This paper presents a comprehensive survey of semantic protocols, protocol learning, and AI-enabled semantic protocols within the context of Open RAN and 6G networks. We systematically review the significant progress achieved in these domains, highlighting key methods such as transformer-based semantic encoders, reinforcement learning–driven protocol adaptation, and federated learning frameworks for distributed training. Across surveyed studies, notable achievements include bandwidth savings of 35-70%, improved robustness under noisy conditions, and enhanced interoperability in multi-vendor environments. By consolidating findings, we identify major challenges such as the lack of standardized semantic KPIs, computational overhead at the edge, interoperability issues, and emerging security vulnerabilities. Furthermore, we categorize open research opportunities into theoretical, methodological, technical, and implementation directions, providing a clear roadmap for future development. This survey ultimately positions semantic communication and AI-enabled protocols as pivotal enablers for meaning-centric, adaptive, and efficient next-generation O-RAN/6G networks.

As communication systems evolve toward 6G, semantic communication is emerging as a transformative paradigm that prioritizes the accurate transmission of meaning rather than just bits. While artificial intelligence enables this shift by facilitating intelligent interpretation and context-aware processing, it also introduces significant challenges related to transparency, reliability, and user trust. XAI has thus become essential in making AI-enabled semantic communication systems more interpretable, auditable, and accountable. To the best of our knowledge, this is the first survey that systematically analyzes how explainability can be embedded across all stages of the semantic communication pipeline, integrating architectural design, metrics, security considerations, and human-in-the-loop mechanisms. Additionally, the survey identifies pressing research challenges, including the lack of standardization, real-time applicability, and vulnerabilities introduced by opaque AI models. By drawing attention to these issues and outlining future research directions, this survey aims to guide the development of responsible and trustworthy semantic communication systems for next-generation wireless networks.

The Cloud-Edge collaborative computing enables the deployment of latency-sensitive and data-intensive applications closer to end users. However, it introduces significant challenges for microservice placement, due to resource heterogeneity, limited edge capacity, and the need to satisfy storage requirements using aggregated resources across multiple nodes. To address these issues, we proposeDiktopos, a topology-aware, two-stage scheduling framework that jointly optimizes microservice placement and distributed storage volume allocation in cloud-edge networks. The joint optimization problem is decomposed into two subproblems: (i) microservice placement and (ii) distributed volume allocation, with the objective of minimizing computation, communication, energy, and storage costs. At its core, Diktopos employs a low-complexity, rank-based heuristic that ensures scalable and accurate placement across heterogeneous edge nodes. Simulation results show that our method achieves near-optimal placement decisions (within 1.67% of the optimal solution), and converges up to 5× faster than state-of-the-art approaches in large-scale deployments. Real-world experiments in Kubernetes environments demonstrate up to 53% latency reduction compared to the default scheduler, and up to 23% improvement over other baselines, confirming Diktopos' effectiveness in dynamic, resource-constrained edge scenarios.

Efficient workload scheduling in Kubernetes is crucial for optimizing energy consumption and resource utilization in large-scale and heterogeneous clusters. However, existing Kubernetes schedulers either ignore power-awareness or rely on simplified, static power models, which limit their effectiveness in managing energy efficiency under dynamic workloads. To address these shortcomings, we present a multi-objective scheduling framework for online Kubernetes pod placement that jointly considers power consumption, resource utilization, and load balancing. The framework follows a two-stage design: (i) a node power–profiling component trains a machine–learning model from real power measurements to predict per-node consumption under varying utilizations; and (ii) an online scheduler uses these predictions within a multi-objective optimization formulation. We implement scheduling optimization using two algorithms, TOPSIS and NSGA-II, adapting them to the Kubernetes context, and also propose a distributed variant of the NSGA-II algorithm that parallelizes fitness evaluation with controlled migration between workers. Experimental results show that the proposed framework outperforms baseline schedulers, achieving a 40% reduction in power consumption and improvements of 74% and 68% in CPU and memory utilization, respectively, while sustaining scalability under high workloads. To the best of our knowledge, this is the first work to integrate learned power models and distributed multi-objective optimization into Kubernetes for power-aware pod scheduling.

Open Radio Access Network (O-RAN) enables modular and intelligent control of radio resources through open interfaces and programmable RAN components. As networks evolve toward sixth-generation (6G) systems, the proliferation of autonomous xApps and rApps introduces a critical challenge: Coordinating concurrent AI-driven control actions under tight near-real-time constraints while avoiding instability and conflicting decisions. This paper focuses on two tightly coupled enablers for AI-native O-RAN orchestration: Conflict-aware control and intent-driven automation. We propose an AI-native orchestration framework centered on a CME integrated into the Near-RT RIC, and a complementary LLM-based intent orchestration module deployed in the Non-RT RIC. The CME is designed to autonomously arbitrate conflicting xApp actions by learning adaptive mitigation policies from structured conflict signals, system context, and performance feedback, rather than relying on static priorities or predefined conflict classes. The LLM module translates high-level operator intents into policy constraints and control objectives that guide conflict resolution and xApp behavior. Overall, this work advances AI-native O-RAN orchestration by grounding conflict-aware control and LLM-assisted intent translation in practical measurements, and by outlining a clear path toward scalable, adaptive, and resilient control mechanisms required for future 6G RIC deployments.

In this paper, we present a programmable data plane design that implements IEEE 802.1Qcr’s Asynchronous Traffic Shaper (ATS) purely in software using Extended Berkeley Packet Filter (eBPF), eliminating specialized hardware requirements for industrial Time-Sensitive Networking (TSN) deployments. Unlike hardware-bound TSN solutions, our approach dynamically decouples and reprograms functions like filtering, metering, and queuing through in-kernel eBPF hooks, enabling adaptive priority management for concurrent streams within shared priority queues. The design explicitly models the ATS scheduler to parameterize per-stream eligibility times in TSN bridges while maintaining deterministic operation. This software-defined method provides a vendor-agnostic path for integrating ATS capabilities into existing industrial networks, particularly for Industry 5.0’s distributed control scenarios requiring flexible traffic multiplexing. The results confirm correct enforcement of ATS scheduling semantics under heterogeneous workloads.

