhancing task processing efficiency and energy sustainability.

Hierarchical federated learning (HFL) is a privacy-preserving distributed machine learning framework with a client-edge-cloud hierarchy, where multiple edge servers perform partial model aggregation to reduce costly communication with the cloud server. Nevertheless, most existing HFL methods require extensive iterative communication and public datasets, which not only increase communication overhead but also raise privacy and security concerns. Moreover, non-independent and identically distributed (non-IID) data among devices can significantly impact the accuracy of the global model in HFL. To address these challenges, we propose a multi-level one-shot HFL framework (MOHFL), which aims to improve the performance of the global model in a single communication round. Specifically, we employ conditional variational autoencoders (CVAEs) as local models and use the aggregated decoders to generate an IID training set for the global model, thereby mitigating the negative impact of non-IID data. We improve the performance of CVAEs under different levels of data heterogeneity through a dominant class-based data selection method. Subsequently, an edge aggregation scheme based on multi-teacher knowledge distillation and contrastive learning is proposed to aggregate the knowledge from local decoders to edge decoders. Extensive experiments on four real-world datasets demonstrate that MOHFL is very competitive against four state-of-the-art baselines under various settings.

Unmanned Aerial Vehicles (UAVs) offer the immense capability for allowing novel applications in a variety of domains including security, military, surveillance, medicine, and traffic monitoring. The prevalence of UAV systems is due to the collaboration and accomplishment of tasks efficiently and effectively. UAVs embedded with camcorders, GPS receivers, and wireless sensors propose enormous promise in realizing the Internet of Things (IoT) service delivery in vast domains. It results in establishing an airborne field of the IoT when empowered with communication protocols of LTE, 4G, and 5G/6G networks. However, numerous difficulties must be addressed before UAVs may be used effectively namely privacy, security, and administration. Conspicuously, in the current article, novel UAV-specific domains enabled by IoT and 5G/6G technology are explored. Moreover, the presented technique assesses sensor requirements and provides an overview of fleet management systems that address aerial networking, privacy, and security concerns. Furthermore, a framework based on the IoT-5G/6G aspect is proposed which can be deployed over UAVs. Finally, in a heterogeneous computational platform, the proposed framework provides a complete IoT architecture that enables secure UAVs.

We study TDMA-scheduled Software Defined Wireless Multihop Networks (SDWMNs), whereby the data traffic and SDN control messages share the same network links and TDMA resources. Since the topology of WMNs dynamically changes, maintaining a responsive SDN plane is essential for meeting data traffic rate requirements. Placing more SDN controllers reduces communication delays at the SDN layer and increases its responsiveness. However, it demands more TDMA resources and reduces the available ones for data traffic. We analyze this trade-off between data traffic performance and SDN layer responsiveness by delving into two distinct resource allocation mechanisms in the WMN, the SDN controller placement and TDMA scheduling. We capture their interaction into an optimization problem formulation, which aims at maximizing the SDN-responsiveness subject to data traffic rate requirements, topology conditions, and the available TDMA resources. We propose a novel heuristic for the hard-to-solve problem that leverages the network state information gathered at the SDN layer. We find that our heuristic can increase the SDN-responsiveness by 44% when varying the rate reserved for rate-elastic data traffic within 40% of what is nominally requested. The heuristic is modular in accommodating different controller placement algorithms and robust to different alternative for the SDN software implementation.

Future advancements in data centers are anticipated to incorporate advanced circuit switching technologies, especially optical switching, which achieve high transmission capacity and energy efficiency. Previous studies addressed a Clos-network design problem to guarantee an admissible blocking probability to maximize the switching capacity, which is defined by the number of terminals connected to the network. However, as the number of available <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N} \times {N}$ </tex-math></inline-formula> switches increases, the switching capacity no longer increases due to the switch port limitation. This paper proposes a design of a multiple-plane twisted-folded (TF) Clos network, named MP-TF, to enhance the switching capacity, which is limited by the original TF-Clos, by guaranteeing an admissible blocking probability. MP-TF consists of identical M TF-Clos planes and pairs of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times {M}$ </tex-math></inline-formula> selector and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M} \times 1$ </tex-math></inline-formula> selector, each pair of which is associated with a transmitter and receiver pair. We formulate a design model of MP-TF as an optimization problem to maximize the switching capacity. We introduce connection admission control in MP-TF, named MP-CAC. We derive the theorem that the MP-TF design model using MP-CAC guarantees the admissible blocking probability. Numerical results observe that MP-TF increases the switching capacity as the number of TF-Clos planes when available <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N} \times {N}$ </tex-math></inline-formula> switches are sufficient; for example, with seven planes, the switching capacity is 1.97 times larger than that of one plane, given a request active probability of 0.6 and an admissible blocking probability of 0.01. We find that the computation time for MP-TF diminishes with an increase in the number of TF-Clos planes. Designing MP-TF is similar to designing a single TF-Clos plane, differing mainly in the handling of connection admission control. With a larger number of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N} \times {N}$ </tex-math></inline-formula> switches, MP-TF enables the design of a smaller TF-Clos plane. We provide the analyses of optical power management and network cost of MP-TF.

Android malware poses a significant challenge for mobile platforms. To evade detection, contemporary malware variants use API substitution or obfuscation techniques to hide malicious activities and mask their shallow semantic characteristics. However, existing research lacks analysis of the hierarchical semantic associated with Android apps. To address this problem, we propose ArchSentry, an enhanced Android malware detection via hierarchical semantic extraction. First, we select entities and their relationships relevant to Android software behavior through the software architecture and represent them using a heterogeneous graph. Then, we structure meta-paths to represent rich semantic information to achieve semantic enhancement and improve efficiency. Next, we design a meta-path semantic selection method based on KL Divergence to identify and eliminate redundant features. To achieve a comprehensive representation of the overall software semantics and improve performance, we construct a feature fusion approach based on Restricted Boltzmann Machines (RBM) and AutoEncoder (AE) during the pre-training phase, while preserving the probability distribution characteristics of various meta-paths. Finally, Deep Neural Networks (DNN) process fusion features for comprehensive feature sets. Experimental results on real-world application samples indicate that ArchSentry achieves a remarkable 99.2% detection rate for Android malware, with a low false positive rate below 1%. These results surpass the performance of current state-of-the-art approaches.

A longstanding problem in the context of computer networks is the issue of recovery from link failures. A promising solution to safeguard all single link failures (SLF) is through partial up gradation of certain IP routers (Internet Protocol(IP)) to Software Defined Networking (SDN) switches using CSA (Candidate Selection Algorithms). The resultant network comprises a combination of IP routers and SDN switches, forming a hybrid IP/SDN network. Most of the existing approaches primarily address SLFs, yet real-world scenarios may involve multiple link failures (MLF). Hence, it’s crucial to investigate CSA for Hybrid IP/SDN with MLF. In this paper, we first introduce four CSA strategies tailored for hybrid IP/SDN networks with MLF. Subsequently, we present a CSA designed to accommodate network scaling or expansion. We emphasize the advantage of CSAs based on non-shortest path approach. Additionally, we propose a budget constraint-based CSA to aid in selecting suitable candidate switches within a specified budget. We evaluate the performance of hybrid IP/SDN networks employing the proposed CSAs, considering average repair path length (AMLU) and average maximum link utilization (AMLU) in the post-recovery network across diverse real-world network topologies. It will be shown that the hybrid IP/SDN with proposed CSA for MLFs will reduce the ARPL by 31.37%, 22.15%, and 18.64%, respectively, for NSF, Attmpls, and Abilene networks.