Dynamic resource allocation in O-RAN is critical for managing the conflicting QoS requirements of 6G network slices. Conventional reinforcement learning agents often fail in this domain, as their unimodal policy structures cannot model the multi-modal nature of optimal allocation strategies. This paper introduces Diffusion Q-Learning (Diffusion-QL), a novel framework that represents the policy as a conditional diffusion model. Our approach generates resource allocation actions by iteratively reversing a noising process, with each step guided by the gradient of a learned Q-function. This method enables the policy to learn and sample from the complex distribution of near-optimal actions. Simulations demonstrate that the Diffusion-QL approach consistently outperforms state-of-the-art DRL baselines, offering a robust solution for the intricate resource management challenges in next-generation wireless networks.

The proliferation of resource-constrained devices in Internet of Things (IoT) environments has amplified the demand for scalable, secure, and efficient cryptographic services. While Encryption-as-a-Service (EaaS) models enable offloading cryptographic tasks to trusted infrastructure, critical challenges remain regarding path integrity, trust management, and resilience to adversarial threats in multi-domain networks. This paper introduces EaaS/PIN, a unified framework that combines cryptographically verifiable path integrity, user-centric trust scoring, collaborative threat intelligence, and machine learning-driven path selection across distributed Autonomous Systems (ASs). The framework integrates: (i) a novel anonymity protocol to conceal complete routes from intermediary ASs, (ii) lightweight, customizable encryption suitable for IoT and edge environments, (iii) real-time, AI-based path recommendation leveraging dynamic trust and performance metrics, and (iv) a blockchain-inspired audit mechanism for tamper-evident reporting and accountability. Comprehensive mathematical modeling, algorithms, and a detailed case study focused on secure data transmission in a multi-AS smart city network demonstrate that EaaS/PIN significantly enhances routing security, reduces latency, and ensures transparent and verifiable operations even under adversarial conditions. Experimental results confirm robust detection of path manipulation and compromised ASs, as well as measurable performance gains over baseline solutions. The proposed framework paves the way for scalable, user-aware, and resilient cryptographic services in next-generation heterogeneous network infrastructures.

With the evolution of sixth generation (6G) technologies and Internet of Things (IoT), base stations and IoT devices are deployed densely to achieve the ultra-high data rate, resulting in the scarcity of spectrum resource. To tackle it, we study a cell-free symbiotic radio network (CF-SRN) that consists of the cell-free network (CFN) and IoT network, and includes multiple access points (APs), multiple backscatter devices (BDs), and a single receiver. APs collaboratively transmit primary radio frequency (RF) signals to the receiver, and BDs split the energy of primary RF signals to perform backscatter communication, and energy harvesting. Existing works focus on the SRN with commensal symbiotic radio (CSR) or parasitic symbiotic radio (PSR) setup, while we design a hybrid CSR-PSR setup to balance the tradeoff between primary communication and backscatter communication in the CF-SRN. Based on the design, we formulate the sum backscatter throughput maximization problem by optimizing the time allocation vector, beamforming vectors of APs and BDs, and reflection coefficients of BDs, subject to the minimum sum primary throughput constraint. Due to the coupling relationship among high-dimensional variables, we decompose the formulated problem into time allocation optimization (TAO) subproblem, beamforming optimization (BO) subproblem, and reflection coefficient optimization (RCO) subproblem. For TAO subproblem, we use a linear programming method to obtain the optimal solution. For BO subproblem and RCO subproblem, we propose a block coordinate descent-based semi-definite relaxation and successive convex approximation (BSS) algorithm. Simulation results validate the superiority of the BSS algorithm and hybrid CSR-PSR setup.

This paper investigates a dynamic heterogeneous mobile edge computing network (HMECN), where mobile devices (MDs) could offload their full tasks to a small base station (SBS) directly or the macro base station (MBS) in direct or relay mode. As age of information (AoI) is a comprehensive and accurate metric to capture the freshness of computation results, we formulate a long-term weighted sum AoI (LWSA) minimization problem in the HMECN by jointly optimizing the offloading decisions of MDs as well as the bandwidth and computation resource allocation of all base stations, subject to energy, delay and peak AoI constraints. To address the formulated non-convex mixed integer nonlinear programming problem, we decompose it into the offloading decision optimization (ODO) top-problem and the resource allocation optimization (RAO) sub-problem. Based on the decomposition, we propose a federated learning (FL)-assisted hybrid DRL and convex approach that is comprised of a safe multi-agent DRL algorithm, convex optimization and FL. The ODO top-problem is solved by the safe multi-agent DRL algorithm, which strictly ensures that the actions of each agent do not exceed its energy constraint and then paves the way for using convex optimization to solve the RAO sub-problem. FL is used to alleviate the training instability problem aggravated by multi agent settings via breaking the limitation of partial knowledge for each individual agent. Simulation results demonstrate the superiority of the proposed approach in terms of the LWSA, convergence, scalability and robustness in dynamic environments.

Benefiting from smartphones' powerful computing and sensing capabilities, biometric authentication is widely applied to them for conveniently verifying users' identities. However, most biometric features can be easily acquired or reproduced, making them vulnerable to replay and impersonation attacks. To address this issue, we propose DBreathLock, a non-contact deep breath-based authentication system that utilizes a smartphone emitting inaudible frequency-modulated continuous waves (FMCW)-based sonar signals and synchronously records breath sounds and sonar echoes of chest-abdominal-joint (C-A-joint) movements. Then, we implement a dual-protection barrier to defend against advanced replay attacks (ARAs). First, by analyzing the energy features of C-A-joint movements, we develop a Deep Breath Activity Detection method to detect deep breath fragments alongside the capability of resisting ARAs. Second, we take C-A-joint movements and smartphone vibrations caused by holding a smartphone as features and design a liveness detection mechanism to further fortify the resistance to ARAs. Furthermore, a multi-stream identity authentication model is designed to verify legitimate users by fusing biometric features from C-A-joint movements, deep breath sounds, and correlation sequences of both. Extensive real-world experiments with 40 users demonstrate DBreathLock's authentication accuracy of 95.97%. Additionally, it successfully defends against advanced replay, impersonation, simple hybrid, and advanced hybrid attacks, achieving the AUC of 0.9792, and FPRs of 2.17%, 2%, and 4.17%, respectively.

For the computation offloading via device-to-device (D2D) terminals and edge servers in a resource-constrained wireless network (RCWN), mobile users can choose to offload their tasks to nearby D2D terminals or edge servers according to quality of service (QoS) requirements (e.g., load balancing at the network edge) by mobile edge computing. To this end, we first formulate computation offloading as a multi-user collaborative resource dynamic management optimization problem that aims to maximize user satisfaction utility function, carefully considering critical issues like the non-uniform distribution of computational resources, user's risk awareness, and the dynamic changes between computing-intensive regions and computing-sparse regions. This is a nonlinear and nonconvex optimization problem, which is generally difficult to be solved. We then construct a resource management scheme for resource allocation of the edge server based on convex optimization. Furthermore, we propose a dynamic offloading update strategy achieving the maximum of user satisfaction utility function based on game theory. The simulation results are presented to show that our proposed method can increase the total system satisfaction utility by nearly 20% and reduce the system energy consumption by nearly 10% compared to the benchmark methods.

Space-air-ground integrated network (SAGIN) represents a pivotal architecture for the future evolution of global mobile communications. However, its inherent high dynamics and stochastic nature pose significant challenges to conventional routing mechanisms. Moreover, the vast spatial-scale openness of SAGIN makes it particularly vulnerable to eavesdropping attacks. This paper presents a novel sleeping multi-armed bandit (SMAB) framework, designed to enable secure routing in SAGIN. Specifically, we first establish channel models for all types of links in SAGIN. Then, we theoretically analyze the statistical properties of secrecy capacity and end-to-end (E2E) delay for message transmission over arbitrary routes, and formulate the secure routing problem to maximize cumulative secure transmission throughput under the delay constraint. The uncertainty in the network state of SAGIN, along with the complexity of the optimization objective and constraint (non-convex, non-linear, and coupled), renders the solution to the secure routing problem highly intractable. To this end, we leverage the MAB model to transform the secure routing problem into a budget-constrained arm-pulling problem and introduce the “sleeping” mode to capture route unavailability due to intermittent link failures. To effectively balance route exploration and exploitation, we further apply the upper confidence bound (UCB) method to design the SMAB-based secure routing algorithm (SMAB-SR), and derive its regret upper bound theoretically. Finally, extensive simulations verify that the SMAB-SR algorithm exhibits significant advantages in E2E secure transmission throughput compared to benchmarks and can maintain highly effective across various SAGIN configurations.

Computing Power Networks (CPNs) represent an innovative, collaborative architecture that integrates resources via the communication network, optimizing resource allocation to support service demands. Due to the increased need for services powered by artificial intelligence across various domains, CPNs are increasingly required to allocate users efficiently to appropriate servers to meet the low-latency needs of service computing. However, challenges such as users' dynamic mobility, weak communication paths, and high-dimensional solution spaces persist in optimizing user allocation in CPNs. In this context, we propose a diffusion-driven optimization approach for mobility-aware user allocation. To tackle the challenge of users' dynamic mobility, we adopt a user location prediction approach incorporating the users' movement patterns to forecast future movement, calledCAMPE. To tackle the challenge of weak communication paths, we establish the new transmission path by reconfigurable intelligent surface and enhance the quality of the communication link by adjusting the phase configurations. Moreover, faced with the challenge of high-dimensional solution spaces associated with phase adjustment and user allocation decisions, we devise an action-generation strategy based on diffusion models namedDiffUser. This approach motivates the generation of optimal solutions even in complex and dynamic environments. Finally, we conduct extensive simulations in user location prediction and system latency optimization. Compared with other solutions, the superiority of our approach has been demonstrated.

Collaboration opportunities for devices are facilitated with Federated Learning (FL). Edge computing facilitates aggregation at edge and reduces latency. To deal with model poisoning attacks, model-based outlier detection mechanisms may not operate efficiently with hetereogenous models or in recognition of complex attacks. This paper fosters the defense line against model poisoning attack by exploiting device-level traffic analysis to anticipate the reliability of participants. FL is empowered with a topology mutation strategy, as a Moving Target Defence (MTD) strategy to dynamically change the participants in learning. Based on the adoption of recurrent neural networks for time-series analysis of traffic and a 6G wireless model, optimization framework for MTD strategy is given. A deep reinforcement mechanism is provided to optimize topology mutation in adaption with the anticipated Byzantine status of devices and the communication channel capabilities at devices. For a DDoS attack detection application and under Botnet attack at devices level, results illustrate acceptable malicious models exclusion and improvement in recognition time and accuracy.

Generative Diffusion Models (GDMs) have emerged as key components of Generative Artificial Intelligence (GenAI), offering unparalleled expressiveness and controllability for complex data generation tasks. However, their deployment in real-time and mobile environments remains challenging due to the iterative and resource-intensive nature of the inference process. Addressing these challenges, this paper introduces a unified optimization framework that jointly tackles service placement and multiple access control for GDMs in mobile edge networks. We propose LEARN-GDM, a Deep Reinforcement Learning-based algorithm that dynamically partitions denoising blocks across heterogeneous edge nodes, while accounting for latent transmission costs and enabling adaptive reduction of inference steps. Our approach integrates a greedy multiple access scheme with a Double and Dueling Deep Q-Learning (D3QL)-based service placement, allowing for scalable, adaptable, and resource-efficient operation under stringent quality of service requirements. Simulations demonstrate the superior performance of the proposed framework in terms of scalability and latency resilience compared to conventional monolithic and fixed chain-length placement strategies. This work advances the state of the art in edge-enabled GenAI by offering an adaptable solution for GDM services orchestration, paving the way for future extensions toward semantic networking and co-inference across distributed environments.

Network slices face challenges in supporting dynamic requests from user equipments (UEs) due to the resource constraints at the edge devices. This may result in potential anomalies due to resource unavailability or latency spikes. Existing schemes are hard to apply due to the lack of flexible steering of UE requests and multiplexed provisioning strategies. In this paper, we present a novel request provisioning model and propose a traffic steering framework to prevent anomalies and reduce the cost of serving normal UEs. Specifically, we categorize the UE requests into stateful and stateless types and utilize a multi-route resource provisioning strategy to address the UE anomalies. Additionally, the framework incorporates bandwidth-aware route selection and load balancing across multiple routes to improve the service bandwidth for UEs. Simulation results demonstrate that the proposed framework effectively reduces both the number of anomaly UEs and cost compared to existing baselines.

Bringing flexibility and scalability to 5G networks has expanded networking technology to facilitate the split of service into microservices and how they can communicate. The network layer dedicated to this communication is called service mesh, and it has become a new target for cyber adversaries. The existing service mesh infrastructures, such as Istio and NGINX, apply the mutual TLS (mTLS) protocol to the connections in the service mesh layer to protect the confidentiality of the data transferred in this layer. However, the main challenge of implementing mTLS is its resource restriction, which significantly conflicts with the scalability and flexibility goals. Therefore, this paper proposes an Encryption as a Service (EaaS) framework that can be implemented on Kubernetes, mitigating man-in-themiddle, (distributed) denial of service, and eavesdropping attacks against service mesh. The implementation results show that the proposed framework decreases the adversary's success rate by at least 45% compared to the cases of having microservices apply the cryptographic processes by themselves.

To address the challenges of small target features being less prominent, susceptibility to background interference, and sample imbalance in road traffic sign detection, which leads to insufficient model detection accuracy, as well as the high complexity of current object detection models that struggle to operate efficiently on resource-constrained edge devices, we propose a traffic sign detection model based on GCA-YOLO. By adding small target detection layers and removing large target detection layers, the model enhances its small target detection capabilities and reduces its parameter size. The introduction of the T-BiFPN (Tiny-BiFPN) structure improves multi-scale feature fusion, while the C2f-CP module increases computational efficiency on edge devices. The GCA (Global Coordinate Attention) mechanism enhances feature extraction, and the Focaler-CIoU loss function enables the model to focus more on difficult samples and accelerate the convergence of bounding boxes. Experimental results show the superiorities of the proposed GCA-YOLO that compared to YOLOv8n, GCA-YOLO improves precision, recall, mAP@50, and mAP@50:95 by 8.6%, 6.1%, 8.7%, and 6.2%, respectively, while reducing the model's parameter count and size by 38.57% and 33.21%, respectively.

The explosive growth of user devices and emerging applications is driving unprecedented traffic demands, accompanied by stringent Quality of Service (QoS) requirements. Addressing these challenges necessitates innovative service orchestration methods capable of seamless integration across the edge-cloud continuum. Terrestrial network-based service orchestration methods struggle to deliver timely responses to growing traffic demands or support users with poor or lack of access to terrestrial infrastructure. Exploiting both aerial and terrestrial resources in service composition increases coverage and facilitates the use of full computing and communication potentials. This paper proposes a service placement and composition mechanism for integrated aerial-terrestrial networks over the edge-cloud continuum while considering the dynamic nature of the network. The service function placement and service orchestration are modeled in an optimization framework. Considering the dynamicity, the Aerial Base Station (ABS) trajectory might not be deterministic, and their mobility pattern might not be known as assumed knowledge. Also, service requests can traverse through access nodes due to users' mobility. By incorporating predictive algorithms, including Deep Reinforcement Learning (DRL) approaches, the proposed method predicts ABS locations and service requests. Subsequently, a heuristic isomorphic graph matching approach is proposed to enable efficient, latency-aware service orchestration. Simulation results demonstrate the efficiency of the proposed prediction and service composition schemes in terms of accuracy, cost optimization, scalability, and responsiveness, ensuring timely and reliable service delivery under diverse network conditions.

Zero Trust (ZT) is poised as a promising paradigm to effectively deal with the envisioned security risks of cloud-native 5G and beyond (B5G) architectures. However, integrating a ZT security model into B5G is still in its nascent stages, with most proposals remaining largely theoretical or limited to a single domain. This paper presents THAALOUB, a novel ZT framework that empowers 3GPP-compliant, end-to-end ZT security in cloud-native B5G networks. The framework leverages the advanced security features of Service Mesh and Container Network Interface (CNI) technologies to enable a multi-layered ZT microsegmentation security model. Moreover, it adopts an intent-based access control approach to foster proactive ZT security management. The experimental results show THAALOUB's high effectiveness in enhancing B5G security stance with minimal impact on latency and resource usage.

With the rapid evolution towards Beyond 5G and future 6G networks, multi-access edge computing (MEC)-enabled wireless networks are expected to support massive device connectivity, ultra-low latency, and high network capacity. However, meeting these stringent requirements in multi-server wireless multihop networks essentially requires the joint orchestration of server selection, multihop routing, and interference management. This paper develops a novel three-stage optimization scheme named broad learning system with Q-learning (BLSQ), consisting of a broad learning system-based server allocation stage, a signal-to-interference-plus-noise ratio-driven Q-learning-based multihop path selection stage, and a consensus transmit power control stage for adaptive interference mitigation. Furthermore, a consensus transmit power control mechanism is incorporated to adaptively adjust the transmit power of user devices, aiming to balance interference mitigation and throughput enhancement. The proposed scheme is particularly suitable for various mission-critical and dynamic scenarios, such as emergency communication in disaster-stricken areas, multihop data exchange between rescue teams and command centers, and flexible network deployment in large-scale events using unmanned aerial vehicles. Extensive simulation results demonstrate that the proposed BLSQ schemes outperforms existing related approaches in terms of network capacity, task completion time, interference management, and quality of servers, validating the superiority and robustness of our design for future MEC-enabled wireless networks.

This article investigates covert communications in a multirelay Internet of Things (IoT) system with multiple energy harvesting jammers, where a transmitter (Alice) attempts to covertly transmit confidential messages to its destination (Bob) through relay forwarding, while a warden (Willie) detects the existence of Alice’s transmission. Specifically, we employ a harvesting-then-jamming protocol with which the jammers first harvest energy from Alice and then send jamming signals to interfere with Willie’s detection. We propose a relay and jammer selection strategy, namely quality of service (QoS)-aware selection, and use the random selection strategy as a comparison strategy. Under these two selection strategies, we derive the optimal detection threshold and minimum detection error probability at Willie, respectively. We then model the covert throughput performance and obtain the maximum covert throughput by jointly optimizing covert transmit power and jamming transmit power. Extensive numerical results are provided to illustrate the impacts of system parameters on covert throughput performance.

In the light of the growing connectivity and sensitivity of industrial data, cyberattacks and data breaches are becoming more common in the Industrial Internet of Things (IIoT). To cope with such threats, this study presents an anomaly detection system based on a novel Federated Learning (FL) framework. This system detects anomalies such as cyberattacks and protects industrial data privacy by processing data locally and training anomaly detection models on industrial agents without sharing raw data. The proposed FL framework incorporates two key components to enhance both privacy and efficiency. The first component is Homomorphic Encryption (HE), which is integrated into the framework to further protect sensitive data transmissions such as model parameters. HE enhances privacy in FL by preventing adversaries from inferring private industrial data through attacks, such as model inversion attacks. The second component is an innovative dynamic agent selection scheme, wherein a selection threshold is calculated based on agent delays and data size. The purpose of this new scheme is to mitigate the straggler effect and the communication bottleneck that occur in traditional FL architectures, such as synchronous and asynchronous architectures. It ensures that agents are not unfairly selected by the different delays resulting from heterogeneous data in IIoT environments, while simultaneously improving model performance and convergence speed. The proposed framework exhibits superior performance over baseline approaches in terms of accuracy, precision, F1-scores, communication costs, convergence speeds, and fairness rate.

Wireless edge networks (WENTs) can provide edge services to support various time-critical Internet of Things (IoT) applications, like autonomous vehicles, where cache content updates are significant to maintaining information freshness quantified as Information of Age (AoI). However, frequent content updates result in high energy consumption at the edge nodes. This article investigates the cache content updates in WENTs, aiming to ensure information freshness and low energy consumption. To this end, we propose a rainbow deep reinforcement learning-based cache content update scheme (RB-DRN). In the RB-DRN scheme, we first establish a Markov decision process (MDP) to characterize the process of cache update. By fully taking advantage of R-Learning empowered Rainbow DQN, we then make optimal strategy to obtain the minimum long-term average overhead associated with energy consumption and information freshness. Extensive simulation results are presented to validate our proposed RB-DRN scheme and also to illustrate that our RB-DRN scheme outperforms the benchmark scheme in terms of information freshness and energy consumption.

In vehicular edge computing networks (VECNs), service migration among edge servers is critical to addressing the challenge of service interruption caused by high mobility of vehicles and limited coverage of each edge server. In this article, we tackle this challenge by optimizing service migration among edge servers through a joint management of resource scheduling and dynamic server selection. Specifically, we aim to minimize system overhead consisting of system time and energy consumption taking account for resource scheduling and dynamic server selection, which is formulated as a constrained optimization problem. To solve this optimization problem, we propose a learning-driven joint resource scheduling and dynamic server selection strategy (LD-JRS3) based on deep reinforcement learning. Under the LD-JRS3 strategy, we first model joint resource scheduling and dynamic server selection as a Markov decision process (MDP). Then, we adopt a recurrent neural network (RNN)-empowered feedback mechanism based on historical information to achieve the optimal system performance. We fully consider the advantages of the soft actor-critic (SAC) algorithm to obtain the optimal decision (i.e., computational resources allocation and servers selection). Notably, we employ an improved SAC algorithm, which takes into account prioritized experience replay and automatic tuning of temperature parameters. Extensive simulation results are presented to verify the effectiveness of our proposed LD-JRS3 algorithm, and also to illustrate the advantage of our algorithm on improving the time consumption and energy consumption compared with the baseline schemes. LD-JRS3 has 19%, 24%, and 11% higher utility values than DDRN, DQN-based, and multiarmed bandit-based systems, respectively.

Distributed Denial of Service (DDoS) attacks are among the most destructive and challenging threats to mitigate for computer networks, particularly in edge IoT environments. Moving Target Defense (MTD) is a promising security mechanism that undermines the adversary’s gathered information by dynamically altering the attack surface. A selection of network nodes is chosen for mutation, and these changes hinder the adversary from achieving their objectives. However, identifying the optimal set of nodes for effectively and efficiently mitigating a DDoS attack remains a significant challenge. Existing MTD approaches have only considered a single factor—either the node’s vulnerability level or connectivity—and often lack generality and scalability for real-world IoT implementations. In this paper, we propose an enhanced MTD approach called CVbMA (Connection- and Vulnerability-based MTD Approach) that jointly considers both the vulnerability levels and connection weights of nodes to inform mutation strategies. To ensure practical applicability and adaptability, we develop a cost-aware Reinforcement Learning (RL) framework that incorporates explicit mutation costs into the reward function and utilizes neural ranking and model compression for scalability. Extensive evaluations are conducted using both Mininet-based simulations and a physical IoT testbed with real attack traces and heterogeneous devices. Comprehensive benchmarking and ablation studies against state-of-the-art MTD baselines demonstrate that the proposed framework significantly reduces the adversary’s success rate and incidents of server crashes, while maintaining low overhead and achieving high adaptivity. A detailed analysis of real-world deployments highlights the robustness of systems under operational constraints, including fluctuating latency, hardware diversity, and asynchronous events. Limitations and future enhancements, including topology-aware RL, adaptive mutation scheduling, and continuous model updates, are discussed. The results affirm the practical, scalable, and robust potential of cost-sensitive RL-based MTD for next-generation IoT security.

Concept-cognitive learning (CCL) is a paradigm that simulates human concept learning by processing given cues through specific cognitive models. However, existing CCL models face significant limitations, such as weak correlations between attributes and decisions, high redundancy within the concept space, and suboptimal learning performance. To address these issues, this article introduces an Attribute Fluctuation-Based CCL (AFFCCL) model. First, a novel measurement method for attribute fluctuation is proposed, based on the variation range of attribute membership degrees. To mitigate redundancy in the concept space, a fuzzy granular concept space is constructed using the concept contribution degree. Second, the model leverages the semantic richness of concepts by integrating similar fuzzy granular concepts, thereby constructing a clustering space. From this, upper and lower approximation spaces are derived. Finally, extensive experiments conducted on multiple benchmark datasets demonstrate that the proposed AFFCCL model outperforms representative fuzzy CCL models, neural network-based classifiers, and traditional similarity-based approaches in termsof accuracy, interpretability, and robustness.

Warden collusion represents a hazardous threat to wireless covert communication, where wardens can combine their observations to perform a more aggressive detection attack. This paper investigates the impact of warden collusion on covert communication in a multi-antenna wireless network consisting of one source, one destination, multiple wardens and interferers. By employing the techniques of Laplace Transform and Cauchy Integral Theorem, we first establish a framework to model the aggregate interference distribution (AID) for covert communication in the network under the typical additive white Gaussian noise (AWGN) and Rayleigh fading channels. Based on the AID results, we then develop theoretical models to reveal the inherent relationship between the collusion intensity and fundamental communication metrics in terms of the covert outage probability, connection outage probability and covert throughput. With the help of these models, we further explore the covert throughput optimization problems and present extensive numerical results to illustrate the impact of warden collusion on the covert throughput under both channel models.

This paper investigates secure communication in multi-UAV networks, where each UAV employs rate-splitting multiple access (RSMA) to simultaneously deliver downlink data services to multiple ground terminals (GTs) under eavesdropping threats. To enhance network security, we propose a two-stage collaborative RSMA transmission scheme. Based on this scheme, we study the optimization of multi-UAV cooperative trajectory, time-step sharing and jamming power (MUCTSJ) to maximize the network’s secrecy rate. Additionally, to ensure fairness in throughput allocation among GTs, we incorporate two typical UAV service principles—Channel Quality First (CQF) and Fair Service First (FSF)—into the optimization objectives. Given the non-convex and NP-hard nature of this optimization problem, we reformulate it as a Markov Decision Process (MDP) and introduce a multi-agent reinforcement learning (MARL) framework based on the Centralized Training and Decentralized Execution (CTDE) paradigm. To address the dynamic topological changes induced by UAV mobility and time-varying channel states, as well as the gradient interference among multiple learning tasks, we design a Multi-Task Attention Deep Recurrent Network (MTA-DRNN). This architecture effectively captures the distinct observed attributes of each UAV while enhancing the coordination between diverse actions, thereby improving the adaptability of the agent and the stability of training. Simulation results demonstrate the superiority of the proposed solution enhances the security of multi-UAV networks over other baseline schemes. Furthermore, deployment on corresponding hardware platforms confirms the solution’s effectiveness and robustness in practical applications.

This paper investigates a wireless powered mobile edge computing (WP-MEC) network with multiple hybrid access points (HAPs) in a dynamic environment, where wireless devices (WDs) harvest energy from radio frequency (RF) signals of HAPs, and then compute their computation data locally (i.e., local computing mode) or offload it to the chosen HAPs (i.e., edge computing mode). In order to pursue a green computing design, we formulate an optimization problem that minimizes the long-term energy provision of the WP-MEC network subject to the energy, computing delay and computation data demand constraints. The transmit power of HAPs, the duration of the wireless power transfer (WPT) phase, the offloading decisions of WDs, the time allocation for offloading and the CPU frequency for local computing are jointly optimized adapting to the time-varying generated computation data and wireless channels of WDs. To efficiently address the formulated non-convex mixed integer programming (MIP) problem in a distributed manner, we propose aTwo-stageMulti-Agent deep reinforcement learning-basedDistributed computationOffloading (TMADO) framework, which consists of a high-level agent and multiple low-level agents. The high-level agent residing in all HAPs optimizes the transmit power of HAPs and the duration of the WPT phase, while each low-level agent residing in each WD optimizes its offloading decision, time allocation for offloading and CPU frequency for local computing. Simulation results show the superiority of the proposed TMADO framework in terms of the energy provision minimization.

This paper explores the joint covert and secure communications in a device-to-device (D2D)-enabled cellular system (DCS) consisting of a base station BS, an eavesdropper Eve, and two user equipments UE and UR. To conduct secure communications with UE against Eve, BS works either under the cellular mode using direct transmission or under the D2D mode replying through UR, while UR is greedy since it opportunistically transmits its own covert message to UE against the detection from BS. To understand the fundamental performance of secrecy rate and covert rate in DCS, we first develop theoretical models to depict the detection probability/secrecy rate of BS and covert rate of UR under different modes (i.e., underlay, overlay, or cellular). Based on these models, we further explore the secrecy rate maximization (SRM) for BS subject to the constraints of detection probability at BS and transmit power at both BS and UR, as well as the covert rate maximization (CRM) for UR subject to the constraints of covertness requirement and covert transmit power. Finally, we employ the Newton-based searching method to solve the SRM/CRM problems and illustrate via numerical results the achievable secrecy rate and covert rate of BS and UR under various DCS scenarios.

Distributed Vehicle Sharing System (DVSS) leverages emerging technologies such as blockchain to create a secure, transparent, and efficient platform for sharing vehicles. In such a system, both efficient matching of users with available vehicles and optimal pricing mechanisms play crucial roles in maximizing system revenue. However, most existing schemes utilize user-to-vehicle (two-sided) matching and pricing, which are unrealistic for DVSS due to the lack of participation of service providers. To address this issue, we propose in this paper a novel Three-sided stable Matching with an optimal Pricing (TRIMP) scheme. First, to achieve maximum utilities for all three parties simultaneously, we formulate the optimal policy and pricing problem as a three-stage Stackelberg game and derive its equilibrium points accordingly. Second, relying on these solutions from the Stackelberg game, we construct a three-sided cyclic matching for DVSS. Third, as the existence of such a matching is NP-complete, we design a specific vehicle sharing algorithm to realize stable matching. Extensive experiments demonstrate the effectiveness of our TRIMP scheme, which optimizes the matching process and ensures efficient resource allocation, leading to a more stable and well-functioning decentralized vehicle sharing ecosystem.

In this paper, we explore covert communications in a device-to-device (D2D) network consisting of an intelligent reflecting surface (IRS), a base station, a cellular user, a D2D pair, and an adversary warden. With the help of the IRS, the D2D pair attempts to perform covert communication, while the warden also tries to detect the very existence of such a transmission. To investigate the covert performance under the scenario, we derive the detection error probability at Warden, the optimal detection threshold for minimizing the probability, and the transmission outage probabilities for D2D and cellular communications, respectively. We further jointly optimize the transmission powers of the cellular user and the D2D transmitter, the reflection phase shifts, and the amplitudes of the IRS reflecting elements to improve covert communication performance. Finally, we provide numerical results to reveal the impact of system parameters on the covert performance and also to exhibit the merits of IRS-enabled D2D networks for achieving covert communications.

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE.

Federated Learning (FL) enables training of a global model from distributed data. However, the singular-model based operation of FL is open with uploading poisoned models compatible with the global model structure and can be exploited as a vulnerability to conduct model poisoning attacks. This paper proposes a multi-model based FL as a proactive mechanism to enhance the opportunity of model poisoning attack mitigation. A master model is trained by a set of slave models. To enhance the opportunity of attack mitigation, the structure of client models dynamically change and the supporter FL protocol is provided. For a MEC system, the model selection problem is modeled as an optimization to minimize loss and recognition time, while meeting a robustness confidence. A deep reinforcement learning based model selection is proposed. For a DDoS attack detection scenario, results illustrate a competitive accuracy gain under poisoning attack with the scenario that the system is without attack, and also a potential of recognition time improvement.

Network function virtualization leverages programmable data plane switches to deploy in-network implementable functions, to improve QoS. The memories of switches can be extended through remote direct memory access to access external memories. This paper exploits the switches external memories to place VNFs at time intervals with ultra-low latency and high bandwidth demands. The reconfiguration decision is modeled as an optimization to minimize the deployment and reconfiguration cost, while meeting the SFCs deadlines. A DRL based method is proposed to reconfigure service chains adoptable with dynamic network and traffic characteristics. To deal with slow convergence due to the complexity of deployment scenarios, static and dynamic filters are used in policy networks construction to diminish unfeasible placement exploration. Results illustrate improvement in convergence, acceptance ratio and cost.

Industrial Internet of Things (IIoT) is highly sensitive to data privacy and cybersecurity threats. Federated Learning (FL) has emerged as a solution for preserving privacy, enabling private data to remain on local IIoT clients while cooperatively training models to detect network anomalies. However, both synchronous and asynchronous FL architectures exhibit limitations, particularly when dealing with clients with varying speeds due to data heterogeneity and resource constraints. Synchronous architecture suffers from straggler effects, while asynchronous methods encounter communication bottlenecks. Additionally, FL models are prone to adversarial inference attacks aimed at disclosing private training data. To address these challenges, we propose a Buffered FL (BFL) framework empowered by homomorphic encryption for anomaly detection in heterogeneous IIoT environments. BFL utilizes a novel weighted average time approach to mitigate both straggler effects and communication bottlenecks, ensuring fairness between clients with varying processing speeds through collaboration with a buffer-based server. The performance results, derived from two datasets, show the superiority of BFL compared to state-ofthe-art FL methods, demonstrating improved accuracy and convergence speed while enhancing privacy preservation.