The Cloud Edge Continuum enables the deployment of latency-sensitive and data-intensive applications closer to end users, but it poses challenges for microservice placement due to resource heterogeneity and limited edge capacity, especially when storage requirements must be met through aggregated node resources. To address this, we propose a two-stage, topology-aware optimization framework that jointly handles microservice deployment and distributed storage volume allocation in edge networks. Our framework decomposes this joint placement problem into two subproblems, microservice placement followed by a distributed volume allocation subproblem, with the goal of optimizing computation, communication, energy, and storage costs. At its core is a lightweight, rank-based heuristic that ensures scalable, accurate placement across distributed edge nodes. Evaluations on real-world scenarios show our method achieves near-optimal placement (within 1.67% of the exact solution), reduces system costs by up to 30%, and accelerates convergence by 5× compared to state-of-the-art approaches, demonstrating its suitability for dynamic, resource-constrained edge environments.

The rise of real-time networking demands has driven the IEEE Time-Sensitive Networking (TSN) task group to develop new standards that ensure high bandwidth and lowlatency Ethernet communication. TSN is an essential component of next-generation 6G networks. It offers features that ensure deterministic data transmission and alleviate network congestion. These features are crucial in time-sensitive applications and systems, whereby both precision and reliability are paramount. While early TSN implementations relied heavily on synchronous communication, newer standards, such as IEEE 802.1Qcr, have introduced asynchronous mechanisms via Urgency-Based Scheduler (UBS). UBS employs advanced queuing and traffic shaping strategies, to guarantee minimal delay for real-time applications. Within the scope of 6G, this study evaluates the queuing and shaping strategies applied to multiple flows within the UBS framework. Moreover, we assess their impact on frame transmission rates at the shaper level, highlighting the optimal use case for each strategy.

This paper proposes a new scheduling framework to optimize the placement of cloud workloads submitted online by users within a Kubernetes-orchestrated environment. The proposed method aims to incorporate power awareness during the scheduling process, along with other criteria, such, load balancing, and bin packing. The framework equitably distributes workloads across cluster nodes while also selecting the most resource-efficient node to reduce the number of active nodes and prevent resource fragmentation. Existing strategies often focus on a single criterion, leading to suboptimal and unsatisfactory workload placements. The proposed framework utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a well-known multi-criteria decision analysis algorithm, to account for power consumption and other criteria defined by cloud operators, such as load balancing and bin packing. The algorithm is implemented as a Kubernetes scheduling plugin to rank worker nodes based on these criteria and the submitted workloads. Simulation results demonstrate the effectiveness of the proposed strategy across various scenarios, reducing power consumption by 26.46% and comparable CPU and memory load balancing performance within a large Kubernetes cluster under heavy workloads.

The Internet of Things (IoT) is rapidly expanding globally, but the limited size of IoT devices restricts their battery capacity, computational resources, and wireless bandwidth, making it difficult to handle resource-intensive tasks. Edge Computing addresses these challenges by enabling task offloading to more capable edge servers. However, optimal task offloading in Edge-IoT networks is complex due to dynamic conditions, such as varying server loads and wireless fluctuations. Traditional and some machine learning-based offloading methods often fall short in adaptability or efficiency. This paper introduces a bi-level optimization approach using Deep Reinforcement Learning (DRL) agents for IoT-level offloading and a priority-aware greedy heuristic for resource allocation on edge servers. The proposed method effectively improves QoS by balancing task execution latency and power consumption, as demonstrated by simulation results.

In recent years, AI inference has seen widespread adoption across fields like finance and healthcare, driving significant demand for high-performing applications. This demand brings about a complex relationship between inference application types, such as real-time applications, and their specific service level objectives (SLOs), like tail-latency. Tail-Latency is a metric requiring a defined percentage of requests to meet a maximum response time, which is crucial for applications where delays can impact user experience or decision-making. This dependency creates a challenging research problem in scheduling inference workloads. The core question becomes: How can we deploy AI workloads in a way that minimizes SLO violations?Specifically, we worked on real-time applications that require tail-latency guarantees. To address this, we developed a tail-latency-aware scheduler designed for resource-constrained devices. Our scheduler employs advanced machine learning techniques to optimize task placement, aiming to minimize SLO violations and enhance performance for latency-sensitive applications. We have developed and integrated our custom scheduler into Kubernetes, which operates on a specially configured cluster designed to test its performance. This cluster features diverse computing capabilities, enabling a comprehensive evaluation of the scheduler’s effectiveness. The experimental results highlight that our proposed scheduler outperforms the native Kubernetes scheduler in terms of efficiency.

In dense urban environments, traditional GNSS-based navigation for Unmanned Aerial Vehicles (UAVs) suffers from multipath interference and signal obstructions, compromising positioning accuracy and increasing risks of collisions and airspace violations. This paper proposes a novel machine learning-based system architecture for autonomous UAV parcel delivery, leveraging standardized 5G Network Exposure Function (NEF) and CAMARA Device Location API to achieve sub-meter location precision. Our approach integrates dynamic geofencing and predictive rerouting at the network edge, powered by a Random Forest-based collision prediction model that proactively adjusts UAV trajectories to avoid restricted zones in real time. Through simulations of six UAVs navigating dynamically updated no-fly zones, we demonstrate that our system significantly reduces time spent in restricted areas to near zero, compared to GNSS-only and rule-based methods, while limiting path-length inflation to approximately 30% for five of six flights. These results underscore the potential of combining 5G-enabled location services with edge intelligence to enhance safety and compliance in urban UAV operations.

