Advances in new technologies are changing the Internet of Things (IoTs) from vehicular ad hoc networks (VANETs) to Internet of Vehicles (IoVs). This IoV needs to construct a network topology in order to efficiently send and receive data to Cloud or Fog nodes. In such a topology construction, there are various methods of configuring a topology considering network interference and configuring a topology for energy saving, such as a mobile sensor network. However, there has been no research on the topology construction considering the rapidly high movement of mobile node, characteristic of IoV. In this article, we proposed the k-Lifetime Tree Topology Construction (k-LTTC) algorithm during emergency situations for IoV with highly dynamic characteristics. By applying the time domain to the topology construction, it is guaranteed to maintain the topology for a specific period of time (k-Life Time). The first algorithm is a centralized algorithm to construct k-lifetime tree topology, and the second algorithm is a decentralized algorithm to construct k-lifetime tree topology. We describe the time complexity and message complexity for the mathematical analysis of our proposed algorithms. Furthermore, we provided the simulation results to verify our proposed method.

The rapid adoption of small drones for applications such as surveillance, disaster response, and infrastructure inspection has increased demand for coordinated multi-drone operations, in which information exchange is essential. However, effective collaboration across different drones remains challenging due to differences in information exchange and communication protocols. This paper proposes a Networked Drone Information Exchange Model (NDIEM), an XML-based model that enables interoperability of the structural information. NDIEM defines five essential element categories: Identification, Telemetry, Command and Control, Sensor, and Mission. NDIEM can be used with protocol-specific adapters for exchanging information in multi-drone operations. The proposed model has been implemented and evaluated on three different drones (an ArduPilot-based Hexacopter, a Crazyflie 2.1, and a Tello EDU) using real sensor telemetry captured under controlled conditions, in which each platform’s onboard sensors were manipulated to generate representative telemetry variation. Experimental results demonstrate information interoperability with 0.013–0.019 ms processing overhead, 0.049–0.073 ms transformation latency per message, and XSD schema validation compliance, achieving 69.7% transformation completeness for telemetry data. These findings show that NDIEM can provide a practical and scalable foundation for software development for drone collaboration and interoperability.

The rapid spread of COVID-19 globally has led to a surge in patients, among whom the incidence of blood clotting has been observed. This phenomenon poses a significant risk, potentially leading to cerebral hemorrhage and severe complications. Early detection of such blood clotting events is crucial for timely intervention and management of secondary complications. Computational Fluid Dynamics (CFD) simulations offer a means to detect these clotting phenomena, albeit with a drawback of time-intensive confirmation, spanning from several days to months. To address this challenge, we propose a framework for early detection of blood clotting events utilizing a deep neural network model. Leveraging patient-specific data derived from CFD simulations, our model learns to predict clotting events, expediting the diagnostic process. Hyperparameter tuning was employed to optimize the deep neural network algorithm for enhanced accuracy and performance. Furthermore, our framework facilitated comparative simulations across various machine learning algorithms, aiding in the identification of optimal data sizes for achieving high predictive accuracy. By combining computational modeling with machine learning techniques, our approach offers a promising avenue for expedited detection and management of thrombogenesis phenomena in COVID-19 patients.