Edge computing has promoted various applications driven by artificial intelligence (AI). However, upgrading AI models during system operation may change resource and performance features. Then, the service management controller (SMC) faces an unprecedented environmental condition and has limited prior knowledge, resulting in high probabilities of policy mismatches. With the proliferation of AI applications, it is an urgent necessity that SMCs can adapt to different conditions to ensure quality of service (QoS) and resource efficiency. Therefore, this paper studies the problem of dynamic edge AI service adaptation and formulates it as a multi-task scenario adaptation problem. After that, we proposed an approach based on off-policy meta-reinforcement learning and digital twin (DT) technology. The DT system emulates a set of encountered conditions, and a meta-policy is obtained by interacting with these DTs. The executed policy is initialized as the meta-policy once AI models are upgraded. Then, it adapts to new service conditions by drawing salient information from limited transition contexts collected from a newly encountered environmental condition. Simulation results reveal that our approach can optimize QoS and adapt to different service situations.

Addressing the pressing requirement for dynamic and intelligent allocation of slicing resources, the dynamic provisioning of resources based on traffic predictions has emerged. Although this method favours proactive scheduling of network slices, more complexities are introduced by the prediction uncertainty. In addition, because multi-tenant networks are always changing in terms of technology and business model, profit-aware network slicing is becoming an important topic of study in the field of resource provision. This paper focuses on profit-aware slicing resource provisioning amid traffic uncertainty in multi-tenancy flexible Ethernet over wavelength division multiplexing networks. Specifically, we develop a profit model for multi-tenant network slicing, accounting for the impact of network prediction uncertainty, and formulate the problem as maximizing the profit of users primarily. To solve this problem, we propose a profit-aware resource provisioning approach that first checks if the slice requests are made by pruning algorithms and then determines the service relationship between slices and tenants by matching games. Simulation results demonstrate the superiority of the proposed algorithm over benchmarks in terms of user profit, total benefit, and denial ratio of service.

Satisfying users' requests based on the service level agreements of network slices is one of the most basic and vital topics of network slicing in 6G networks, and anomaly detection is regarded as a key technique for locating the abnormal status of slices. However, current studies on slice anomaly detection mostly focused on real-time monitoring of slices and ignored the prediction of potential anomalies. Generally, when anomalies trigger, it is hard for slices to adjust the resources in time due to resource competition among physical/virtual nodes. Besides, the resource provisioning strategies can also be optimized when slices are running normally, which is seldom considered when performing slice anomaly detection. To cope with these challenges, in this paper, we are motivated to locate the potential slice anomalies and optimize the resource allocation strategies in a holistic view by learning users' historical behaviors. Specifically, we design a general network architecture, model the process of slice resource provisioning, and formulate the problem as maximizing the long-term system net promoter score (NPS). To solve this problem, we propose a framework to locate the potential slice anomalies and decide the resource allocation strategies simultaneously by predicting the users' future requests and positions. As a result, simulation results demonstrate that our proposed scheme outperforms other baselines in improving the long-term system NPS and reducing the average latency of users.

Anticipation for 6G's arrival comes with growing concerns about increased energy consumption in computing and networking. The expected surge in connected devices and resource-demanding applications presents unprecedented challenges for energy resources. While sustainable resource allocation strategies have been discussed in the past, these efforts have primarily focused on single-domain orchestration or ignored the unique requirements posed by 6G. To address this gap, we investigate the joint problem of service instance placement and assignment, path selection, and request prioritization, dubbed PIRA. The objective function is to maximize the system's overall profit as a function of the number of concurrently supported requests while simultaneously minimizing energy consumption over an extended period of time. In addition, end-to-end latency requirements and resource capacity constraints are considered for computing and networking resources, where queuing theory is utilized to estimate the Age of Information (AoI) for requests. After formulating the problem in a non-linear fashion, we prove its NP-hardness and propose a method, denoted ORIENT. This method is based on the Double Dueling Deep Q-Learning (D3QL) mechanism and leverages Graph Neural Networks (GNNs) for state encoding. Extensive numerical simulations demonstrate that ORIENT yields near-optimal solutions for varying system sizes and request counts.

The coexistence between enhanced Mobile Broad-band (eMBB) and Ultra Reliable Low Latency Communications (URLLC) is challenging in modern communication systems. To support such diversity, Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) emerge as complementary paradigms that shall offer fine-grained on-demand distributed resources closer to the User Equipment (UE). In this work, we address the combination of MEC, NFV and dynamic virtual resource allocation to overcome the challenge of resource dimensioning in the network edge. A Continuous Time Markov Chain (CTMC) based model was designed to evaluate how requests are managed by the virtualization resources of a single MEC node, with a primary focus on meeting the requirements of both eMBB and URLLC services. Practical factors such as resource failures, service prioritization, and setup (repair) times were integrated into the formulation. Some of our key findings include the idea that higher eMBB arrival rates decrease availability and increase response times, while URLLC availability remains stable, and that the container setup rates and failure rates substantially affect both availability and response times, with higher setup rates enhancing both availability and reducing response times.

5G slices are susceptible to indirect Distributed Denial of Service (DDoS) attacks, where overwhelming traffic directed to one slice can also disrupt other slices sharing the same infrastructure Many current mitigation methods rely on a detection phase, which may not be effective against unknown or sophisticated attacks. Moving Target Defense (MTD) is a security mechanism that invalidates the adversary's collected information, and it can be deployed without the detection phase. In this paper, we propose a Slice Mutation technique based on Reinforcement Learning (SMRL) that reduces the impact of DDoS attacks on 5G slices while keeping the number of allocated slices acceptable. SMRL proposes a general RL model that considers ternary and ranking numbers to improve learning performance. We tested SMRL on computer networks attacked by a real botnet called Mirai and assessed its performance using various measures, including a new functionality analysis method The results indicate that SMRL decreases the number of slices impacted by a DDoS attack and enhances the distribution of slices among infrastructure resources by 46 % and 20 %, respectively.

The distributed and disaggregated nature of 5G and beyond (B5G) networks has spurred interest in federated learning (FL) for empowering privacy-preserving collaborative network anomaly detection at the edge. However, FL is prone to catastrophic forgetting (CF), where prior knowledge is forgotten while sequentially learning new attack patterns from a stream of data. Few studies addressed CF issue in network anomaly detection using Continual Learning (CL), but focusing on centralized models rather than FL and overlooking integration in B5G. To fill this gap, we propose TenaxDoS, a novel framework that combines FL with a replay memory-based CL strategy to foster sustainable and cooperative network anomaly detection in an Open Radio Access Network (O-RAN) environment in B5G networks. The experimental results on a dataset from a real 5G test network show TenaxDoS's superior overall performance, stability and effective mitigation of CF, yielding a remembering of past knowledge of above 98.8%.

The emergence of the semantic-aware paradigm presents opportunities for innovative services, especially in the context of 6G-based applications. Although significant progress has been made in semantic extraction techniques, the incorporation of semantic information into resource allocation decision-making is still in its early stages, lacking consideration of the requirements and characteristics of future systems. In response, this paper introduces a novel formulation for the problem of multiple access to the wireless spectrum. It aims to optimize the utilization-fairness trade-off, using the a-fairness metric, while accounting for user data correlation by introducing the concepts of self- and assisted throughputs. Initially, the problem is analyzed to identify its optimal solution. Subsequently, a Semantic-Aware Multi-Agent Double and Dueling Deep Q-Learning (SAMA-D3QL) technique is proposed. This method is grounded in Model-free Multi-Agent Deep Reinforcement Learning (MADRL), enabling the user equipment to autonomously make decisions regarding wireless spectrum access based solely on their local individual observations. The efficiency of the proposed technique is evaluated through two scenarios: single-channel and multi-channel. The findings illustrate that, across a spectrum of a values, association matrices, and channels, SAMA-D3QL consistently outperforms alternative approaches. This establishes it as a promising candidate for facilitating the realization of future federated, dynamically evolving applications.

To address the evolving landscape of next-generation mobile networks, characterized by an increasing number of connected users, surging traffic demands, and the continuous emergence of new services, a novel communication paradigm is essential. One promising candidate is the integration of network slicing and in-network computing, offering resource isolation, deterministic networking, enhanced resource efficiency, network expansion, and energy conservation. Although prior research has explored resource allocation within network slicing, routing, and in-network computing independently, a comprehensive investigation into their joint approach has been lacking. This paper tackles the joint problem of network slicing, path selection, and the allocation of in-network and cloud computing resources, aiming to maximize the number of accepted users while minimizing energy consumption. First, we introduce a Mixed-Integer Linear Programming (MILP) formulation of the problem and analyze its complexity, proving that the problem is NP-hard. Next, a Water Filling-based Joint Slicing, Routing, and In-Network Computing (WF-JSRIN) heuristic algorithm is proposed to solve it. Finally, a comparative analysis was conducted among WF-JSRIN, a random allocation technique, and two optimal approaches, namely Opt-IN (utilizing in-network computation) and Opt-C (solely relying on cloud node resources). The results emphasize WF-JSRIN's efficiency in delivering highly efficient near-optimal solutions with significantly reduced execution times, solidifying its suitability for practical real-world applications.

Honeypot technologies are becoming increasingly popular in cybersecurity as they offer valuable insights into adversary behavior with a low rate of false detections. By diverting the attention of potential attackers and siphoning off their resources, honeypots are a powerful tool for protecting critical assets within a network. However, the cybersecurity landscape constantly evolves, and professional attackers are always working to uncover and bypass honeypots. Once an adversary successfully identifies a deception mechanism in place, they may change their tactics, potentially causing significant harm to the network. Maintaining a high level of deception is crucial for honeypots to remain undetectable. This paper explores various deception techniques designed specifically for honeypots to enhance their performance while making them impervious to detection. Previous research has not provided a detailed comparison of these techniques, particularly those tailored to honeynets. Therefore, we categorize the presented techniques into relevant classes, subject them to a comparative analysis, and evaluate their effectiveness in simulation scenarios. We also present a mathematical model that comprehensively represents and compares various honeynet research endeavors. In addition, we provide insightful suggestions that highlight the existing research gaps in this field and offer a roadmap for future expansion. This includes extending deception techniques to emulate vulnerabilities inherent in 5G and software-defined networks, which address the evolving challenges of the cybersecurity landscape. The findings and insights presented in this paper are valuable to honeypot developers and cybersecurity researchers alike, providing a vital resource for advancing the field and fortifying network defenses against ever-evolving threats.

Advancements in deep learning and the Internet of Things (IoT) enable early fire detection through vision-based systems, reducing ecological, social, and economic damage. These systems necessitate lightweight, cost-effective convolutional neural networks (CNNs) for real-time operation. Effective deployment on AI-assisted edge devices is crucial for optimal performance. To mitigate this problem, we present the optimized fire attention network (OFAN) for effective and efficient fire detection. In the re-engineered attention block, we swapped the convolution layers by dilated variants and integrated additional dense layers to capture global context and refine more weight optimization. We calibrate the OFAN for real-time processing using a lightweight and efficient feature extractor backbone model. Additionally, a challenging fire dataset is a critical contribution that contains extremely diverse, blazing, and non-fire, captured in lighting and foggy environments. It advances traditional fire detection samples by considering low-light and foggy conditions. A comprehensive experiment is conducted over three widely used fire detection datasets, and our proposed OFAN outperforms state-of-the-art. The proposed OFAN achieved 96.23%, 96.54% and 94.63% accuracies over BoWFire, FD and the newly proposed DiverseFire dataset, respectively. Our research sets a standard for fire detection over edge devices, offering improved accuracy and better frames per second (FPS) performance.

The integration of communication and radar systems could enhance the robustness of future communication systems to support advanced application demands, e.g., target sensing, data exchange, and parallel computation. In this article, we investigate the beamforming design for integrated sensing, computing, and communication (ISCC) in the Internet of Robotic Things (IoRT) scenario. Specifically, we assume that each robot uploads its preprocessed sensing information to the access point (AP). Meanwhile, leveraging the additive features of the spatial wireless channels between robots and AP, over-the-air computation (AirComp) through multirobot cooperation could bolster system performance, particularly in tasks like target localization through sensing. To get a full picture of the effects of antenna array structures and beampatterns on the ISCC system, we evaluate the performance by considering the shared and separated antenna structures, as well as the omnidirectional and directional beampatterns. Based on these setups, the nonconvex optimization problems for the performance tradeoff between sensing and AirComp are formulated to minimize the mean-squared error (MSE) of AirComp and sensing. To efficiently solve these optimization problems, we designed the gradient descent augmented Lagrangian (GDAL) algorithm, which involves dynamically adjusting the step sizes while updating the variables. Simulation results show that the separated antenna structure achieves a lower AirComp MSE than the shared antenna setup because it has greater beam steering Degrees of Freedom. Moreover, the beampattern types have almost no effect on the AirComp MSE for the given antenna structure setup. This comprehensive investigation provides useful guidelines for ISCC framework implementation in IoRT applications.

The main goal of Encryption as a Service (EaaS) is to deliver cryptography services to limited-resource devices. However, due to the massive number of devices connecting EaaS platforms, they face challenging issues, such as high service delays and uncovered requests. The existing EaaS architectures lack in adequately taking advantage of both cloud and fog layers, by which the performance can be improved. Therefore, this article proposes a novel EaaS architecture called full-cloud-fog that focuses on increasing the EaaS throughput by locating the frequently accessed components on the fog layer and resolving resource allocations utilizing the cloud nodes. We have analyzed the security aspects of the proposed architecture and then implemented it in a real testbed. The evaluation results show that the proposed full-cloud-fog architecture improves the EaaS throughput by 81%.

The widespread adoption of Internet of Things (IoT) technology has introduced new cybersecurity challenges. Encryption services are being offloaded to cloud and fog platforms to mitigate these risks. Encryption as a Service (EaaS) emerges as a remedy, offering cryptographic solutions tailored to the resource constraints of IoT devices. This study thoroughly examines existing EaaS platforms, categorizing them based on encryption algorithms and service offerings. Additionally, we outline various EaaS architecture types depending on the placement of key components. Practical implementations of these platforms are explored through different testbeds. A key focus lies in dissecting the challenges that EaaS faces, particularly in the context of IoT, while suggesting potential remedies. This work stands out as an all-encompassing exploration, bridging the gap left by previous surveys.

Multiaccess edge computing (MEC) provides services for resource-sensitive and delay-sensitive Internet of Things (IoT) applications by extending the capabilities of cloud computing to the edge of the networks. However, the high mobility of IoT devices (e.g., vehicles) and the limited resources of edge servers (ESs) affect the service continuity and access latency. Service migration and reasonable resource (re-)allocation consequently become needed to ensure Quality of Service (QoS). However, service migration results in additional latency. In addition, different mobile IoT users have different resource requirements and different resource allocation policies of target ESs also determine whether service migration is necessary. Subsequently, how to jointly optimize service migration and resource allocation (SMRA) is a challenge that needs to be carefully addressed. To this end, this article investigates the joint optimization problem of SMRA in MEC environments to minimize the access delay of IoT users. It proposes a joint SMRA algorithm based on deep reinforcement learning (DRL), which takes into account the mobility of IoT users and decides whether to migrate services, where to migrate, and how to allocate resources through the long short time memory (LSTM) algorithm and the parameterized deep$Q$-network (PDQN) algorithm. Moreover, the PDQN algorithm effectively solves the discrete-continuous hybrid action space challenge in the SMRA problem. Finally, we conduct evaluation using a real-world data set of Beijing cab trajectories to verify the effectiveness and superiority of our proposed SMRA solution.

This article investigates sum-rate maximization while achieving seamless coverage with the minimum number of unmanned aerial vehicles (UAVs) in a device-to-device (D2D)-enabled UAV network. Toward this end, we formulate it as a nonlinear and nonconvex optimization problem, and then propose a max-rate-min-number (MRMN) scheme to solve this optimization problem. First, we derive UAV’s coverage radius which can depict the maximum coverage for user equipments, and then implement the optimal deployment for UAV swarm by exploiting the disk covering theory. Furthermore, we apply the coalitional game theory to design the cooperative strategy between UAV swarm and ground equipments. Finally, a coalition formation algorithm is presented for achieving maximum system sum-rate while reducing the number of UAVs under seamless coverage constraint. Extensive simulation results are provided to validate the effectiveness of our proposed MRMN scheme, and also illustrate that the scheme can improve the system sum-rate and reduce the number of deployed UAVs. Meanwhile, we further conduct a performance comparison between our scheme and the existing benchmark schemes.

This paper explores a new secure wireless communication scenario for the data collection in the Internet of Things (IoT) where the physical layer security technology is applied to counteract both the detection and eavesdropping attacks, such that the critical covertness and secrecy properties of the communication are jointly guaranteed. We first provide theoretical modeling for covertness outage probability (COP), secrecy outage probability (SOP) and transmission probability (TP) to depict the covertness, secrecy and transmission performances of the wireless communication system. To understand the fundamental security performance under the wireless communication system, we then define a new metric -covert secrecy rate (CSR), which characterizes the maximum transmission rate subject to the constraints of COP, SOP and TP. We further conduct detailed theoretical analysis to identify the CSR under various scenarios determined by the detector-eavesdropper relationships and the secure transmission schemes adopted by transmitters. Finally, numerical results are provided to illustrate the achievable performances under the secure wireless communication system.

This paper investigates the covert communications via cooperative jamming and relay selection in a wireless relay system, where a source intends to transmit a message to its destination with the help of a selected relay, and a warden attempts to detect the existence of wireless transmissions from both the source and relay, while friendly jammers send jamming signals to prevent warden from detecting the transmission process. To this end, we first propose two relay selection schemes, namely random relay selection (RRS) and max-min relay selection (MMRS), as well as their corresponding cooperative jamming (CJ) schemes for ensuring covertness in the system. We then provide theoretical modeling for the covert rate performance under each relay selection scheme and its CJ scheme and further explore the optimal transmit power controls of both the source and relay for covert rate maximization. Finally, extensive simulation/numerical results are presented to validate our theoretical models and also to illustrate the covert rate performance of the relay system under cooperative jamming and relay selection.

Network slicing is envisaged as the key to unlocking revenue growth in 5 G and beyond (B5G) networks. However, the dynamic nature of network slicing and the growing sophistication of DDoS attacks rises the menace of reshaping a stealthy DDoS into an Economical Denial of Sustainability (EDoS) attack. EDoS aims at incurring economic damages to service provider due to the increased elastic use of resources. Motivated by the limitations of existing defense solutions, we propose FortisEDoS, a novel framework that aims at enabling elastic B5G services that are impervious to EDoS attacks. FortisEDoS integrates a new deep learning-powered DDoS anomaly detection model, dubbed CG-GRU, that capitalizes on the capabilities of emerging graph and recurrent neural networks in capturing spatio-temporal correlations to accurately discriminate malicious behavior. Furthermore, FortisEDoS leverages transfer learning to effectively defeat EDoS attacks in newly deployed slices by exploiting the knowledge learned in a previously deployed slice. The experimental results demonstrate the superiority of CG-GRU in achieving higher detection performance of more than 92% with lower computation complexity. They show also that transfer learning can yield an attack detection sensitivity of above 91%, while accelerating the training process by at least 61%. Further analysis shows that FortisEDoS exhibits intuitive explainability of its decisions, fostering trust in deep learning-assisted systems.

Benefiting from the booming of Big Data and artificial intelligence (AI) technologies, data-as-a-service is gradually transforming into knowledge-as-a-service. Extracting knowledge from massive raw data is becoming a popular paradigm to save network resources and improve efficiency, and establishing knowledge markets is receiving increasing attention from academia and industry. In this paper, we propose a one-stop knowledge acquisition ecosystem termed BWKA that covers the whole process from upper-layer knowledge trading to underlying knowledge generation. In the knowledge trading process, the knowledge-as-a-service platform (KSP) is the buyer and publishes knowledge demands to multiple local knowledge sellers (LKSs). In the knowledge generation process, each LKS aggregates data from its sensors and then trains data into knowledge according to the KSP's requirements. We resort to blockchain technology and provide a series of tailored operating rules and functions to protect the truthfulness of data gathering and the fairness of knowledge trading. In addition, we introduce incentive mechanisms to stimulate selfish and rational entities in the BWKA ecosystem to participate in knowledge acquisition. To analyze the strategic interactions among entities theoretically, we develop a nested hierarchical game model, where the upper-layer knowledge trading is evaluated based on the Contract Theory, and the lower-layer knowledge generation is formulated as a two-stage Stackelberg game. By solving the nested hierarchical game in a backward inductive way, we identify the optimal strategy for each entity in closed form. Experiments on the Ethereum blockchain and simulation results demonstrate the practical operability and outstanding performance of the BWKA ecosystem.

Underwater acoustic sensor networks (UASNs) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning systems. The security of UASNs, particularly insider threats, is of growing concern. Internal attacks carried out via compromised normal nodes are more damaging and stealthy than external attacks, such as signal stealing, data decryption, and identity forgery. As a security mechanism for internal threat detection based on interaction data, trust models have proven to enhance the security of UASNs. However, traditional trust models lack sufficient scalability when faced with movable underwater devices, heterogeneous network environments, and variable attack patterns. Therefore, in this paper, a novel trust model based on federated deep reinforcement learning is proposed for UASNs. First, the evidence acquisition mechanism, including communication, energy, and data evidence, is improved based on existing ones to better accommodate the topological dynamics of UASNs. Second, acquired trust evidence is fed into the corresponding deep reinforcement learning-based local trust model to accomplish trust prediction and model training. Finally, a federated learning-based update method periodically aggregates and updates the parameters of the local models. The experimental results prove that the proposed scheme exhibits satisfactory performance in terms of improving trust prediction accuracy and energy efficiency.

Mobile crowdsensing (MCS) has emerged as a popular and promising paradigm for solving challenging problems by utilizing collective wisdom and resources. However, the system architecture and operational rules for MCS have not been well-defined, and obtaining accurate and reliable results from conflicting data collected by workers is difficult due to discrepancies in sensor quality and privacy protection requirements. In this paper, we combine the methodologies of Dynamic Truth Discovery (DTD), Combinatorial Multi-Armed Bandit (CMAB), and Multi-Attribute Reverse Auction to develop a novel MCS ecosystem, with the objective of maximizing the sensing accuracy-aware utility under the budget constraint. We first establish the data collection model by jointly considering the task completion duration as well as the deviation caused by both endogenous errors and privacy protection-oriented injected noise. Then, we theoretically evaluate the accuracy of truth discovery and quantify the contribution of each worker to MCS to form the worker selection criterion. As the qualities of workers are initially unknown, the platform faces the exploration-exploitation dilemma. Therefore, we apply CMAB to transform the worker recruitment problem into a combinatorial arm-pulling problem and elaborately design an Upper Confidence Bound (UCB) algorithm to achieve a desirable exploration-exploitation tradeoff. Moreover, we design an auction-based payment method for the platform, stimulating workers to provide their quoted price honestly while enabling individual rationality. Extensive simulations and comparison results demonstrate the feasibility and effectiveness of our proposed MCS ecosystem.

Network slices are generally coupled with services and face service continuity/unavailability concerns due to the high mobility and dynamic requests from users. Network slice mobility (NSM), which considers user mobility, service migration, and resource allocation from a holistic view, is witnessed as a key technology in enabling network slices to respond quickly to service degradation. Existing studies on NSM either ignored the trigger detection before NSM decision-making or didn't consider the prediction of future system information to improve the NSM performance, and the training of deep reinforcement learning (DRL) agents also faces challenges with incomplete observations. To cope with these challenges, we consider that network slices migrate periodically and utilize the prediction of system information to assist NSM decision-making. The periodical NSM problem is further transformed into a Markov decision process, and we creatively propose a prediction-based federated DRL framework to solve it. Particularly, the learning processes of the prediction model and DRL agents are performed in a federated learning paradigm. Based on extensive experiments, simulation results demonstrate that the proposed scheme outperforms the considered baseline schemes in improving long-term profit, reducing communication overhead, and saving transmission time.

Nowadays, as the need for capacity continues to grow, entirely novel services are emerging. A solid cloud-network integrated infrastructure is necessary to supply these services in a real-time responsive, and scalable way. Due to their diverse characteristics and limited capacity, communication and computing resources must be collaboratively managed to unleash their full potential. Although several innovative methods have been proposed to orchestrate the resources, most ignored network resources or relaxed the network as a simple graph, focusing only on cloud resources. This paper fills the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including function placement and assignment, traffic prioritization, and path selection considering capacity constraints and quality requirements, to minimize total cost. We formulate the problem as a non-linear programming model and propose two approaches, dubbed B&B-CCRA and WF-CCRA, based on the Branch & Bound and Water-Filling algorithms to solve it when the system is fully known. Then, for partially known systems, a Double Deep Q-Learning (DDQL) architecture is designed. Numerical simulations show that B&B-CCRA optimally solves the problem, whereas WF-CCRA delivers near-optimal solutions in a substantially shorter time. Furthermore, it is demonstrated that DDQL-CCRA obtains near-optimal solutions in the absence of request-specific information.

This paper investigates the fundamental covert rate performance in a D2D-enabled cellular network consisting of a cellular user Alice, a base station BS, an active warden Willie, and a D2D pair with a transmitter$D_{t}$and a full-duplex receiver$D_{r}$. To conduct covert communication between Alice and BS, the full-duplex$D_{r}$transmits jamming signal to confuse the active Willie and also receives signal from$D_{t}$simultaneously. With spectrum sharing,$D_{t}$can operate over either an underlay mode reusing cellular spectrum or an overlay mode using dedicated spectrum. With power control,$D_{r}$can send jamming signal to confuse Willie's detection of the transmission from Alice. We first provide theoretical results for the outage probabilities of the cellular and D2D transmissions, the average minimum detection error probability at Willie, and the achievable covert rate from Alice to BS. We then explore the power control for covert rate maximization (CRM) under the underlay mode as well as the joint designs of power control and spectrum partition for CRM under the overlay mode. We further consider a mode selection that flexibly switches between these two modes with a probability, and also investigate the covert rate modeling and joint designs of power control, spectrum partition and mode selection probability for CRM. Finally, numerical results are presented to illustrate the covert rate performances of the network under the underlay mode, overlay mode and mode selection.

The popularity of microservices in industry has sparked much attention in the research community. Despite significant progress in microservice deployment for resource-intensive services and applications at the network edge, the intricate dependencies among microservices are often overlooked, and some studies underestimate the importance of system context extraction in deployment strategies. This paper addresses these issues by formulating the microservice deployment problem as a max-min problem, considering system cost and quality of service (QoS) jointly. We first study the attention-based microservice representation (AMR) method to achieve effective system context extraction. In this way, the contributions of different computing power providers (users, edge servers, or cloud servers) in the networks can be effectively paid attention to. Subsequently, we propose the attention-modified soft actor-critic (ASAC) algorithm to tackle the microservice deployment problem. ASAC leverages attention mechanisms to enhance decision-making and adapt to changing system dynamics. Our simulation results demonstrate ASAC's effectiveness, prioritizing average system cost and reward compared to the other state-of-the-art algorithms.

Offering intelligent services with ultra low latency and high reliability is one of the main objectives of 6G networks. Federated Learning (FL) is a solution to enhance the security of data and the accuracy, in comparison with local training of data in devices. The transmission cost in conventional FL is high. Performing FL using edge infrastructure is a solution. However, edge servers might not be available at every location or the communication with edge resources may prolong the learning process. This paper proposes a collaborative federated learning approach to provide intelligent services through collaboration of various learning levels including central cloud level, edge cloud level, and device level. Computational capabilities of neighbourhood devices are exploited to provide a fast recognition via 6G D2D communication. The learning is modeled as an optimization that performs trade-off between recognition accuracy and response time of recognition for devices. Considering the dynamicity in communication and computation status of the network/devices, a deep reinforcement learning method is proposed to decide about the collaboration of learning levels, and performing the appropriate trade-off. For a DDoS attack detection scenario, the evaluation results show improvement in the gained rewards, the attack detection accuracy, the response time of recognition, and the accumulation of accuracy and response time.

Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of Economical Denial of Sustainability (EDoS) attack, causing economic losses to service provider due to the increased elastic use of resources. Motivated by the limitations of existing solutions, we propose FortisEDoS, a novel framework that aims at enabling EDoS-aware elastic B5G services. FortisEDoS integrates a new deep learning-based DDoS anomaly detection model, called CG-GRU, that leverages the capabilities of emerging graph and recurrent neural networks in capturing spatio-temporal correlations to accurately identify malicious behavior, allowing proactive mitigation of EDoS attacks. Moreover, FortisEDoS uses transfer learning to effectively counteract EDoS attacks in newly deployed slices by leveraging the knowledge acquired in previously deployed slice. The experimental results show the superiority of transfer learning-powered CG-GRU in achieving higher detection performance with lower computation overhead, compared to other baseline methods.

Novel applications such as the Metaverse have high-lighted the potential of beyond 5G networks, which necessitate ultra-low latency communications and massive broadband connections. Moreover, the burgeoning demand for such services with ever-fluctuating users has engendered a need for heightened service continuity consideration in B5G. To enable these services, the edge-cloud paradigm is a potential solution to harness cloud capacity and effectively manage users in real time as they move across the network. However, edge-cloud networks confront a multitude of limitations, including networking and computing resources that must be collectively managed to unlock their full potential. This paper addresses the joint problem of service placement and resource allocation in a network-cloud integrated environment while considering capacity constraints, dynamic users, and end-to-end delays. We present a non-linear programming model that formulates the optimization problem with the aiming objective of minimizing overall cost while enhancing latency. Next, to address the problem, we introduce a DDQL-based technique using RNNs to predict user behavior, empowered by a water-filling-based algorithm for service placement. The proposed framework adeptly accommodates the dynamic nature of users, the placement of services that mandate ultra-low latency in B5G, and service continuity when users migrate from one location to another. Simulation results show that our solution provides timely responses that optimize the network's potential, offering a scalable and efficient placement.

Massive device connections in upcoming 6G networks have led to a sharp increase in network traffic volume, posing significant challenges in providing reliable performance guarantees, e.g., low latency. The Computing Power Network (CPN) is a new framework for resource integration involving multiple parties. It integrates the resources of various owners via the network, providing users with efficient and adaptable services. Due to the uncertainty of the signal quality, the majority of existing studies do not adequately organize the topology of user allocation in CPNs when optimizing network resources. Reconfigurable Intelligent Surface (RIS) is a new type of network node for constructing future smart radio environments with high spectral efficiency and nearly zero energy consumption that can offer new access options for user allocation in CPNs. In this paper, we investigate the user access allocation in a RIS-assisted Ad Hoc Edge (RAHE) scenario where the users are with service-intensive demands. To maximize the overall service tasks of the system constrained by a service time threshold, we propose a RIS-assisted interval scheduler strategy (RS3) approach to balancing the whole system service completion and total latency. Specifically, RS3is a graph-theoretic optimization method based on the interval scheduling problem. The numerical simulation results demonstrate that our proposed RS3approach is superior to commonly utilized methods in terms of the number of serves given the service time constraint.

Mafia, also known as Werewolf, is a game of uncertainty between two teams, which aims to eliminate the other team's players from the game. The similarities between detecting the Mafia members in this game and botnet detection in a computer network motivate us to solve the botnet detection problem using this game's winning strategies. None of the state-of-the-art researches have used the Mafia game strategies to detect the network's malicious nodes. In this paper, we first propose the Mafia detection strategies, which are applied using linear relation and reinforcement learning techniques. We then use the suggested strategies in a network infected by the Mirai botnet, using Mininet, to evaluate the performance of botnet detection. The average results show that the suggested strategies are 11% more accurate than the existing ones for the Mafia game. Additionally, the true positive and true negative detection rates of a network modeled by the proposed Mafia game are 71% and 91%, respectively.

Network slicing within 5G networks encounters two significant challenges: catering to a maximum number of requests while ensuring slice isolation. To address these challenges, we present an innovative actor-critic Reinforcement Learning (RL) model named ‘Slice Isolation based on RL’ (SIRL). This model employs five optimal graph features to construct the problem environment, the structure of which is adapted using a ranking scheme. This scheme effectively reduces feature dimensionality and enhances learning performance. SIRL was assessed through a comparative analysis with nine state-of-the-art RL models, utilizing four evaluation metrics. The average results demonstrate that SIRL outperforms other models with a 70% higher coverage rate of requests and an 8% reduction in damage resulting from DoS/DDoS attacks.

The open radio access network (O-RAN) architecture's native virtualization and embedded intelligence facilitate RAN slicing and enable comprehensive end-to-end services in post-5G networks. However, any vulnerabilities could harm security. Therefore, artificial intelligence (AI) and machine learning (ML) security threats can even threaten O-RAN benefits. This paper proposes a novel approach to estimating the optimal number of predefined VNFs for each slice while addressing secure AI/ML methods for dynamic service admission control and power minimization in the O-RAN architecture. We solve this problem on two-time scales using mathematical methods for determining the predefined number of VNFs on a large time scale and the proximal policy optimization (PPO), a Deep Reinforcement Learning algorithm, for solving dynamic service admission control and power minimization for different slices on a small-time scale. To secure the ML system for O-RAN, we implement a moving target defense (MTD) strategy to prevent poisoning attacks by adding uncertainty to the system. Our experimental results show that the proposed PPO-based service admission control approach achieves an admission rate above 80% and that the MTD strategy effectively strengthens the robustness of the PPO method against adversarial attacks.