The rapid development of reconfigurable intelligent surfaces (RISs) has sparked transformative advancements in wireless communication systems. These intelligent metasurfaces, adept at dynamically manipulating electromagnetic (EM) waves, hold vast potential for enhancing network capacity, coverage, and efficiency. However, to fully unleash the capabilities of RIS-aided communication systems, effective optimization is crucial. This article provides a recent development of RIS-assisted communication from the viewpoint of physically-consistent EM models. We delve into the realm of physically-consistent EM models, highlighting their pivotal role in achieving robust and efficient RIS designs. Furthermore, this paper offers a survey of the different optimization models utilized for RIS-assisted wireless communication systems, which consider various EM and physical aspects of RIS. We explore solution approaches aimed at optimizing different objectives like sum-rate/spectral efficiency and energy efficiency, spanning traditional optimization models to machine learning-based methods. Additionally, we discuss some open research issues in this field.

Recent advancements in serverless computing and the cloud-edge continuum have increased interest in WebAssembly (WASM). This technology enables portability and interoperability across diverse computing environments while achieving near-native execution speeds. Currently, WASM supports Single Instruction Multiple Data (SIMD), which allows for data-level parallelism that is particularly beneficial for vectorizable operations such as general matrix-matrix multiplication (GEMM) and convolutional layers. However, WASM lacks native integration with specialized hardware accelerators like GPUs, TPUs, and NPUs, as well as the ability to benefit from multi-core processing capabilities, which are critical for efficiently running Deep-Learning (DL) workloads. In contrast, despite these gains, WASM still lacks native support for heterogeneous accelerators such as GPUs, TPUs, and NPUs, as well as full multi-core parallelism capabilities that are critical for meeting the latency and throughput requirements of modern DL inference services. To bridge this gap, WASI-NN was developed, enabling WASM to integrate with external runtimes such as OpenVINO and ONNX Runtime, which leverage hardware acceleration. However, these current integrations often introduce performance overhead on certain devices, restricting their usability across the CECC. To address these challenges, we propose a new integration focusing on TVM as an external runtime for WASI-NN to enhance WASM’s performance and expand support to a broader range of devices. Additionally, we integrate this solution into Knative, a serverless framework, to provide a scalable and flexible platform for DL deployment. Using WASM technology, we evaluate our TVM-based solution through comparative studies. Results on AMD CPUs demonstrate the effectiveness of our approach, achieving 58% overall gain over other WASI-NN integrations (e.g., ONNX Runtime and OpenVINO) for CNN-based models while also achieving optimal performance on different platforms, such as Intel GPUs. These findings highlight the effectiveness of our solution.

The emergence of the Beyond Visual Line of Sight (BVLOS) operations for Unmanned Aerial Vehicles (UAVs) unlocked a wide range of new applications across various domains, such as urban transportation, package delivery, and aerial surveillance. However, due to the possibility of losing control and collisions, BVLOS operations present several risks to people on the ground. Therefore, it is crucial to minimize safety risks by flying UAVs along paths that traverse less populated areas. Nevertheless, implementing such a solution requires access to real-time data on the population density distribution across UAV operational areas. Consequently, in this paper, we harness the network exposure capabilities of 5G mobile networks, proposing a framework that integrates the UAV Traffic Management (UTM) system with the 5G Core (5GC). The proposed framework can collect real-time information about the density of mobile users in Areas of Interest (AoI), leveraging this data to estimate ground risks and subsequently devise optimized flight paths. Moreover, we propose a Deep Reinforcement Learning (DRL) solution to compute optimized flight paths. The simulation results show the efficiency of our proposed solution to achieve the designed goals in terms of reducing the experienced ground risk and total flight distance.

Software-Defined Networking (SDN) has emerged as a transformative technology that revolutionizes network management and architecture by providing unparalleled flexibility and control over data traffic flows. This flexibility is increasingly crucial in managing the complex demands of modern networks, whereby efficient traffic management is essential for mitigating congestion and enhancing operational efficiency. This paper introduces a novel traffic management model that employs Deep Reinforcement Learning (DRL) to transcend the conventional limitations typically associated with routing strategies that prioritize the shortest path or make non-optimal decisions when forwarding the traffic between different peers. Our model not only reduces overall network congestion but also aims to minimize bandwidth usage and enhance routing mechanisms within SDN environments. By incorporating DRL-based load balancing mechanisms, the model intelligently redistributes traffic across multiple pathways, shifting the focus from proximity to efficiency. This strategic redistribution prioritizes routes that optimize both, transmission time and network performance, rather than merely the shortest path. Moreover, the integration of DRL allows for real-time decision-making, enabling our system to dynamically adapt to changing traffic conditions and user demands. This capability is instrumental in significantly reducing transmission times and improving the overall efficiency of traffic flow across the network. Our findings highlight the substantial benefits of integrating SDN with advanced DRL techniques, offering a pioneering perspective on traffic routing within SDN networks. We evaluated the proposed framework via simulations and the obtained results demonstrated the efficiency of our solution compared to the baseline approaches.

Training without sharing data is one of the drivers that makes Federated Learning (FL) more attractive, compared to centralized approaches. However, requiring each learner to train the full model may not be efficient, particularly for devices with restricted resources, such as those available in Unmanned Aerial Vehicles (UAVs). To address this issue, a variation of FL technique, specifically Split Federated Learning (SFL), has recently been proposed. Unlike FL, the key concept of SFL is to divide the layers of the neural network among the involved learners. Therefore, each individual client will train only a segment of the model (submodel) rather than the entire model. Clearly, this technique, besides data privacy, optimizes the utilization of computational resources, reduces client-side training time, and enhances model privacy. However, there are questions that require answers: How should we split the model? Shall we systematically divide it in half, or is there a more optimal approach? In this line of thought, this paper provides a detailed analysis of possible splitting schemes of a power consumption prediction model for UAV s. First, the SFL-enabled model is presented. Second, an experimental analysis is conducted in which different splitting alternatives are made and numerically analyzed to examine the influence of network layering on split federated learning performance.

