Constraint hubs deployment for efficient machine-type communications

Abstract 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

Toward a UTM-based service orchestration for UAVs in MEC-NFV environment

Abstract 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 5th 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

Orchestrating 5G network slices to support industrial internet and to shape next-generation smart factories

Abstract Industry 4.0 aims at shaking the current manufacturing landscape by leveraging the adoption of smart industrial equipment with increased connectivity, sensing, and actuation capabilities. By exploring access to real-time production information and advanced remote control features, servitization of manufacturing firms promise novel added value services for industrial operators and customers. On the other hand, industrial networks would face a transformation process in order to support the flexibility expected by the next-generation manufacturing processes and enable inter-factory cooperation. In this scenario, 5G systems can play a key role in enabling Industry 4.0 by extending the network slicing paradigm to specifically support the requirements of industrial use cases over heterogeneous domains. We present a novel 5G-based network slicing framework that aims at accommodating the requirements of Industry 4.0. To interconnect different industrial sites up to the extreme edge, different slices of logical resources can be instantiated on-demand to provide the required end-to-end connectivity and processing features. We validate our proposed framework in three realistic use cases that enabled us to highlight the envisioned benefits for industrial stakeholders

LEARNET:reinforcement learning based flow scheduling for asynchronous deterministic networks

Abstract 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

Energy and delay aware task assignment mechanism for UAV-based IoT platform

Abstract 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

Trust-based video management framework for social multimedia networks

Abstract 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

Edge cloud resource-aware flight planning for unmanned aerial vehicles

Abstract 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

Lightweight virtualization based security framework for network edge

Abstract The following topics are dealt with: 3G mobile communication; Internet of Things; virtualisation; 5G mobile communication; telecommunication traffic; cloud computing; telecommunication network routing; cellular radio; Internet; protocols

Coalition game-based approach for improving the QoE of DASH-based streaming in multi-servers scheme

Abstract 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

Toward a real deployment of network services orchestration and configuration convergence framework for 5G network slices

Abstract A seamless interworking between network function virtualization (NFV) and software defined networking (SDN) to orchestrate network services for the 5G systems is very fundamental for network slice creation. The orchestration of large scale network slices across multiple administrative as well as technological domains with heterogeneous resources and a distributed form of slice management can benefit from harnessing existing NFV orchestration (NFVO) solutions. In this regard, this article presents a network service orchestration and configuration convergence framework that is capable of providing a large scale network slicing solution for 5G network operators. Using this framework, 5G network operators can orchestrate and configure network slices directly from their infrastructure and that of credible registered slice providers who have resources for the orchestration of only a subset of the overall network slice. The framework is equipped with mechanisms that allow a distributed form of slice configuration and management