The industrial Internet of Things (IIoT) involves the integration of Internet of Things (IoT) technologies into industrial settings. However, given the high sensitivity of the industry to the security of industrial control system networks and IIoT, the use of software-defined networking (SDN) technology can provide improved security and automation of communication processes. Despite this, the architecture of SDN can give rise to various security threats. Therefore, it is of paramount importance to consider the impact of these threats on SDN-based IIoT environments. Unlike previous research, which focused on security in IIoT and SDN architectures separately, we propose an integrated method including two components that work together seamlessly for better detecting and preventing security threats associated with SDN-based IIoT architectures. The two components consist in a convolutional neural network-based Intrusion Detection System (IDS) implemented as an SDN application and a Blockchain-based system (BS) to empower application layer and network layer security, respectively. A significant advantage of the proposed method lies in jointly minimizing the impact of attacks such as command injection and rule injection on SDN-based IIoT architecture layers. The proposed IDS exhibits superior classification accuracy in both binary and multiclass categories.

This paper investigates the fundamental traffic steering issue for cellular-enabled unmanned aerial vehicles (UAVs), where each UAV needs to select one from different Mobile Network Operators (MNOs) to steer its traffic for improving the Quality-of-Service (QoS). To this end, we first formulate the issue as an optimization problem aiming to minimize the maximum outage probabilities of the UAVs. This problem is non-convex and non-linear, which is generally difficult to be solved. We propose a solution based on the framework of deep reinforcement learning (DRL) to solve it, in which we define the environment and the agent elements. Furthermore, to avoid sharing the learned experiences by the UAV in this solution, we further propose a federated deep reinforcement learning (FDRL)-based solution. Specifically, each UAV serves as a distributed agent to train separate model, and is then communicated to a special agent (dubbed coordinator) to aggregate all training models. Moreover, to optimize the aggregation process, we also introduce a FDRL with DRL-based aggregation (DRL2A) approach, in which the coordinator implements a DRL algorithm to learn optimal parameters of the aggregation. We consider deep Q-learning (DQN) algorithm for the distributed agents and Advantage Actor-Critic (A2C) for the coordinator. Simulation results are presented to validate the effectiveness of the proposed approach.

Our vision of the future world is one wherein everything, anywhere and at any time, can reliably communicate in real time. 5G, the fifth generation of cellular networks, is anticipated to use heterogeneity to deliver ultra-high data rates to a vastly increased number of devices in ultra-dense areas. Improving the backhaul network capacity is one of the most important open challenges for deploying a 5G network. A promising solution is Integrated Access and Backhaul (IAB), which assigns a portion of radio resources to construct a multi-hop wireless backhaul network. Although 3GPP has acknowledged the cost-effectiveness of the IAB-enabled framework and its orchestration has been extensively studied in the literature, its transmission capacity (i.e., the number of base stations it can support) has not been sufficiently investigated. In this paper, we formulate the problem of maximizing transmission capacity and minimizing transmit powers for IAB-enabled multi-hop networks, taking into account relay selection, channel assignment, and power control constraints. Then, the solution space of the problem is analyzed, two optimality bounds are derived, and a heuristic algorithm is proposed to investigate the bounds. The claims are finally supported by numerical results.

Beyond 5G applications, future 6G services would need to support very large data volumes for emerging industry verticals, such as holographic-type communications, as well as time-sensitive services, e.g., industrial control. Network slicing is the key technology to deliver such customizable services. Slices and their dedicated resources should be provisioned optimally where the services will be run with low network latencies and associated expenses. However, the user dynamics on resource demands within and between slices result in different resource re-allocation triggers, ultimately lead to distinct mobility patterns, e.g., scaling, migration, where sufficient resources must be transferred. Efficient slice mobility requires increasing flexibility in network operation and management to ensure the customized QoS while minimizing the corresponding mobility cost. In this paper, a prediction-based intelligent network analytic is proposed to facilitate the optimized network slice mobility scheme. We will investigate how to utilize the user and network prediction as the auxiliary information to make the slice mobility decision with the objective of maximizing the long-term profits while minimizing the latency and mobility cost. Finally, we evaluate the proposed prediction-based network slice mobility scheme in a simulated environment and compare its performance in terms of system costs, revenues, and profits with two benchmark solutions.

As a way of obtaining useful information about the adversaries behavior with a low rate of false detection, honeypots have made significant advancements in the field of cybersecurity. They are also powerful in wasting the adversaries resources and attracting their attention from other critical assets in the network. A deceptive network with multiple honeypots is called a honeynet. The honeypots in a honeynet aim to cooperate in order to increase their deception power. Professional adversaries utilize strong detection mechanisms to discover the existence of the honeypots in a network. When an adversary finds that a deception mechanism is deployed, it may change their behavior and cause malicious effects on the network. Therefore, a honeynet has to be deceptive enough in order not to be identified. This paper aims to review the techniques that are designed for the honeynets to make them improve their deception performance. The recent related surveys do not focus on the honeynet-specific techniques, and also have no comparison analysis. The main presented techniques in this paper are fully investigated through comparative analysis and simulation scenarios. Some suggestions on the research gap are also provided. The results of this paper can be used by the honeynet developers and researchers to improve their work.

Identifying the top-K flows that require much more bandwidth resources in a large-scale Software-Defined Network (SDN) is essential for many network management tasks, such as load balancing, anomaly detection, and traffic engineering. However, identifying such top-K flows is not trivial, not only because of the fluctuations in flow bandwidth requirements but also because of the combinatorial explosion of problem instance sizes. In this paper, we weaken the tradeoff between exploration and exploitation and innovatively define the online top-K flows identification problem as identifying the top-K arms in a Combinatorial Multi-Armed Bandit (CMAB) model. Then, we propose a general greedy selection mechanism with some identification strategies that focus on temporal variations in the rewards. Extensive simulation experiments based on real traffic data are conducted to evaluate the performance of different strategies. In addition, the results of numerical simulations demonstrate that our proposed greedy selection mechanism significantly outperforms existing counterparts on top-K arms identification.

Multi-services are of fundamental importance in Unmanned Aerial Vehicle (UAV)-enabled aerial communications for the Internet of Things (IoT). However, the multi-services are challenging in terms of requirements and use of shared resources such that the traditional solutions for a single service are unsuitable for the multi-services. In this paper, we consider a UAV-enabled aerial access network for ground IoT devices, each of which requires two types of services, namely ultra Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB), measured by transmission delay and effective rate, respectively. We first consider a communication model that accounts for most of the propagation phenomena experienced by wireless signals. Then, we derive the expressions of the effective rate and the transmission delay, and formulate each service type as an optimization problem with the constraints of resource allocation and UAV deployment to enable multi-service support for the IoT. These two optimization problems are nonlinear and nonconvex and are generally difficult to be solved. To this end, we transform them into linear optimization problems, and propose two iterative algorithms to solve them. Based on them, we further propose a linear program algorithm to jointly optimize the two service types, which achieves a trade-off of the effective rate and the transmission delay. Extensive performance evaluations have been conducted to demonstrate the effectiveness of the proposed approach in reaching a trade-off optimization that enhances the two services.

This article investigates the covertness and secrecy of wireless communications in an untrusted relay-assisted device-to-device (D2D) network consisting of a full-duplex base station (BS), a user equipment (UE), and an untrusted relay${R}$. For the covertness, we attempt to prevent Willie from detecting the very existence of communications via a D2D link from UE to R and cellular link from R to BS, while for the secrecy, we aim to prevent the untrusted relay from eavesdropping the UE message. To explore the fundamental covertness and secrecy in such a network, we first provide theoretical modelings for the average minimum detection error rate of Willie, and the average covert/secrecy rate from UE to BS under the underlay and overlay modes, respectively. Based on these models, th we further explore the optimal power control at UE, R, and BS to achieve the average covert rate maximization (MCR) for UE with the constraints of covertness and security requirements under the underlay mode. We also identify the optimal transmit powers and the optimal spectrum partition factor for MCR under the overlay mode. Finally, the exhaust searching method is adopted to solve the MCR problems, and extensive numerical and simulation results are presented to validate our theoretical analysis and to illustrate the average covert rate and secrecy rate of UE under various scenarios.

Emerging cross-reality (XR) applications, including holography, augmented, virtual, and mixed reality, are characterized by unprecedented requirements for Quality of Experience (QoE), largely exceeding those currently attainable. To cope with these requirements, noticeable efforts and a number of initiatives are ongoing to enhance the current communications technologies, especially in the direction of supporting ultralow latency and increased bandwidth. This work proposes an architecture that puts together the key enablers to support future XR applications, highlighting the shortcomings of existing technologies and leveraging the ongoing innovations. It demonstrates the feasibility of the proposed architecture by describing the processes driving the platform with relevant use case scenarios, and mapping the envisioned functionality to existing tools.

The management of remote services, such as remote surgery, remote sensing, or remote driving, has become increasingly important, especially with the emerging 5G and Beyond 5G technologies. However, the strict network requirements of these remote services represent one of the major challenges that hinder their fast and large-scale deployment in critical infrastructures. This article addresses certain issues inherent in remote and immersive control of virtual reality (VR)-based unmanned aerial vehicles (UAVs), whereby a user remotely controls UAVs, equipped with 360° cameras, using their head-mounted devices (HMD) and their respective controllers. Remote and immersive control services, using 360° video streams, require much lower latency and higher throughput for true immersion and high service reliability. To assess and analyze these requirements, this article introduces a real-life testbed system that leverages different technologies (e.g., VR, 360° video streaming over 4G/5G, and edge computing). In the performance evaluation, different latency types are considered. They are namely: 1) glass-to-glass latency between the 360° camera of a remote UAV and the HMD display; 2) user/pilot’s reaction latency; and 3) the command/execution latency. The obtained results indicate that the responsiveness (dubbed Glass-to-Reaction-to-Execution—GRE–latency) of a pilot, using our system, to a sudden event is within an acceptable range, i.e., around 900 ms.

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EV), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this paper, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the self-interested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker’s coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing “currency”, such that an IDA-based Data-Energy Transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well-functioning. Extensive numerical results are presented to show the performance of the IDADET ecosystem and demonstrate its advantages in terms of economic properties, operational feasibility, fast convergence, and market social welfare.

Device-to-device (D2D) communications underlaid cellular networks have emerged as a promising network architecture to provide extended coverage and high data rate for various Internet of Things (IoT) applications. However, because of the inherent openness and broadcasting nature of wireless communications, such networks face severe risks of data privacy disclosure. This paper investigates the covert communications in such networks for providing enhanced privacy protection. Specifically, this paper explores the critical covert rate performance in a full-duplex D2D communication underlaid cellular network consisting of a base station, a cellular user, a D2D pair with a transmitter and a full-duplex receiver, and a warden, where the D2D receiver can operate over either the full-duplex (FD) mode or the half-duplex (HD) mode. We first derive transmission outage probabilities of cellular and D2D links under the FD and HD modes, respectively. Based on these probabilities, we further provide theoretical modelling for the covert rate under each mode and explore the corresponding covert rate maximization by jointly optimizing the transmit powers of the D2D pair and the cellular user. To improve the covert rate performance, we propose a general mode in which the D2D receiver can flexibly switch between these two modes. Under the general mode, we also investigate the theoretical modelling and maximization problems of covert rate. Finally, we present extensive numerical results to illustrate the covert rate performances under the FD, HD, and general modes.

Protecting large-scale networks, especially Software-Defined Networks (SDNs), against distributed attacks in a cost-effective manner plays a prominent role in cybersecurity. One of the pervasive approaches to plug security holes and prevent vulnerabilities from being exploited is Moving Target Defense (MTD), which can be efficiently implemented in SDN as it needs comprehensive and proactive network monitoring. The critical key in MTD is to shuffle the least number of hosts with an acceptable security impact and keep the shuffling frequency low. In this paper, we have proposed an SDN-oriented Cost-effective Edge-based MTD Approach (SCEMA) to mitigate Distributed Denial of Service (DDoS) attacks at a lower cost by shuffling an optimized set of hosts that have the highest number of connections to the critical servers. These connections are named edges from a graph-theoretical point of view. We have proposed a three-layer mathematical model for the network that can easily calculate the attack cost. We have also designed a system based on SCEMA and simulated it in Mininet. The results show that SCEMA has lower complexity than the previous related MTD field with acceptable performance.

Deterministic networking (DetNet) has recently drawn much attention by investigating deterministic flow scheduling. Combined with artificial intelligent (AI) technologies, it can be leveraged as a promising network technology for facilitating automated network configuration in the Industrial Internet of Things (IIoT). However, the stricter requirements of the IIoT have posed significant challenges, that is, deterministic and bounded latency for time-critical applications. This paper incorporates deep reinforcement learning (DRL) in Cycle Specified Queuing and Forwarding (CSQF) and proposes a DRL-based Deterministic Flow Scheduler (Deep-DFS) to solve the Deterministic Flow Routing and Scheduling (DFRS) problem. Novel delay aware network representations, action masking and criticality aware reward function design are proposed to make Deep-DFS more scalable and efficient. Simulation experiments are conducted to evaluate the performances of Deep-DFS, and the results show that Deep-DFS can schedule more flows than the other benchmark methods (heuristic-based and AI-based methods).

Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) technologies are increasingly recognized as key levers of the future 5G transport networks (TNs) due to their capabilities for providing deterministic Quality-ofService and enabling the coexistence of critical and best-effort services. Additionally, they rely on programmable and costeffective Ethernet-based forwarding planes. In this article, we address the flow allocation problem in 5G backhaul networks realized as asynchronous TSN networks, whose building block is the Asynchronous Traffic Shaper. We propose an offline solution, dubbed Next Generation Transport Network Optimizer (NEPTUNO), that combines exact optimization methods and heuristic techniques and leverages data analytics to solve the flow allocation problem. NEPTUNO aims to maximize the flow acceptance ratio while guaranteeing the deterministic Qualityof-service requirements of the critical flows. We carried out a performance evaluation of NEPTUNO in terms of the degree of optimality, execution time, and flow rejection ratio. Furthermore, we compare NEPTUNO with two online baseline solutions. Online methods compute the flows allocation configuration right after the flow arrives at the network, whereas offline solutions like NEPTUNO compute a long-term configuration allocation for the whole network. Our results highlight the potential of the data analytics for the self-optimization of the future 5G TNs.

Service Function Chaining (SFC) is regarded as an important concept for next-generation communication networks because it can flexibly tackle diverse usage scenarios. Due to SFC requests’ life-cycle and resource adjustment, the distribution of the remaining physical resources may become unbalanced, which brings negative effects to subsequent SFC requests as well as network operators. In this paper, we investigate the network SFC migration problem in the core cloud under the premise of considering the migration cost and the balance of physical resource distribution. We first model the SFC migration problem as an integer linear program and propose an aggressive migration strategy that can effectively reduce the imbalance of physical resource distribution. Then, we employ two state-of-the-art heuristics to allocate resources for subsequent SFC requests. The simulation results show that migrating SFC requests in the initial service queue can bring favorable feedback to subsequent requests as well as network operators. Compared to the conservative migration strategy, our proposed migration strategy can mitigate the imbalance of physical resource distribution more effectively, and thus the acceptance ratio of subsequent SFC requests, physical resources utilization, and the long-term profit of network operators can be further improved.

With the increasing demand of network slices in vertical industries, slice resource provisioning in transport networks has encountered two challenges, one is efficient slice resource provisioning in the presence of traffic uncertainty of slices, and another is flexible slice resource isolation for customizable isolation needs. In this paper, we propose an innovative flexible hybrid isolation model to support any customized resource isolation from complete isolation to full sharing, and solve the slice resource provisioning problem named Hybrid Slicing Minimum Bandwidth (HSMB) by considering traffic prediction error to mitigate the negative impact of traffic uncertainty in the proposed model. After analyzing the HSMB problem, 1) we first try to solve the problem in steps and decompose the HSMB problem into grouping sub-problem and adjusting sub-problem, 2) we then propose a low-complexity dynamic programming grouping algorithm and a fast iterative adjustment algorithm for the two sub-problems based on probabilistic feature-based analysis, 3) we combine the algorithms of the two sub-problems and further propose a linking algorithm for the potential insufficient resource dilemma and high computational complexity dilemma to improve the efficiency of the solution. The numerical results show that the proposed flexible hybrid isolation model with different factors can facilitate flexible slice isolation with customized isolation demands, while the proposed algorithm can realize efficient slice resource provisioning with a probabilistic guarantee. The comparison result shows the proposed algorithms outperform the other benchmark algorithms.

Network slicing in 5G networks can be modeled as a Virtual Network Embedding (VNE) problem, wherein the slice requests must be efficiently mapped on the core network. This process faces two major challenges: covering the maximum number of requests and providing slice isolation. Slice isolation is a mechanism for protecting the slices against Distributed Denial of Service (DDoS) attacks. To overcome these two challenges, we have proposed a novel actor-critic Reinforcement Learning (RL) model, called Slice Isolation-based Reinforcement Learning (SIRL), using five optimal graph features to create the problem environment, the form of which is changed based on a ranking scheme. The ranking procedure reduces the dimension of the features and improves learning performance. We evaluated SIRL by comparing it against four non-RL and nine state-of-the-art RL models. The average results show that the ratio of the covered requests and the damage caused by a DDoS attack of SIRL is 54% higher and 23% lower than that of the other models, respectively. It also has an acceptable learning performance and generality, regarding the reported results that show SIRL agents trained and tested with different networks outperform the other agents by 97%.

Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyzes an attention-based microservice representation approach for extracting system context. The attention-modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm's priorities in terms of average system cost and system reward.

This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach.

This paper considers the scenario of Unmanned Aerial Vehicles (UAVs) acting as flying base stations (UAV-BSs) to provide network connectivity to ground Internet of Things (IoT) devices. More precisely, we investigate the issue where a UAV-BS needs to be replaced by a new one in a seamless way. First, we formulate the issue as an optimization problem aiming to maximize the minimum transmission rate of the served IoT devices during the UAV-BS replacement process. This is translated into jointly optimizing the trajectory of the source UAV-BS (the one to be replaced) and the target UAV-BS (the replacing one), while pushing the IoT devices to seamlessly transfer their connections to the target UAV-BS. We therefore consider a target replacement zone where the UAV-BS replacement can happen, along with IoT connections transfer. Furthermore, we propose a solution based on Deep Reinforcement Learning (DRL). More precisely, we introduce a Multi-Heterogeneous Agent-based approach (MHA-DRL), where two types of agents are considered, namely the UAV-BS agents and the IoT agents. Each agent implements a DQN (Deep Q-Learning) algorithm, where UAV-BS agents learn optimal policies to perform replacement while IoT agents learn optimal policies to transfer their connections to the target UAV-BS. The conducted performance evaluations show that the proposed approach can achieve near optimal optimization.

Protecting large-scale networks, especially Software-Defined Networks (SDNs), against distributed attacks in a costeffective manner plays a prominent role in cybersecurity. One of the pervasive approaches to plug security holes and prevent vulnerabilities from being exploited is Moving Target Defense (MTD), which can be efficiently implemented in SDN as it needs comprehensive and proactive network monitoring. The critical key in MTD is to shuffle the least number of hosts with an acceptable security impact and keep the shuffling frequency low. In this paper, we have proposed an SDN-oriented Cost-effective Edge-based MTD Approach (SCEMA) to mitigate Distributed Denial of Service (DDoS) attacks with a lower cost by shuffling an optimized set of hosts have the highest number of connections to the critical servers. These connections are named edges from a graph-theoretical point of view. We have designed a system based on SCEMA and simulated it in Mininet. The results show that SCEMA has lower (52.58%) complexity than the previous related MTD methods with improving the security level by 14.32%.

Nowadays, while the demand for capacity continues to expand, the blossoming of Internet of Everything is bringing in a paradigm shift to new perceptions of communication networks, ushering in a plethora of totally unique services. To provide these services, Virtual Network Functions (VNFs) must be established and reachable by end-users, which will generate and consume massive volumes of data that must be processed locally for service responsiveness and scalability. For this to be realized, a solid cloud-network Integrated infrastructure is a necessity, and since cloud and network domains would be diverse in terms of characteristics but limited in terms of capability, communication and computing resources should be jointly controlled to unleash its full potential. Although several innovative methods have been proposed to allocate the resources, most of them either ignored network resources or relaxed the network as a simple graph, which are not applicable to Beyond 5G because of its dynamism and stringent QoS requirements. This paper fills in the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including VNF placement and assignment, traffic prioritization, and path selection considering capacity constraints as well as link and queuing delays, with the goal of minimizing overall cost. We formulate the problem as a non-linear programming model, and propose two approaches, dubbed B&B-CCRA and WF-CCRA respectively, based on the Branch & Bound and Water-Filling algorithms. Numerical simulations show that B&B-CCRA can solve the problem optimally, whereas WF-CCRA can provide near-optimal solutions in significantly less time.

Edge computing (EC) is a promising technology to support a variety of performance-sensitive intelligent applications, especially in the Industrial Internet of Things (IIoT). The sensing-data-driven applications whose task processing requires sensing data from various sensors are typical applications in IIoT systems. The placement of caching and computing edge service functions for such applications is vital to ensure system performance and resource utilization in EC-enabled IIoT systems. Therefore, this paper investigates the joint caching and computing edge service placement (JCCESP) for multiple sensing-data-driven IIoT applications in an EC-enabled IIoT system. The JCCESP problem is formulated as a Markov Decision Process (MDP). Then, a deep reinforcement learning (DRL)-based approach is proposed to address the challenges like limited prior knowledge and the heterogeneity of such IIoT systems. Under such an approach, the policy network of the DRL agent is constructed based on an encoder-decoder model to tackle various applications requiring different numbers of service functions. A REINFORCE-based method is further employed to train the policy network. Simulation results indicate that the performances achieved by our proposed approach can converge after training and are significantly superior to benchmarks.

The Software Defined Network (SDN) architecture decouples the control functionality from the forwarding devices and implements it in a separate entity known as the controller. This raises new concerns on securing the control messages exchanged between the controller and the forwarding devices. In this paper, we propose TopoTrust, a novel fully trustless authenticity and integrity verification mechanism that relies on a Blockchain protocol to detect network topology poisoning attacks, namely Host Tracking Service (HTS) and OpenFlow Discovery Protocol (OFDP). The key merit of TopoTrust is its ability to operate in a zero trust SDN environment where no controller or switch is trusted. The evaluation of our protocol shows that it can successfully detect any spoofing-based and packet tampering attacks; and up to 96% and 100% of Fast Relocation and Link Fabrication attacks respectively within a short detection time, while introducing small overhead to the network.

Unmanned Aerial Vehicles (UAVs) are set to become an integral part of 5G and beyond systems with the promise of assisting cellular communications and enabling advanced applications and services, such as public safety, caching, and virtual/mixed reality-based remote inspection. However, safe and secure navigation of UAVs is a key requisite for their integration in the airspace. The GPS spoofing is one of the major security threats to remotely and autonomously controlled UAVs. In this paper, we propose a machine learning-based, mobile network-assisted UAV monitoring and control system that allows live monitoring of UAVs' locations and intelligent detection of spoofed positions. We introduce the Convolutional Neural Network (CNN) in the edge UAV Flight Controller (UFC) to locate a UAV and detect any GPS spoofing by comparing differences between the theoretical path loss computed by UFC and the corresponding path loss reported by the connected base station (BS). To reduce the detection latency as well as to increase the detection accuracy, transfer learning is leveraged to transfer the CNN knowledge between edge servers when the UAV handovers from one BS to another. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with an accuracy rate above 88% using only one BS.

Along with the high demand for network connectivity from both end-users and service providers, networks have become highly complex; and so has become their lifecycle management. Recent advances in automation, data analysis, artificial intelligence, distributed ledger technologies (e.g., Blockchain), and data plane programming techniques have sparked the hope of the researchers’ community in exploring and leveraging these techniques towards realizing the much-needed vision of trustworthy self-driving networks (SelfDNs). In this vein, this article proposes a novel framework to empower fully distributed trustworthy SelfDNs across multiple domains. The framework vision is achieved by exploiting (i) the capabilities of programmable data planes to enable real-time in-network telemetry collection; (ii) the potential of P4 – as an important example of data plane programming languages – and AI to (re)write the source code of network components in a fashion that the network becomes capable of automatically translating a policy intent into executable actions that can be enforced on the network components; and (iii) the potential of blockchain and federated learning to enable decentralized, secure and trustable knowledge sharing between domains. A relevant use case is introduced and discussed to demonstrate the feasibility of the intended vision. Encouraging results are obtained and discussed.

Edge computing (EC) extends the ability of cloud computing to the network edge to support diverse resource-sensitive and performance-sensitive IoT applications. However, due to the limited capacity of edge servers (ESs) and the dynamic computing requirements, the system needs to dynamically update the task allocation policy according to real-time system states. Service migration is essential to ensure service continuity when implementing dynamic task allocation. Therefore, this article investigates the long-term dynamic task allocation and service migration (DTASM) problem in edge-cloud IoT systems where users’ computing requirements and mobility change over time. The DTASM problem is formulated to achieve the long-term performance of minimizing the load forwarded to the cloud while fulfilling the seamless migration constraint and the latency constraint at each time of implementing the DTASM decision. First, the DTASM problem is divided into two subproblems: 1) the user selection problem on each ES and 2) the system task allocation problem. Then, the DTASM problem is formulated as a Markov decision process (MDP) and an approach based on deep reinforcement learning (DRL) is proposed. To tackle the challenge of vast discrete action spaces for DTASM task allocation in the system with a mass of IoT users, a training architecture based on the twin-delayed deep deterministic policy gradient (DDPG) is employed. Meanwhile, each action is divided into a differentiable action for policy training and one mapped action for implementation in the IoT system. Simulation results demonstrate that the proposed DRL-based approach obtains the long-term optimal system performance compared to other benchmarks while satisfying seamless service migration.

By placing edge service functions in proximity to IoT facilities, edge computing can satisfy various IoT applications’ resource and latency requirements. Sensing-data-driven IoT applications are prevalent in IoT systems, and their task processing relies on sensing data from sensors. Therefore, to ensure the Quality of Service (QoS) of such applications in an edge-enabled IoT system, dedicated caching functions (CFs) are required to cache necessary sensing data. This article considers an edge-enabled IoT system and investigates the joint caching and computing service placement (JCCSP) problem for sensing-data-driven IoT applications. Then, deep reinforcement learning (DRL) is exploited to address the problem since it can adapt to a heterogeneous system with limited prior knowledge. In the proposed DRL-based approaches, a policy network based on the encoder–decoder model is constructed to address the issue of varying sizes of JCCSP states and actions caused by different numbers of CFs related to applications. Then, an on-policy REINFORCE-based method is adopted to train the policy network. After that an off-policy training method based on the twin-delayed (TD) deep deterministic policy gradient (DDPG) is proposed to enhance the training efficiency and experience utilization. In the proposed DDPG-based method, a weight-averaged twin-$Q$-delayed (WATQD) algorithm is introduced to reduce the bias of$Q$-value estimation. Simulation results show that our proposed DRL-based JCCSP approaches can achieve converged performance that is significantly superior to benchmarks. Moreover, compared with the original TD method, the proposed WATQD method can significantly improve the training stability.

Unmanned aerial vehicles (UAVs) are an emerging technology in the 5G-and-beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the opensource simulator can use commercial software-defined radio tools to generate fake global positioning system (GPS) signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations (BSs) at the same time. In this article, we propose a novel deep-ensemble-learning-based, mobile-network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between BSs and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multilayer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.

The efficient buffer space management in intermittently connected Internet of Things (IC-IoT) is of great importance for data delivery performance guarantee in such networks. This article considers two typical buffer space management policies for IC-IoT, i.e., buffer-space sharing (BS) and buffer-space allocation (BA). The BS policy allows the buffer space of each device to be fully shared by the exogenous packets and the packets from other devices, while the BA policy divides the buffer space into the source buffer and relay buffer for storing the two kinds of packets separately. With the help of the queueing theory and Markov chain theory, we develop a theoretical framework to capture the sophisticated queueing processes for the buffer space under either BS or BA policy, which enables the limiting distribution of the buffer occupation state to be determined. We then provide theoretical modeling for throughput and expected end-to-end delay to evaluate the fundamental performance of the IC-IoT under the BS and BA policies. Finally, extensive simulation and numerical results are presented to validate theoretical models and to demonstrate the effects of BS and BA policies on the IC-IoT performance.

Unmanned aerial vehicles (UAVs) have recently attracted both academia and industry representatives due to their utilization in tremendous emerging applications. Most UAV applications adopt visual line of sight (VLOS) due to ongoing regulations. There is a consensus between industry for extending UAVs’ commercial operations to cover the urban and populated area-controlled airspace beyond VLOS (BVLOS). There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges related to UAVs’ autonomy for detecting and avoiding static and mobile objects. An intelligent component should either be deployed onboard the UAV or at a multiaccess-edge computing (MEC) that can read the gathered data from different UAV’s sensors, process them, and then make the right decision to detect and avoid the physical collision. The sensing data should be collected using various sensors but not limited to Lidar, depth camera, video, or ultrasonic. This article proposes probabilistic and deep-reinforcement-learning (DRL)-based algorithms for avoiding collisions while saving energy consumption. The proposed algorithms can be either run on top of the UAV or at the MEC according to the UAV capacity and the task overhead. We have designed and developed our algorithms to work for any environment without a need for any prior knowledge. The proposed solutions have been evaluated in a harsh environment that consists of many UAVs moving randomly in a small area without any correlation. The obtained results demonstrated the efficiency of these solutions for avoiding the collision while saving energy consumption in familiar and unfamiliar environments.

Zero-touch network and service management (ZSM) exploits network function virtualization (NFV) and software-defined networking (SDN) to efficiently and dynamically orchestrate different service function chaining (SFC), whereby reducing capital expenditure and operation expenses. The SFC is an optimization problem that shall consider different constraints, such as Quality of Service (QoS), and actual resources, to achieve cost-efficient scheduling and allocation of the service functions. However, the large-scale, complexity and security issues brought by virtualized IoT networks, which embrace different network segments, e.g., Fog, Edge, Core, Cloud, that can also exploit proximity (computation offloading of virtualized IoT functions to the Edge), imposes new challenges for ZSM orchestrators intended to optimize the SFC, thereby achieving seamless user-experience, minimal end-to-end delay at a minimal cost. To cope with these challenges, this paper proposes a cost-efficient optimized orchestration system that addresses the whole life-cycle management of different SFCs, that considers QoS (including end-to-end delay, bandwidth, jitters), actual capacities of Virtual Network Functions (VNFs), potentially deployed across multiple Clouds-Edges, in terms of resources (CPU, RAM, storage) and current network security levels to ensure trusted deployments. The proposed orchestration system has been implemented and evaluated in the scope of H2020 Anastacia EU project,1showing its feasibility and performance to efficiently manage SFC, optimizing deployment costs, reducing overall end-to-end delay and optimizing VNF instances distribution.

While the 5G network technology is maturing and the number of commercial deployments is growing, the focus of the networking community is shifting to services and service delivery. 5G networks are designed to be a common platform for very distinct services with different characteristics. Network Slicing has been developed to offer service isolation between the different network offerings. Cloud-native services that are composed of a set of inter-dependent micro-services are assigned into their respective slices that usually span multiple service areas, network domains, and multiple data centers. Due to mobility events caused by moving end-users, slices with their assigned resources and services need to be re-scoped and re-provisioned. This leads to slice mobility whereby a slice moves between service areas and whereby the inter-dependent service and resources must be migrated to reduce system overhead and to ensure low-communication latency by following end-user mobility patterns. Recent advances in computational hardware, Artificial Intelligence, and Machine Learning have attracted interest within the communication community to study and experiment self-managed network slices. However, migrating a service instance of a slice remains an open and challenging process, given the needed co-ordination between inter-cloud resources, the dynamics, and constraints of inter-data center networks. For this purpose, we introduce a Deep Reinforcement Learning based agent that is using two different algorithms to optimize bandwidth allocations as well as to adjust the network usage to minimize slice migration overhead. We show that this approach results in significantly improved Quality of Experience. To validate our approach, we evaluate the agent under different configurations and in real-world settings and present the results.

