Supporting Next-Generation Network Management with Intelligent Moving Devices

The concept of fixed infrastructures capable of fulfilling the requirements of moving devices in terms of connectivity and reliability has been the optimal solution for the past few decades. Today, such a solution is no longer feasible in the Internet of Things (IoT) era. All things are now connected, and a significant number of them are mobile, hence leading to connectivity and reliability issues. Connected and autonomous vehicles, in addition to more contemporary flying and moving devices such as unmanned aerial vehicles (UAVs) and IoT devices, will play a significant role in next-generation networks (NGNs). Node-to-node communication will also play a key role in NGNs and will provide alternative solutions toward connectivity in many complex environments for applications such as smart transportation. With that said, today\u27s wide availability of smart moving devices provides a wider set of alternatives to autonomy for NGNs. In this article, we discuss some of the existing solutions that use connected vehicles, UAVs, and other moving intelligent devices to not only provide connectivity support, but also perform on-location data collection, anal-ysis, and decision making to enable the management of moving NGNs for intelligent services and applications. We envision a solution that is capa-ble of adapting generalized and decentralized learning on mobile devices, such as federated learning, with the advances in deep learning to support the autonomy and configurability aspects of moving NGNs

Blockchain and FL-based Network Resource Management for Interactive Immersive Services

Advanced services leveraged for future smart cities have played a significant role in the advancement of 5G networks towards the 6G vision. Interactive immersive applications are an example of those enabled services. Such applications allow for the interaction between multiple users in a 3D environment created by virtual presentations of real objects and participants using various technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Digital Twin (DT) and holography. These applications require advanced computing models which allow for the processing of massive gathered amounts of data. Motions, gestures and object modification should be captured, added to the virtual environment, and shared with all the participants. Relying only on the cloud to process this data can cause significant delays. Therefore, a hybrid cloud/edge architecturewith an intelligent resource orchestration mechanism, that is able to allocate the available capacities efficiently is necessary. In this paper, a blockchain and federated learning-enabled predicted edge-resource allocation (FLP-RA) algorithm is introduced to manage the allocation of computing resources in B5G networks. It allows for smart edge nodes to train their local data and share it with other nodes to create a global estimation of future network loads. As such, nodes are able to make accurate decisions to distribute the available resources to provide the lowest computing delay

A Federated Learning and Blockchain-enabled Sustainable Energy-Trade at the Edge: A Framework for Industry 4.0

Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and AI, enables smart and secure micro-grid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end-devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and Federated Learning-enabled solution provides secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario

Securing Critical IoT Infrastructures with Blockchain-Supported Federated Learning

Network trustworthiness is considered a very crucial element in network security and is developed through positive experiences, guarantees, clarity and responsibility. Trustworthiness becomes even more compelling with the ever-expanding set of Internet of Things (IoT) smart city services and applications. Most of today;s network trustworthy solutions are considered inadequate, notably for critical applications where IoT devices may be exposed and easily compromised. In this article, we propose an adaptive framework that integrates both federated learning and blockchain to achieve both network trustworthiness and security. The solution is capable of dealing with individuals’ trust as a probability and estimates the end-devices’ trust values belonging to different networks subject to achieving security criteria. We evaluate and verify the proposed model through simulation to showcase the effectiveness of the framework in terms of network lifetime, energy consumption, and trust using multiple factors. Results show that the proposed model maintains high accuracy and detection rates with values of ≈0.93 and ≈0.96, respectively