Dynamic Time Division Duplex (D-TDD) is a promising solution to address newly emerging 5G and 6G services characterized by asymmetric and dynamic uplink (UL) and downlink (DL) traffic demands. However, there are two major issues: (i) determining the TDD scheme (i.e., the number of slots devoted to UL and DL) to meet the dynamic traffic demands of the Users Equipment (UE); (ii) cross-link interference between cells that use different TDD schemes. The 3GPP standard neither specifies algorithms or solutions to derive the TDD configuration nor solves the cross-link interference. To fill this gap, we model the dynamic TDD problem in 5G NR as a linear programming problem. Then, we design Multi-Agent Deep Reinforcement Learning based 5G RAN TDD Pattern (MADRP), a fully decentralized solution based on the Multi-Agent Deep Reinforcement Learning (MADRL) approach. Based on the simulation results, the algorithm effectively prevents buffer overflows, avoids cross-link interference, and adapts to changes in the traffic pattern, ensuring its versatility. We compared our solution with the optimal solution and different static TDD configurations. We found that MADRP outperforms the static TDD configurations. We finally discuss the algorithm's limitations in terms of the number of cells, traffic variance, and cross-link interference probability.

To ensure low latency, service providers are increasingly turning to edge computing, pushing services and resources from the Cloud to the Edge of the network, as close as possible to users. However, since video and image processing applications are particularly computationally intensive, their deployment is typically based on distributed provisioning between the Edge and the Cloud, which can increase the risk of failure when relying on unreliable networks. In this work, we proposed the algorithm RAP-G (Reliability-Aware service Placement with Genetics), which considers the reliability of network links and distributes services between the Cloud and the Edge using a genetic algorithm (GA). We have also developed a new variant of the first-fit algorithm called RF2 (Reliability-Aware First-Fit) that considers reliability within a reasonable time. The performance of the RAP-G algorithm was evaluated and compared with the RF2 algorithm. The experimental results show the importance of considering reliability in service delivery and the superiority of RAP-G.

Nowadays, Internet of Things (IoT) devices are gaining momentum globally. However, due to their limited size, these devices have limited battery capacity, computational resources, and wireless bandwidth, making it impossible to run resource-intensive applications on these devices. Fortunately, Edge Computing has emerged as a promising solution to meet this demand by enabling data processing in more capable devices. Task offloading is a crucial technique used in Edge Computing to overcome the limitations of IoT devices by offloading some of their computational tasks to more powerful edge servers. The traditional methods used for task offloading are often based on heuristics or simple rules, which may result in sub-optimal solutions. Moreover, the increasing complexity and heterogeneity of edge networks, as well as the stochastic nature of the wireless channel, pose significant challenges for these methods. In this paper, we leverage Federated Learning (FL) to efficiently train Deep Reinforcement Learning (DRL) agents to make the best offloading and power allocation decisions by achieving the near-optimal trade-off between task execution latency and the power consumption of the end device. The obtained simulation results of the proposed method demonstrate its remarkable and superior performance in comparison to central DQN.

Due to the increase in the number of Internet of Things (IoT) devices in recent years, managing and supporting the diversity of services is becoming more difficult. Network Slicing will be the solution, in which the network slices are tailored to the requirements of the services. In this paper, network slicing is investigated in LoRaWAN networks using the Heuristic-Deep Q-Network (H-DQN) solution that manages the network resource allocation. We propose an intra-service allocation based on the deep Q-Network (DQN) algorithm by allocating virtual resource blocks to services. In addition, the intra-service allocation is based on a heuristic algorithm that assigns the transmission probability to the LoRa nodes of each service for each block in a way to maximizes the Packet Delivery Rate (PDR) of the network while ensuring that the priority of services is maintained. Simulation results show that the proposed approach improves the PDR, and ensures prioritization among services.

Autonomous driving has gained an increased interest in both academia and industry, as autonomous vehicles (AVs) significantly improve road safety by reducing traffic accidents and human injuries. Motion control remains one of the main functions of Autonomous Vehicles, which generates the steering angle and velocity of the vehicle. While traditional Machine Learning techniques have been extensively used in the past to improve motion control in AVs, the attention has been recently drawn to the use of Deep Learning (DL) and Deep Reinforcement Learning (DRL) techniques. These techniques have been applied to improve motion control of AVs and to help them learn from their environment. However, existing works are limited to dealing with simple scenarios without taking into consideration other road participants (e.g., other vehicles, pedestrians, cyclists, and motorcycles). In this paper, we propose a DRL-based model using Deep-Q Networks to control the AV in a complex scenario with dense traffic involving road participants. The AV learns the policy of different actions to reach its destination in an intersection without accidents. We tested and validated our proposed approach using the CARLA simulator. The obtained results demonstrated the efficiency of our solution by achieving better learning in terms of travel delay and avoiding collisions.

Dynamic Time Duplex Division (D-TDD) is a promising solution to accommodate the new emerging 5G and 6G services characterised by asymmetric and dynamic Uplink (UL) and Downlink (DL) traffic demands. D-TDD dynamically changes the TDD configuration of a cell without interrupting users’ connectivity, hence balancing the bandwidth for UL or DL communication according to the traffic pattern. However, 3GPP standard does not specify algorithms or solutions to derive the TDD configuration, i.e., the number of slots to dedicate to UL and DL. In [1], we have proposed a Machine Learning (ML)-based solution relaying on Deep Reinforcement Learning (DRL) to allow the base station (or gNB) to self-adapt to the traffic pattern of the cell by periodically adapting the number of slots dedicated to UL and DL. In this work, we implemented the DRL algorithm on top of an open-source gNB based on OpenAirInterface (OAI) [2] to demonstrate its efficiency. To this end, we relied on the O-RAN architecture [3], where the proposed DRL algorithm is deployed as xApp at the Near Real-time RAN Intelligent Controller (RIC) and communicates with the base station using O-RAN E2 interface. We developed xTDD Service Model (SM) following the E2SM standard [3], allowing the DRL solution to monitor DL and UL buffers from the gNB to deduce the optimal TDD configuration that accommodates the current traffic. Then, the decision (i.e., TDD configuration) is pushed to the base station. We implemented the solution on top of the OAI 5G StandAlone (SA) platform and Flexric RIC [4]. To the best of our knowledge, this is the first demonstration of a ML-based D-TDD on top of a real 5G network, showing the advantage of O-RAN architecture to building Self Organized Network (SON) function for dynamic configuration of D-TDD.

A novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (motion-related) parameters of the target. The aim is to calculate the distance between the Gaussian-distributed probability densities of kinematic data and to generate the optimal distribution that maximizes the precision. The proposed data fusion method can be used in several smart environments and Internet of Things (IoT) applications that call for target tracking, such as smart building applications, security surveillance, smart healthcare, and intelligent transportation, to mention a few. The diverse estimation techniques were compared with the state-of-the-art solutions by measuring the estimation root mean square error in different settings under different conditions, including high-noise environments. The simulation results show that the proposed algorithm is scalable and outperforms the standard particle filter, the improved particle filter based on KLD, and the consensus-based particle filter algorithm.

This work targets a comprehensive model enabling energy-constrained IoT (Internet of Things) sensor devices to be inactive for extended periods while estimating their readings of real-time events. Although events seem semantically uncoupled, they are usually spatially and temporally related. We propose GSAVES (Graph Sensor AdVersarial for Energy Saving), which uses readings from active devices and spatial correlations to generate the missing data due to sensor inactivity. The missing readings are generated with Graph Convolutional Network (GCN) that learns embeddings from data and the graph structure. GSAVES is evaluated against four state-of-the-art solutions using three network sizes and four performance metrics. The results demonstrate the efficiency of GSAVES for providing the best balance between the considered metrics, outperforming all the solutions in reducing energy consumption and improving accuracy.

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.

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.

5G networks and beyond are shifting from dominant Downlink (DL) traffic to a more equilibrate DL/UpLink (UL) and dominant UL traffic for specific emerging services. Particularly for ultra-Reliable and Low Latency Communications (uRLLC) services, the UL latency becomes an essential factor to consider. However, current UL scheduling methods are not efficient in terms of Physical Resource Blocks (PRBs) allocation, latency, or link adaptation. In this paper, we address the emerging challenge related to the UL latency in 5G networks and beyond. We introduce a solution based on Deep Reinforcement Learning (DRL) to dynamically allocate the future UL grant by learning from the dynamic traffic pattern. Simulation results demonstrate the efficiency of the proposed methodology in reducing the UL latency down to 0.25 ms and ensuring the generality by reacting to different traffic models.

Currently deployed NAT devices are designed primarily around the client/server paradigm, in which relatively anonymous client machines inside a private network initiate connections to public servers with stable IP addresses and DNS names. Thus, the asymmetric addressing and connectivity regimes established by NAT devices have created unique problems for Peer-to-Peer (P2P) applications and protocols. Multiple NAT-traversal techniques have been developed to overcome these shortcomings, each offering a different set of pros and cons. In the context of a P2P collaborative virtual reality (CVR) system, the difficulty of selecting a convenient and effective NAT-traversal technique increases exponentially because of the added constraints related to CVR. In this view, this article discusses the trade-offs of different NAT-traversal techniques and the CVR challenges that need to be taken into account when choosing a NAT-traversal technique. Finally, it presents a relay-based approach that leverages container migration to mitigate the drawbacks that come with this solution and accentuate its advantages.

5G New Radio (NR) introduces several key features to support the new emerging vertical industry use-cases, mainly: (1) Different numerology that gives more flexibility in managing time slot duration, and hence satisfying different delay requirements; (2) Bandwidth part that permits dedicating parts of the bandwidth to ensure different data rate requirements. However, although 5G NR introduces several enhancements, it makes radio resource management, more precisely resource scheduling, more complex and challenging. In this paper, we address the challenge of radio resource management in 5G NR featuring network slicing. We introduce a novel scheduling solution based on Deep Reinforcement Learning (DRL) to allocate resources and numerology for UEs to satisfy their different requirements. We evaluated the solution for different network configurations and compared its performance with the maximum achievable throughput. Simulation results demonstrated the efficiency of the proposed algorithm to allocate resources and the ability to scale for larger bandwidths covering both Frequency Range 1 (FR1) and FR2, as well as serving a higher number of User Equipment (UE).

The new emerging technologies enable the appearance of the 5G system and beyond that offers a plethora of services and target new verticals. One of these verticals is the large-scale Internet of Things (IoT) that is expected to be used everywhere in our daily lives. The traffic would likely be increased due to these emerging verticals. To overcome such challenges, network slicing and softwarization will play a crucial role in addressing these requirements and ensuring service level agreements (SLAs). Thus, there is a need to provide efficient and flexible network slice management mechanisms to handle the hurdles that come with the emerging industrial verticals. This paper focuses on network slicing in LoRa networks. We suggest a centralized coalition game-based network slicing strategy to manage LoRa nodes efficiently. According to the K-means clustering algorithm, the proposed solution is deployed within clustered players to maximize reliability while ensuring the SLA of the LoRa slices. Simulation results clearly show that our proposed approach improves Packets' Success Rate (PSR), Network Energy Consumption (NEC), and guarantees prioritization between slices.

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.

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 deployment of 5G and 6G is highly motivated by the emerging network services that demand more band-width and very low latency. Besides, these services are shifting from dominant Downlink (DL) Traffic to a more equilibrate DL/UpLink (UL) and dominant UL traffic for specific emerging services. One option to accommodate this new behavior is to use Time Duplex Division (TDD), where the radio frame is shared between UL and DL time slots, namely UL/DL pattern. While 4G TDD has a fixed number of configurations that cannot be updated on runtime, 5GNR allows complete flexibility to define the UL/DL pattern. Therefore, 5G base stations can dynamically change the pattern to adapt to the type of traffic (i.e., UL or DL). However, the 5G standard does not specify algorithms or solutions to derive the UL/DL pattern. To fill this gap, we propose a Deep Reinforcement Learning (DRL) that adds intelligence to the base station to self-adapt to the traffic pattern of the cell type. The proposed DRL algorithm monitors UL and DL buffers at the 5G base station to derive the optimal UL/DL pattern in respect to the current traffic configuration. The proposed solution delivers the optimal configuration in a timely and efficient manner. Simulation results demonstrated the efficiency of the proposed algorithm to avoid buffer overflow and ensure the generality by reacting to traffic pattern changes.