Deterministic Networking (DetNet) has recently attracted much attention. It aims at studying the deterministic bounded latency and low latency variation for time-sensitive applications (e.g., industrial automation). To improve the quality of service (QoS) guarantee and make the network management efficient, it is desirable for Internet Service Provider (ISP) to obtain an optimal service function chain (SFC) provision strategy while providing deterministic service performance for the time-sensitive applications. In this paper, we will study the deterministic SFC lifetime management problem in beyond 5G edge fabric with the objective of maximizing the overall profits and ensuring the deterministic latency and jitter of SFC requests. We first formulate this problem as a mathematical model with the maximal profits for ISP. Then, the novel Deterministic SFC Deployment algorithm (Det-SFCD) and SFC Adjustment algorithm (Det-SFCA) due to traffic load variation are proposed to efficiently solve the SFC lifetime management problem. Extensive simulation results show that our proposed algorithms can achieve better performance in terms of SFC request acceptance rates, overall profits and latency variation compared with the benchmark algorithm.

Even a wider set of highly critical and latency-sensitive applications with resource needs from the access network and the edge will be supported by the 6G networks. Therefore, the 6G network will deal with diversification of service platforms. Optimizing the resource consumption of network slicing on top of a shared infrastructure will become essential to keep the operating costs on an acceptable level. Each vertical, e.g., eMBBPlus, BigCom, holographic and tactile communications can run on top of network slice with specific KPIs. Different verticals can have contradicting requirements running on top of the same infrastructure. This paper investigates the orchestration of network services within a federated end-to-end network slice, which may span over multiple cloud domains as expected to be a common scenario in 6G deployments. We introduce three optimization solutions that consider two conflicting objectives, the end-to-end delay and service relocation, for orchestrating network slice. While the first solution optimizes the end-to-end delay, the second solution optimizes the service relocation. Meanwhile, the third solution leverages the bargaining game theory for achieving optimal Pareto fair trade-off configuration to optimize both objectives. The simulation results demonstrate the efficiency of the proposed solutions to achieve their main design goals

The explosion of the number of things connected to the Internet gave birth to a new set of services. Customers now are expecting next-generation networks to satisfy vertical applications with different requirements. For instance, 5G networks can carry these various demands by logically dividing the physical network into multiple slices, each slice features specific characteristics based on the type of service. Software-Defined Network (SDN) and Network Function Virtualization (NFV) introduced the term “Network Softwarization” which is the main enabler of network slicing and 5G networks. Specifically, SDN separates the control plane from the data plane, this concept brings many benefits such as dynamism, flexibility, and innovation. However, when it comes to the assurance of Quality of Service (QoS), SDN is still behind. Not only SDN was not optimized for real-time communications, but also SDN networks cannot offer a deterministic End-to-End (E2E) delay. In this paper, we study OpenFlow-based communications with a focus on the delay. The modeling of queuing delays shows a stable linear development of the mean waiting time under a probability of 0.2 that 10 switches generate packet_in message. After that, the increase becomes exponential and thus hard to predict.

Multi-Access Edge Computing (MEC) is considered as one of the key enablers of Unmanned Aerial Vehicles (UAVs) use cases. However, the envisioned MEC deployments introduce new challenges related to the management of the mobility of services across the distributed MEC hosts, following the UAVs movements and possible handovers to ensure sustainable Quality-of-Service (QoS). A major challenge for MEC service mobility is the decision-making on where and when to relocate services. In this paper, we motivate the use of the predefined flight plans of UAVs for devising proactive relocation strategies that can deal efficiently with realistic asynchronous relocation processes. Moreover, we formulate the Proactive Service Relocation for UAV (PSRU) problem using linear programming, and we validate the gains introduced by the proactive relocation strategy and the use of the predefined flight plans of UAVs.

This paper investigates the fundamental connection steering issue in cellular-enabled Unmanned Aerial Vehicles (UAVs), whereby a UAV steers the cellular connection across multiple Mobile Network Operators (MNOs) for ensuring enhanced Quality-of-Service (QoS). We first formulate the issue as an optimization problem for minimizing the maximum outage probability. This is a nonlinear and nonconvex problem that is generally difficult to be solved. To this end, we propose a new approach for solving the optimization problem based on Deep Reinforcement Learning (DRL), considering two important reinforcement learning algorithms (i.e., Deep Q-Learning (DQN) and Advantage Actor Critic (A2C)). Simulation results show that under the proposed approach, the UAVs can make optimal decisions to select the most suitable connection with MNOs for achieving the minimization of the maximum outage probability. Furthermore, the results also show that in our new approach, the A2C-based algorithm is better than the DQN-based one, especially when the number of MNOs increases, while the DQN-based algorithm can be executed in a shorter time.

Along with the increasing demand for latency-sensitive services and applications, Deterministic Network (DetNet) concept has been recently proposed to investigate deterministic latency assurance for services featured with bounded latency requirements in 5G edge networks. The Network Function Virtualization (NFV) technology enables Internet Service Providers (ISPs) to flexibly place Virtual Network Functions (VNFs) achieving performance and cost benefits. Then, Service Function Chains (SFC) are formed by steering traffic through a series of VNF instances in a predefined order. Moreover, the required network resources and placement of VNF instances along SFC should be optimized to meet the deterministic latency requirements. Therefore, it is significant for ISPs to determine an optimal SFC deployment strategy to ensure network performance while improving the network revenue. In this paper, we jointly investigate the resource allocation and SFC placement in 5G edge networks for deterministic latency assurance. We formulate this problem as a mathematic programming model with the objective of maximizing the overall network profit for ISP. Furthermore, a novel Deterministic SFC deployment (Det-SFCD) algorithm is proposed to efficiently embed SFC requests with deterministic latency assurance. The performance evaluation results show that the proposed algorithm can provide better performance in terms of SFC request acceptance rate, network cost reduction, and network resource efficiency compared with benchmark strategy.

The integration of cellular networks and unmanned aerial vehicle (UAV) swarm communications is expected to be a promising technology to provide ubiquitous network connectivity for various UAV assisted Internet of Things (IoT) applications. To support these IoT applications with stringent requirement of rate performance, this paper explores the maximum sum rate performance for the cellular-connected UAV swarm communications. The sum rate maximization can be formulated as a nonlinear and nonconvex optimization problem with the constraints of transmit power of UAVs, elevation angle, azimuth angle and height of antenna array equipped at base station (BS). According to the Karush–Kuhn–Tucker (KKT) optimality conditions and the standard interference function, we propose an iterative algorithm to solve the problem, wherein the problem is transformed into a concave optimization problem by utilizing the rate approximation and logarithmic transformations. The iterative algorithm is proved to converge to a global solution for the approximated concave optimization problem. Finally, simulation results are provided to indicate the effect of some important system parameters on the sum rate performance in the system.

With the emergence of 5G, wireless sensor networks, and related technologies, Internet of Things (IoT) has gained prominence as an emerging paradigm to meet the demands of flexible, agile, and ubiquitous accessibility of cyberspace from physical systems. However, the current centralized IoT architecture is heavily restricted by the problems of single points of failure, data privacy, security, and robustness. Recently, blockchains have been found attractive as potential solutions to some of these problems, due to their ability to maintain immutable open ledgers that are accessible to everyone but are tamper-proof. In addition, rapid development of edge computing has enabled a large range of new IoT applications. Edge computing pushes cloud services from the network core to the network edges in closer proximity to IoT devices. Thus, blockchain and edge computing are attractive technologies to meet new and existing challenges by enabling new IoT applications and services through secure, reliable, flexible, and powerful devices and systems while motivating new business models in the growing digital economies. They can provide attractive solutions, such as schemes for decentralized services, service virtualization, rapid resource optimization, and flexible and reliable management and maintenance.

This article focuses on the secure routing problem in the decentralized Internet of Things (IoT). We consider a typical decentralized IoT scenario composed of peer legitimate devices, unauthorized devices (eavesdroppers), and selfish helper jamming devices (jammers), and propose a novel incentive jamming-based secure routing scheme. For a pair of source and destination, we first provide theoretical modeling to reveal how the transmission security performance of a given route is related to the jamming power of jammers in the IoT. Then, we design an incentive mechanism with which the source pays some rewards to stimulate the artificial jamming among selfish jammers, and also develop a two-stage Stackelberg game framework to determine the optimal source rewards and jamming power. Finally, with the help of the theoretical modeling as well as the source rewards and jamming power setting results, we formulate a shortest weighted path-finding problem to identify the optimal route for secure data delivery between the source-destination pair, which can be solved by employing the Dijkstra's or Bellman-Ford algorithm. We prove that the proposed routing scheme is individually rational, stable, distributed, and computationally efficient. Simulation and numerical results are provided to demonstrate the performance of our routing scheme.

Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility's impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers' multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods' efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic.

Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this article investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively.

The promise of disparate features envisioned by the 3GPP for 5G, such as offering enhanced Mobile Broadband connectivity while providing massive Machine Type Communications likely with very low data rates and maintaining Ultra Reliable Low Latency Communications requirements, create a very challenging environment for protecting the 5G networks themselves and associated assets. To overcome such complexity, future 5G networks must employ a very high degree of network and service management automation, which is a security challenge by itself as well as an opportunity for smarter and more efficient security functions. In this paper, we present the smart, trustworthy and liable 5G security platform being designed and developed in the INSPIRE-5Gplus1 project. This platform takes advantage of new techniques such as Machine Learning (ML), Artificial Intelligence (AI), Distributed Ledger Technologies (DLT), network softwarization and Trusted Execution Environment (TEE) for closed-loop and end-to-end security management following a zero-touch model in 5G and Beyond 5G networks. To this end, we specifically elaborate on two key aspects of our platform, namely security management with Security Service Level Agreements (SSLAs) and liability management, in addition to the description of the overall architecture.

Network Function Virtualization (NFV) and Software Distributed Networking (SDN) technologies play a crucial role in enabling 5G system and beyond. A synergy between these both technologies has been identified for enabling a new concept dubbed service function chains (SFC) that aims to reduce both the capital expenditures (CAPEX) and operating expenses (OPEX). The SFC paradigm considers different constraints and key performance indicators (KPIs), that includes QoS and different resources, for enabling network slice services. However, the large-scale, complexity and security issues brought by these technologies create an extra overhead for ensuring secure network slicing. To cope with these challenges, this paper proposes a cost-efficient optimized SFC management system that enables the creation of SFCs for enabling efficient and secure network slices. The proposed system considers the network and computational resources and current network security levels to ensure trusted deployments. The simulation results demonstrated the efficiency of the proposed solution for achieving its designed objectives. The proposed solution efficiently manages the SFCs by optimizing deployment costs and reducing overall end-to-end delay.

The envisioned key role of Unmanned Aerial Vehicles (UAVs) in assisting the upcoming mobile networks calls for addressing the challenge of their secure and safe integration in the airspace. The GPS spoofing is a prominent security threat of UAVs. In this paper, we propose a 5G-assisted UAV position monitoring and anti-GPS spoofing system that allows live detection of GPS spoofing by leveraging Uplink received signal strength (RSS) measurements to cross-check the position validity. We introduce the Adaptive Trustable Residence Area (ATRA); a novel strategy to determine the trust area within which the UAV's GPS position should be located in order to be considered as non-spoofed. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with a rate of above 95%.

Dynamic Adaptive Streaming over HTTP (DASH) is becoming the de facto method for effective video traffic delivery at large scale. Its primer success factor returns to the full autonomy given to the streaming clients making them smarter and enabling decentralized logic of video quality decision at granular video chunks following a pull-based paradigm. However, the pure autonomy of the clients inherently results in an overall selfish environment where each client independently strives to improve its Quality of Experience (QoE). Consequently, the clients will hurt each other, including themselves, due to their limited scope of perception. This shortcoming could be addressed by employing a mechanism that has a global view, hence could efficiently manage the available resources. In this paper, we propose a game theoretical-based approach to address the issue of the client's selfishness in multi-server setup, without affecting its autonomy. Particularly, we employ the coalitional game framework to affect the clients to the best server, ultimately to maximize the overall average quality of the clients while preventing re-buffering. We validate our solution through extensive experiments and showcase the effectiveness of the proposed solution.

5G system and beyond will build on the network slicing for offering high customizable services with different requirements that run on top of the same shared infrastructure. Each network slice, such as Ultra-Reliable Low latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB), has different requirements that can be even contradicting from a slice to another. A network slice consists of a set of physical or virtual network functions (VNF/PNF) that have various capabilities and run across multiple administrative and cloud domains of different technology. A user can simultaneously request multiple services from different network slices. In this paper, we address the problem of initial placement and live migration of multiple mobile services across centralized and edge cloud by taking into account service types, network conditions and users' mobility features. As a solution to this problem, in this paper, we suggest and evaluate a solution that orchestrates the network services in a cost-efficient way, ensuring that each user could be simultaneously served by multiple slices while perceiving a high QoS and ensuring that the service level agreements (SLAs) of the consumed services are not violated.

Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) standards come to satisfy the needs of many industries for deterministic network services. That is the ability to establish a multi-hop path over an IP network for a given flow with deterministic Quality of Service (QoS) guarantees in terms of latency, jitter, packet loss, and reliability. In this work, we propose a reinforcement learning-based solution, which is dubbed LEARNET, for the flow scheduling in deterministic asynchronous networks. The solution leverages predictive data analytics and reinforcement learning to maximize the network operator's revenue. We evaluate the performance of LEARNET through simulation in a fifth-generation (5G) asynchronous deterministic backhaul network where incoming flows have characteristics similar to the four critical 5GQoS Identifiers (5QIs) defined in Third Generation Partnership Project (3GPP) TS 23.501 V16.1.0. Also, we compared the performance of LEARNET with a baseline solution that respects the 5QIs priorities for allocating the incoming flows. The obtained results show that, for the scenario considered, LEARNET achieves a gain in the revenue of up to 45% compared to the baseline solution.

The Next Generation of Mobile Networks (NGMN) alliance advocates the use of different means to support vehicular communications. This aims to cope with the massive data generated by these devices which could affect the Quality of Service (QoS) of the associated applications, but also the overall operation carried out by the vehicles. However, efficient communication strategies must be considered in order to select, for each vehicle, the communication mean ensuring the best QoS. In this paper, we tackle this issue and we propose efficient communication strategies for Unmanned Aerial Vehicles (UAVs). In addition to direct UAV-to-Infrastructure communications (U2I), we also consider UAV-to-UAV scheme (U2U) to transmit data via relay UAVs. The goal is to select for each UAV the best communication strategy and the relay node to maximize the spectral efficiency. The expressions of the effective rate are derived for the different strategies and the problem is formulated using linear programming. Performance evaluations are conducted and the obtained results demonstrate the effectiveness of the proposed solution.

Unmanned aerial vehicles (UAVs) is one of the promising technology in the future. A recent study claims that by 2026, the commercial UAVs, for both corporate and customer applications, will have an annual impact of 31 billion to 46 billion on the country's GDP. Shortly, many UAVs will be flying everywhere. For this reason, there is a need to suggest efficient mechanisms for preventing the collisions among the UAVs. Traditionally, the collisions are prevented using dedicated sensors, however, those would generate uncertainty in their reading due to their external conditions sensitivity. From another side, the use of those sensors could create an extra overhead on the UAVs in terms of cost and energy consumption. To deal with these challenges, in this paper, we have suggested a solution that leverages the chance-constrained optimization technique for avoiding the collision in an energy-efficient manner. Building on the expressions for the non-central Chi-square CDF and expected value, and through the convexification of the resulting expressions, the chance-constrained optimization program is transformed into a convex Mixed Binary Nonlinear one. The resulting program allows us to find the optimal safety distance that extends UAVs life-time and allows every UAV to move with a guaranteed probability of collision between any pair of UAVs.

The expected high bandwidth of 5G and the envisioned massive number of connected devices will open the door to increased and sophisticated attacks, such as application-layer DDoS attacks. Application-layer DDoS attacks are complex to detect and mitigate due to their stealthy nature and their ability to mimic genuine behavior. In this work, we propose a robust application-layer DDoS self-protection framework that empowers a fully autonomous detection and mitigation of the application-layer DDoS attacks leveraging on Deep Learning (DL) and SDN enablers. The DL models have been proven vulnerable to adversarial attacks, which aim to fool the DL model into taking wrong decisions. To overcome this issue, we build a DL-based application-layer DDoS detection model that is robust to adversarial examples. The performance results show the effectiveness of the proposed framework in protecting against application-layer DDoS attacks even in the presence of adversarial attacks.

Duplicated download has been a big problem that affects the users' quality of service/experience (QoS/QoE) of current mobile networks. Edge caching and Device-to-Device communication are two promising technologies to release the pressure of repeated traffic downloading from the cloud. There are many researches about the edge caching policy. However, these researches have some limitations in the real scenarios. Traditional methods are lacking the self-adaptive ability in the dynamic environment and privacy issues will occur in centralized learning methods. In this paper, based on the virtue of Deep Q-Network (DQN), we propose a weighted distributed DQN model (WDDQN) to solve the cache replacement problem. Our model enables collaboratively to learn a shared predictive model. Trace-driven simulation results show that our proposed model outperforms some classical and state-of-the-art schemes.

Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the expectation convergence of FADE. Trace-driven simulation results demonstrate the effectiveness of the proposed FADE framework on reducing the performance loss and average delay, offloading backhaul traffic, and improving the hit rate.

5G is raising high expectations, offering new avenues for research, and paving the way to a safer and highperforming set of innovative use-cases. For use-cases related for instance to Internet of Things (IoT), self-driving vehicles, or unmanned aerial vehicles (UAVs), the programming and management of the underlying network resources will be carried out in a dynamic and scalable manner. In this vein, many enabling technologies are adopted by service providers, cloud providers and enterprises, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV).

Network Slicing (NS) is a key enabler of the upcoming 5G and beyond system, leveraging on both Network Function Virtualization (NFV) and Software Defined Networking (SDN), NS will enable a flexible deployment of Network Functions (NFs) belonging to multiple Service Function Chains (SFC) over various administrative and technological domains. Our novel architecture addresses the complexities and heterogeneities of verticals targeted by 5G systems, whereby each slice consists of a set of SFCs, and each SFC handles specific traffic within the slice. In this paper, we propose and evaluate a MILP optimization model to solve the complexities that arise from this new environment. Our proposed model enables a cost-optimal deployment of network slices allowing a mobile network operator to efficiently allocate the underlying layer resources according to its users' requirements. We also design a greedy-based heuristic to investigate the possible trade-offs between execution runtime and network slice deployment. For each network slice, the proposed solution guarantees the required delay and the bandwidth, while efficiently handling the use of both the VNF nodes and the physical nodes, reducing the service provider's Operating Expenditure (OPEX).

Network slicing allows different applications and network services to be deployed on virtualized resources running on a common underlying physical infrastructure. Developing a scalable system for the orchestration of end-to-end (E2E) mobile network slices requires careful planning and very reliable algorithms. In this paper, we propose a novel E2E Network Slicing Orchestration System (NSOS) and a Dynamic Auto-Scaling Algorithm (DASA) for it. Our NSOS relies strongly on the foundation of a hierarchical architecture that incorporates dedicated entities per domain to manage every segment of the mobile network from the access, to the transport and core network part for a scalable orchestration of federated network slices. The DASA enables the NSOS to autonomously adapt its resources to changes in the demand for slice orchestration requests (SORs) while enforcing a given mean overall time taken by the NSOS to process any SOR. The proposed DASA includes both proactive and reactive resource provisioning techniques. The proposed resource dimensioning heuristic algorithm of the DASA is based on a queuing model for the NSOS, which consists of an open network of G/G/m queues. Finally, we validate the proper operation and evaluate the performance of our DASA solution for the NSOS by means of system-level simulations.

5G is the next telecommunications standards that will enable the sharing of physical infrastructures to provision ultra shortlatency applications, mobile broadband services, Internet of Things, etc. Network slicing is the virtualization technique that is expected to achieve that, as it can allow logical networks to run on top of a common physical infrastructure and ensure service level agreement requirements for different services and applications. In this vein, our paper proposes a novel and complete solution for planning network slices of the LTE EPC, tailored for the enhanced Mobile BroadBand use case. The solution defines a framework which consists of: i) an abstraction of the LTE workload generation process, ii) a compound traffic model, iii) performance models of the whole LTE network, and iv) an algorithm to jointly perform the resource dimensioning and network embedding. Our results show that the aggregated signaling generation is a Poisson process and the data traffic exhibits self-similarity and long-range-dependence features. The proposed performance models for the LTE network rely on these results. We formulate the joint optimization problem of resources dimensioning and embedding of a virtualized EPC and propose a heuristic to solve it. By using simulation tools, we validate the proper operation of our solution.

We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN service instance which is created upon a content provider's request, is autonomously managed, and spans multiple, potentially heterogeneous, edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video service, we derive empirical models of video QoE as a function of service workload, which, coupled with multi-level service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.

Software Defined Networking (SDN) is a driving technology for enabling the 5th Generation of mobile communication (5G) systems offering enhanced network management features and softwarization. This paper concentrates on reducing the operating expenditure (OPEX) costs while i) increasing the quality of service (QoS) by leveraging the benefits of queuing and multi-path forwarding in OpenFlow, ii) allowing an operator with an SDN-enabled network to efficiently allocate the network resources considering mobility, and iii) reducing or even eliminating the need for over-provisioning. For achieving these objectives, a QoS aware network configuration and multipath forwarding approach is introduced that efficiently manages the operation of SDN enabled open virtual switches (OVSs). This paper proposes and evaluates three solutions that exploit the strength of QoS aware routing using multiple paths. While the two first solutions provide optimal and approximate optimal configurations, respectively, using linear integer programming optimization, the third one is a heuristic that uses Dijkstra short-path algorithm. The obtained results demonstrate the performance of the proposed solutions in terms of OPEX and execution time.

Cellular networks are expected to be the main communication infrastructure to support the expanding applications of Unmanned Aerial Vehicles (UAVs). As these networks are deployed to serve ground User Equipment (UEs), several issues need to be addressed to enhance cellular UAVs' services. In this article, we propose a realistic communication model on the downlink, and we show that the Quality of Service (QoS) for the users is affected by the number of interfering BSs and the impact they cause. The joint problem of sub-carrier and power allocation is therefore addressed. Given its complexity, which is known to be NP-hard, we introduce a solution based on game theory. First, we argue that separating between UAVs and UEs in terms of the assigned sub-carriers reduces the interference impact on the users. This is materialized through a matching game. Moreover, in order to boost the partition, we propose a coalitional game that considers the outcome of the first one and enables users to change their coalitions and enhance their QoS. Furthermore, a power optimization solution is introduced, which is considered in the two games. Performance evaluations are conducted, and the obtained results demonstrate the effectiveness of the propositions.

We study transmitter authentication in massive multiple-input multiple-output (MIMO) systems with non-ideal hardware for the fifth generation (5G) and beyond networks. A new channel-based authentication scheme is proposed by taking hardware impairments into account. Based on signal processing theory, we first formulate channel estimation under hardware impairments and determine error covariance matrix to assess the quantity caused by hardware impairments on authentication performance. With the help of hypothesis testing and matrix transformation theories, we are then able to derive exact expressions for the probabilities of false alarm and detection under different channel covariance matrix models. Extensive simulations are carried out to validate theoretical results and illustrate the efficiency of the proposed scheme. Impacts of system parameters on performance are revealed as well.

Given the indispensable need for a reliable network architecture to cope with 5G networks, 3GPP introduced a covet technology dubbed 5G Service Based Architecture (5G-SBA). Meanwhile, Multi-access Edge Computing (MEC) combined with SBA conveys a better experience to end- users by bringing application hosting from centralized data centers down to the network edge, closer to consumers and the data generated by applications. Both the 3GPP and the ETSI proposals offered numerous benefits, particularly the ability to deliver highly customizable services. Nevertheless, compared to large data- centers that tolerate the hosting of standard virtualization technologies (Virtual Machines (VMs) and servers), MEC nodes are characterized by lower computational resources, thus the debut of lightweight micro-service based applications. Motivated by the deficiency of current micro-services-based applications to support users' mobility and assuming that all these issues are under the umbrella of Service Function Chain (SFC) migrations, we aim to introduce, explain and evaluate diverse SFC migration patterns. The obtained results demonstrate that there is no clear vanquisher, but selecting the right SFC migration pattern depends on users' motion, applications' requirements, and MEC nodes' resources.

The increased use of Unmanned Aerial Vehicles (UAVs) in numerous domains, will result in high traffic densities in the low-altitude airspace. Consequently, UAVs Traffic Management (UTM) systems that allow the integration of UAVs in the low-altitude airspace are gaining a lot of momentum. Furthermore, the 5 h generation of mobile networks (5G) will most likely provide the underlying support for UTM systems by providing connectivity to UAVs, enabling the control, tracking and communication with remote applications and services. However, UAVs may need to communicate with services with different communication Quality of Service (QoS) requirements, ranging form best-effort services to Ultra-Reliable Low-Latency Communications (URLLC) services. Indeed, 5G can ensure efficient Quality of Service (QoS) enhancements using new technologies, such as network slicing and Multi-access Edge Computing (MEC). In this context, Network Functions Virtualization (NFV) is considered as one of the pillars of 5G systems, by providing a QoS-aware Management and Orchestration (MANO) of softwarized services across cloud and MEC platforms. The MANO process of UAV's services can be enhanced further using the information provided by the UTM system, such as the UAVs' flight plans. In this paper, we propose an extended framework for the management and orchestration of UAVs' services in MECNFV environment by combining the functionalities provided by the MEC-NFV management and orchestration framework with the functionalities of a UTM system. Moreover, we propose an Integer Linear Programming (ILP) model of the placement scheme of our framework and we evaluate its performances. The obtained results demonstrate the effectiveness of the proposed solutions in achieving its design goals.

The consideration of mobile networks as a communication infrastructure for unmanned aerial vehicles (UAVs) creates a new plethora of emerging services and opportunities. In particular, the availability of different mobile network operators (MNOs) can be exploited by the UAVs to steer connection to the MNO ensuring the best quality of experience (QoE). While the concept of traffic steering is more known at the network side, extending it to the device level would allow meeting the emerging requirements of today's applications. In this vein, an efficient steering solutions that take into account the nature and the characteristics of this new type of communication is highly needed. The authors introduce, in this paper, a mechanism for steering the connection in mobile network-enabled UAVs. The proposed solution considers a realistic communication model that accounts for most of the propagation phenomena experienced by wireless signals. Moreover, given the complexity of the related optimization problem, which is inherent from this realistic model, the authors propose a solution based on coalitional game. The goal is to form UAVs in coalitions around the MNOs, in a way to enhance their QoE. The conducted performance evaluations show the potential of using several MNOs to enhance the QoE for mobile network-enabled UAVs and prove the effectiveness of the proposed solution.

HTTP Adaptive Streaming (HAS) is becoming the de-facto video delivery technology over best- effort networks nowadays, thanks to the myriad advantages it brings. However, many studies have shown that HAS suffers from many Quality of Experience (QoE)-related issues in the presence of competing players. This is mainly caused by the selfishness of the players resulting from the decentralized intelligence given to the player. Another limitation is the bottleneck link that could happen at any time during the streaming session and anywhere in the network. These issues may result in wobbling bandwidth perception by the players and could lead to missing the deadline for chunk downloads, which result in the most annoying issue consisting of rebuffering events. In this paper, we leverage the Software-Defined Networking paradigm to take advantage of the global view of the network and its powerful intelligence that allows reacting to the network changing conditions. Ultimately, we aim at preventing the re-buffering events, resulting from deadline misses, and ensuring high QoE for the accepted clients in the system. To this end, we use Deterministic Network Calculus (DNC) to guarantee a maximum delay for the download of the video chunks while maximizing the perceived video quality. Simulation results show that the proposed solution ensures high efficiency for the accepted clients without any rebuffering events which result in high user QoE. Consequently, it might be highly useful for scenarios where video chunks should be strictly downloaded on- time or ensuring low delay with high user QoE such as serving video premium subscribers or remote control/driving of an autonomous vehicle in future 5G mobile networks.

Network Function Virtualization ecosystem enables the automation of deployment and scaling of softwarized network services (SNSs), thus reducing their operational expenditures. This enables operators to handle workload fluctuations, to keep the desired performance, with great agility and reduced costs. However, to realize the automation of such management practices, it is needed to determine the amount of required resources to allocate the SNS so that its performance requirements are met. This problem is commonly referred to as resources dimensioning problem. In this paper, we address the derivation of a closed-form expression for the optimal resources dimensioning of an SNS in terms of cost or energy efficiency. The performance requirement considered for the SNS is a limit on its mean response time. The performance model considered for the SNS is practical and accurate. The usefulness of the derived closed-form expression is successfully validated by means of simulation. The scenario considered for the validation is a video optimization chain located at the SGi-LAN of a mobile network.

The fifth generation (5G) and beyond wireless networks are foreseen to operate in a fully automated manner, in order to fulfill the promise of ultra-short latency, meet the exponentially increasing resource requirements, and offer the quality of experience (QoE) expected from end- users. Among the ingredients involved in such environments, network slicing enables the creation of logical networks tailored to support specific application demands (i.e., service level agreement SLA, quality of service QoS, etc.) on top of physical infrastructure. This creates the need for mechanisms that can collect spatiotemporal information on users' service consumption, and identify meaningful insights and patterns, leveraging machinelearning techniques. In this vein, our paper proposes a framework dubbed "SOCL" for the Service Oriented CLustering, analysis and profiling of users (i.e., humans, sensors, etc.) when consuming enhanced Mobile BroadBand (eMBB) applications, internet of things (IoT) services, and unmanned aerial vehicles services (UAVs). SOCL relies mainly on the realistic network simulation framework "network slice planner" (NSP), and two clustering methods namely K-means and hierarchical clustering. The obtained results showcase interesting features, highlighting the benefit of the proposed framework.

Unmanned Aerial Vehicles (UAVs) can offer a plethora of applications, provided that the appropriate ground control and complementary computing and storage services are available in close proximity. To accomplish this, edge cloud platforms, deployed at or close to the base stations, are essential. However, current UAV travel planning does not take into account the resource constraints of such edge cloud platforms. This paper introduces an aligned process for UAV flight planning and networking resource allocation, minimizing the total traveled distance. It proposes two solutions, namely (i) a Multi-access Edge Computing (MEC)-Aware UAVs' Path planning (MAUP) based on integer linear programming and (ii) an Accelerated MAUP (AMAUP), i.e., a heuristic and scalable approach that adopts the shortest weighted path algorithm considering directed graphs. The performance of the two solutions are evaluated using computer-based simulations and the obtained results demonstrate the effectiveness of the two solutions in achieving their design goals.

Along the increasing demands for complex networking services, big networking infrastructures, and network service providers aim to provide customized services to the users. Complex services require a composition of intermediate networking Service Functions (SFs). Service Function Chaining (SFC) is a networking concept enabling to compose and force the order of invoking SFs. New technologies such as Software Defined Networking and Network Function Virtualization promote the SFC dynamic composition and management. However, it is still challenging to implement flexible and scalable SF chains in large networking infrastructures. In this paper, we discuss the concept of hierarchical SFC and show its ability to enhance the network scalability and to simplify SFC management. Moreover, we propose a novel traffic steering method to implement hierarchical SFC without requiring data plane components modification. Our proposed approach enhances the scalability of hierarchical SFC and eases its deployment.

The efficient control of mobile network-enabled unmanned aerial vehicles (UAVs) is targeted in this paper. In particular, a downlink scenario is considered, in which control messages are sent to UAVs via cellular base stations (BSs). Unlike terrestrial user equipment (UEs), UAVs perceive a large number of BSs, which can lead to increased interference causing poor or even unacceptable throughput. This paper proposes a framework for efficient control of UAVs. First, a communication model is introduced for flying UAVs taking into account interference, path loss and fast fading. The characteristics of UAVs make such model different compared to traditional ones. Thereafter, in order to ensure the efficient control, a solution is proposed for reducing interference. This is achieved by efficiently assigning sub-carriers to the UAVs in a way to reduce interference. A maximum independent set formulation is proposed along with an algorithm for optimal sub-carrier allocation. The obtained results demonstrate the efficiency of the proposed solution in terms of enhancing the link quality of UAVs.

The key concept beneath Multi-Access Edge Computing (MECs) is to place cloud resources in closer proximity to end-users, through the installation of small-scale cloud infrastructures at the network edge. In MEC environments, we identify two issues: 1) data about users' activities are not always available, and 2) the available virtual resource planning mechanisms (i.e., algorithms for the placement of Virtual Network Functions - VNFs) are not efficient enough to fulfill the QoS requirements and deployment costs. In this vein, we design a layered framework to define the presence of Mobile BroadBand User Equipments (UEs) and automate the underlying virtual resource placement and management based on the Fuzzy Logic Controller paradigm (FLC). Experimentation results show that our framework, compared to baseline solutions, achieves good performance results; the end-to-end delay is enhanced by 25%, the resource consumption is reduced by 30%, and the environmental impact, reflected by the carbon footprint that depends on the amount of deployed Virtual Machines (VMs), is reduced by 50%.