Realizing Health 4.0 in Beyond 5G Networks

The advancements of Edge and Internet of Things (IoT) devices in terms of their processing, storage and communication capabilities, in addition to the advancements in wireless communication and networking technologies, have led to the rise in Intelligent Edge-enabled IoT architectures. Federated Learning (FL) is one example in which intelligence is adapted to the edge to offload some of the processing load from centralized entities and maintain secure localized model training. With Health 4.0, it is anticipated that distributed and edge-supported Artificial Intelligence (AI) will enable faster and more accurate early-stage disease discovery that relies significantly on intelligent remote and on-site IoT devices. Given that healthcare systems are highly scrutinized by both governments and patients to maintain high levels of data privacy and security, FL coupled with the support of blockchain will provide an optimal solution to reinforce today\u27s healthcare frameworks. In this paper, we propose a FL-enabled framework for healthcare systems that is supported by edge-computing, blockchain and intelligent IoT devices. The solution considers a pneumonia detection use-case as a proof-of-concept and is applicable to an extended set of health-related use-cases. Different pre-trained models are compared against the proposed FL-supported model, namely, CNN, GG16, VGG19, InceptionV3, ResNet, DenseNet, and Xception. Results show high model accuracy attainment and significant improvements in terms of data privacy

Preventing and Controlling Epidemics Through Blockchain-Assisted AI-Enabled Networks

The COVID-19 pandemic, which spread rapidly in late 2019, has revealed that the use of computing and communication technologies provides significant aid in preventing, controlling, and combating infectious diseases. With the ongoing research in next-generation networking (NGN), the use of secure and reliable communication and networking is of utmost importance when dealing with users\u27 health records and other sensitive information. Through the adaptation of artificial-intelligence-enabled NGN, the shape of healthcare systems can be altered to achieve smart and secure healthcare capable of coping with epidemics that may emerge at any given moment. In this article, we envision a cooperative and distributed healthcare framework that relies on state-of-the-art computing, communication, and intelligence capabilities, namely, federated learning, mobile edge computing, and blockchain, to enable epidemic (or suspicious infectious disease) discovery, remote monitoring, and fast health authority response. The introduced framework can also enable secure medical data exchange at the edge and between different health entities. This technique, coupled with the low latency and high bandwidth functionality of 5G and beyond networks, would enable mass surveillance, monitoring, and analysis to occur at the edge. Challenges, issues, and design guidelines are also discussed in this article with highlights on some trending solutions

Lightweight IDS for UAV Networks: A Periodic Deep Reinforcement Learning-based Approach

The use of intrusion detection systems (IDS) has become crucial for modern networks. To ensure the targeted performance of such networks, diverse techniques were introduced to enhance system reliability. Many network designs have adapted the use of Unmanned Aerial Vehicles (UAVs) to provide wider coverage and meet performance targets. However, the cybersecurity aspect of UAVs has not been fully considered. In this paper, we propose a lightweight intrusion detection and prevention system (IDPS) module for UAVs. The IDPS module is trained using Deep Reinforcement Learning (DRL), specifically Deep Q-learning (DQN), to enable UAVs to autonomously detect suspicious activities and to take necessary action to ensure the security of the network. A customized reward function is used to take into consideration the dataset unbalanced nature, which encourages the IDPS module to detect minor classes. Also, considering the limited availability of resources for UAVs, a periodic offline-learning approach is introduced to ensure that UAVs are capable to learn and adapt to the evolution of intrusion attacks autonomously. Numerical simulations show the efficiency of the proposed IDPS in detecting suspicious activities and corroborating the advantages brought by the periodic offline learning in comparison with similar online learning approaches, in terms of accuracy and energy consumption

An Adaptive UAV Positioning Model for Sustainable Smart Transportation

Several research works are being considered to adopt the use of UAVs to support smart transportation systems due to their movement flexibility. In this article, a UAV-supported vehicular network solution is developed which considers both power and coverage limitations of UAVs to attain the vision of sustainable smart cities. Nodes communicate with each other through the 5G connection and ad-hoc links. The solution is solved for as a predictive optimization problem that determines the height of the UAV to dynamically change it to ensure the optimal communication coverage of vehicular nodes. Moreover, the solution considers UAV energy consumption constraints when setting the optimal height of the UAV. Additionally, the optimal distance between every two adjacent UAVs is also considered to avoid any coverage overlapping while protecting their connectivity. Extensive evaluations were considered in terms of both implementation and simulation to test the proposed model. Evaluation results show that the proposed solution can predict vehicle traffic patterns accurately to ensure proper adjustments of the UAV height. Moreover, network coverage is ensured for areas with and without fixed BS availability with the support of the self-positioning UAVs while adhering to QoS requirements