LoRaWAN has imposed itself as a promising and suitable technology for massive machine-type communications. With the acceleration of deployment, improving the energy efficiency of LoRaWAN networks has become paramount, especially with the limitations of throughput and battery resources. However, LoRaWAN suffers from the Aloha access scheme, which leads to a high probability of collision at large scales, especially in dense environments such as cities. In this paper, we propose EE-LoRa, an algorithm to improve the energy efficiency of LoRaWAN networks with multiple gateways via spreading factor selection and power control. We proceed in two steps, where we first optimize the energy efficiency of the network, defined as the ratio between the throughput and consumed energy. Solving this problem involves determining the optimal node distribution among different spreading factors. Then, in the second step, power control is applied to minimize the transmission power at nodes without jeopardizing the reliability of communications. The simulation results show that our proposed algorithm greatly improves the energy efficiency of LoRaWAN networks compared to legacy LoRaWAN and relevant state-of-the-art algorithms.

Recently, the advent of digital twins (DTs) has sparked significant interest in both business and academics offering new perspectives into our increasingly digitalized society. The aim of DT is to create a digital representation of a physical entity, be it a process, an object, or a system, by using the entity’s characteristics and behavior. This enables real-time prediction, optimization, monitoring, control, and better decision-making. The adoption of digital twins is growing in various industry verticals due to the rapid proliferation of the Internet of Things (IoT), computing power, artificial intelligence, and big data. Furthermore, the incorporation of metaverse and augmented reality technologies with digital twins bridges the gap between the physical and virtual worlds in manufacturing by providing intuitive ways to visualize and interact with DT data. Identifying the main performance metrics of a DT is crucial to develop a high-fidelity replica of the physical entity to ensure better informed decisions. Research and industry have focused on accuracy and synchronization as key performance metrics with varying definitions. To this end, the aim of this study is to survey the synchronization metrics used in the existing related literature and to establish a more representative synchronization measure for digital twins. Among all metrics, the freshness of the information delivered to the DT, coined as Age of Information (AoI), remains the most adopted measure. In this paper, we review the evaluation of AoI in different network scenarios and introduce Age of Digital Twin (AoDT) to reliably measure the synchronization gap between digital and physical twins. We proceed to formulating AoDT expressions in common network scenarios to be used for planning and developing high-fidelity digital twins.