The Trickle algorithm is one of the main components of the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). Trickle is used to maintain and to control messages in the network. However, this algorithm has some limitations in terms of power consumption, overhead, and convergence time. In this paper, we present a new improvement of the Standard Trickle algorithm, named Flexible Trickle Algorithm (FL-Trickle). Based on the T time parameters and the Minimum Interval values, the new trickle allows reducing the delay needed to transmit control messages as well as the transmission rate. A comparison has been made between the FL-Trickle, the standard Trickle and the Trickle-Plus algorithms. Simulation results show that our proposed approach outperforms both the standard trickle and the Trickle-Plus in terms of convergence time, overhead and energy consumption. FL-Trickle increases the convergence time by up 58%, reduces the overhead by up to 69% and the energy consumption by up to 62%.

The great attraction that unmanned vehicles have gained in the past years has marked this century as the era of automation. In particular, automated vehicles have recently become the center of attention of many research activities. All of which have had one goal in common: achieving a complete or partial automation of driving functions, all while ensuring safety and security of other traffic agents. It is with this purpose in mind that the concept of dynamic maps has been introduced to allow vehicles to be aware of their surroundings and have precise knowledge of their environment and all of its components. Aiming for a mapping system that sets out a layered view of a vehicle vicinity ranging from highly-static to highly-dynamic data layers, researchers have taken different approaches. In this paper, we present a system, inspired from the concept of dynamic maps, that collects data from user vehicles and maps them out by location and time, keeping a history of all recorded information. We then evaluate the performance of the system module in charge of localization and tracking of users' devices, and present and discuss the obtained results.

Unmanned aerial vehicles (UAVs) are gaining much momentum due to the vast number of their applications. In addition to their original missions, UAVs can be used simultaneously for offering value added Internet of Things services (VAIoTS) from the sky. VAIoTS can be achieved by equipping UAVs with suitable Internet of Things (IoT) payloads and organizing UAVs' flights using a central system orchestrator (SO). SO holds the complete information about UAVs, such as their current positions, their amount of energy, their intended use-cases or flight missions, and their onboard IoT device(s). To ensure efficient VAIoTSs, there is a need for developing a smart mechanism that would be executed at the SO in order to take into account two major factors: 1) the UAVs' energy consumption and 2) the UAVs' operation time. To effectively implement this mechanism, this paper presents three complementary solutions, named energy aware UAV selection (EAUS), delay aware UAV selection (DAUS), and fair tradeoff UAV selection (FTUS), respectively. These solutions use linear integer problem (LIP) optimizations. While the EAUS solution aims to reduce the energy consumption of UAVs, the DAUS solution aims to reduce the operational time of UAVs. Meanwhile, FTUS uses a bargaining game to ensure a fair tradeoff between the energy consumption and the operation time. The results obtained from the performance evaluations demonstrate the efficiency and the robustness of the proposed schemes. Each solution demonstrates its efficiency at achieving its planned goals.

With the 5G era approaching, many operators and service providers are on the race to deploy the mobile 5G and propose standards-based 5G products. With such an evolving ecosystem, the research community is urged to enhance the key components of this network generation, namely Network Function Virtualization (NFV) and Software Defined Networking (SDN), taking into account several vital objectives, such as the Capital Expenditure (CAPEX), Operational Expenditure (OPEX), security, Quality of Service (QoS), and Quality of Experience (QoE).

Network softwarization emerged with the promise of network flexibility, rapid service revenues, while reducing at the same time the Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). This has attracted operators, service providers, and stakeholders to invest in enabling technologies, such as Network Function Virtualization (NFV) and Software Defined Networking (SDN). On the verge of 5G networks, the research community is still investigating the enhancement of these key components, taking into account several vital objectives, such as ultra-short latency, availability, Quality of Service (QoS), and Quality of Experience (QoE).

On the verge of 5G devices commercialization, network softwarization is required to offer new capabilities for rapid service revenues, and better performance. This involves investing in enabling technologies, such as Network Function Virtualization (NFV) and Software Defined Networking (SDN). The research community is still investigating the enhancement of these key components with the sole purpose of reaching ultra-short latency, availability, and very high bandwidth.

The trend towards the cloudification of the 3GPP LTE mobile network architecture and the emergence of federated cloud infrastructures call for alternative service delivery strategies for improved user experience and efficient resource utilization. We propose Follow-Me Cloud (FMC), a design tailored to this environment, but with a broader applicability, which allows mobile users to always be connected via the optimal data anchor and mobility gateways, while cloud-based services follow them and are delivered via the optimal service point inside the cloud infrastructure. Follow-Me Cloud applies a Markov-decision-process-based algorithm for cost-effective performance-optimized service migration decisions, while two alternative schemes to ensure service continuity and disruption-free operation are proposed, based on either software defined networking technologies or the locator/identifier separation protocol. Numerical results from our analytic model for follow-me cloud, as well as testbed experiments with the two alternative follow-me cloud implementations we have developed, demonstrate quantitatively and qualitatively the advantages it can bring about.

Social multimedia networks (SMNs) have attracted much attention from both academia and industry due to their impact on our daily lives. The requirements of SMN users are increasing along with time, which make the satisfaction of those requirements a very challenging process. One important challenge facing SMNs consists of their internal users that can upload and manipulate insecure, untrusted, and unauthorized contents. For this purpose, controlling and verifying content delivered to end users is becoming a highly challenging process. So far, many researchers have investigated the possibilities of implementing a trustworthy SMN. In this vein, the aim of this paper is to propose a framework that allows collaboration between humans and machines to ensure secure delivery of trusted video content over SMNs while ensuring an optimal deployment cost in the form of CPU, RAM, and storage. The key concepts beneath the proposed framework consist in assigning to each user a level of trust based on his/her history, creating an intelligent agent that decides which content can be automatically published on the network and which content should be reviewed or rejected, and checking the videos' integrity and delivery during the streaming process. Accordingly, we ensure that the trust level of the SMNs increases. Simultaneously, efficient capital expenditure and operational expenditures can be achieved.

The use cases of the upcoming 5G mobile networks introduce new and complex user demands that will require support for fast reconfiguration of network resources. Software Defined Network (SDN) is a key technology that can address these requirements, as it decouples the control plane from the data plane of the network devices and logically centralizes the control plane in the SDN controller. SDN network operating system (ONOS) is a state-of-art SDN controller that aims to address this important scalability limitation from its design. An important feature of ONOS is that it allows network administrators to configure and manage networks with a high-level of abstraction by using Intent specifications. An Intent is a policy expression describing what is the desired outcome rather than how the outcome should be reached. The concept of Intents coupled with the distributed storage space are the key components for the theoretical scalability of ONOS. In this paper, we present our evaluation of the ONOS Intent northbound interface using a methodology that takes into consideration the interface access method, type of Intent and number of installed Intents. Our preliminary analysis indicates a linear increase in the computational cost with regards to the number of submitted Intents, with the access method being a major factor in the overall computational cost.

Given the constantly growing demand for inter- data-center services that 5G networks are bringing, live migration has become a covet and very challenging technology. Meanwhile, the emergence of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies has completely transformed modern networks by offering more flexibility and at the same time more complexity. So far, investigations have been confined to integrating the live migration process with SDN/NFV paradigms in order to ensure the desired Quality of Experience (QoE). However, the simple integration is not sufficient to handle unexpected cases such as resources' unavailability, networking issues, and system control. For this purpose, we present MIRA!, a novel framework for managing reliable live migrations of virtual resources across different Infrastructure as a Service (IaaS), handling unexpected cases, while ensuring high QoS and a very low downtime without human intervention using an SDN aware solution. To validate our proposed framework, we performed a set of experimental evaluations under different configurations. The obtained results of our proposed framework show a 21% time reduction compared to a prior work and an interesting behavior while modifying the number of allocated CPU cores.

Network Slicing (NS) is expected to be a key functionality of the upcoming 5G systems. Coupled with Software Defined Networking (SDN) and Network Function Virtualization (NFV), NS will enable a flexible deployment of Network Functions belonging to multiple Service Function Chains (SFC) over a shared infrastructure. To address the complexities that arise from this new environment, we formulate a MILP optimization model that enables a cost- optimal deployment of network slices, allowing a Mobile Network Operator to efficiently allocate the underlying layer resources according to the users' requirements. For each network slice, the proposed solution guarantees the required delay and the bandwidth, while efficiently handling the usage of underlying nodes, which leads to reduced cost. The obtained results show the efficiency of the proposed solution in terms of cost and execution time for small-scale networks, while it shows an interesting behavior in the optimization of the mapping of slices into underlay nodes of the large-scale topologies.

Given the continuously increasing use of Unmanned Aerial Vehicles (UAVs) in different domains, their management in the uncontrolled airspace has become a necessity. This has given rise to new systems called UAVs Traffic Management (UTM) systems. Nevertheless, currently, there is a lack of communication infrastructures that can support the requirements of UTM systems. Luckily, the envisioned 5G mobile network has introduced the concept of Multi-access Edge Computing (MEC) in its architecture to support mission-critical applications by decreasing the end-to-end latency and the unreliability of communication. In this paper, we evaluate the impact of the network latency and reliability on the control of UAVs' flights. The obtained results show that a UAV can deviate from its intended path with more than 5m if the network latency exceeds 400ms and with more than 2m if the packet loss probability exceeds 0.2. To overcome these limitations, we have leveraged MEC to provide a new UTM framework that enables an efficient traffic management. Moreover, due to MEC resource-limited nature and in order to give an insight about the resource provisioning, we have evaluated the scalability of the proposed solution in terms of the number of UAVs that can be handled without affecting the efficiency of the proposed UTM framework.

In this article, we leverage Network Function Virtualization(NFV) and Multi-Access Edge Computing (MEC) technologies, proposing a system which integrates ICN (Information-Centric Network) with CDN (Content Delivery Network) to provide an efficient content delivery service. The proposed system combines the dynamic CDN slicing concept with the NDN(Named Data Network) based ICN slicing concept to avoid core network congestion. A dynamic CDN slice is deployed to cache content at optimal locations depending on the nature of the content and the geographical distributions of potential viewers. Virtual cache servers, along with supporting virtual transcoders, are placed across a cloud belonging to multiple-administrative domains, forming a CDN slice. The ICN slice is, in turn, used for the regional distribution of content, leveraging the name-based access and the autonomic in-network content caching. This enables the delivery of content from nearby network nodes,avoiding the duplicate transfer of content and also ensuring shorter response times. Our experiments demonstrate that integrated ICN/CDN is better than traditional CDN in almost all aspects, including service scalability, reliability, and quality of service.

The next generation of Unmanned Aerial Vehicles (UAVs) will rely on mobile networks as a communication infrastructure. Several issues need to be addressed to enable the expected potentials from this communication. In particular, it was demonstrated that flying UAVs perceive a high number of base stations (BSs), consequently causing more interferences on non-serving BSs. This unfortunately results in decreased throughput for ground user equipments (UEs) already connected. Such a problem could be a limiting factor for mobile network-enabled UAVs, due to its consequences on the quality of experience (QoE) of served UEs. This underpins the focus of this article, wherein the effect of UAVs' communication on ground UEs in the uplink scenario is studied. First, given the fact that the nature of flying UAVs introduces particularities that make the underlying communication models different from traditional ones, this work proposes a model for mobile network-enabled UAVs (considering interferences, path loss, and fast fading). Moreover, we also tackle the QoE issue and propose an optimization solution based on adjusting the transmission power of UAVs. Simulations are conducted to evaluate the mobile network performance in the presence of flying UAVs. Our results reveal that as the number of added UAVs increases, a significant increase in the outage is observed. We demonstrate that our power optimization strategy guarantees the QoE for UEs, offers good communication links for UAVs, and reduces the overall interference in the network.

Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy- and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDCUAV. The primary goal of EDC-UAV is to build in-flight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collision free mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.

The network softwarization paradigm, enabled by Network Function Virtualization (NFV), facilitates the automation of management operations and orchestration of future networks, thus reducing their operational expenditures. The envisioned management practices include the introduction of automation in the scaling of network services. This may enable operators to handle workload fluctuations, to keep the desired performance, with great agility and reduced costs. This procedure introduces a non-negligible delay in allocating or releasing virtual resources. Therefore, waiting until the system is overloaded or underutilized so as to scale resources up or down could negatively impact the users' Quality of Experience, or lead to inefficient resource utilization. In this vein, this paper proposes a novel and agile Dynamic Auto Scaling Algorithm for the Control Plane (CP) of the Long Term Evolution' (LTE) virtualized Evolved Packet Core (vEPC). The resources dimensioning stage of the algorithm is based on an original queuing model for the CP. To model the CP, we use an open network of G/G/m queues. We also provide expressions to derive the steady state transition probabilities of the queuing network. Finally, we validate the proper operation of our solution using accurate simulation tools.

5G, as the next phase of mobile communications standards, intends to offer connectivity with greater throughput, higher capacity, lower latency and higher mobility range. 5G is promised to meet the demands of emerging applications, such as Internet of things (IoT) (e.g., wearables, connected cars, mobile phones, robots, and smart home appliances) to access to the Internet. However, the devices on IoT are expected to grow exponentially in the following years, resulting a dramatic increase in bandwidth requirements. Thus, more efficient management and planning of the network's bandwidth resources are essential in the evolution of mobile systems. In this vein, we propose an Intelligent Scheme (IS) to schedule the communications in the mobile systems for enabling emerging applications, such as connected cars. In our scheme, a core component controller is added to the network, which consists of two parts: the controller database and the controller server. The resource availability status of each cell is recorded in the database. The controller server cooperates with the Intelligent Start Algorithm (ISA) on the end-user controlling the traffic of the whole network. The uploading of time-non-sensitive content is delayed if there are not enough resources at the located cell. We evaluate the performance of this Intelligent Scheme (IS) based on NS-3. The obtained results indicate that our Intelligent Scheme (IS) manages to reduce the packet loss and improve the Quality of Experience (QoE) for users. As most of the added functions can run in the format of software, no dedicated hardware is needed and the overall system cost is expected to be minimal.

Large-scale networking infrastructures such as service providers deploy complex services to deal with the growing network traffic demand, security concerns, and user preferences. Using Service Function Chaining (SFC), a set of networking and management operations permits to steer the traffic through a list of intermediate services. Traffic steering for SFC is usually based on packet headers to share the SFC information, however, such headers introduce encapsulation overhead and require service functions support. In this paper, we present a novel traffic steering technique based on a compact SFC header. The proposed header does not increase the packet size and allows network operators to deploy SFC using legacy service functions. We also present a new SDN architecture for SFC based on compact headers. Our proposal permits a scalable and a flexible SFC deployment in real-life infrastructures.

Many ongoing research activities relevant to 5Gmobile systems concern the virtualization of the Evolved Packet Core (EPC) elements aiming for system scalability, elasticity, flexibility, and cost-efficiency. Virtual Evolved Packet Core (vEPC) will principally rely on some key technologies, such as Network Function Virtualization (NFV), Software Defined Networking (SDN) and Cloud Computing, for enabling the concept of Mobile Carrier Cloud. The key idea beneath this concept, known also as EPC as a Service (EPCaaS), consists in deploying virtual instances (i.e., Virtual Machines or Containers) of key core network functions (i.e., Virtual Network Functions - VNF), such as the Mobility Management Entity (MME), Serving GateWay (SGW), and Packet Data network gateWay (PGW) over a federated cloud. In this vein, an efficient VNF placement algorithm is highly needed to sustain the Quality of Service (QoS) while reducing the deployment cost. Our contribution, in this paper, is to devise an algorithm that derives the optimal number and locations of vEPC's virtual instances over the federated cloud. The proposed algorithm is based on coalition formation game, wherein the aim is to build optimal coalitions of Cloud Networks (CNs) to host the virtual instances of the vEPC elements. The obtained results clearly indicate the advantages of the proposed algorithm in ensuring QoS given a fixed cost for vEPC deployment, while maximizing the profits of cloud operators.

Content delivery networks (CDNs) have been widely implemented to provide scalable cloud services. Such networks support resource pooling by allowing virtual machines to be dynamically running or stopping according to current users' demands. Recently, there has been an increasing interest in Network Function Virtualization (NFV) as an emerging technology that aims to reduce cost, enable scalability and flexibility by decoupling network functions from the underlying hardware. In this regard, this paper designs a novel architecture to provide CDN Slices as a Service and that is across multiple administrative cloud domains. The architecture is aligned with the NFV Management and Orchestration (MANO) models. The proposed platform consists of three virtual network functions (VNFs), namely virtual caches, virtual video streamers, and virtual video transcoders. Regarding the latter, the paper also proposes a scheme for load balancing the transcoding tasks of the uploaded videos over a distributed network of virtual transcoders. In this article, an extensive benchmark analysis is conducted in order to study the virtual transcoding behavior in different cloud environments. The experiment evaluations provides a solid knowledge base to predict the estimated transcoding time for an optimal workload management of videos, aiming to optimize the incurred efficient cost in terms of delivery time and latency.

The future 5G systems ought to meet diverse requirements of new industry verticals, such as Massive Internet of Things (IoT), broadband access in dense networks and ultra-reliable communications. Network slicing is an important concept that is expected to support these 5G verticals and cope with the conflicting requirements of their respective services. Network slicing allows the deployment of multiple virtual networks, or slices, over the same physical infrastructure as well as supporting on-demand resource allocation to those slices. In this paper, we propose an architecture that will explore how both Network Function Virtualization (NFV) and Software Defined Networking (SDN) may be leveraged to secure a network slice on-demand, addressing the new security concerns imposed to the network management by the flexibility and elasticity support. Our proposed framework aims to ensure an optimal resource allocation that manages the slice security strategy in an efficient way. Moreover, experimental performance evaluations are presented to evaluate the security overhead in virtualized environments.

The existing conventional mobile networks are not flexible: if there is a new service, it unfortunately cannot be integrated automatically. Their traffic routing is not optimal and users' traffic is forwarded to the core network without considering the optimal path. This causes high latency to access the desired service, and the use of resources is inefficient. This has motivated the evolution towards 5G. The 5G vision consists of managing highly dynamic network slices and provisions networks in an as-a-service fashion. In this vein, to answer to the elasticity and low-latency specifications of the upcoming 5G services, the optimal placement of Virtual Network Functions (VNFs) must overcome the non-uniform service demand and the irregular nature of the underlying network topologies. This paper addresses this issue by mapping the non-uniform signaling messages to a new uniform environment, namely, the canonical domain, whereby the placement of core functions is more feasible and efficient. This is carried out by using Schwartz-Christoffel conformal mappings. The conducted experimentation shows the efficiency of our approach, compared to some baseline approaches, in the virtual resource allocation (i.e. Virtual CPU, Virtual DISK) and that is in terms of reducing the overall cost, end-to-end delay and number of activated Virtual Machines (VMs; virtual resources in general).

The amount of data generated by sensors, actuators, and other devices in the Internet of Things (IoT) has substantially increased in the last few years. IoT data are currently processed in the cloud, mostly through computing resources located in distant data centers. As a consequence, network bandwidth and communication latency become serious bottlenecks. This paper advocates edge computing for emerging IoT applications that leverage sensor streams to augment interactive applications. First, we classify and survey current edge computing architectures and platforms, then describe key IoT application scenarios that benefit from edge computing. Second, we carry out an experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming. To this end, we consider a resource-intensive 3-D application as a paradigmatic example and evaluate the response delay in different deployment scenarios. Our experimental results show that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality. We conclude by discussing what can be achieved with current edge computing platforms and how emerging technologies will impact on the deployment of future IoT applications.

Many ongoing research activities relevant to 5G mobile systems concern the virtualization of the mobile core network, including the evolved packet core (EPC) elements, aiming for system scalability, elasticity, flexibility, and cost-efficiency. Virtual EPC (vEPC)/5G core will principally rely on some key technologies, such as network function virtualization, software defined networking, and cloud computing, enabling the concept of mobile carrier cloud. The key idea beneath this concept, also known as core network as a service, consists in deploying virtual instances (i.e., virtual machines or containers) of key core network functions [i.e., virtual network functions (VNF) of 4G or 5G], such as the mobility management entity (MME), Serving GateWay (SGW), Packet Data network gateWay (PGW), access and mobility management function (AMF), session management function (SMF), authentication server function (AUSF), and user plane functions, over a federated cloud. In this vein, an efficient VNF placement algorithm is highly needed to sustain the quality of service (QoS) while reducing the deployment cost. Our contribution in this paper is twofold. First, we devise an algorithm that derives the optimal number of virtual instances of 4G (MME, SGW, and PGW) or 5G (AMF, SMF, and AUSF) core network elements to meet the requirements of a specific mobile traffic. Second, we propose an algorithm for the placement of these virtual instances over a federated cloud. While the first algorithm is based on mixed integer linear programming, the second is based on coalition formation game, wherein the aim is to build coalitions of cloud networks to host the virtual instances of the vEPC/5G core elements. The obtained results clearly indicate the advantages of the proposed algorithms in ensuring QoS given a fixed cost for vEPC/5G core deployment, while maximizing the profits of cloud operators.

This paper introduces a content delivery network as a service (CDNaaS) platform that allows dynamic deployment and life-cycle management of virtual content delivery network (CDN) slices running across multiple administrative cloud domains. The CDN slice consists of four virtual network function (VNF) types, namely virtual transcoders, virtual streamers, virtual caches, and a CDN-slice-specific Coordinator for the management of the slice resources across the involved cloud domains. To create an efficient CDN slice, the optimal placement of its composing VNFs using adequate amount of virtual resources for each VNF is of vital importance. In this vein, this paper devises mechanisms for allocating an appropriate set of VNFs for each CDN slice to meet its performance requirements and minimize as much as possible the incurred cost in terms of allocated virtual resources. A mathematical model is developed to evaluate the performance of the proposed mechanisms. We first formulate the VNF placement problem as two Linear Integer problem models, aiming at minimizing the cost and maximizing the quality of experience (QoE) of the virtual streaming service. By applying the bargaining game theory, we ensure an optimal tradeoff solution between the cost efficiency and QoE. Extensive simulations are conducted to evaluate the effectiveness of the proposed models in achieving their design objectives and encouraging results are obtained.

The evolution towards 5G consists of managing highly dynamic networks and making decisions related to the provisioning of networks in an as-a-service and cost-aware fashion. This is translated by 5G verticals that are dedicated to specific services, applications, or use cases fulfilling the constant demand of vertical industries. In this vein, to achieve the high-level goals defined by operators and service providers, and to answer to the elasticity and low-latency specifications of the upcoming 5G mobile system, the optimal placement of virtual network functions must cope with the non-uniform service demands and the irregular nature of network topologies. This paper addresses this issue by mapping the non-uniform distribution of signaling messages in the physical domain to a new uniform environment (i.e., canonical domain) whereby the placement of core functions is more feasible and efficient by means of Schwartz-Christoffel conformal mappings. The experimentation results, compared to some baseline approaches, have proven the efficiency of the conformal mapping based placement in allocating the virtual resources (i.e., virtual CPU and virtual storage) with regard to the optimal end-to-end delay, cost and activated virtual machines. Another interesting contribution is that all placement decisions are based on a realistic spatio-temporal user-centric model, which defines both the mobility of user equipments and the underlying service usage.

Massive Internet of Things (mIoT) is an important use case of 5G. The main challenge for mIoT is the huge amount of uplink traffic as it dramatically overloads the radio access network (RAN). To mitigate this shortcoming, a new RAN technology has been suggested, where small cells are used for interconnecting different devices to the network. The use of small cells will alleviate congestion at the RAN, reduce the end-to-end (E2E) delay, and increase the link capacity for communications. In this paper, we devise three solutions for deploying and interconnecting small cells that would handle mIoT traffic. A realistic physical model is considered in these solutions. The physical model is based on a composite fading channel that captures path loss, fast fading, shadowing, and interference to derive the signal-to-interference-plus-noise ratio. The three solutions consider two conflicting objectives, namely the cost and the E2E delay for deploying and backhauling small cells. The first solution minimizes the cost while the second reduces the E2E delay. The third solution uses bargaining game theory for reducing both the cost and the E2E delay. The proposed solutions are evaluated through simulations. The obtained results demonstrate the efficiency of each solution in achieving its design goals.

IoT covers various scales and types of wireless networks. The first constraint to be respected, for an efficient application, is to reduce as much as possible the amount of energy consumption. The idle listening process in existing Medium Access Control (MAC) protocols is a very energy consuming task. Recently, a new emerging technology based on a low power wake-up radio has shown real benefits by completely eliminating the problem of idle listening. Thanks to the use of this technology, an IoT device keeps its main radio in deep sleep until a wake-up message is received by the wake-up radio that consumes less energy. However, collision can occur among wake-up messages (i.e., wake-up plane). Collision in the wake-up plane, if not handled efficiently, leads to collision at the data plane which is more complicated. In this paper, we address this issue by modeling the wake-up decision using a Markov Decision Process (MDP). The goal is to formulate a decision policy that determines whether to send a wake-up message in the actual time slot or to report it, taking into account the time factor. Experiments have been conducted to determine the decision policies. Results of the proposed approach have been compared against those of RFIDImpulse, a CSMA\CA-based wake-up MAC protocol. The obtained results show the efficiency of the proposed approach.

Software Defined Networking (SDN) is an emerging technology that will play an important role in enabling 5G, since it offers enhanced network management features. SDN allows programmability of the control plane, abstracting the underlying network infrastructure for applications and network services, e.g. through the OpenFlow protocol. In this paper, we propose a solution that enables the end-to-end Quality of Service (QoS) based on the queue support in OpenFlow, allowing an operator with a SDN-enabled network to efficiently allocate the network resources according to the users' demands, reducing or even eliminating the need for over-provisioning. For each traffic flow, the proposed solution guarantees the required end-to- end QoS, while efficiently managing the utilization of open virtual switches (OVSs), which leads to reduced cost. The cost could be also reduced as a fewer number of OVSs are needed, which are enabled in different data centers. For ensuring these objectives, the proposed solution explores the strength of multi-path routing based on SDN with a precise bandwidth allocation. The obtained results show the efficiency of the proposed solution in terms of cost and execution time.

To support the much desired ultra-short latency of 5G mobile systems, many micro-data centers will be deployed in the vicinity of mobile users, defining a distributed edge cloud. Over this edge cloud, it is important to create optimal network slices to support different 5G verticals. Optimality is defined in terms of cost efficiency and QoS support. Therefore, it is important to understand the behavior of mobile users in terms of mobile service consumption. In this paper, we present, on one hand, a tool for developing a spatio-temporal model of mobile service usage over a particular geographical area. This tool will help to define the behavior of mobile users in terms of mobility patterns and mobile service consumption. On the other hand, based on this tool, we present a benchmark of some interesting Virtualized Network Functions (VNF) placement algorithms, among them our enhanced version of the predictive placement strategy. The comparison is based on data overload, overload of Virtual Machines (VMs) and QoS.

Given the constantly growing demand for live streaming services, live transcoding has become compulsory and very challenging. So far, investigations have been confined to satisfy a huge number of users for ensuring the Quality of Experience (QoE). The aim of this paper is to propose a framework architecture following ESTI-NFV (Network Function Virtualization) model, whereby the transcoding and streaming Virtual Network Functions (VNFs) would be running on top of multiple cloud domains. By respecting ESTI-NFV model, we ensure the flexibility of our virtual delivery platform that scales up/down and in/out relative to the changing demands of the end-users in order to reduce cost. For this purpose, this paper presents a new framework for managing the virtual live transcoding and streaming VNFs on top of multiple cloud domains for ensuring the QoE while reducing the cost. In order to develop such a framework, we have done a set of experimental benchmarking of transcoding and streaming VNFs using variant flavors (i.e., in terms of CPU and Memory resources). The obtained results will be explored later for developing an intelligent algorithm that will be integrated with the proposed framework in managing different transcoding and streaming VNFs in an efficient manner.

The many advantages of Dynamic Adaptive streaming over HTTP (DASH) made it one of the most prevalent video streaming technologies in recent years. Unfortunately, many studies have unveiled the QoE issue of users when multiple DASH clients compete for the bandwidth of a bottleneck link. This issue consists of several aspects, namely the frequent encoding changes, the unfair bandwidth allocation, the inefficient bandwidth utilization, and the relatively long convergence time. These aspects are indeed conflicting each other and resolving them entails tradeoffs. In this paper, we propose a new mathematical model that leverages a score matrix to ensure a fair sharing of the server's bottleneck link between competing clients and satisfies the requests of as many clients as possible and that is for efficient bandwidth utilization. The proposed solution is compared against notable solutions through computer-based simulations, and the results show that the proposed solution achieves high scores in terms of both efficiency and fairness.

In this paper, we deal with the store-and-forward paradigm for self-organizing Delay Tolerant Networks (DTN). To overcome the decentralized nature and the infrastructureless constraint of such a network, highly distributed design and efficient incentive mechanisms are needed in order to convince relay nodes to disseminate the content. Here, we exhibit a new way to set the store-and-forward scheme based on the emerging matching game theory. This approach serve to match between on one hand different kinds of files generated by a source node and on the second hand relay nodes that may forward these files. In order to make incentive for cooperation, the source node offers a strategic reward to relay nodes that have accepted to forward a given file. Moreover, each relay and file can be defined by a context, i.e. its characteristics. Based on that, the source would prefer maximize the overall delivery probability at the same time as the relay would try to guarantee the highest possible reward while considering its battery status. Our matching-game-based scheme promises an efficient tradeoff between the overall delivery probability and the energy consumption. For practical considerations, we propose an algorithmic solution to achieve a stable matching between the sets of source files and the set of relay stations. Extensive simulations show that our scheme outperforms the legacy two-hop routing and illustrate the impact of preferences of each set involved in the game, and how such a tool can meet a high delivery rate at a reasonable energy budget.

Extending cloud infrastructure to the Network Edge represents a breakthrough to support delay-sensitive applications in next 5G cellular systems. In this context, to enable ultrashort response times, fast relocation of service instances between edge nodes is required to cope with user mobility. To face this issue, proactive service replication is considered a promising strategy to reduce the overall migration time and to guarantee the desired Quality of Experience (QoE). On the other hand, the provisioning of replicas over multiple edge nodes increases the resource consumption of constrained edge nodes and the relevant deployment cost. Given the two conflicting objectives, in this paper we investigate different optimization models for proactive service migration at the Network Edge, which can exploit prediction of user mobility patterns. In particular, we define two Integer Linear Problem optimization schemes, which aim at respectively minimizing the QoE degradation due to service migration, and the cost of replicas' deployment. Performance evaluation shows the effectiveness of our proposed solutions.

Edge Cloud infrastructure will play a key role in extending the range of supported real-time cloud applications, by guaranteeing extremely fast response times. However, user mobility requires fast relocation of service instances, which represents an open challenge for resource-constrained cloudlets interconnected by high-latency and low-bandwidth links. In this paper, we investigate container-based virtualization techniques to support dynamic Mobile Edge Computing (MEC) environments. In particular, we design a framework to guarantee fast response time, by proactively exploiting service replication. A preliminary performance analysis is conducted to identify the possible advantages introduced by the proposed approach compared to classic migration procedures.

This paper introduces a Quality of Experience (QoE) estimation-based server benchmarking system, which can be utilized as a part of QoE-optimized resource provisioning in our envisioned virtual video delivery platform. The system has been targeted for benchmarking virtual video streaming servers, i.e., virtual server flavors deployed in a cloud environment, based on resulting QoE estimates. The paper also presents another layer to the benchmarking by showing how to optimize stream segment duration in terms of estimated QoE. The QoE estimation in the system is based on a Pseudo-Subjective Quality Assessment (PSQA) method developed for video streaming. Output of the system, i.e., QoE estimation-based benchmarks, helps to find out how different factors can affect video streaming QoE which in turn makes parameter and resource optimizations possible. Moreover, the paper presents experimental benchmarking results obtained in a cloud environment.

In Telco cloud environment, virtual network functions (VNFs) can be shipped in the form of virtual machine images and hosted over commodity hardware. It is likely that these VNF images will contain highly sensitive data and mission critical network operations. For this reason, these VNF images are prone to malicious tampering during shipping and even after uploaded to the cloud image database. Furthermore, due to various applications, there is a requirement from mobile network operators to seal VNFs on specific platforms which satisfy certain hardware and software configurations. This requires cloud service providers to introduce some mechanisms to verify VNF image integrity and host sealing before the instantiation of VNFs. In this paper, we present a proof of concept demonstrated with the help of an experimental setup to solve the above-mentioned problems. We also evaluate the performance of the envisioned setup and present some insights on its usability.

This paper presents a mechanism for steering connections to different mobile networks for UAV-based reliable communications. This connection steering mechanism works by selecting the best Radio Signal Strength Indicator (RSSI) quality among the available networks in order to ensure the highest availability. In this work, we developed a test-bed to evaluate the performance of the steering mechanism. In addition, to mimic the mobility of UAVs, we analyze our work by applying Discrete Time Markov Chain (DTMC) to evaluate the performance of the testbed results. The results obtained from our analysis and testbed-based evaluation show the efficiency of the proposed connection steering mechanism. These results demonstrate the efficiency of the proposed connection steering mechanism in terms of data packet transmission rate and energy consumption saving.

