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.
Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service ...(DoS) to network intrusion and data leakage. This paper presents a novel machine learning (ML) based security framework that automatically copes with the expanding security aspects related to IoT domain. This framework leverages both Software Defined Networking (SDN) and Network Function Virtualization (NFV) enablers for mitigating different threats. This AI framework combines monitoring agent and AI-based reaction agent that use ML-Models divided into network patterns analysis, along with anomaly-based intrusion detection in IoT systems. The framework exploits the supervised learning, distributed data mining system and neural network for achieving its goals. Experiments results demonstrate the efficiency of the proposed scheme. In particular, the distribution of the attacks using the data mining approach is highly successful in detecting the attacks with high performance and low cost. Regarding our anomaly-based intrusion detection system (IDS) for IoT, we have evaluated the experiment in a real Smart building scenario using one-class SVM. The detection accuracy of anomalies achieved 99.71%. A feasibility study is conducted to identify the current potential solutions to be adopted and to promote the research towards the open challenges.
This article provides an overview of enhanced network services, while emphasizing the role of UAVs as core network equipment with radio and backhaul capabilities. Initially, we elaborate the various ...deployment options, focusing on UAVs as airborne radio, backhaul and core network equipment, pointing out the benefits and limitations. We then analyze the required enhancements in the SBA to support UAV services including UAV navigation and air traffic management, weather forecasting and UAV connectivity management. The use of airborne UAVs network services is assessed via qualitative means, considering the impact on vehicular applications. Finally, an evaluation has been conducted via a testbed implementation, to explore the performance of UAVs as edge cloud nodes, hosting an ACS function responsible for the control and orchestration of a UAV fleet.
Time synchronization in wireless sensor networks (WSNs) is a topic that has been attracting the research community in the last decade. Most performance evaluations of the proposed solutions have been ...limited to theoretical analysis and simulation. They consequently ignored several practical aspects, e.g., packet handling jitters, clock drifting, packet loss, and mote limitations, which affect real implementation on sensor motes. Authors of some pragmatic solutions followed empirical approaches for the evaluation, where the proposed solutions have been implemented on real motes and evaluated in testbed experiments. This paper gives an insight on issues related to the implementation of synchronization protocols in WSN. The challenges related to WSN environment are presented; the importance of real implementation and testbed evaluation are motivated by some experiments we conducted. The most relevant implementations of the literature are then reviewed, discussed, and qualitatively compared. While there are several survey papers that present and compare the protocols from the conception perspectives, as well as others that deal with mathematical and signal processing issues of the estimators, a survey on practical aspects related to the implementation is missing. To our knowledge, this paper is the first one that takes into account the practical aspect of existing solutions.
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 <inline-formula> <tex-math notation="LaTeX">i </tex-math></inline-formula>) increasing the quality of service (QoS) by leveraging the benefits of queuing and multi-path forwarding in OpenFlow, <inline-formula> <tex-math notation="LaTeX">ii </tex-math></inline-formula>) allowing an operator with an SDN-enabled network to efficiently allocate the network resources considering mobility, and <inline-formula> <tex-math notation="LaTeX">iii </tex-math></inline-formula>) 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.
IoT systems can be leveraged by Network Function Virtualization (NFV) and Software-Defined Networking (SDN) technologies, thereby strengthening their overall flexibility, security and resilience. In ...this sense, adaptive and policy-based security frameworks for SDN/NFV-aware IoT systems can provide a remarkable added value for self-protection and self-healing, by orchestrating and enforcing dynamically security policies and associated Virtual Network Functions (VNF) or Virtual network Security Functions (VSF) according to the actual context. However, this security orchestration is subject to multiple possible inconsistencies between the policies to enforce, the already enforced management policies and the evolving status of the managed IoT system. In this regard, this paper presents a semantic-aware, zero-touch and policy-driven security orchestration framework for autonomic and conflict-less security orchestration in SDN/NFV-aware IoT scenarios while ensuring optimal allocation and Service Function Chaining (SFC) of VSF. The framework relies on Semantic technologies and considers the security policies and the evolving IoT system model to dynamically and formally detect any semantic conflict during the orchestration. In addition, our optimized SFC algorithm maximizes the QoS, security aspects and resources usage during VSF allocation. The orchestration security framework has been implemented and validated showing its feasibility and performance to detect the conflicts and optimally enforce the VSFs.
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.
Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, ...particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. The revolution of 6G networks is driven by massive data availability, moving from centralized and big data towards small and distributed data. This trend has motivated the adoption of distributed and collaborative ML/DL techniques. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique that recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.