The concept of federated learning enhances the security and privacy of Internet of Vehicles (IoV) networks while accelerating the development of more intelligent, secure, and efficient vehicular applications. Federated learning-based approaches allow the training of machine learning models directly on the IoVs without transferring the sensitive information to a Mobile Edge Computing (MEC) server. One persisting drawback in these approaches is that malicious IoVs may deliberately engage in malicious behaviors to manipulate the edge model training and reduce the trustworthiness of the system. To overcome this challenge, we propose a digital twin-enabled federated misbehavior detection model to collaboratively detect malicious behaviors in IoV networks without sharing sensitive data. Combining digital twin with IoV networks, we analyze real-world vehicle behavior and investigate various attack scenarios. We develop a federated misbehavior detection algorithm, which continuously learns and improves system performance by identifying malicious activities of potential attacks. This algorithm allows each IoV to process data locally and contribute to an edge model for a more robust and secure misbehavior detection model. Our simulation results show that the proposed federated learning model with the XGBoost algorithm detects attacks with a maximum accuracy of 99%, while also ensuring the trustworthiness of the data.

The integration of the Internet of Things (IoT) with Mobile Edge Computing (MEC) has the potential to deliver ubiquitous connectivity and low-latency computational services. However, the growing proliferation of IoT devices and the burden of computational services requested at the network edge present significant challenges in enhancing the Quality of Service (QoS). More specifically, the service migration problem is particularly crucial for the timely data processing of latency-constrained services. Service migration involves the dynamic movement of computation services between MEC servers in order to prevent MEC servers from being overloaded. To handle the service migration problem, digital twin, as one of the promising technologies, simulates and predicts IoT edge network behaviors to find the best assignment between IoT devices and MEC servers. In this regard, this paper proposes a digital twin-assisted service migration model in IoT edge networks. Taking the computation time and energy consumption as QoS metrics, we formulate a service migration optimization problem to minimize both average latency and energy consumption on MEC servers. Then, we design a service migration algorithm to define a list of migration candidates and find the best assignment between IoT devices and MEC servers to balance the traffic load on MEC servers. Through comprehensive evaluations, we demonstrate the effectiveness of the proposed digital twin-based computation and energy-efficient model, optimize service migration decision by achieving lower computation time and energy consumption.

The emergence of a number of network communication facilities such as Network Function Virtualization (NFV), Software Defined Networking (SDN), the Internet of Things (IoT), Unmanned Aerial Vehicles (UAV), and in-network packet processing, holds a potential to meet the low latency, high precision requirements of various future multimedia applications. However, this raises the corresponding issues of how all of these elements can be used together in future networking environments, including newly developed protocols and techniques. This paper describes the architecture of an end-to-end video streaming platform for video surveillance, consisting of a UAV network domain, an edge server implementing in-network packet trimming operations with the use of Big Packet Protocol (BPP), utilization of Scalable Video Coding (SVC) and multiple video clients which connect to a network managed by an SDN controller. A Virtualized Edge Function at the drone edge utilizes SVC and in communication with the Drone Control Unit to manage the transmitted video quality. Experimental results show the potential that future multimedia applications can achieve the required high precision with the use of future network components and the consideration of their interactions.

The sudden changes in human mobility, the immense increase in demand for logistics and delivery systems, governmental restrictions, and uncertainty of the spread dynamics have introduced several transportation and location-related decision problems during the COVID-19 pandemic. Hence, a variety of Operations Research (OR) tools and techniques have been applied to tackle these problems for mitigating the adverse effects of the spread. In this study, we first investigate the emerging decision problematics observed during epidemics/pandemics under four research clusters as: (${i}$) effects of epidemics on transportation, (ii) effect of mobility on pandemic spread, (iii) logistics and delivery systems, and (iv) medical waste management and wastewater-based epidemiology. Next, we explore the OR tools implemented to solve the transportation and location-related decision problems in each cluster.

Wireless RF energy transfer for indoor sensors is an emerging paradigm ensuring continuous operation without battery limitations. However, high power radiation within ISM band interferes with packet reception for existing WiFi devices. The paper proposes the first effort in merging RF energy transfer within a standards compliant 802.11 protocol, realizing practical and WiFi-friendly Energy Delivery with Mobile Transmitters (WiFED Mobile). WiFED Mobile architecture is composed of a centralized controller coordinating the actions of multiple energy transmitters (ETs), and deployed sensors that periodically requires charging. The paper first describes 802.11 supported protocol features that can be exploited by sensors to request energy and for ETs to participate in energy transfer. Second, it devises a controller-driven bipartite matching algorithm, assigning appropriate number of ETs to sensors for efficient energy delivery. Thirdly, it detects outlier sensors (OS), which have limited power reception from static ETs and utilizes mobile ETs (METs) to satisfy their charging cycles. The proposed in-band and protocol supported coexistence in WiFED Mobile is validated via simulations and partly in a software defined radio testbed, showing that METs reduce latency by 42% and improve throughput by 83% in scenarios where using only static ETs fails to satisfy charging cycles of OS.

Wireless RF energy transfer for indoor sensors is an emerging paradigm that ensures continuous operation without battery limitations. However, high power radiation within the ISM band interferes with the packet reception for existing WiFi devices. The paper proposes the first effort in merging the RF energy transfer functions within a standards compliant 802.11 protocol to realize practical and WiFi-friendly Energy Delivery (WiFED). The WiFED architecture is composed of a centralized controller that coordinates the actions of multiple distributed energy transmitters (ETs), and a number of deployed sensors that periodically request energy from the ETs. The paper first describes the specific 802.11 supported protocol features that can be exploited by sensors to request energy and for the ETs to participate in the energy delivery process. Second, it devises a controller-driven bipartite matching-based algorithmic solution that assigns the appropriate number of ETs to energy requesting sensors for an efficient energy transfer process. The proposed in-band and protocol supported coexistence in WiFED is validated via simulations and partly in a software defined radio testbed, showing 15% improvement in network lifetime and 31% reduction in the charging delay compared to the classical nearest distance-based charging schemes that do not anticipate future energy needs of the sensors and are not designed to co-exist with WiFi systems.