Internet Protocol Television (IPTV) has become widely used to deliver TV channels over the Internet. Tremendous efforts have been carried out for making IPTV services an alternative to traditional TV by offering low-cost TV channels. The cloud network offers many advantages that give more flexibility for sharing the content and reducing the cost for endusers. In this paper, we explore the strength of cloud by allowing different users to create cost-efficient TV channels on top of the cloud. The proposed algorithm reduces the cost by exploring the shared content in the cloud network. The simultaneous streaming of the content shared among different channels will reduce the number of streams in the network, and consequently the otherwise incurred cost. This can be achieved through a smart scheduling mechanism that schedules the streaming of the same video to a large number of channels at the same time. The obtained results prove the efficiency of the proposed solution in terms of cost efficiency.

Unmanned Aerial Vehicles (UAVs) are expected to be used everywhere to provision different services and applications, impacting different aspects of our daily lives. Basically, UAVs are characterized by their high mobility. Some may remain motionless for a specific time to perform pre-programmed missions. Whilst UAVs would be used for specific applications, they could additionally offer numerous IoT (Internet of Things) value-added services (VAS) when they are equipped with suitable IoT devices. Many IoT VAS applications require high amount of resources and/or diverse IoT devices that cannot be offered by a single UAV. In order to overcome this limitation, this paper aims to explore, i) the diversity of IoT devices on-board UAVs, and ii) the mobility of UAVs for offering UAVs-based IoT VAS. Two solutions are proposed for carrying out different IoT VAS. Both solutions are modeled using linear integer programming. While the first solution aims to reduce the energy consumption, the second one aims to shorten the response time. The simulation results demonstrate the efficiency of both solutions in achieving their design goals.

Content Delivery Networks (CDNs) emerged to manage the great amount of content, as well as the transmissions over long distances. In recent years, this concept proves to be a promising solution for emergent enterprises. In this paper, we present a Content Delivery Network as a Service (CDNaaS) platform which can create virtual machines (VMs) through a network of data centers and provide a customized slice of CDN to users. CDNaaS manages a great number of videos by means of caches, transcoders, and streamers hosted in different VMs. However, an optimal placement of VMs with adequate flavors for the different images is required to obtain an efficient slice of CDN. In this work, we argue the need to find a convenient slice for the CDN owner while respecting his performance requirements and minimizing as much as possible the incurred cost. We first formulate the VMs placement problem as two Linear Integer problem solutions, aiming at minimizing the cost and maximizing the quality of experience of streaming. Then, extensive simulation results are presented to illustrate the effectiveness of the proposed models.

In this paper we discuss the major modifications required in the current LTE network to realize a decentralized LTE architecture and develop a novel IP mobility management solution for it. The proposed solution can handle traffic redirecting and IP address continuity above the distributed anchor points in a scalable and resource efficient manner. Our approach is based on the NAT (Network Address Translation) mechanism, which is a well- known and widely used procedure in the current Internet. We extend the NS3-LENA to implement a decentralized LTE network as well as the proposed scheme. The evaluation results show that the proposed solution efficiently fulfills the functionality and performance requirements (e.g.,latency and signaling load) related to the mobility management.

The constant and fast increase in the number of heterogeneous Internet of Things (IoT) devices that populate everyday life environments brings new challenges to the full exploitation of the computation, memory, sensing, and actuation resources associated to them. In this context, device virtualization solutions and platforms may definitely play a key role in enabling the desired tradeoff between flexibility and performance. This paper focuses on lightweight virtualization technologies for IoT devices, suitably thought to effectively deploy new integrated applications and to create a novel distributed and virtualized ecosystem. Two different frameworks for container-based IoT service provisioning are compared, the one based on a direct interaction between two cooperating devices and the other based on the presence of a manager supervising the operations between cooperating devices forming a cluster. In the latter case, accounting for the growing impetus to move intelligence toward the edge of the network, management features are implemented at the network access point to provide short latency responses. We also introduce the outcomes of a thorough performance evaluation campaign conducted via a real IoT testbed. The measurements, performed by accounting for the constraints of typical IoT nodes, shed light on the actual feasibility of container-based IoT frameworks.

This paper tackles the challenge of optimally placing relay nodes (RNs) in wireless sensor networks given a limited set of positions. The proposed solution consists of: (1) the usage of a realistic physical layer model based on a Rayleigh block-fading channel; (2) the calculation of the signal-to-interference-plus-noise ratio (SINR) considering the path loss, fast fading, and interference; and (3) the usage of a weighted communication graph drawn based on outage probabilities determined from the calculated SINR for every communication link. Overall, the proposed solution aims for minimizing the outage probabilities when constructing the routing tree, by adding a minimum number of RNs that guarantee connectivity. In comparison to the state-of-the art solutions, the conducted simulations reveal that the proposed solution exhibits highly encouraging results at a reasonable cost in terms of the number of added RNs. The gain is proved high in terms of extending the network lifetime, reducing the end-to-end- delay, and increasing the goodput.

In virtualized networks, network functions are delivered as software running on generic hardware allowing service providers to dynamically allocate resources based on traffic and service demands. Network Function Virtualization (NFV) is becoming a key enabler and consequently a hot research topic. Dynamic scaling of resources in NFV is a highly important challenge towards its implementation in real-life networks. In this paper, we propose a method to predict the required resources in the appropriate time to sustain true elasticity in NFV. The capacity of different Virtualized Network Functions (VNFs) would increase/decrease in a way that the CPU utilization is maximized while the overall cost is minimized. In this paper, we present two strategies to predict the day-ahead CPU utilization. The first strategy is an offline scheduling method that helps managing elasticity in virtualized networks by predicting normal days events. The second one is an online scheduling approach that predicts the day-ahead CPU utilization during sudden peaks due to some unusual circumstances. In this paper, we also present new promising results that show the correlation between the control and data planes. Finally, we propose a hybrid algorithm that uses both strategies to efficiently handle elasticity in virtualized networks. The obtained results are encouraging and are all based on real-life data of mobile operator networks.

To enhance video streaming experience for mobile users, we propose an approach towards Quality-of-Experience (QoE) aware on-the-fly transcoding. The proposed approach relies on the concept of Mobile Edge Computing (MEC) as a key enabler in enhancing service quality. Our scheme involves an autonomic creation of a transcoding service as a Virtual Network Function (VNF) and ensures dynamic rate switching of the streamed video to maintain the desirable quality. This edge-assistive transcoding and adaptive streaming results in reduced computational loads and reduced core network traffic. The proposed solution represents a complete miniature content delivery network infrastructure on the edge, ensuring reduced latency and better quality of experience.

To integrate Software Defined Networking (SDN) in the envisioned 5G system, a separation of the control and user data plane functions of the Evolved Packet Core (EPC) is required. This separation will impact mainly the functions available at the Serving GateWay (SGW) and Packet data GateWay (PGW) elements, and will result in two new entities; i.e. the S/PGW-C and S/PGW-U (PGW-C and PGW-U). The S/PGW-C integrates all the control plane functions (such as signaling and tunnel creation), while S/PGW-U contains only forwarding functions. The S/PGW-C will control the S/PGW-U in order to forward the UE traffic to the appropriate destinations by enforcing rules e.g., using the Openflow protocol. Usually, the S/PGW-C will run as a Virtual Network Function (VNF) running on a Virtual Machine or Container instantiated over a federated cloud. In this paper, we focus on the problem of the SGW-C placement, where a tradeoff is needed between reducing the SGW relocation frequency and balancing the traffic load among the underlying SGW-C VNFs. We formulate this problem using optimisation models, and a fair solution (i.e. Pareto optimal) is derived using Nash Bargaining game and the threat point.

This paper presents a UAV-based integrative IoT platform that leverages UAVs to deliver different IoT services from height. One of the major tasks of the platform is to select the appropriate UAVs for a particular IoT task. This selection may be based on different criteria, such as UAV's equipment, energy budget and geographical proximity of the UAV to the area of interest. For the selection mechanism, this paper proposes and formulates two Linear Integer Problem (LIP) optimization solutions by aiming at minimizing the energy consumption and shortening the UAV operation time. These two solutions are dubbed Energy-Aware Selection of UAVs (EAS) and Delay- Aware Selection of UAVs (DAS). They are both evaluated through simulations. The obtained results show that if the objective is energy efficiency, EAS is more efficient than DAS in terms of reducing the total energy consumption by the UAVs. Additionally, if the time is the objective, DAS exhibits better performance than EAS in terms of operation time.

Data backup in data center networks (DCNs) is critical to minimize the data loss under disaster. This paper considers the cost-efficient data backup for DCNs against a disaster with ε early warning time. Given geo-distributed DCNs and such a ε-time early warning disaster, we investigate the issue of how to back up the data in DCN nodes under risk to other safe DCN nodes within the ε early warning time constraint, which is significant because it is an emergency scheme for data protection against a predictable disaster and also help DCN operators to build a complete backup scheme, i.e., regular backup and emergency backup. Specifically, an Integer Linear Program (ILP)-based theoretical framework is proposed to identify the optimal selections of backup DCN nodes and data transmission paths, such that the overall data backup cost is minimized. Extensive numerical results are also provided to illustrate the proposed framework for DCN data backup.

Typically, maintaining static pool of cloud resources to meet peak requirements with good service quality makes the cloud infrastructure costly. To cope with this, this paper proposes an approach that enables a cloud-infrastructure to automatically and dynamically scale-up or scale-down resources of a virtualized environment aiming for efficient resource utilization and improved quality of experience (QoE) of the offered services. The QoE-aware approach ensures a truly elastic infrastructure, capable of handling sudden load surges while reducing resource and management costs. The paper also discusses the applicability of the proposed approach within the ETSI NFV MANO framework for cloud-based 5G mobile systems.

Internet Service Providers are becoming more involved in the audiovisual content delivery chain. One manifestation of this trend is the emergence of telco CDNs, i.e., content delivery networks operated by telecom service providers. In this work, we make the case for opening the telco CDN infrastructure to content providers by means of network function virtualization (NFV) and cloud technologies. We design and implement a CDN-as-a-Service architecture, where content providers can lease CDN resources on demand at regions where the ISP has presence. Using open northbound RESTful APIs, content providers can express performance requirements and demand specifications, which can be translated to an appropriate service placement on the underlying cloud substrate. To gain insight which can be applied to the design of such service placement mechanisms, we evaluate the capabilities of key enabling virtualization technologies by extensive testbed experiments.

In this paper we address the problem of Software Defined Networking (SDN) controller placement in large networks. Indeed, to solve the scalability issue raised by the centralized architecture of SDN, multi-controllers deployment (or distributed controllers system) is envisioned. However, the number and the location of controllers in large networks remain an issue. In this context, several works have been proposed to find the optimal placement of SDN controllers. Most of them consider latency among SDN controllers and switches as the main metric. In this work, we go beyond the state of art by proposing a solution that considers at the same time three critical objectives for the optimal placement of controllers: (i) the latency and communication overhead between switches and controllers; (ii)the latency and communication overhead between controllers; (iii) the guarantee of load balancing between controllers. We then solve the system by using Bargaining Game in order to find a fair trade off between these objectives. Simulation results clearly demonstrate the effectiveness of the proposed solution in finding the optimal placement of controllers that enforces this trade-off.

Recently extensive works have been devoted to the performance analysis of physical layer security in wireless communication systems. However, the combination of physical layer security and quality of service (QoS) for route selection in multi-hop wireless ad hoc networks (WANETs) still remains an open technical challenge. As an initial step towards this end, this paper focuses on a multi-hop WANET with two typical transmission schemes amplify-and-forward (AF) and decode-and-forward (DF), and explores the route selection with the consideration of both security and QoS. We first derive the closed-form expressions of secrecy outage probability (SOP) and connection outage probability (COP) for a single hop link, and further extend the results to an end-to-end route. Then we conduct the performance comparison between the AF scheme and DF scheme. Finally, based on both the SOP and COP of a route, we formulate the route metric and propose a flexible route selection algorithm which enables us to select the suitable route for message delivery according to different security and QoS requirements.

In Long Term Evolution (LTE) networks, User Equipments (UE)s should proceed Random Access CHannel (RACH) procedure to attach to the Base Station and access the channel. One limitation of this procedure is the congestion that may appear when high number of UEs are simultaneously trying to attach to the channel. Such use-case happens when high number of Machine Type Communication (MTC) devices are deployed in one LTE cell. In order to evaluate the RACH performances, in terms of success, collision and idle probabilities, when the traffic load is high, accurate models are needed. However, most of existing models ignore one important constraint, which is the fact that the eNB can knowledge only a limited number of UEs in each RACH round, leading to a mis-formulation of these metrics in the context of LTE, and especially in the presence of high number of devices competing for the channel access. In this paper, we tackle the above-mentioned issue by devising a new model for the RACH procedure taking in consideration this constraint. Computer simulation demonstrates that unlike the existing models, our proposed model achieve high accuracy to estimate the performance of the RACH procedure, whatever the traffic load.

Mobile Operators are looking for new ways to cope with ever-increasing data traffic while improving the operational and capital efficiency of their networks. Cloud computing and network function virtualization (NFV) have emerged as key enablers to optimize resource utilization and at the same time reduce network operational expenditure (OPEX). In virtualized networks, network functions are delivered as software running on generic hardware allowing service providers to dynamically allocate resources based on traffic and service demands. In this paper, we analyze resource utilization using real-life data of two different mobile networks and evaluate the impact virtualization would have on these networks. Some conclusions are drawn based on the analysis.

Recent studies foresee that there would be roughly 50 billion of machine type communication (MTC) devices by 2020. Coping with the massive signaling overhead expected from these devices in 5G network is an important hurdle to tackle. In this paper, we have proposed two optimal solutions that use Device-to-Device (D2D) communications to lightweight the overhead of MTC devices on 5G network. Each scheme has a specific objective, and aims to manage the communications between devices and eNodeBs to achieve its objective. The proposed solutions nominate the devices that should communicate through D2D communication fashion and those that should directly communicate with eNodeBs. The first solution aims to reduce the energy consumption, whereas the second one aims to reduce the data transfer delay at the eNodeBs. The performance of the proposed schemes is evaluated via simulations and the obtained results demonstrate their feasibility and ability in achieving their design goals.

This paper introduces the concept of elastic bearer in Evolved Packet System (EPS), which allows, on one hand, the users to enhance on-demand the performance of certain applications and on the other hand, it permits the network to efficiently manage the resource allocation considering the application type. In particular, the paper introduces a set of mechanisms to trigger and support bearer elasticity in EPS based on Quality of Experience (QoE) perceived by users or based on feedback from Radio Access Network (RAN). Bearer elasticity can be attained through potential Packet Data Network/Serving Gateway (PDN/S-GW) relocation to eventually improve QoE within and beyond the mobile network operator premises. The paper also introduces a set of methods to identify and cope with a “storm” of requests for particular applications at densely populated areas.

It is expected that in two-tier LTE heterogeneous networks, an extensive deployment of small cell networks (SCNs) will take place in the near future, especially in dense urban zones; hence a hyper density of SCNs randomly distributed within macro cell networks (MCNs) will emerge with many overlapping zones of neighboring SCNs. Therefore, the problems of interferences in co-channel deployment will be more complicated and then the overall throughput of downlink will substantially decrease. In order to mitigate the effect of interferences in a hyper density of SCNs scenarios, a solution based on a fully distributed algorithm for sharing time access to SCNs and multi-homing capabilities of macro cellular users is proposed to improve the overall data rate of downlink and at the same time to satisfy QoS throughput requirements of macro and home cellular users. Our tentative scheme will also reduce the signaling overhead due to the absence of coordination among small base stations (SBSs) and macro base station (MBS). Results validate our solution and show the improvement attained in a hyper density of SCNs within MCNs compared to open, closed and shared time access mechanisms based on single network selection.

Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented.

Next generation cellular networks (5G) have to deal with massive deployment of machine-type-communication (MTC) devices, expected to cause congestion and system overload in both the radio access network (RAN) and the core network (CN). Moreover, not only would the network suffer from the system overload, but also the MTC devices would experience high latency to access the channel and high power consumption due to the retransmission attempts. Indeed, power consumption is a critical issue in MTC, as the devices are not plugged into the electrical supply, e.g., in the case of sensor devices. To alleviate system overload (caused by the massive MTC deployment), the 3GPP proposed the group paging (GP) method. However, its performances dramatically decrease when increasing the number of MTC devices being paged. In this paper, we devise a novel method, named further improvement-traffic scattering for group paging (FI-TSFGP), which aims to improve the performance of GP when the number of MTC devices is high. FI-TSFGP scatters the paging operation of the MTC devices over a GP interval instead of letting all of the devices start the channel access procedure at nearly the same time. By doing so, FI-TSFGP achieves high-channel access probability for MTC devices, leading to the reduction of both the channel access latency and power consumption. Compared to GP and two other schemes, simulation results clearly demonstrate the high performance of FI-TSFGP in terms of: success and collision probabilities, average access delay, average number of preamble transmissions, and ultimately energy conservation.

To cope with the tremendous growth in mobile data traffic on one hand, and the modest average revenue per user on the other hand, mobile operators have been exploring network virtualization and cloud computing technologies to build cost-efficient and elastic mobile networks and to have them offered as a cloud service. In such cloud-based mobile networks, ensuring service resilience is an important challenge to tackle. Indeed, high availability and service reliability are important requirements of carrier grade, but not necessarily intrinsic features of cloud computing. Building a system that requires the five nines reliability on a platform that may not always grant it is, therefore, a hurdle. Effectively, in carrier cloud, service resilience can be heavily impacted by a failure of any network function (NF) running on a virtual machine (VM). In this paper, we introduce a framework, along with efficient and proactive restoration mechanisms, to ensure service resilience in carrier cloud. As restoration of a NF failure impacts a potential number of users, adequate network overload control mechanisms are also proposed. A mathematical model is developed to evaluate the performance of the proposed mechanisms. The obtained results are encouraging and demonstrate that the proposed mechanisms efficiently achieve their design goals.

Social networks and mobile networks are exposing human beings to a big data era. With the support of big data analytics, conventional intelligent transportation systems (ITS) are gradually changing into data-driven ITS (D2ITS). Along with traffic growth, D2ITS need to solve more real-life problems, including the issue of unlicensed taxis and their identification, which potentially disrupts the taxi business sector and endangers society safety. As a remedy to this issue, a smart model is proposed in this paper to identify unlicensed taxis. The proposed model consists of two submodel components, namely, candidate selection model and candidate refined model. The former is used to screen out a coarse-grained suspected unlicensed taxi candidate list. The list is taken as an input for the candidate refined model, which is based on machine learning to get a fine-grained list of suspected unlicensed taxis. The proposed model is evaluated using real-life data, and the obtained results are encouraging, demonstrating its efficiency and accuracy in identifying unlicensed taxis, helping governments to better regulate the traffic operation and reduce associated costs.

One important objective of 5G mobile networks is to accommodate a diverse and ever-increasing number of user equipment (UEs). Coping with the massive signaling overhead expected from UEs is an important hurdle to tackle so as to achieve this objective. In this paper, we devise an efficient tracking area list management (ETAM) framework that aims to find optimal distributions of tracking areas (TAs) in the form of TA lists (TALs) and assigning them to UEs, with the objective of minimizing two conflicting metrics, namely paging overhead and tracking area update (TAU) overhead. ETAM incorporates two parts (online and offline) to achieve its design goal. In the online part, two strategies are proposed to assign in real time, TALs to different UEs, while in the offline part, three solutions are proposed to optimally organize TAs into TALs. The performance of ETAM is evaluated via analysis and simulations, and the obtained results demonstrate its feasibility and ability in achieving its design goals, improving the network performance by minimizing the cost associated with paging and TAU.

An important requirement on 5G mobile systems is to accommodate massive numbers of wireless devices and users. Heterogeneous networks are expected to play a crucial role in meeting this requirement. In this vein, small cells are expected to become an integral part of these heterogeneous networks. However, their success would not last longer unless they offer services at a quality similar to that currently ensured by the macro cellular networks. Mitigating congestion of the backhaul links to small cell networks is a crucial factor. With this regard, this paper proposes an admission control that makes decisions to redirect IP flows, fully or partially, to the macro or small cell networks, or to reject the incoming flows. The decision mechanism is based on predictions of users' Quality of Experience (QoE). It is modeled as a Markov decision process (MDP), whereby the aim is to derive the optimal policy (i.e. reject or accept flows in the macro or the small cell) that maximizes users' QoE. Through computer simulations, we evaluate the performance of the proposed admission control and compare it against a random policy decision. We also numerically illustrate its optimal policies in different scenarios under different traffic load conditions.

User generated content (UGC)-based applications are gaining lots of popularity among the community of mobile internet users. They are populating video platforms and are shared through different online social services, giving rise to the so-called mobile social media applications. These applications are characterized by communication sessions that frequently and dynamically update content, shared with a potential number of mobile users, sharing the same location or being dispersed over a wide geographical area. Since most of UGC content of mobile social media applications are exchanged through mobile devices, it is expected that along with online social applications, these content will cause severe congestion to mobile networks, impacting both their core and radio access networks. In this paper, we address the challenges introduced by these applications devising a complete framework that 1) identifies such applications/sessions and 2) initiates multicast-based delivery (or offload through WiFi) of the relevant content. The proposed framework leverages the network function virtualization (NFV) paradigm to dynamically integrate its functionalities to the operators' service function chaining (SFC) process, allowing fast deployment and lowering both capital and operational expenditures (CAPEX and OPEX) of the mobile operators. The performance of the proposed framework is evaluated through mathematical analysis and computer simulations, taking Twitter-like social applications as an example.

Abstract Traditional vertical handover schemes postulate that vertical handovers (VHOs) of users come on an individual basis. This enables users to know previously the decision already made by other users, and then the choice will be accordingly made. However, in case of group mobility, almost all VHO decisions of all users, in a given group (e.g., passengers on board a bus or a train equipped with smart phones or laptops), will be made at the same time. This concept is called group vertical handover (GVHO). When all VHO decisions of a large number of users are made at the same time, the system performance may degrade and network congestion may occur. In this paper, we propose two fully decentralized algorithms for network access selection, and that is based on the concept of congestion game to resolve the problem of network congestion in group mobility scenarios. Two learning algorithms, dubbed Sastry Algorithm and Q‐Learning Algorithm, are envisioned. Each one of these algorithms helps mobile users in a group to reach the nash equilibrium in a stochastic environment. The nash equilibrium represents a fair and efficient solution according to which each mobile user is connected to a single network and has no intention to change his decision to improve his throughput. This shall help resolve the problem of network congestion caused by GVHO. Simulation results validate the proposed algorithms and show their efficiency in achieving convergence, even at a slower pace. To achieve fast convergence, we also propose a heuristic method inspired from simulated annealing and incorporated in a hybrid learning algorithm to speed up convergence time and maintain efficient solutions. The simulation results also show the adaptability of our hybrid algorithm with decreasing step size‐simulated annealing (DSS‐SA) for high mobility group scenario. Copyright © 2015 John Wiley & Sons, Ltd.

Machine-Type-Communication (MTC) is a key enabler for a variety of novel smart systems, such as smart grid, eHealth, Intelligent Transport System (ITS), and smart city, opening the area of the cyber physical systems. These systems may require the use of a huge number of MTC devices, which will put a great pressure on the whole network, i.e. Radio Access Network (RAN) and Core Network (CN) parts, resulting in the shape of congestion and system overload. Aiming at better evaluating the network performance under the existence of MTC traffic and also the effectiveness of the congestion control methods, the 3rd Generation Partnership Project (3GPP) group has proposed two traffic models: Uniform Distribution (over 60 s) and Beta Distribution (over 10 s). In this paper, a recursive operation-based analytical model, namely General Recursive Estimation (GRE), for modeling the performance of RACH procedure in the existence of MTC with Beta traffic is proposed. In order to show the effectiveness of our analytical model GRE, many metrics have been considered, such as the total number of MTC devices in each Random Access (RA) slot, the number of success MTC devices in each RA slot, and the Cumulative Distribution Function (CDF) of preamble transmission. Numerical results demonstrate the accuracy of GRE. Moreover, our model GRE could be used to model the performance of RACH procedure with any type of traffic.

One important objective of 5G mobile networks is to accommodate a diverse and ever-creasing number of user equipment (UEs). Coping with the massive signaling overhead expected from UEs is an important hurdle to tackle to achieve this objective. In this paper, we propose three solutions that aim for finding optimal distributions of tracking areas (TAs) in the form of TA lists (TALs) and assigning them to UEs, with the objectives of minimizing two conflicting metrics, namely paging overhead and tracking area update (TAU) overhead. Two solutions favors one objective than the other. The third one incorporates a novel scheme, dubbed Fair and Optimal TAL Assignment (FOTA), based on Nash bargaining game theory. FOTA improves overall network performance minimizing overhead due to both paging and TAU messages, taking into account the behavior and mobility features of UEs. The performance of proposed schemes are evaluated via simulations and the obtained results demonstrate their feasibility and ability in achieving their design goals, improving network performance by minimizing cost associated with paging and TAU.

Along with an ever-growing demand for rich video applications by a rapidly increasing population of mobile users, it is becoming difficult for the Internet backbone to cope with a constantly increasing mobile traffic. Though multi-source mobile streaming (MS2) was proposed to solve the bottleneck issue of the Internet backbone considering simultaneous multiple low streaming rate transmissions to mobile users, it does not consider redundant transmissions of popular contents. Recently, Content Centric Networking (CCN) is proposed as a content name-oriented approach to disseminate content to edge gateways/routers. In CCN, if the content is popular, the previously queried content can be reused for multiple times to save bandwidth capacity, reduce overall energy consumption, and improve users' Quality of Experience (QoE). Inheriting all advantages of CCN, a novel architecture "MM3C", which integrates CCN with MS2, is proposed as a better solution to the problem. Using OPNET, the performance of MM3C is evaluated. Compared to MS2 under the same network configuration, the simulation results show that MM3C exhibits less bottleneck links, shorter round trip times, and better performance in terms of traffic offloading.

Machine-Type-Communication (MTC) is a promising service of the envisioned 5G mobile networks. However, deploying a massive number of MTC devices in these networks remains a challenge due to the overload that may appear at the Radio Access Network (RAN), hence degrading the Quality of Services (QoS) for both MTC and Non-MTC devices. One of the methods used to address the congestion's problem in RAN is Group Paging (GP), wherein a single message is used to activate a group of devices. Whilst the GP method has several advantages, its performance quickly decreases when the number of MTC devices increases. In this paper, we devise a new method, namely Traffic Scattering For Group Paging (TSFGP) to improve the performance of the GP method for massive deployment of MTC devices. Numerical results demonstrate that TSFGP highly improves the performance of GP in terms of several performance metrics, such as success probability, collision probability, and access delay.

Cloud offerings represent a promising solution for mobile network operators to cope with the surging mobile traffic. The concept of carrier cloud has therefore emerged as an important topic of inquiry. For a successful carrier cloud, algorithms for optimal placement of Virtual Network Functions (VNFs) on federated cloud are of crucial importance. In this paper, we introduce different VNF placement algorithms for carrier cloud with two main design goals: i) minimizing path between users and their respective data anchor gateways and ii) optimizing their sessions' mobility. The two design goals effectively represent two conflicting objectives, that we deal with considering the mobility features and service usage behavioral patterns of mobile users, in addition to the mobile operators' cost in terms of the total number of instantiated VNFs to build a Virtual Network Infrastructure (VNI). Different solutions are evaluated based on different metrics and encouraging results are obtained.

A Long Term Evolution-Frequency Division Duplexing (LTE-FDD) small cell is one of the promising solutions for improving service quality and data rate in both the uplink and downlink of home users. Small cell (e.g., femtocell, picocell, microcell) is short range, low cost and low power base station installed by the indoor consumers. However, the avoidance of interferences is still an issue that needs to be addressed for successful deployment of small base stations (SBS) within existing macro cell networks mainly in co-channel deployment. Moreover, interferences are strongly dependent on the type of access control of small cells. Closed and open access are in conflict interests for macro users and home users in the uplink and downlink. To mitigate this conflict, we propose a fully distributed algorithm based on the shared time access and executed by LTE-FDD small cells, in order to reduce the effect of interferences, improve QoS of users, and maximize the overall capacity of downlink and uplink in two-tier LTE networks when small cells are deployed randomly. Simulation results validate our algorithm and show the improvement attained in offloading macro cell and satisfying QoS requirements of home users compared to the closed and open access mechanisms in both the uplink and downlink.

As (M2M) Machine-To-Machine, communication continues to grow rapidly, a full study on overload control approach to manage the data and signaling of H2H traffic from massive MTC devices is required. In this paper, a new M2M resource-scheduling algorithm for Long Term Evolution (LTE) is proposed. It provides Quality of Service (QoS) guarantee to Guaranteed Bit Rate (GBR) services, we set priorities for the critical M2M services to guarantee the transportation of GBR services, which have high QoS needs. Additionally, we simulate and compare different methods and offer further observations on the solution design.

Machine-type-Communication (MTC) is seen as a major service in next generation cellular mobile networks. However, the forecasted very large number of MTC devices may overload the RAN (Radio Access Network) part of the network, which may impact Non-MTC communications. Group paging is considered as one of the most efficient mechanisms proposed to alleviate the problem of the RAN overload. In this paper, we introduce a new solution to improve the performance of the current group paging method and overcome its disadvantages. The proposed solution is intended for MTC devices in the RRC CONNECTED OUT OF SYNC state, in which MTC devices have an RRC context without being synchronized with the network. Numerical results demonstrate that the proposed solution highly improves the performance of existing group paging mechanisms.

The Follow-Me Cloud (FMC) concept enables service mobility across federated data centers (DCs). Following the mobility of a mobile user, the service located in a given DC is migrated each time an optimal DC is detected. The detailed criterion for optimality is defined by operator policy, but it may be typically derived from geographical proximity or load. Service migration may be an expensive operation given the incurred cost in terms of signaling messages and data transferred between DCs. Decision on service migration defines therefore a tradeoff between cost and user perceived quality. In this paper, we address this tradeoff by modeling the service migration procedure using a Markov Decision Process (MDP). The aim is to formulate a decision policy that determines whether to migrate a service or not when the concerned User Equipment (UE) is at a certain distance from the source DC. We numerically formulate the decision policies and compare the proposed approach against the baseline counterpart.

This paper describes a device triggering optimization technique for controlling system overload when deploying massive Machine Type Communication (MTC) devices in 3GPP-based cellular networks. Triggering a large number of MTC devices can dramatically overload the underlying transport network system and incur congestion in both the Radio Access Network (RAN) and the Evolved Packet Core (EPC). The proposed solution aims at controlling the rate of device trigger requests that MTC servers can generate in order to reduce the system overload. For this purpose, we propose that the Mobility Management Entity (MME), or an alike core network node, computes the device trigger rate that alleviates congestion, and communicates this value to the MTC-Interworking Function (MTC-IWF) element that enforces MTC traffic control, via admission control or data aggregation, on the device trigger request rate received from the different MTC servers. The proposed solution is evaluated through computer simulations and encouraging results are obtained.

Content Centric Networking (CCN) is a content name-oriented approach to disseminate content to edge gateways/routers. In CCN, a content is cached at routers for a certain time. When the associated deadline is reached, the content is removed to cope with the limited size of content storage. If the content is popular, the previously queried content can be reused for multiple times to save bandwidth capacity. It is, therefore, critical to design an efficient replacement policy to keep popular content as long as possible. Recently, a novel caching strategy, named Most Popular Content (MPC), was proposed for CCN. It considers the high skewness of content popularity and outperforms existing default caching approaches in CCN such as Least Recently Used (LRU) and Least Frequency Used (LFU). However, MPC has some undesirable features, such as slow convergence of hitting rate and unstable hitting rate performance for various cache sizes. In this paper, a new caching policy, dubbed Fine-Grained Popularity-based Caching (FGPC), is proposed to overcome the above-mentioned weak points. Compared to MPC, FGPC always caches coming content when storage is available. Otherwise, it keeps only most popular content. FGPC achieves higher hitting rate and faster convergence speed than MPC. Based on FGPC, we further propose a Dynamic-FGPC (D-FGPC) approach that regularly adjusts the content popularity threshold. D-FGPC exhibits more stability in the hitting rate performance in comparison to FGPC and that is for various cache sizes and content sizes. The performance of both FGPC and D-FGPC caching policies are evaluated using OPNET Modeler. The obtained simulation results show that FGPC and D-FGPC outperform LRU, LFU, and MPC.

Carrier Cloud is a promising concept towards the decentralization of mobile networks, to, in turn, alleviate mobile traffic load and reduce mobile operator cost. Carrier cloud is enabled by two main approaches, namely virtualization of the mobile network functions and networking over federated cloud. For intelligent carrier cloud dimensioning, the placement of mobile network functions over federated cloud is of vital importance. In this vein, this paper argues the need for adopting service/application type and requirements as metrics for (i) creating virtual instances of the Packet Data Network Gateways (PDN-GW) and (ii) selecting adequate virtual PDN-GWs for User Equipment receiving specific application type. After modeling this procedure as a nonlinear Optimization Problem and proving it as a NP-hard problem, we propose three solutions to solve it. The proposed solutions are evaluated through computer simulations and encouraging results are obtained.