Radio Link Failure (RLF) is a challenging problem in 5G networks as it may decrease communication reliability and increases latency. This is against the objectives of 5G, particularly for the ultra-Reliable Low Latency Communications (uRLLC) traffic class. RLF can be predicted using radio measurements reported by User Equipment (UE)s, such as Reference Signal Receive Power (RSRP), Reference Signal Receive Quality (RSRQ), Channel Quality Indicator (CQI), and Power HeadRoom (PHR). However, it is very challenging to derive a closed-form model that derives RLF from these measurements. To fill this gap, we propose to use Machine Learning (ML) techniques, and specifically, a combination of Long Short Term Memory (LSTM) and Support Vector Machine (SVM), to find the correlation between these measurements and RLF. The RLF prediction model was trained with real data obtained from a 5G testbed. The validation process of the model showed an accuracy of 98% when predicting the connection status (i.e., RLF). Moreover, to illustrate the usage of the RLF prediction model, we introduced two use-cases: handover optimization and UAV trajectory adjustment.

The fifth generation of the mobile network aims to revolutionize mobile communication by offering both unparalleled performance and broader service offerings. 5G technology responds to the growing demand for higher bandwidth and lower latency, caused by a significant increase of connected resources. Leveraging on Software-Defined Networking (SDN) and artificial intelligence (AI) technologies, the 6G system can autonomously adapt to user requirements. This paper proposes a framework, named intelligent optimization framework (IoF), that leverages both network optimization and machine learning techniques for achieving the best performance results. The IoF framework allows for finding an exemplary resource allocation configuration of the mobile network by leveraging SDN technology. This work aims to configure the SDN-enabled switches and Open vSwitchs (OVSs) to enable an optimized data plane that reduces the overall operational expense (OPEX) cost and capital expense (CAPEX) cost within an optimized execution time. The evaluation results show our proposed framework's efficiency for delivering optimal configurations by reducing the number of allocated OVSs in a reasonable execution time.

Service Function Chaining (SFC) has emerged as a promising technology for 5G and beyond. It leverages Network Function Virtualization (NFV) and Software Defined Networking (SDN) and allows the decomposition of a given service into a set of blocks that successively process data. The SFC placement issue has been extensively studied in the literature, and different solutions have been proposed using mathematical models and heuristics. More recently, Reinforcement Learning (RL) has emerged as a tool for decision-making that allows agents to elaborate policies based on the environment's feedback. In this paper, we study the benefits of using Deep Reinforcement Learning methods for the multi-domain SFC placement problem. We propose a Deep Deterministic Policy Gradient (DDPG) approach, where Linear Physical Programming is employed to generate rewards that reflect the solution's quality in terms of cost and latency. Through our experiments, we are able to demonstrate the efficiency of our approach with results that satisfy the SLA requirements.

Service Function Chaining (SFC) is the process of decomposing a network service into multiple functions that successively process packets to deliver the end-to-end service. In a multi-domain context, SFC placement is a challenging problem due to limited knowledge of the infrastructure of the local domains, which complicates the process of finding the optimal placement solutions. On the other hand, Reinforcement Learning has gained momentum as a tool for decision-making, allowing agents to construct and improve policies using feedback from the environment. In this paper, we leverage Deep Reinforcement Learning (DRL) to perform SFC placement on multiple domains. We devise a hierarchical architecture where the local domain agents and the multi-domain agent are trained using different DRL models to perform SFC and sub-SFC placement while satisfying the SLA requirements.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

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.

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).

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.

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.

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.

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.

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.

This paper considers the problem of communication coverage for sustainable data forwarding in wireless sensor networks, where an energy-aware deployment model of relay nodes (RNs) is proposed. The model used in this paper considers constrained placement and is different from the existing one-tiered and two-tiered models. It supposes two different types of sensor nodes to be deployed, energy rich nodes (ERNs), and energy limited nodes (ELNs). The aim is thus to use only the ERNs for relaying packets, while ELN's use will be limited to sensing and transmitting their own readings. A minimum number of RNs is added if necessary to help ELNs. This intuitively ensures sustainable coverage and prolongs the network lifetime. The problem is reduced to the traditional problem of minimum weighted connected dominating set (MWCDS) in a vertex weighted graph. It is then solved by taking advantage of the simple form of the weight function, both when deriving exact and approximate solutions. Optimal solution is derived using integer linear programming (ILP), and a heuristic is given for the approximate solution. Upper bounds for the approximation of the heuristic (versus the optimal solution) and for its runtime are formally derived. The proposed model and solutions are also evaluated by simulation. The proposed model is compared with the one-tiered and two-tiered models when using similar solution to determine RNs positions, i.e., minimum connected dominating set (MCDS) calculation. Results demonstrate the proposed model considerably improves the network life time compared to the one-tiered model, and this by adding a lower number of RNs compared to the two-tiered model. Further, both the heuristic and the ILP for the MWCDS are evaluated and compared with a state-of-the-art algorithm. The results show the proposed heuristic has runtime close to the ILP while clearly reducing the runtime compared to both ILP and existing heuristics. The results also demonstrate scalability of the proposed solution.

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 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.

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.

Small Cells (SCs) are considered as a key enabling technique for future 5G cellular networks; whether they are deployed for Macro Cells networks densification or ensuring a standalone broadband access service. However, one of the critical challenges facing SCs deployments is a stable and an economical backhaul network. Particularly, if we consider SC for green-field deployments where operator transport infrastructure is bad or absent. Indeed, the question that arises in this specific case: what is the backhaul design that best meets economic constraints and end users Quality of Service (QoS) requirements? In this paper, we propose a novel method to design a cost-efficient SCs backhaul for green field deployment, while respecting linking technologies constraints. In fact, we formulate the problem of backhaul planning as a Mixed Integer Linear Programming (MILP), wherein the objective is to minimize SCs-Core Network connection cost while providing necessary access to operator mobile broadband by using a set of different technologies. Implementation results corroborate the benefit of wireless backhaul over wired one, and give to mobile operator insightful guidelines for designing a SCs backhauls for green field deployment.

