YA-DA: YAng-Based DAta Model for Fine-Grained IIoT Air Quality Monitoring

With the development of industrialization, air pollution is also steadily on the rise since both industrial and daily activities generate a massive amount of air pollution. Since decreasing air pollution is critical for citizens' health and well-being, air pollution monitoring is becoming an essential topic. Industrial Internet of Things (IIoT) research focuses on this crucial area. Several attempts already exist for air pollution monitoring. However, none of them are improving the performance of IoT data collection at the desired level. Inspired by the genuine Yet Another Next Generation (YANG) data model, we propose a YAng-based DAta model (YA-DA) to improve the performance of IIoT data collection. Moreover, by taking advantage of digital twin (DT) technology, we propose a DT-enabled fine-grained IIoT air quality monitoring system using YA-DA. As a result, DT synchronization becomes fine-grained. In turn, we improve the performance of IIoT data collection resulting in lower round-trip time (RTT), higher DT synchronization, and lower DT latency.Comment: This paper has been accepted at the 4th Workshop on Future of Wireless Access and Sensing for Industrial IoT (FUTUREIIOT) in IEEE Global Communications Conference (IEEE GLOBECOM) 202

Digital Twin Enriched Green Topology Discovery for Next Generation Core Networks

Topology discovery is the key function of core network management since it utilizes the perception of data and mapping network devices. Nevertheless, it holds operational and resource efficiency complexities. For example, traditional discovery cannot perform predictive analysis to learn the network behavior. Moreover, traditional discovery periodically visits IP ports without considering the utilization levels, which leads to high resource usage and energy consumption. Hence, it is necessary to integrate intelligent methods into traditional discovery to deeply understand the behavioral pattern of a core network and recommend action to avoid these intrinsic complexities. Therefore, we propose a Digital Twin (DT) enriched Green Discovery Policy (DT-GDP) to serve a green discovery by using the increased intelligence and seamless assistance of DT. DT-GDP jointly uses the outputs of two modules to calculate the total energy consumption in Watts. In the energy module, we consider the service power, idle state power, and the cooling power of an IP port and derive a novel energy formula. In the visit decision module, we use Multilayer Perceptron (MLP) to classify the IP ports and recommend visit action. According to experimental results, we achieve a significant reduction in the visited ports by 53% and energy consumption by 66%

Network-Aware AutoML Framework for Software-Defined Sensor Networks

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the Internet of Things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a network-aware automated machine learning (AutoML) framework which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays

TwinPot: Digital Twin-assisted Honeypot for Cyber-Secure Smart Seaports

The idea of next-generation ports has become more apparent in the last ten years in response to the challenge posed by the rising demand for efficiency and the ever-increasing volume of goods. In this new era of intelligent infrastructure and facilities, it is evident that cyber-security has recently received the most significant attention from the seaport and maritime authorities, and it is a primary concern on the agenda of most ports. Traditional security solutions can be applied to safeguard IoT and Cyber-Physical Systems (CPS) from harmful entities. Nevertheless, security researchers can only watch, examine, and learn about the behaviors of attackers if these solutions operate more transparently. Herein, honeypots are potential solutions since they offer valuable information about the attackers. It can be virtual or physical. Virtual honeypots must be more realistic to entice attackers, necessitating better high-fidelity. To this end, Digital Twin (DT) technology can be employed to increase the complexity and simulation fidelity of the honeypots. Seaports can be attacked from both their existing devices and external devices at the same time. Existing mechanisms are insufficient to detect external attacks; therefore, the current systems cannot handle attacks at the desired level. DT and honeypot technologies can be used together to tackle them. Consequently, we suggest a DT-assisted honeypot, called TwinPot, for external attacks in smart seaports. Moreover, we propose an intelligent attack detection mechanism to handle different attack types using DT for internal attacks. Finally, we build an extensive smart seaport dataset for internal and external attacks using the MANSIM tool and two existing datasets to test the performance of our system. We show that under simultaneous internal and external attacks on the system, our solution successfully detects internal and external attacks.Comment: Accepted on WS01 IEEE ICC 2023 Workshop on The Evolution of Digital Twin Paradigm in Wireless Communication

DTWN: Q-learning-based Transmit Power Control for Digital Twin WiFi Networks

Interference has always been the main threat to the performance of traditional WiFi networks and next-generation moving forward. The problem can be solved with transmit power control(TPC). However, to accomplish this, an information-gathering process is required. But this brings overhead concerns that decrease the throughput. Moreover, mitigation of interference relies on the selection of transmit powers. In other words, the control scheme should select the optimum configuration relative to other possibilities based on the total interference, and this requires an extensive search. Furthermore, bidirectional communication in real-time needs to exist to control the transmit powers based on the current situation. Based on these challenges, we propose a complete solution with Digital Twin WiFi Networks (DTWN). Contrarily to other studies, with the agent programs installed on the APs in the physical layer of this architecture, we enable information-gathering without causing overhead to the wireless medium. Additionally, we employ Q-learning-based TPC in the Brain Layer to find the best configuration given the current situation. Consequently, we accomplish real-time monitoring and management thanks to the digital twin. Then, we evaluate the performance of the proposed approach through total interference and throughput metrics over the increasing number of users. Furthermore, we show that the proposed DTWN model outperforms existing schemes

Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines

Wind turbines are one of the primary sources of renewable energy, which leads to a sustainable and efficient energy solution. It does not release any carbon emissions to pollute our planet. The wind farms monitoring and power generation prediction is a complex problem due to the unpredictability of wind speed. Consequently, it limits the decision power of the management team to plan the energy consumption in an effective way. Our proposed model solves this challenge by utilizing a 5G-Next Generation-Radio Access Network (5G-NG-RAN) assisted cloud-based digital twins’ framework to virtually monitor wind turbines and form a predictive model to forecast wind speed and predict the generated power. The developed model is based on Microsoft Azure digital twins infrastructure as a 5-dimensional digital twins platform. The predictive modeling is based on a deep learning approach, temporal convolution network (TCN) followed by a non-parametric k-nearest neighbor (kNN) regression. Predictive modeling has two components. First, it processes the univariate time series data of wind to predict its speed. Secondly, it estimates the power generation for each quarter of the year ranges from one week to a whole month (i.e., medium-term prediction) To evaluate the framework the experiments are performed on onshore wind turbines publicly available datasets. The obtained results confirm the applicability of the proposed framework. Furthermore, the comparative analysis with the existing classical prediction models shows that our designed approach obtained better results. The model can assist the management team to monitor the wind farms remotely as well as estimate the power generation in advance

Digital Twin for 6G: Taxonomy, Research Challenges, and the Road Ahead

The concept of digital twin (DT) is constantly revealing as a key enabling technology for the deployment of mobile communication services envisaged for the sixth-generation (6G) Internet-of-Things (IoT). This paper aims at providing a comprehensive review of the current state-of-the-art DT-enabled 6G oriented network services. The main characteristics of this new key enabling technology and its critical aspects are highlighted. An overview of the 6G network requirements for the deployment of its innovative envisioned services is firstly provided, emphasizing how the DT concept represents a complementary key enabling technology for them. This is followed by a brief introduction of the DT technology. Subsequently, a comprehensive classification and analysis of the research advancements on DT-enabled 6G services currently available in literature is provided. This paper is concluded by highlighting the most representative challenges and future directions necessary for the deployment of this promising and innovative technology

Energy-efficient RL-based aerial network deployment testbed for disaster areas

Rapid deployment of wireless devices with 5G and beyond enabled a connected world. However, an immediate demand increase right after a disaster paralyzes network infrastructure temporarily. The continuous flow of information is crucial during disaster times to coordinate rescue operations and identify the survivors. Communication infrastructures built for users of disaster areas should satisfy rapid deployment, increased coverage, and availability. Unmanned air vehicles (UAV) provide a potential solution for rapid deployment as they are not affected by traffic jams and physical road damage during a disaster. In addition, ad-hoc WiFi communication allows the generation of broadcast domains within a clear channel which eases one-to-many communications. Moreover, using reinforcement learning (RL) helps reduce the computational cost and increases the accuracy of the NP-hard problem of aerial network deployment. To this end, a novel flying WiFi ad-hoc network management model is proposed in this paper. The model utilizes deep-Q-learning to maintain quality-of-service (QoS), increase user equipment (UE) coverage, and optimize power efficiency. Furthermore, a testbed is deployed on Istanbul Technical University (ITU) campus to train the developed model. Training results of the model using testbed accumulates over 90% packet delivery ratio as QoS, over 97% coverage for the users in flow tables, and 0.28 KJ/Bit average power consumption

Software-Defined Management Model for Energy-Aware Vehicular Networks

This paper investigates the energy efficiency of Road Side Units (RSUs) by proposing a novel flow and energy management model in Software-Defined Networking (SDN)-based vehicular networks. In the considered scenario, high vehicular mobility and limited coverage area of RSUs cause a degradation in Quality of Experience (QoE) of vehicles and this significantly affects the quality of communication by decreasing the percentage of flow satisfied. In addition, the growth in energy consumption of RSUs leads to inefficient network management. Being inspired from SDN, a centralized controller can schedule RSUs by providing a fair share of network resources and reduce the total energy consumption of RSUs by switching on/off. More specifically, in this paper, the controller classifies to vehicles based on QoE and defines unsatisfactory vehicles. Then the controller estimates the right amount of power level of these vehicles to connect a new assigned RSU. In this manner, RSUs can be scheduled by switching on/off so that the growth in energy consumption of RSUs can be managed. The evaluations show that the proposed model provides a better flow satisfied and throughput by guaranteeing energy efficiency in SDN-based vehicular networks

Q-learning driven routing for aeronautical Ad-Hoc networks

The aeronautical ad-hoc network (AANET) is one of the critical methodologies to satisfy the Internet connectivity requirement of airplanes during their flights. However, the ultra-dynamic topology and unstable air-to-air link characteristics increase the need for AANETs to a particular routing algorithm compared to the terrestrial networks. This need is mainly because these AANET-specific characteristics increase the delays, packet losses, and network load with accuracy reduction by continuously changing topology and breaking air-to-air links during routing. The works in the literature do not satisfy the ultra-dynamic topology and unstable air-to-air link characteristics of AANETs during routing. On the other hand, the routing algorithm can adapt to the dynamic conditions of AANETs by utilizing Artificial Intelligence (AI) based methodologies. For adaptation to this dynamic environment, we aim to let the airplanes find their routing path through exploration and exploitation by mapping the AANET environment to QLR. Clearly, this article proposes an updated Layered Hidden Markov Model (updated-LHMM) estimation-based Q-learning (QLR) routing for AANETs to solve the delay, packet loss, network load, and accuracy problems. For this aim, the Bellman Equation is adapted to the AANET environment by proposing different methodologies for its related QLR components. Results reveal that the proposed strategy mainly reduces the routing delay and packet losses by 30% and 33% compared to the methods in the literature