Recently, issues of power consumption at base stations (BSs) in wireless cellular networks have attracted great interest in both research communities and the industry. In this paper, we investigate the BS power consumption in multiple-input single-output (MISO) Poisson-Voronoi tessellation (PVT) random cellular networks. Taking into account the inter-cell interference, the impact of the traffic demands of users and the spatial traffic intensity on the BS power consumption are jointly considered for MISO PVT random cellular networks. Furthermore, the power consumption required at the BS in a typical PVT cell is modeled through characteristic functions. Simulation results are employed to evaluate the BS power consumption and the performance of the random cellular networks.

In this paper, we address the problem of efficient routing in delay tolerant networks. We propose a new routing protocol dubbed as ORION. In ORION, only a single copy of a data packet is kept in the network and transmitted, contact by contact, towards the destination. The aim of the ORION routing protocol is twofold: on one hand, it enhances the delivery ratio in networks where an end-to-end path does not necessarily exist, and on the other hand, it minimizes the routing delay and the network overhead to achieve better performances. With ORION, nodes are aware of their neighborhood by the mean of actual and statistical estimation of new contacts. ORION makes use of autoregressive moving average (ARMA) stochastic processes for best contact prediction and geographical coordinates for optimal greedy data packet forwarding. Simulation results have demonstrated that ORION outperforms other existing DTN routing protocols such as PRoPHET in terms of end-to-end delay, packet delivery ratio, hop count, first packet arrival and queues occupancy.

Bandwidth is an extremely valuable and scarce resource in wireless networks. Therefore, efficient bandwidth management is necessary to support service continuity, guarantee acceptable quality of service and ensure steady quality of experience for users of mobile multimedia streaming services. Indeed, the support of uniform streaming rate during the entire course of a streaming service, whereas the user is on the move is a challenging issue. In this paper, we propose a framework, together with schemes, which integrates user mobility prediction models with bandwidth availability prediction models to support the requirements of mobile multimedia services. More specifically, we propose schemes that predict paths to destinations, times when users will enter/exit cells along predicted paths, and available bandwidth in cells along predicted paths. With these predictions, a request for a mobile streaming service is accepted only when there is enough (predicted) available bandwidth, which is along the path to destination, to support the service. Simulation results show that the proposed approach outperforms existing bandwidth management schemes in better supporting mobile multimedia services.

Whilst Machine Type Communication (MTC) represents an important business opportunity for mobile operators, mobile operators fear the congestion that could come with the deployment of billions/trillions of MTC devices. In this paper, we present a new mechanism that anticipates system overload due to MTC signaling messages in 3GPP networks. This mechanism proactively avoids system congestion by compacting the signaling message content for a group of MTC devices sharing redundant Information Elements (IE) by creating a profile ID for this group. Furthermore, along with this solution, we propose a dynamic grouping solution, which groups MTC devices with common subscription features in order to control the MTC signaling traffic when the network is overloaded. Simulation results show that compared to the Access Class Baring (ACB) mechanism, introduced by 3GPP, our proposed solution can avoid system overload without dropping MTC signaling messages, which is highly beneficial for MTC applications requiring service reliability.

This paper introduces an analytical model for the Follow-Me Cloud (FMC) concept whereby service mobility is enabled across data centers following the mobility of a mobile user. Given a network and cloud setup and a mobility pattern of a mobile user, the proposed analytical model provides the performance of the FMC concept related to: (i) the user experience with the service (such as: UE average distance from the optimal DC, average end-to-end delay, service disruption duration); and (ii) to the cloud/mobile operator (such as the service migration cost). Obtained results are encouraging. They confirm the advantage of the FMC concept, but stress the need for careful consideration when triggering the service migration.

A good understanding of the behaviour of the traffic of a mobile network is essential for an efficient planning and management of the mobile network's scarce bandwidth resources. In this paper, we propose a probabilistic approach, called Handoff Time Estimation MODel (HTEMOD), to estimate the time window when a user will perform handoffs along his/her movement/path to a destination. We derive the probability distribution function of time taken to transit each road segment along the path, using a sample of users that is selected according to navigation zone characteristics, current data on road segments, and current behaviour of users on the road segment. We evaluate our model via simulations, and compare it with the model proposed in [1]. Regardless of the given probability value to obtain a time window, the road segment density and the number of road segments to handoff, HTEMOD provides a better accuracy and good duration of predicted time window when handoff will occur. Whilst the proposed HTEMOD model can be applied to any type of user equipment, its efficiency becomes more appealing in the context of vehicles (i.e., for the support of road to vehicles communications - RVC) or highly mobile nodes travelling in urban areas constrained by predefined roads and whose velocities are also restricted according to speed limits, level of congestion in roads, and traffic control mechanisms (e.g., stop signs and traffic lights).

Emerging social media applications are expected to cause severe congestion to mobile networks, both mobile core network and mobile radio access network. These social media applications are characterized by the fact that they involve sessions with frequently and dynamically updated content, shared with a potential number of mobile users sharing the same location, or being dispersed over a wide area. A method to dynamically identify such applications/sessions and initiate multicast based delivery of the relevant content is proposed. The performance of the proposed method is evaluated through computer simulations, taking Twitter as an example. Encouraging results are obtained.

Existing gateway selection mechanisms base their selection on gateway load and/or geographical proximity of users to the gateways. In this paper, we mainly argue the need for other metrics to improve the gateway selection mechanisms in distributed mobile networks. We therefore propose considering the end-to-end connection and the service/application type as two important additional metrics in the selection of data anchor gateways in the context of the Evolved Packet System (EPS). To enable this, two solution variants are proposed. Simulations were also conducted to evaluate the performance of the proposed solutions and encouraging results are obtained.

This paper introduces the concept of dynamic Service Boost and proposes deployment solutions in mobile networks focusing on the 3GPP Long Term Evolution (LTE) architecture. The main idea is to introduce a time bound preferential service to subscribers based on predefined service contracts. By applying a light-weight, dynamic Quality of Service (QoS) control, operators achieve both, efficient network utilization and adequate QoS for users and content/application providers. This helps operators to use resources more efficiently, for example to enable a more efficient capacity sharing in the presence of increasing mobile traffic. We initially investigate the impact of Service Boost on Over-The-Top (OTT) traffic transmitted without any specific QoS guarantees over the so-called default bearer in LTE. Then we consider the design and analysis of a Service Boost architecture and framework for managing and prioritizing service requirements for certain applications within LTE. Finally, we elaborate the realization of Service Boost through congestion accountability.

Building mobile networks, on demand and in an elastic manner, represents a vital solution for mobile operators to cope with the modest Average Revenues per User (ARPU), on one hand, and the ever-increasing mobile data traffic, on the other hand. An important research problem towards this vision of carrier cloud pertains to the development of adequate technologies and methods for the on-demand and dynamic provision of a decentralized and elastic mobile network as a cloud service over a distributed network of cloud-computing data centers, forming a federated cloud. An efficient mobile cloud cannot be built without efficient algorithms for the placement of network functions over this federated cloud. In this vein, this paper argues the need for avoiding or minimizing the frequency of mobility gateway relocations and discusses how this gateway relocation avoidance can be reflected in an efficient network function placement algorithm for the realization of mobile cloud. The proposed scheme is evaluated through computer simulations and encouraging results are obtained.

The increasing adoption of Machine Type Communication (MTC) applications using the Long Term Evolution (LTE) brings new challenges for the traditional bearer allocation and network management. MTC devices are high in number, each requiring an individual bearer for a short data transmission; an inefficient process considering the signaling and management effort to the duration of the actual bearer use. This paper introduces the notion of a virtual bearer that can be shared among a number of MTC devices with similar Quality of Service (QoS) characteristics. The virtual bearer support at the MTC device side is based on the paradigm of Network Function Virtualization (NFV) and can migrate from one MTC device to another eliminating the need for individual device bearer establishment for both the radio and backhaul. The use of virtual bearers is enhanced by considering additionally MTC device-to-device (D2D) connectivity allowing devices without direct access to the network to use the virtual bearer via other devices, which act as gateways. Gateway devices may also be re-located dynamically based on NFV principles. In this way, a finer control is achieved on prioritizing certain services and providing load balancing. Our simulation results demonstrate the benefits of our proposed scheme compared to conventional approaches wherein MTC devices use individual bearers to send data in response to a trigger or according to a predetermined schedule.

Scheduling different types of packets, such as realtime and non-real-time data packets, at sensor nodes with resource constraints in Wireless Sensor Networks (WSN) is of vital importance to reduce sensors' energy consumptions and end-to-end data transmission delays. Most of the existing packet-scheduling mechanisms of WSN use First Come First Served (FCFS), non-preemptive priority and preemptive priority scheduling algorithms. These algorithms incur a high processing overhead and long end-to-end data transmission delay due to the FCFS concept, starvation of high priority real-time data packets due to the transmission of a large data packet in nonpreemptive priority scheduling, starvation of non-real-time data packets due to the probable continuous arrival of real-time data in preemptive priority scheduling, and improper allocation of data packets to queues in multilevel queue scheduling algorithms. Moreover, these algorithms are not dynamic to the changing requirements of WSN applications since their scheduling policies are predetermined. In this paper, we propose a Dynamic Multilevel Priority (DMP) packet scheduling scheme. In the proposed scheme, each node, except those at the last level of the virtual hierarchy in the zone-based topology of WSN, has three levels of priority queues. Real-time packets are placed into the highest-priority queue and can preempt data packets in other queues. Non-real-time packets are placed into two other queues based on a certain threshold of their estimated processing time. Leaf nodes have two queues for real-time and non-real-time data packets since they do not receive data from other nodes and thus, reduce end-to-end delay. We evaluate the performance of the proposed DMP packet scheduling scheme through simulations for real-time and non-real-time data. Simulation results illustrate that the DMP packet scheduling scheme outperforms conventional schemes in terms of average data waiting time and end-to-end delay.

This paper designs a complete framework that anticipates QoS/QoE (Quality of Experience) degradation and proactively defines policies for LTE-connected cars (UEs) to select the most adequate radio access out of WiFi and LTE. For a particular application, the proposed framework considers the application type, the mobility feature (e.g., speed, user mobility entire/partial path, user final/intermediate destination), and the traffic dynamics over the backhauls of both LTE and WiFi networks in order to predict and allow the UE to select the best network that maximize user QoE throughout the mobility path. Simulations are conducted to evaluate the performance of the proposed framework in achieving its design objectives and encouraging results are obtained.

One of the most important problems posed by cellular-based machine type communications is congestion. Congestion concerns both the radio access network and the mobile core network, impacting both the user data and the control planes. In this paper, we address the problem of congestion in machine type communications. We propose a congestion-aware admission control solution that selectively rejects signaling messages from MTC devices at the radio access network following a probability that is set based on a proportional integrative derivative controller reflecting the congestion level of a relevant core network node. We evaluate the performance of our proposed solution using computer simulations. The obtained results are encouraging. In fact, we succeed in reducing the amount of signaling, to reach a target utilization ratio of resources in the core network.

Scheduling real-time and non-real time packets at the sensor nodes is significantly important to reduce processing overhead, energy consumptions, communications bandwidth, and end-to-end data transmission delay of Wireless Sensor Network (WSN). Most of the existing packet scheduling algorithms of WSN use assignments based on First-Come First-Served (FCFS), non-preemptive priority, and preemptive priority scheduling. However, these algorithms incur a large processing overhead and data transmission delay and are not dynamic to the data traffic changes. In this paper, we propose three-class priority packet scheduling scheme. Emergency real-time packets are placed into the highest priority queue and can preempt the processing of packets at other queues. Other packets are prioritized based on the location of sensor nodes and are placed into two other queues. Lowest priority packets can preempt the processing of their immediate higher priority packets after waiting for a certain number of timeslots. Simulation results show that the proposed three-class priority packet scheduling scheme outperforms FCFS and multi-level queue schedulers in terms of end-to-end data transmission delay.

Along with the recent and ongoing advances in the wireless and mobile access technologies, a wide plethora of mobile multimedia services have emerged. Ensuring an acceptable level of Quality of Service (QoS) is a crucial requirement to allow users enjoy these mobile multimedia services. One means to ensure QoS is to minimize the frequency and magnitude of fluctuations in the mobile multimedia streaming rates during the multimedia service and while users are on the move. For this purpose, there is need for tools to predict a user's long-term movement. In this vein, this paper proposes a Path Prediction Model (PPM) to predict a user's movement path. PPM is based on historical movement trace, current movement data and spatial conceptual maps; it assumes a priori knowledge of the destination. At each road intersection, the probability of selecting the next road segment is evaluated, based on historical data, towards the destination. These probabilities are computed via (a) filtering historical data according to the day of the week (e.g., weekend, holiday) and the time of the day; and (b) applying conditional probability rules taking into account the path used between the origin of movement, current position, and the destination. Simulations are conducted using real-life data to evaluate the performance of the proposed model. Encouraging results are obtained in terms of average prediction accuracy and mitigation of the impact of learning period and the remaining distance to reach the destination on the path prediction performance.

Mobile Wireless Network technology has enabled the development of increasingly diverse applications and devices resulting in an exponential growth in usage and services. One challenge in mobility management is the movement prediction. Prediction of the user's longer-term movement (e.g., 10 min in advance) with reasonable accuracy is very important to a broad range of services. To cope with this challenge, this paper proposes a new method to estimate a user's future destination, called Destination Prediction Model (DPM). This method combines two types of approaches: one based on the use of filtered historical movement pattern and another based on contextual knowledge; both approaches use spatial conceptual maps. The filter is based on the day and the time of the day to increase accuracy. The current movement direction, that takes into account the recent data, is used by the proposed method to reduce historical and contextual knowledge mistakes. Simulations are conducted using real-life data to evaluate the performance of the proposed model. For subjects with low predictability degree, DPM reaches an average prediction accuracy of 79%; it reaches 91% for subjects with high predictability and 86% for other subjects. Simulation results also indicate that DPM significantly reduces the impact of learning period and the remaining distance to reach the destination on prediction performance. In the future, we plan to extend our research work by proposing a full Path Prediction Model (PPM) based on the Destination Prediction Model (DPM).

Due to their numerous advantages, current trends show a growing number of femtocell deployments. However, femtocells would become less attractive to the general consumers if they cannot keep up with the service quality that the macro cellular network should provide. Given the fact that the quality of mobile services provided at femtocells depends largely on the level of congestion on the backhaul link, this paper introduces a flow mobility/handover admission control method that makes decisions on layer-three handovers from macro network to femtocell network and/or on entire or partial flow mobility between the two networks based on predicted QoS taking into account metrics such as network load/congestion indications and based on predicted QoE metrics. The performance of the proposed admission control is evaluated via simulations and encouraging results are obtained.

In this paper, we propose a new method for assessing the sociability scalar of a mobile peer by the network, most importantly with no involvement of the mobile peer. The sociability metric can help in the Application Layer Traffic Optimization (ALTO) guidance in Mobile Peer-to-Mobile Peer (MP2MP) scenario to scale up the database search of an ALTO server. The proposed method models encounters of mobile peers with predetermined areas, such as cells, tracking areas, gateway service areas, etc, depending on the targeted granularity. The obtained metrics, pertaining to inter-mobile peer relationship (i.e., sociability) and mobile peers mobility, are adopted to ALTO in a MP2MP scenario. In addition, metrics reflecting the energy budget of a mobile peer, the type of a mobile terminal, history of a mobile terminal in sharing contents with other mobile peers, etc, can be also taken into account by ALTO in the peer recommendation.

There is a general trend towards the decentralization of mobile operator networks. Such network decentralization will not be efficient without rethinking mobility management schemes, particularly for users moving for a long distance and/or at a high speed (e.g., vehicles). To support such highly mobile users, this paper introduces a data anchor gateway relocation method based on user mobility, history information, and user activity patterns. The performance of the proposed schemes is evaluated through simulations and encouraging results are obtained.

ABSTRACT Different security measures have emerged to encounter various Internet security threats, ensuring a certain level of protection against them. However, this does not come without a price. Indeed, there is a general agreement that high security measures involve high amount of resources, ultimately impacting the perceived Quality of Service (QoS). The objective of this paper is to define a framework, dubbed QoS2, that provides means to find a tradeoff between security requirements and their QoS counterparts. The QoS2 framework is based on the multiattribute decision‐making theory. The performance of the QoS2 framework is evaluated through computer simulations. A use‐case considering worm e‐mail detection is used in the performance evaluation. Copyright © 2012 John Wiley & Sons, Ltd.

In mobile multimedia streaming services, important requirements consist of the support of service continuity, the guarantee of acceptable Quality of Service (QoS) and insurance of steady Quality of Experience (QoE). How to get a uniform data exchange rate during the entire (or partial) course of a streaming service while a user is on the move is an important challenge. Generally speaking, the streaming rate of a multimedia service may heavily fluctuate due to the unavailability or deficiency of resources along the movement path of a user. To cope with this challenge, this paper proposes a framework that integrates user mobility prediction models with resource availability prediction models to keep a constant or less fluctuating streaming rate and to ultimately ensure steady QoE. Simulations are conducted to evaluate the performance of the proposed framework in achieving its design objectives and encouraging results are obtained.

In the Evolved Packet System, service resiliency can be heavily impacted by a node failure in its control plane. Ensuring service resiliency in EPS via defining efficient and proactive restoration mechanisms is of vital importance. In this paper, we address the case of MME failure. We propose schemes for MME failure detection and restoration considering both UEs in idle mode and UEs in active mode. As a MME failure may concern a potential number of UEs, network overload control, via randomized paging and handling signaling messages in bulk, is considered. The proposed schemes are evaluated through simulations and encouraging results are obtained.

In this paper, we consider the Selected IP Traffic Offload (SIPTO) approach to handle increased data traffic of both local and macro-cellular networks. We devise different ap- proaches based on operator defined offload policies on a per des- tination domain name basis, which offer operators fine-grained control of whether a new IP connection should be offloaded or provided via the core network. Two of our solutions are based on Network Address Translation (NAT) named simple-NATing and twice-NATing, while a third one employs simple tunneling. These solutions support all kinds of UEs including those that support a single Packet Data Protocol (PDP) context/Packet Data Network (PDN) connection. In a forth solution, we consider the case where a User Equipment (UE) supports multiple PDP contexts/PDN connections, with at least one dedicated for SIPTO traffic. A qualitative analysis and a simulation study are presented.

Mobility management is a critical issue in vehicular networks. In this paper, we consider the case of highly mobile users in heterogeneous wireless environments. We propose a mobility management, based on a recently proposed mobile IP-based mobility management architecture, optimizing the calculation of its dynamic registration message frequency. The new calculation takes into account both the size of the radio access networks and the velocity of the mobile users. Simulation results show that this approach yields an effective control of the policy function and alleviates the high signaling cost introduced by high registration message frequencies. The derived mobility management thus allows an efficient control of the use of registration messages at congested access networks and guarantees appropriate handoff decisions.

Coupling the high data rates of IEEE 802.11p-based VANETs and the wide coverage area of 3GPP networks (e.g., UMTS), this paper envisions a VANET-UMTS integrated network architecture. In this architecture, vehicles are dynamically clustered according to different related metrics. From these clusters, a minimum number of vehicles, equipped with IEEE 802.11p and UTRAN interfaces, are selected as vehicular gateways to link VANET to UMTS. Issues pertaining to gateway selection, gateway advertisement and discovery, service migration between gateways (i.e., when serving gateways lose their optimality) are all addressed and an adaptive mobile gateway management mechanism is proposed. Simulations are carried out using NS2 to evaluate the performance of the envisioned architecture incorporating the proposed mechanisms. Encouraging results are obtained in terms of high data packet delivery ratios and throughput, reduced control packet overhead, and minimized delay and packet drop rates.

Along with recent Internet security threats, different security measures have emerged. Whilst these security schemes ensure a level of protection against such threats, they sometimes have significant impact on perceived Quality of Service (QoS). There is thus need to retrieve ways for an efficient integration of security requirements with their QoS counterparts. In this paper, we devise a Quality of Protection framework that tunes between security requirements and QoS using a multi-attribute decision making model. The performance of the proposed approach is evaluated and verified via a use case study using computer simulations.

Vehicle Ad Hoc Networks (VANETs), based on IEEE 802.11p, and 3G & beyond networks are characterized by their high date transmission rates and wide range communication, respectively. This paper presents an architecture that integrates between the two, making advantage of the features of each. Design guidelines pertaining to vehicle clustering and gateway management are defined. The former aims for enhancing the link stability within the VANET, whereas the latter sustains inter-connectivity of the VANET with the backhaul 3G & beyond network. Simulations are carried out using NS2 to evaluate the performance of the integrated network architecture and encouraging results are obtained in terms of high data packet delivery ratio, reduced control packet overhead, and reduced packet drop rate.

Delay/disruption tolerant networking (DTN) technology offers a new solution to highly stressed communications in space environments, especially those with long link delay and frequent link disruptions in deep space missions. To date, little work has been done in evaluating the effectiveness of the available DTN protocols when they are applied to an interplanetary Internet. In this paper, we present an experimental evaluation of the Bundle Protocol (BP) running over various "convergence layer" protocols in a simulated cislunar communications environment characterized by varying degrees of signal propagation delay and data loss. We focus on the Licklider Transmission Protocol (LTP) convergence layer adapter running on top of UDP/IP (i.e., BP/LTPCL/UDP/IP). The performance of BP/LTPCL/UDP/IP in realistic file transfers over a PC-based testbed is compared with that of two other DTN protocol stacks, BP/TCPCL/TCP/IP and BP/UDPCL/UDP/IP. The experiment results show that LTPCL has a significant performance advantage over TCPCL for link delays longer than 4 sec when bit error rate (BER) is 10E-6 or lower. For a lossy channel with a BER of around 10E-5, LTPCL has a significant goodput advantage over TCPCL at all the link delay levels studied, with an advantage of around 3000 bytes/s for delays longer than 1.5 sec.

For an efficient usage of the sensor technology, several design factors (e.g., topology and sensing coverage) should be taken into account. In this paper, we focus on the underlying topology of sensor networks in two-dimensional environments and enhance a set of recently proposed graphs. The new enhanced graphs are referred to as the Derived Circles version 2 (DCαv2) graphs. We show that DCαv2 graphs are locally constructed, connected, have the rotation-ability property, and have the Euclidean Minimum Spanning Tree (EMST) as their subgraphs. Moreover, we show that the new set of graphs has a bounded Euclidean/length and power dilation when 0.5 ≤ α ≤ 1. Furthermore, via simulations, we confirm most of these properties, and demonstrate that the DCαv2 graphs also have bounded Euclidean and power dilations when 0αv2 graphs outperform the Half Space Proximal (HSP) and the Relative Neighbourhood Graph (RNG) graphs in terms of the network dilation, Euclidean dilation, and power dilation. This, in turn, increases the speed for message delivery, reduces the energy consumption of nodes and accordingly prolongs the network lifetime.

In the System Architecture Evolution (SAE) study of the nextgeneration mobile network in 3GPP, Serving Gateways (SGWs) and Mobility Management Entities (MMEs) are grouped to form a number of service and pool areas, respectively. While this concept of SGW service areas in the Evolved Packet Core is interesting to limit the administrative scope of SGWs and also provides a means to optimize the routing, the use of fixed/hard area boundaries can result in frequent unnecessary SGW relocations and can severely impact the Quality of Experience (QoE) of users. To avoid the drawback of fix/hard service (or pool) area boundaries, this paper proposes a scheme whereby every SGW can have a flexibly configurable service area, which is defined by a set of LTE (Long Term Evolution) cells or Tracking Areas (TAs). The service area of a SGW defines the LTE area (e.g., cells or TAs) that the SGW can serve. The working of the proposed mechanism is validated via computer simulations and encouraging results are obtained.

Available doorbell interphone systems are designed under the assumption that residents would be at home to enable the communication between them and their visitors. However, people spend a large fraction of their time away from home, thus undermining the basic assumptions of existing doorbell solutions. Recent developments in 3GPP Home Node B (HNB or Femtocell) and home gateway technologies, along with the growing proliferation of smart-phones, can offer interesting opportunities for the design and development of innovative doorbell solutions. Along this line, the paper describes a 3GPP-enhanced eDoorbell application prototype that relies on IMS to enable video-based communication between visitors and (possibly remote available) home residents. In our prototype, the visibility of context information, such as current location of residents, their diary, and their personal preferences, provides solid basis for definition and enactment of customizable management policies that determine the best suited home resident whom to route the notification of a visit to. While the paper presents no experimental results, it aims at assisting organizations such as the Femto Forum or the ETSI TISPAN to identify the requirements for standards and the different methods used to implement HNB-based or Home Gateway-based services, respectively.

The arousal of emotion might have consequences for physical health is a broadly acknowledged idea. Therapy for depression, prevention for heart pathologies, and rehabilitation treatments for drug addiction are just a few examples of application domains that may benefit from technologies capable of monitoring, detecting, representing, and disseminating information pertaining to patients' physical and psychological/emotional states. However, the design and development of healthcare applications of this kind is a rather challenging issue that requires to integrate sensor infrastructures, which are able to detect changes in patients' physiological and emotional states, and of sharing this information to interested caregivers, such as professional medical staff, relatives, and friends. This paper proposes the Pervasive Environment for AffeCtive Healthcare (PEACH) framework, a middleware level support for affective healthcare that incarnates these ideas and describes its effective functions in a drug addiction treatment application scenario.

The unbridled growth of the Internet and the network-based applications has contributed to enormous security leaks. Even the cryptographic protocols, which are used to provide secure communication, are often targeted by diverse attacks. Intrusion detection systems (IDSs) are often employed to monitor network traffic and host activities that may lead to unauthorized accesses and attacks against vulnerable services. Most of the conventional misuse-based and anomaly-based IDSs are ineffective against attacks targeted at encrypted protocols since they heavily rely on inspecting the payload contents. To combat against attacks on encrypted protocols, we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs). We have categorized various attacks against cryptographic protocols. The MSs, by sniffing the encrypted traffic, extract features for detecting these attacks and construct normal usage behavior profiles. Upon detecting suspicious activities due to the deviations from these normal profiles, the MSs notify the victim servers, which may then take necessary actions. In addition to detecting attacks, the MSs can also trace back the originating network of the attack. We call our unique approach DTRAB since it focuses on both Detection and TRAceBack in the MS level. The effectiveness of the proposed detection and traceback methods are verified through extensive simulations and Internet datasets.

Cooperative diversity has emerged as a promising technique to facilitate fast handoff mechanisms in mobile ad-hoc environments. The key concept behind a prominent cooperative diversity based protocol, namely, Partner-based Hierarchical Mobile IPv6 (PHMIPv6), is to enable mobile nodes anticipate handover events by selecting suitable partners to communicate on their behalves with Mobility Anchor Points (MAPs). In the original design of PHMIPv6, mobile hosts choose partners based on their signal strength. Such a naive selection procedure may lead to scenarios where mobile hosts lose communication with the selected partners before the completion of the handoff operations. In addition, PHMIPv6 overlooks security considerations, which can easily lead to vulnerable mobile hosts and/or partner entities. As a solution to these two shortcomings of PHMIPv6, this paper first proposes an extended version of PHMIPv6 called Connection Stability Aware PHMIPv6 (CSA-PHMIPv6). In CSA-PHMIPv6, mobile hosts select partners with whom communication can last for a sufficiently long time by employing the Link Expiration Time (LET) parameter. To tackle the security issues, the simple yet effective use of two distinct authentication keys is envisioned. Furthermore, to shorten the communication time between mobile hosts and their corresponding partners, a second handoff management approach called Partner Less Dependable PHMIPv6 (PLD-PHMIPv6) is proposed.

Abstract Secure time synchronization is one of the key concerns for some sophisticated sensor network applications. Most existing time synchronization protocols are affected by almost all attacks. In this paper, we consider heterogeneous sensor networks (HSNs) as a model for our proposed novel time synchronization protocol based on pairing and identity‐based cryptography (IBC). This is the first approach for time synchronization protocol using pairing‐based cryptography (PBC) in HSNs. The proposed protocol reduces the communication overhead of the nodes as well as prevents from all the major security attacks. Security analysis shows, it robust against reply attacks, masquerade attacks, delay attacks, and message manipulation attacks. Copyright © 2009 John Wiley & Sons, Ltd.

Due to their ability to overcome many shortcomings associated with the contemporary client-server paradigm, Peer-to-Peer (P2P) networks have attracted phenomenal interests from researchers in both academia and industry. Interactive and multimedia streaming applications using P2P networks are, however, often prone to long startup delays, which disrupt the smooth playback and undermine users' perceived quality of service. In addition, P2P networks must be able to support a potential number of users while ensuring that the resources are efficiently utilized. In this paper, by addressing these shortcomings in the traditional P2P framework, we envision a novel scheme to effectively provide a Video-on-Demand (VoD) using P2P-based mesh overlay networks. The proposed scheme covers two main phases, namely requesting and scheduling modes. The former aims at dynamically selecting the required contents from the available peers. On the other hand, in the scheduling mode, the incoming requests are scheduled in a priority-based manner for minimizing the startup latency and sustaining the playback rate to an acceptable level. Computer simulations have been conducted to verify the effectiveness of the proposed scheme. The obtained results demonstrate the scalability of our envisioned scheme in addition to its capability to reduce the startup delay and provide a sustainable playback rate.

TCP experiences severe performance degradation in cislunar communications because of some assumptions built into its design. Delay/disruption tolerant networking (DTN) is a class of network techniques that is developed to work over Internet protocols to accommodate long link delay and frequent link disruptions in space communication environment. Most of the work concerning DTN protocols and techniques focused on its application in terrestrial wireless network and sensor-based networks. In this paper, we present an experimental evaluation of the DTN protocols with BP/TCPCL running on top of TCP/IP over a long-delay cislunar communication channel with and without link disruption. We hope the experiment results and analysis in this paper will apply equally well in any deep space mission with a round-trip time (RTT) that is comparable to that of the Earth-Moon system.

The problem of local positioning for geostatic satellite networks operating at frequencies above 10 GHz is studied in the present paper. Based on angle of arrival (AOA) and received signal strength (RSS) techniques, a simple yet effective algorithm is provided to estimate the position of a satellite terminal (ST). Since the accuracy of RSS techniques can be affected by the propagation model, two operational conditions are examined, namely the clear sky and the raining one. This distinction becomes critical since modern satellite networks operate at frequencies above 10 GHz, where rain attenuation constitutes the dominant factor impairing link performance and therefore causing uncertainty in the localization of a satellite station. Both cases are studied and useful conclusions, concerning the probability of inaccurate location estimation due to rain, are drawn. Moreover, the effect of various factors on the accuracy of localization is investigated through extended numerical results. Finally, an algorithm that is able to identify the position of a ST independently of the climatic conditions is provided.

Recently, different types of sensors have been developed to detect environmental changes (e.g., instability of the earth's crust) and to reduce the associated damage. For an efficient usage of the sensor technology, several design factors (e.g., topology and sensing coverage) should be taken into account. In this paper, we focus on the underlying topology of sensor networks in two-dimensional environments and propose a new set of graphs referred to as the Derived Circles (DCalpha) graphs. We show that DCalphagraphs are locally constructed, connected, power efficient, and orientation-invariant. We also show that DCalphagraphs have a minimum degree of one and an Euclidean dilation of one. Furthermore, via simulations, we demonstrate that DCalphagraphs outperform the half space proximal (HSP) graph in terms of the network dilation, Euclidean dilation, and power dilation. This, in turn, reduces the energy consumption of nodes and accordingly prolongs the network lifetime.

Owing to the diverse geographical distributions of users, multi-layered satellite networks tend to exhibit high variances causing traffic concentrations at particular satellites to increase drastically. This results in high packet drop rates and severe degradation of Quality of Service (QoS). The Explicit Load Balancing (ELB) scheme was developed to address these issues in Low Earth Orbit (LEO) satellite networks by having the satellites, which experience heavy traffic, redirect a portion of the traffic via alternative paths. To cope with network congestion (over a single layer) and for better traffic distribution, multi layer satellites were proposed. In this paper, we propose an efficient traffic distribution scheme for multi-layered satellite networks based on ELB in which we extend the range for exchanging the traffic-load information for achieving further reductions in packet drop rates. We also present an enhanced technique for efficiently computing the detouring ratio. The effectiveness of the envisioned approach is validated via simulations.