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.

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.

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.

Energy scavenging from ambient sources represents a promising solution for sustaining continual operation for wireless sensor networks. An energy harvesting wireless sensor network (EH-WSN) can have two node types, harvesting enabled nodes (HNs) and non-harvesting enabled nodes (NHNs). In this paper, we consider the problem of achieving network longevity in EH-WSN when in-network data aggregation is used. The aim is to construct an aggregation tree that extends the network lifetime through reducing the overhead on NHN as much as possible. Two solutions are proposed; the first model the problem using a integer linear program, whereas the second solution uses minimum directed spanning tree. The objective of these protocols is to extend the network lifetime while reducing the runtime complexity. Both solutions are evaluated through extensive simulation experiments. The obtained results demonstrate their feasibility and ability in achieving the design goals.

In-network data aggregation is one of the popular optimization methodologies in the realm of wireless sensor networks (WSNs). To enable effective implementation, a routing tree is formed and the node transmissions are carefully scheduled to meet flow constraints. Minimizing the data delivery latency has been the most common objective of the data aggregation scheduling optimization. Prior work on this optimization problem pursued heuristics to overcome the complexity of the problem and used an upper bound on latency as a metric to assess the quality of the solution. In this paper we argue that for small and medium sized networks it is computationally feasible to obtain the optimal solution. We formulate the data aggregation scheduling problem as a linear integer program. Two variants of the problem are considered. The first assumes that the routing tree is predefined, e.g., through a network layer protocol, and node transmissions are to be scheduled to minimize delay. For the second variant, the routing topology formation and node schedule are to be optimized in an integrated manner. The proposed solutions are compared to the existing heuristics via extensive simulation experiments.

This paper deals with power-efficient coverage in wireless sensor networks (WSN) by taking advantage of energy-harvesting capabilities. A general scenario is considered for deployed networks with two types of sensor nodes, harvesting enabled nodes (HNs), and none-harvesting nodes (NHNs). The aim is to use only the HNs for relaying packets, while NHNs use will be limited to sensing and transmitting their own readings. The problem is modeled using graph theory and reduced to finding the minimum weighted connected dominating set in a vertex weighted graph. A limited number of relay nodes is added at the positions close to the NHNs in the resulted set. The weight function ensures minimizing the number of NHNs in the set, and thus reducing the relay nodes to be added. Our contribution is to consider relay node placement (addition) in energy harvesting WSN, where only HNs are used to forward packets. This is to preserve the limited energy of NHNs. Extensive simulation results show that the proposed relay node addition strategy prolongs the network lifetime, from the double, to factors of several tens of times. This is at a reasonable cost in terms of the number of relay nodes added, which is compared to a lower-bound derived in the paper.

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.

This article considers the data aggregation scheduling problem, where a collision-free schedule is determined in a distributed way to route the aggregated data from all the sensor nodes to the base station within the least time duration. The algorithm proposed in this article (Distributed algorithm for Integrated tree Construction and data Aggregation (DICA)) intertwines the tree formation and node scheduling to reduce the time latency. Furthermore, while forming the aggregation tree, DICA maximizes the available choices for parent selection at every node, where a parent may have the same, lower, or higher hop count to the base station. The correctness of the DICA is formally proven, and upper bounds for time and communication overhead are derived. Its performance is evaluated through simulation and compared with six delay-aware aggregation algorithms. The results show that DICA outperforms competing schemes. The article also presents a general hardware-in-the-loop framework (DAF) for validating data aggregation schemes on Wireless Sensor Networks (WSNs). The framework factors in practical issues such as clock synchronization and the sensor node hardware. DICA is implemented and validated using this framework on a test bed of sensor motes that runs TinyOS 2.x, and it is compared with a distributed protocol (DAS) that is also implemented using the proposed framework.

In wireless sensor networks (WSNs), it may be necessary to have a unified time reference for all network nodes. Such a necessity may be imposed by the management strategy in the network, e.g., using time based medium access arbitration, or simply due to the dynamic nature of the application, e.g. target tracking. Since each node more or less operates autonomously, the clocks of the individual nodes have to be synchronized. Contemporary clock synchronization protocols introduce significant messaging overhead and thus do not suit the resource-constrained WSNs. In the paper, we propose a novel solution called Synchronization through Piggybacked Reference Timestamps (SPiRT). SRiRT exploits the popularity of two-tier network architectures in WSN, where nodes are grouped into disjoint clusters and each cluster is lead by a cluster-head that aggregates the data from its members. Each cluster-head synchronizes its clock to that of a reference node in the network through message exchange. Since cluster members can overhear the cluster-head transmissions, SPiRT takes advantages of such synchronization traffic to adjust the clock of the cluster members. SPiRT calls for appending the reference timestamps in the cluster-head messages so that a cluster member can estimate their clock adjustment. This cuts on energy consumption and increases the synchronization efficiency of SPiRT. SPiRT is validated through simulation and implementation on a Micaz based testbed. The validation results confirm the effectiveness of SPiRT and show that it outperforms competing schemes in the literature.