In the modern era of Internet, providing Quality of Service (QoS) is a challenging issue, particularly in resource-constrained wireless networks with delay-sensitive multimedia traffic. Real-time and multimedia services are now available to end-users over wired networks, Wireless Local Area Networks (WLANs), and Wireless Personal Area Networks (WPANs). While the usual trend is to provide the best possible QoS for these services, it is also imperative to deploy security requirements along with the QoS parameters. In this paper, we argue that the existing approaches for including security parameters (such as encryption/decryption key lengths) with QoS parameters (e.g., end-to-end delay requirements) lead to further security risks and consequently fail to provide an adequate solution. Through simulations, we point out the pitfalls of integrating delay and security support in the contemporary approaches. We also envision QoS2, a framework integrating both quality of security and QoS, in order to provide possible solutions for solving these problems. We also demonstrate via simulation the effectiveness and strength of our adopted approach.

In this paper, we propose a cognitive radio based Medium Access Control (MAC) protocol for packet scheduling in wireless networks. Cognitive MAC protocols allow a class of users, called secondary users, to identify the unused frequency spectrum and to communicate without interfering with the primary users. In our proposed MAC protocol, each secondary user is equipped with two transceivers. One of the transceivers is used for control messages while the other periodically senses and dynamically utilizes the unused data channel. The secondary users report the status of channels on control channel and negotiate on the selected data channel itself for onward data transmission. Each channel is used by different set of secondary users. Contrary to existing protocols, data transmission takes place in the time slot in which spectrum opportunity is found. We develop a new analytical model, while taking into account the backoff mechanism. Our simulation results show that throughput increases with the increase in the number of channels.

From the beginning of data networking, dynamic routing has been a challenge. Due to the constantly increasing number of devices and the introduction of multi-hop wireless networks, dynamic routing will remain an important issue for any future network architecture. Using more elaborate routing metrics for such environments is regarded as the general solution but neglects the introduction of significant overhead required to exchange the information. In this paper, we present a routing scheme which is based on metric dependent neighborhood to calculate the forwarding graph. The proposed routing scheme supports aggregation of this metric related information while disseminating routing updates to retain scalability. Simulation results with an exemplary metric based on link stability information show the feasibility of this aggregation approach and the improvement with respect to node reachability and reliable communication in self-organizing wireless networks. Moreover, we implemented this routing scheme on an autonomic networking architecture (ANA).

Recent advances in wireless technology, sensors and portable devices offer interesting opportunities to enable ubiquitous assistance to individuals in need of prompt help. Providing healthcare services to mobile users, such as, patients, elders, or potential drug abusers, is a rather challenging task. Novel middleware-level supports are required to integrate sensor infrastructures capable of detecting changes in the monitored subjects' health conditions and of alerting medical personnel, and the victim's relatives and friends in case of emergency situations. Along this line, the paper envisions a context-aware middleware-level solution dubbed Pervasive Environment for Affective Healthcare (PEACH). PEACH integrates together various sensors in a Wireless Body Area Network (WBAN) to detect alterations of monitored subjects' affective and physical conditions, aggregate the sensed information, and also detect potentially dangerous situations for the monitored subject. Finally, PEACH aims at providing outdoor assistance to the victim/patient by quickly promoting the formation of ad hoc rescue groups comprising nearby volunteers. Through encouraging results obtained from both simulations and a practical drug-rehabilitation application testbed, the effectiveness of the envisioned PEACH framework is verified.

Fast handover management in mobile IPv6 environments has been a research subject for a long time. Exploiting the cooperative diversity paradigm in partner-based hierarchical MIPv6 (PHMIPv6) promises an acceleration of the handoff management operation by relaying some signaling over a selected partner node prior to the actual handover to the new access point. For this purpose, a suitable partner node, that stays in communication range for sufficient time until the signaling in the pre-handoff phase is finalized, should be selected. PHMIPv6 proposes to select the node with the highest signal strength as the partner node. In this paper, we show that using the link expiration time (LET) metric to select the partner node can significantly improve handovers in mobile IP (MIP) networks. The basis of this new metric is the relative position and the relative speed of the mobile node to the potential partner nodes. A set of simulations is conducted to evaluate the performance of the proposed scheme and encouraging results are obtained.

The ever growing percentage of elderly people within modern societies poses welfare systems under relevant stress. In fact, partial and progressive loss of motor, sensorial, and/or cognitive skills renders elders unable to live autonomously, eventually leading to their hospitalization. This results in both relevant emotional and economic costs. Ubiquitous computing technologies can offer interesting opportunities for in-house safety and autonomy. However, existing systems partially address in-house safety requirements and typically focus on only elder monitoring and emergency detection. The paper presents ANGELAH, a middleware-level solution integrating both "elder monitoring and emergency detection" solutions and networking solutions. ANGELAH has two main features: i) it enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and ii) provides a solid framework for creating and managing rescue teams composed of individuals willing to promptly assist elders in case of emergency situations. A prototype of ANGELAH, designed for a case study for helping elders with vision impairments, is developed and interesting results are obtained from both computer simulations and a real-network testbed.

Abstract In this paper, we propose a signature‐based hierarchical email worm detection (SHEWD) system to detect e‐mail worms in large‐scale networks. The proposed system detects novel worms and instantly generates their signatures. This feature helps to check the spread of any kind of worm—knownorunknown. We envision a two‐layer hierarchical architecture comprising local security managers (LSMs), metropolitan security managers (MSM), and a global security manager (GSM). Local managers collectsuspiciousflows and hand them to metropolitan managers. Metropolitan managers then use cluster analysis to sort worms from the suspicious flows. The sorted worms are used to generate the worm signature which is relayed to the global manager and then to all the collaborating networks. A separate scheme is proposed to automatically select suitable values of the system parameters. This parameter selection procedure takes into account the current network state and thethreat levelof the ongoing attack. The performance of the whole system is investigated using real network traffic with traces of worms. Experimental results demonstrate that the proposed scheme is capable to accurately detect email worms during the early phase of their propagations. Copyright © 2008 John Wiley & Sons, Ltd.

In next generation wireless networks, Internet service providers (ISPs) are expected to offer services through several wireless technologies (e.g., WLAN, 3G, WiFi, and WiMAX). Thus, mobile computers equipped with multiple interfaces will be able to maintain simultaneous connections with different networks and increase their data communication rates by aggregating the bandwidth available at these networks. To guarantee quality-of-service (QoS) for these applications, this paper proposes a dynamic QoS negotiation scheme that allows users to dynamically negotiate the service levels required for their traffic and to reach them through one or more wireless interfaces. Such bandwidth aggregation (BAG) scheme implies transmission of data belonging to a single application via multiple paths with different characteristics, which may result in an out-of-order delivery of data packets to the receiver and introduce additional delays for packets reordering. The proposed QoS negotiation system aims to ensure the continuity of QoS perceived by mobile users while they are on the move between different access points, and also, a fair use of the network resources. The performance of the proposed dynamic QoS negotiation system is investigated and compared against other schemes. The obtained results demonstrate the outstanding performance of the proposed scheme as it enhances the scalability of the system and minimizes the reordering delay and the associated packet loss rate.

This paper introduces a robust buffer occupancy-based connection admission control (CAC) mechanism to alleviate both uplink and downlink congestions in converged IP and broadcasting networks. The scheme also ensures a fair share of downlink bandwidth among competing satellite terminals (subnetworks) in the event of congestion. The proposed scheme is dubbed Weighted Fair CAC (W-FCAC). It accepts or rejects connections based on an adaptive fuzzy-based approach. The use of the fuzzy-based mechanism is for the purpose of overcoming issues related to instantaneous link capacity assessment, flow characterization and the associated high computational complexity, and use of traffic descriptors for new flows. Additionally, the adaptive fuzzy logic makes the proposed CAC approach robust to traffic dynamics. These features make the scheme highly suitable for DVB-S2 environments where the link capacity frequently fluctuates due to the adaptive coding/modulation of the physical layer during noisy periods. Simulation results elucidate that the proposed W-FCAC scheme prevents downlink congestion and fairly allocates network resources among satellite terminals. It also minimizes the frequency of congestion events while maintaining efficient utilization of network resources.

The next generation networks are considered one of the most interesting research and application fields in days to come.The current advances in wireless and mobile networking architectures, services, and applications have spurred an unprecedented emergence of various techniques that may be adopted in next generation wireless communications and mobile network environments.It is, therefore, the right time for an issue in our journal which presents a compilation of the recent advances in this field.The Wireless Communications and Mobile Computing (WCMC) Journal continues to grow swiftly, not only in the number of papers being submitted but also in terms of the diverse technical areas it covers.The papers bring much needed expertise from a wide spectrum of areas in which wireless communications, mobile computing, and emerging technologies converge at.We appreciate the contributions of our authors for bringing together different but relevant areas of wireless innovations, and also appreciate the tremendous support from our readers to make our effort a success.This issue called for papers in various aspects of next generation wireless communications and mobile computing/networking.As a result, nine papers have been selected.The papers cover both topical and innovative areas, each of which is highlighted as follows:

Secure time synchronization is one of the key concerns for some sophisticated sensor network applications. Most existing time synchronization protocols are affected by almost all attacks. In this paper we consider heterogeneous sensor networks (HSNs) as a model for our proposed novel time synchronization protocol based on pairing and identity based cryptography (IBC). This is the first approach for time synchronization protocol using pairing-based cryptography in heterogeneous sensor networks. The proposed scheme reduces the key spaces of nodes as well as it prevents from all major security attacks. Security analysis indicated that the proposed scheme is robust against reply attacks, masquerade attacks, delay attacks, and message manipulation attacks.

In this paper, we propose a fair and dynamic auction-based QoS negotiation scheme that allows users to dynamically negotiate their agreed service levels with their service provider. The scheme has three major design goals: ensuring a high degree of fairness among competing users, efficient utilization of the underlying network resources, and maximization of the service provider's revenue. A mathematical analysis is provided to demonstrate that when the three design goals are taken into account, the resource allocation function proposed in this paper represents a Pareto optimal solution.

Despite recent advances in networking technologies, video streaming applications still suffer from limited bandwidth and highly varying network conditions. As a solution, this paper proposes a novel multi-source streaming strategy specifically tailored for next generation mobile networks to deliver multimedia services to mobile users. The proposed solution consists of a set of mechanisms that aim at ensuring seamless, continuous, and smooth playback of video for users, guaranteeing an efficient and fair utilization of network resources while meeting the playback and buffer constraints of the clients, preventing redundant transmissions and minimizing packet reordering. A set of computer simulations is conducted to evaluate the performance of the proposed strategy and encouraging results are obtained.

Video streaming to mobile users is gaining momentum within the communities of both industrial and academic researchers. In a previous research work, the authors proposed a multi-source streaming method for video streaming to mobile users. The focus was on video fragmentation and packet scheduling to avoid packet reordering and packet redundancy. As a continuation to the work, this paper presents a handoff management method to support userspsila mobility and to guarantee continuous and smooth playback of video data for users while they are on move. For performance evaluation, some simulation results are presented.

In this paper, we develop an efficient vehicle collision avoidance strategy for intelligent transport systems. The proposed strategy first clusters vehicles according to the features of their movement (e.g., direction of movement, inter-vehicle distance, and relative velocity). To enhance the responsiveness of the proposed CCA system, a risk-aware MAC protocol is designed. In the proposed scheme, an emergency level is defined for each vehicle based on its order in its respective cluster. The emergency level reflects the risk of a vehicle to meet an emergency situation in the platoon. The medium access delay of each vehicle is set as a function of its emergency level in a way that enables vehicles in emergency situations to promptly disseminate warning messages to neighboring vehicles and to accordingly minimize chain collisions. The proposed CCA system is dubbed cluster- based risk-aware CCA (C-RACCA). The performance of the proposed C-RACCA system is evaluated via simulations and encouraging results are obtained.

In multi-hop sensor networks, information obtained by the monitoring nodes need to be routed to the sinks. If we assume that the transmitter power level can be adjusted to use the minimum energy required to reach the intended next hop receiver, the energy consumption rate per unit information transmission depends on the choice of the next hop node. In a power-aware routing approach, most proposed algorithms aim at minimizing the total energy consumption or maximizing network lifetime. In this paper, we propose a new routing algorithm with two goals: minimizing the total energy consumption and ensuring fairness of energy consumption between nodes. We formulate this as a nonlinear programming problem and use a sub-gradient algorithm to solve the problem. We also evaluate the proposed algorithm via simulations at the end of this paper.

This paper presents a novel scheme that enables IPv6 mobile networks to perform optimal route optimization. The proposed scheme exploits features of the widely deployed border gateway protocol (BGP). When a mobile network is about to change its point of attachment to the Internet, its mobile router (MR) gets a new care-of-address (CoA) from the visited location and sends a binding update to its home agent (HA). Additionally, MR gets a new temporary network prefix (TNP) at the new location using the prefix delegation protocol. MR then advertises this TNP to its subnet via a router advertisement (RA) message and enables the mobile network nodes (MNNs) to build their own respective CoAs. Simultaneously, this TNP is also sent to the border router (BR) of the home network to enable BR update its BGP routing table. This operation is performed to build an association between the TNP and the mobile network prefix (MNP). BR then notifies its peers of this new update. This procedure will enable any correspondent node (CN) to directly communicate with MNNs, avoiding therefore ingress filtering and reducing both signaling and processing overhead on MR and the home agent (HA). A comparison of the proposed scheme against the NEMO Basic Support scheme, in terms of communication delay, is made via a simple performance analysis.

The transmission of high quality video requires high bandwidth. Ensuring constantly high bandwidth in wireless environments is a challenging task given constraints in the current wireless network resources. Current mobile computers are equipped with multiples wireless interfaces that can be used to improve the video quality by aggregating the bandwidth of these interfaces. Such Bandwidth Aggregation (BAG) approach involves multiple paths in communication and gives rise to a number of issues related to the management of the Service Level Agreement (SLA) and packet reordering. To guarantee an efficient and fair management of SLA, this paper presents a bandwidth aggregation-aware QoS negotiation mechanism that enables users to dynamically negotiate their desired service levels and to reach them through the use of bandwidth aggregation. This operation is performed while ensuring a fair use of the network resources among all competing users. To cope with packet reordering, a new scheduling strategy is presented. The performance evaluation of the proposed bandwidth aggregation-aware QoS negotiation scheme and the proposed scheduling algorithm are conducted via simulations and the results are discussed.

Attacks against encrypted protocols are becoming increasingly popular. They pose a serious challenge to the conventional intrusion detection systems (IDSs) which heavily rely on inspecting the network packet fields and are consequently unable to monitor encrypted sessions. IDSs can be broadly categorized into two types: signature-based and anomaly-based IDSs. The signature-based IDSs rely on previous attack signatures but are often ineffective against new attacks. On the other hand, anomaly-based detection systems depend on detecting the change in the protocol behavior caused by an attack. The latter can be employed to detect novel attacks, and therefore are often preferred over their signature-based counterpart. In this paper, we envision an anomaly-based IDS which can detect attacks against popular encrypted protocols, such as SSH and SSL. The proposed system creates a normal behavior profile and uses non-parametric Cusum algorithm to detect deviation from the normal profile. Upon detecting an anomaly, the proposed mechanism generates an alert, sets a delay to the protocol response, and traces back the attacker. The effectiveness of the proposed detection scheme is verified via simulations.

Satellite communication systems exhibit important and unique features that qualify them to be an integral part of a global ubiquitous information system. Given the universality of the Internet protocol (IP), traffic over satellite network is expected to be all IP. Success of these all-IP satellite systems depends on their abilities to guarantee QoS. QoS provisioning has been a hot topic in terrestrial wired networks. It has been, however, highly overlooked in wireless networks. An efficient QoS provisioning in wireless networks in general, and in satellite networks in particular, can be possible only with the development of new schemes that are able to dynamically (re)negotiate service levels in an adaptive manner to changes in network conditions upon handoff occurrences. This paper surveys major dynamic service level negotiation schemes proposed for terrestrial wireless networks and discusses their limitations when applied to satellite networks. As a solution, a dynamic service level negotiation scheme specifically tailored to satellite networks is portrayed. Comparison of the proposed scheme to other dynamic negotiation approaches, via a qualitative and quantitative analysis, is also presented.

Mobile users are expected to be highly dynamic in next generation mobile networks. Additionally they will be served a wide variety of services with different transmission rates and expect high quality of service (QoS). Since the number of mobile subscribers is rapidly increasing and given the limited resources of any robust network, guarantee of high QoS is possible only by the deployment of network elements that optimally allocate network resources and instantly adapt to network conditions. In attempt to support mobility in IP networks, the hierarchical mobile IPv6 (HMIPv6) has been proposed. An important issue that has been highly overlooked in the design of HMIPv6 consists in its lack of a mechanism that can efficiently control and distribute traffic among multiple mobility anchor points (MAPs). In the absence of such mechanism, some MAPs may get congested while others remain underutilized. In such scenario, mobile users connecting to congested MAPs may experience significant packet drops and excessive queuing delays. This ultimately affects QoS. In this vein, this paper proposes an application-driven mechanism for selection of MAPs. The key idea behind the proposed scheme consists in the reference of access points to the transmission rate of the users' applications to decide which MAP visiting users should be registering with. The decision of MAPs is performed in a way that the load variance of all MAPs, serving the access point in question, is minimized. Issues related to the frequency of binding update messages are also considered in the selection of MAPs. The performance of the proposed scheme is evaluated via computer simulations. In terms of QoS, encouraging results are obtained: better traffic distribution among MAPs and lower handoff delays.

Attacks against encrypted protocols have become increasingly popular and sophisticated. Such attacks are often undetectable by the traditional Intrusion Detection Systems (IDSs). Additionally, the encrypted attack-traffic makes tracing the source of the attack substantially more difficult. In this paper, we address these issues and devise a mechanism to trace back attackers against encrypted protocols. In our efforts to combat attacks against cryptographic protocols, we have integrated a traceback mechanism at the monitoring stubs (MSs), which were introduced in one of our previous works. While we previously focused on strategically placing monitoring stubs to detect attacks against encrypted protocols, in this work we aim at equipping MSs with a traceback feature. In our approach, when a given MS detects an attack, it starts tracing back to the root of the attack. The traceback mechanism relies on monitoring the extracted features at different MSs, i.e., in different points of the target network. At each MS, the monitored features over time provide a pattern which is compared or correlated with the monitored patterns at the neighboring MSs. A high correlation value in the patterns observed by two adjacent MSs indicates that the attack traffic propagated through the network elements covered by these MSs. Based on these correlation values and a prior knowledge of the network topology, the system can then construct a path back to the attacking hosts. The effectiveness of the proposed traceback scheme is verified by simulations.

Energy consumption is a critical issue in wireless sensor networks as the battery of a sensor node, in most cases, cannot be recharged or replaced after deployment. In order to detect an event, a sensor node spends most of the time in monitoring its environment, during which a significant amount of energy can be saved by placing the radio in the low power sleep mode when no reception and/or transmission of data is involved. In this paper, we discuss the design of a new MAC protocol for wireless sensor networks, which mainly avoids overhearing, collisions, and frequent commutation between sleep and active modes. These issues are generally considered to be the most important reasons behind energy waste in heavy loaded conditions of wireless sensor networks. The proposed protocol, called Reservation-MAC (R-MAC), uses two separate periods during the communication process. In the first period, nodes compete for time slots reservation for their future transmissions, and in the second period, each node transmits its data or receive data from a corresponding sender. Once a node is aware of its transmission and/or reception time slot, it stays active only for these time slots and goes back to the sleep mode during the remaining time of the transmission period. In our experiments, the performance of the R-MAC protocol is studied in saturated conditions and compared with the well known S-MAC and T- MAC protocols. Depending on the traffic load, the proposed MAC protocol significantly improves the energy consumption compared to S-MAC and T-MAC.

Users in next generation wireless networks are expected to be highly dynamic while maintaining connectivity through different devices with different processing and communication capabilities. In wireless environments, bandwidth is scarce and channel conditions are time-varying. To guarantee quality of service (QoS) to users roaming between heterogeneous wireless networks, a dynamic QoS negotiation mechanism, which allows users to dynamically negotiate their service-levels with the network, is required. Several protocols for dynamic service level negotiation have been proposed, each focusing on a particular mode. This paper presents an overview of these protocols and discusses their limitations. To alleviate these shortcomings, a dynamic QoS negotiation scheme to allow users to change their service levels in response to changes in both network conditions and their own resource requirements is proposed. In the proposed scheme, upon an intra-domain handoff of a mobile node, the visited access point consults the previously used access point to confirm the legitimacy of the service negotiation request issued by the mobile node. The performance of the proposed scheme has been investigated and compared with other dynamic negotiation approaches. It was demonstrated that the proposed scheme outperforms the state-of-the-art method, in terms of the signaling overhead and data storage, at the expense of a slight increase in the overall negotiation delay.

Flooding-based approaches are incorporated in reactive routing protocols as the fundamental strategy for route discovery. They overtly affect traffic as the frequency of route discovery increases along with the mobility of users in a mobile ad hoc network (MANET). This paper presents a scheme for reducing overall traffic and end-to-end delay in highly MANET networks. Firstly a new routing algorithm is proposed to reduce the frequency of flood requests by elongating the link duration of the selected paths. In order to increase the path duration, non-disjoint paths are also considered. This concept is a novel approach in route discovery as previous reactive routing protocols seek only disjoint paths. Secondly another novel approach is presented to estimate the link expiration time without the need for global positioning system (GPS) devices. To prevent broadcast storms that may be intrigued during the path discovery operation, another scheme is also introduced. The basic concept behind the proposed scheme is to broadcast only specific and well-defined packets, referred to as "best packets" in the paper. The new protocol is simulated with regard to traffic overhead. Although our main aim in this paper is to reduce the net control traffic in a MANET network, there are other benefits arising from the proposed schemes, namely the increase in link duration, reduction in the end-to-end communication delay, less disruption in data flow, and fewer path setups.

In light of the fast propagation of recent Internet worms, human intervention in securing the Internet during worm outbreaks is of little significance. In order to reduce the damage worms may cause, existing intrusion detection systems (IDSs) need to be adaptive to the security-related requirements of their monitoring networks. This paper presents a Multilevel security based Autonomic Parameter Selector (MAPS) that can be implemented over any existing IDSs. The deployment architecture consists of a number of hierarchically placed local security managers, metropolitan security managers, and a global security manager. These security managers report events to a worm advisory system (WAS). WAS accordingly sets the threat level of the network. Based on this level, MAPS selects the most optimum parameters for the entire IDS to combat against the propagating worm. The MAPS architecture maintains the system performance by constantly evaluating three metrics, namely False Negative Avoidance, False Positive Avoidance, and performance overhead. Extensive experiments, using real network traffic and a recently proposed worm detection system, demonstrate that MAPS is capable of advising an IDS with optimum parameter values to effectively and promptly hinder further propagation of worms.

Due to geographical and/or climatic constraints, the community of future satellite users will exhibit a significant variance in its density over the Globe. This density variance will yield a scenario where some satellite links are congested while others are underutilized. To ensure an intelligent engineering of traffic over satellite networks, this paper proposes a routing protocol that enables neighboring satellites to explicitly exchange information on their congestion status. A "soon-to-be-congested" satellite requests its neighboring satellites to decrease their data forwarding rates. In response, the neighboring satellites search for less congested paths that do not include the satellite in question and communicate a portion of data, primarily destined to the satellite, via the retrieved paths. By so doing, congestion, and the resulting packet drops, can be avoided. A better distribution of traffic among satellites can be guaranteed as well. The proposed scheme is dubbed "Explicit Load Balancing" (ELB) scheme. A set of simulations is conducted to evaluate the performance of the ELB scheme using the Network Simulator. In terms of Quality of Service, encouraging results are obtained: better traffic distribution, higher throughput, and lower packet drops.

This paper describes the architecture of a global sensor network based on a constellation of LEO satellites. The considered sensor network is heterogeneous: two types of sensor nodes are envisioned. One type does the sensing and relays the gathered data to the other type that performs data aggregation and communicates it directly to the satellites. The main challenging tasks in the design of the architecture are explored and adequate solutions are provided. A set of data dissemination techniques is then presented. Following this, a mathematical model is developed to evaluate the energy use of the sensors. Open research issues for the realization of such architecture are finally discussed.

Internet Worms pose a serious threat to today's Internet. Signature matching is an important approach to detect worms. However, as most signature development processes are manual, they require significant time. They are thus not efficient in reducing the damage worms may cause. In this paper, an efficient signature-based method is proposed for automatic detection of worms over large-scale networks. In the proposed system, detection is performed in a hierarchical manner. Security managers of local networks collect worm-like or suspicious flows and handle these flows to high-hierarchy metropolitan managers. In response, the latter use this information to generate robust signature. The global manager which lies on top of the hierarchy, multicasts the signature to local managers via metropolitan managers. This enables local managers to detect worms that try to penetrate into their networks. The proposed system is evaluated using an off-line real network traffic that contains traces of worms. Experimental results indicate that the proposed system exhibits high detection rates with low false alarm rates.

Over the past few years, a number of private networks have emerged. In these private networks, a server provides its subscribed clients with Internet services, forming a one-to-many network topology. Given the fact that users are located at different distances from the server, usage of the Transmission Control Protocol (TCP) for communication results in drastically unfair bandwidth allocations among the users. In this regard, this paper addresses the fairness and efficiency issues of TCP in such one-to-many IP (Internet Protocol) networks. The efficiency of TCP is controlled by matching the aggregate traffic rate of all TCP connections to the sum of the link capacity and total buffer size. On the other hand, its unfairness issue is mitigated by allocating bandwidth among individual flows in relative proportion with their RTTs. Simulation results elucidate that the proposed method makes better utilization of the network resources, reduces the number of packet drops, and provides a fair service to users.

In the near future, RTP/RTCP-based multimedia streaming will become the norm not only in wired networks but also in mobile environments. Presently when a handoff occurs between heterogeneous networks (with different available bandwidths), a RTP sender cannot stream media at a suitable rate over the new network. Furthermore, RTCP fails to precisely adapt to sudden changes in network resources due to handoff. In order to solve these issues, 1) senders should be aware when mobile nodes are about to perform a handoff; 2) senders should then efficiently probe the available bandwidth in the new network and accordingly adjust their streaming rates. In this paper, we propose a scheme that allows mobile nodes to explicitly notify their handoff timing by using newly-defined RTCP packets. In the proposed scheme, senders probe the available bandwidth in the new network using low-priority RTP dummy packets. The performance of the proposed scheme is evaluated and compared with conventional schemes through extensive simulations. The simulation results show that the proposed scheme achieves appropriate bandwidth utilization immediately after a handoff occurrence and lowers packet losses during the handoff. The proposed scheme exhibits also high TCP-friendliness

Internetworking over vehicle ad-hoc networks (VANETs) is getting increasing attention from all major car manufacturers. The design of effective vehicular communications poses a series of technical challenges. Guaranteeing a stable and reliable routing mechanism over VANETs is an important step towards the realization of effective vehicular communications. In current ad-hoc routing protocols, the control messages in reactive protocols and route update timers in proactive protocols are not used to anticipate link breakage. They solely indicate presence or absence of a route to a given node. Consequently, the route maintenance process at both protocol types is initiated only after a link breakage event takes place. This paper argues the use of information on vehicle headings to predict a possible link breakage event prior to its occurrence. Vehicles are grouped according to their velocity vectors. When a vehicle shifts to a different group and a route, involving the vehicle, is to be broken, the proposed protocol searches for a more stable and "more durable" route that includes vehicles from the same group. The proposed scheme is dubbed velocity-heading based routing protocol (VHRP). Whilst the proposed scheme can be implemented on any existing routing protocol, the paper considers the case of VHRP over destination-sequenced distance vector (DSDV) routing protocol. The performance of the scheme is evaluated through computer simulations. Simulation results indicate that knowledge on the vehicles' heading adds major benefits to routing in terms of reducing the number of link breakage events and increasing the end-to-end throughput

Protection of mobile IP networks from denial-of-service (DoS) attacks, a serious security threat in today's Internet, is a one major step toward making this paradigm a reality. The paper proposes a method to detect DoS attacks, issued from mobile users, in the vicinity of flooding sources and in early stages before they cripple the targeted system. The fundamental challenge in attack detection consists in distinguishing between simple flash events and DoS attacks so as not to deprive innocent users from having legitimate accesses. In the proposed mechanism, this distinction is based on the fact that legitimate TCP flows obey the congestion control protocol, whereas misbehaving sources remain unresponsive. Suspicious flows are sent a test feedback and are required to decrease their sending rates. Legitimacy of such flows is decided based on their responsiveness. The scheme performance is evaluated through a set of simulations and encouraging results are obtained: short detection latency and high detection accuracy

While mobile communication systems provide certainly more flexibility to end-users, they present complex mobility management issues. To tackle mobility management issues, the concept of mobility anchor points (MAPs) was introduced and its use was proposed within the framework of the hierarchical mobile IPv6 (HMIPv6) protocol. However, due to traffic dynamics, the protocol performance remains critically affected by the selection of MAPs. This paper proposes a dynamic and efficient mobility management strategy for the selection of the most appropriate MAP with the lightest traffic load. The MAP selection is based on an estimation of MAP load transition using the exponential moving average (EMA) method. The proposed selection scheme is referred to as dynamic and efficient MAP selection (DEMAPS). The scheme performance is evaluated through simulations. Simulation results show that the DEMAPS scheme substantially reduces the number of packet drops, guarantees shorter service delays, makes better utilization of the network resources, avoids redundant transmissions, and maintains a fair and efficient distribution of the network load.

In order to realize the dream of global broadband coverage, the need for satellite communication systems has grown rapidly during the last few years. Several low Earth orbit (LEO), medium Earth orbit (MEO), and geostationary (GEO) satellite constellations have been thus proposed in the recent literature. However, these constellations either require a potential number of satellites or are unable to provide data transmission with high elevation angles. This paper proposes a new satellite constellation composed of quasi geostationary satellites. The main advantage of the constellation is in its ability to provide global coverage with a significantly small number of satellites while, at the same time, maintaining high elevation angles. Since end-terminals can be simultaneously covered by plural satellites in the proposed constellation, a scheme is proposed to select the most appropriate satellite for communication. The selection is based on the geographical location information of end-terminals. The efficiency of the proposed scheme is verified through a set of simulations. Simulation results reveal the good performance of the proposed method in reducing the delay, the delay variation, and ultimately improving the overall quality of service.

In mobile environments, the fundamental challenge upon a handoff phenomenon consists in an efficient probing of the availability of the new network resources and an appropriate rate adjustment in the new network cell. This paper proposes the usage of low-priority dummy packets to probe the availability of the new network resources. Indeed, when a mobile node enters a cell overlapping area and is about to change its point-of-attachment to the network, two connections are simultaneously set between the mobile node and the sender: one through the old point-of-attachment and another through the new one. The sender transmits actual data through the old connection. Meanwhile, it sends dummy segments through the new connection to verify the bandwidth availability of the new network. The proposed scheme is dubbed dummy segment based bandwidth probing (DSBP). The performance of the DSBP scheme is evaluated and compared with existing schemes through extensive simulations. The results show that DSBP substantially improves the system efficiency, reduces the number of packet drops, and makes better utilization of the network bandwidth.

As originally specified, TCP did not perform well over satellite network systems, systems known with their rapidly time-varying network topologies. This paper addresses some vexing attributes that impair TCP performance in LEO satellite networks. The paper proposes a scheme that allows satellite systems to automatically adapt to the number of active TCP flows, the free buffer size and the bandwidth-delay product of the network. The proposed scheme controls the efficiency of the system by matching the aggregate traffic rate to the sum of the link capacity and total buffer size. This attribute helps to adjust TCP's aggressiveness and to prevent persistent queues from forming. The system min-max fairness is achieved by allocating bandwidth among individual flows in proportion with their RTTs. Simulation results elucidate that the proposed scheme substantially improves the system fairness, reduces the number of packet drops and makes better utilization of the bottleneck link. The results demonstrate also that the proposed scheme works properly in more complicated environments where connections traverse multiple bottlenecks and the available bandwidth may change over data transmission time.

Transmission control protocol (TCP) is the most widely used transport protocol in today's Internet. Despite the fact that several mechanisms have been presented in recent literature to improve TCP, there remain some vexing attributes that impair TCPs performance. This paper addresses the issue of the efficiency and fairness of TCP in multihops satellite constellations. It mainly focuses on the effect of the change in flows count on TCP behavior. In case of a handover occurrence, a TCP sender may be forced to be sharing a new set of satellites with other users resulting in a change of flows count. This paper argues that the TCP rate of each flow should be dynamically adjusted to the available bandwidth when the number of flows that are competing for a single link, changes over time. An explicit and fair scheme is developed. The scheme matches the aggregate window size of all active TCP flows to the network pipe. At the same time, it provides all the active connections with feedbacks proportional to their round-trip time values so that the system converges to optimal efficiency and fairness. Feedbacks are signaled to TCP sources through the receiver's advertised window field in the TCP header of acknowledgments. Senders should accordingly regulate their sending rates. The proposed scheme is referred to as explicit and fair window adjustment (XFWA). Extensive simulation results show that the XFWA scheme substantially improves the system fairness, reduces the number of packet drops, and makes better utilization of the bottleneck link.