Wireless sensors and actors networks (WSANs) have the capacity for not only monitoring some phenomena through sensor nodes but also performing appropriate actions. Most of the contemporary WSAN management solutions focus on defining communication path among sensors and actors and on tasking appropriate actors to handle the detected events. In this paper we classify events based on how they evolve over time into continuous and discrete and categorize the WSAN management strategies accordingly. Unlike discrete events, a continuous event spreads quickly and becomes more serious as time passes. Such a characteristic introduces more challenges and motivates a non-conventional management strategies. This paper presents an approach for Sensor-Actuator Coordination for Handling Spreading events (SACHS). SACHS opts to enable the network to respond quickly in order to avoid the event from growing in scope, e.g., prevent a fire from spreading, while reducing the energy overhead due to the coordination messages and due to actor's relocation to the event region. SACHS limits sensor-actor and actor-actor interactions and exploits local sensor-sensor communication to determine the scope of the event, define spots for actors to position at, and schedule the actors' response. The simulation results confirm the performance advantage of SACHS compared to competing schemes.

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.

In wireless sensor networks, the sensor data are often aggregated en-route to the base-station in order to eliminate redundancy and conserve the network resources. The basestation not only acts as a destination for the upstream data traffic, but it also configures the network by transmitting commands downstream to nodes. The data delivery latency is a critical performance metric in time-sensitive applications and is considered by a number of data aggregation schemes in the literature. However, to the best of our knowledge, no solution has considered the scheduling of downstream packets, originated from the base-station, in conjunction with upstream data aggregation traffic. This paper fills such a gap and proposes MASAUD, which intertwines the medium access schedule of upstream and downstream traffic in order to reuse time slots in a non-conflicting manner and reduce delay. MASAUD can be integrated with any scheme for data aggregation scheduling. The simulation confirms the effectiveness of MASAUD.

In wireless sensor networks, in-network data aggregation filters out redundant sensor readings in order to reduce the energy and bandwidth consumed in disseminating the data to the base-station. In this paper, we investigate the problem of reliable collection of aggregated data with minimal latency. The aim is to form an aggregation tree such that there are k disjoint paths from each node to the base-station and find a collision-free schedule for node transmissions so that the aggregated data reaches the base-station in minimal time. We propose a novel algorithm for Reliable and Timely dissemination of Aggregated Data (RTAD). RTAD intertwines the formation of the aggregation tree and the allocation of time slots to nodes, and assigns parents to the individual nodes in order to maximize time slot reuse. The simulation results show that RTAD outperforms competing algorithms in the literature.

In-network data aggregation is often pursued to remove redundancy and correlate the data en-route to the base-station in order to save energy in wireless sensor networks (WSNs). In this paper, we present a novel cross-layer approach for reducing the latency in disseminating aggregated data to the base-station over multi-frequency radio links. Our approach forms the aggregation tree with the objective of increasing the simultaneity of transmissions and reducing buffering delay. Aggregation nodes are picked and time-slots are allocated to the individual sensors so that the most number of ready nodes can transmit their data without delay. Colliding transmissions are avoided by the use of different radio channels. Our approach is validated through simulation and is shown to outperform previously published schemes.

This paper focuses on data aggregation scheduling problem in wireless sensor networks (WSNs), to minimize time latency. Prior works on this problem have adopted a structured approach, in which a tree-based structure is used as an input for the scheduling algorithm. As the scheduling performance mainly depends on the supplied aggregation tree, such an approach cannot guarantee optimal performance. To address this problem, we propose approaches based on Semi-structured Topology (DAS-ST) and Unstructured Topology (DAS-UT). The approaches are based on two key design features, which are: (1) simultaneous execution of aggregation tree construction and scheduling, and (2) parent selection criteria that maximize the choices of parents for each node and maximize time slot reuse. We prove that the latency of DAS-ST is upper-bounded by ([2π/arccos(1/1+ϵ)]+4)R+Δ-4, where R is the network radius, Δ is the maximum node degree, and 0.05 <; ϵ ≤ 1. Simulations results show that DAS-UT outperforms DAS-ST and four competitive state-of-the-art aggregation scheduling algorithms in terms of latency and network lifetime.

The area-based localization algorithms use only the location information of some reference nodes, called anchors, to give the residence area of the remaining nodes. The current algorithms use triangle, ring or circle as a geometric shape to determine the sensors' residence area. Existing works suffer from two major problems: (1) in some cases, they might issue wrong decisions about nodes' presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error. In this paper, we deal with the localization problem by introducing a new way to determine the sensors' residence area which shows a better accuracy than the existing algorithms. Our new localization algorithm, called HSL (Half Symmetric Lens based localization algorithm for WSN), is based on the geometric shape of half-symmetric lens. We also uses the Voronoi diagram in HSL to mitigate the problem of unlocalizable sensor nodes. Finally, we conduct extensive simulations to evaluate the performance of HSL. Simulation results show that HSL has better locatable ratio and location accuracy compared to representative state-of-the-art area-based algorithms.

In wireless sensor networks, reliability represents a design goal of a primary concern. To build a comprehensive reliable system, it is essential to consider node failures and intruder attacks as unavoidable phenomena. In this paper, we present a new intrusion-fault tolerant routing scheme offering a high level of reliability through a secure multi-path communication topology. Unlike existing intrusion-fault tolerant solutions, our protocol is based on a distributed and in-network verification scheme, which does not require any referring to the base station. Furthermore, it employs a new multi-path selection scheme seeking to enhance the tolerance of the network and conserve the energy of sensors. Extensive simulations with Tiny OS showed that our approach improves the overall Mean Time To Failure (MTTF) while conserving the energy resources of sensors.

Energy is a scarce resource in Wireless Sensor Networks. Some studies show that more than 70% of energy is consumed in data transmission. Since most of the time, the sensed information is redundant due to geographically collocated sensors, most of this energy can be saved through data aggregation. Furthermore, data aggregation improves bandwidth usage. Unfortunately, while aggregation eliminates redundancy, it makes data integrity verification more complicated since the received data is unique. In this paper, we present a new protocol that provides secure aggregation for wireless sensor networks. Our protocol is based on a two hops verification mechanism of data integrity. Our solution is essentially different from existing solutions in that it does not require referring to the base station for verifying and detecting faulty aggregated readings, thus providing a totally distributed scheme to guarantee data integrity. We carried out simulations using TinyOS environment. Simulation results show that the proposed protocol yields significant savings in energy consumption while preserving data integrity.