Junhee Lee

Buildings constitute a significant portion of global energy demand, accounting for approximately 30% of final energy consumption and over 50% of global electricity usage. This sector is responsible for 26% of energy-related emissions, with demand projected to increase by 4% in 2024 as per International Energy Agency. A critical component of this consumption is lighting, which continues to be a major contributor to electricity use. In South Korea, a marked increase in residential electricity consumption underscores these global trends. Traditional building energy models inadequately address the complexity of human behavior, often relying on static schedules. This study leverages statistical and machine learning techniques to analyze lighting energy consumption across varying temporal scales, using real empirical data from South Korean residential buildings. The analysis reveals insights into behavior-driven energy consumption, highlighting the necessity for dynamic energy modeling that integrates human behavioral variability, their intrinsic routines and temporal patterns. The findings have profound implications for enhancing energy efficiency and optimizing conservation strategies in an era of escalating global energy demands.

In this paper, we propose a novel MAC protocol, called DG-LoRa, for improving scalability in low power wide area networks. DG-LoRa is backward compatible with legacy LoRaWAN and adds new features, such as group acknowledgment transmissions in the time-synchronized frame structure that supports determinism on channel access. In DG-LoRa, the number of responses to data frames that are transmitted from end devices is maximized by allocating the spreading factor and timeslot in the frame structure. We evaluate the performance of DG-LoRa using the Monte-Carlo simulation and then compare it with the performance of legacy LoRaWAN in terms of data drop rate and the number of retransmissions. Our numerical results show that DG-LoRa supports approximately five times more connections to the LoRa network satisfying a 5% data drop rate. Also, it is observed that DG-LoRa enables low overhead by reducing the number of data frame retransmissions.

In this article, we propose a time division multiple access scheduling algorithm for end-to-end on-time packet delivery in multi-hop wireless sensor networks. Our proposed algorithm establishes a new communication path to substitute the old path including the link failed and schedules communication links on the new path by allocating timeslots satisfying end-to-end delay bound. Max–min optimization is employed to maximize the number of dedicated timeslots to establish the substitute path in the event of the next link failure. We evaluate the performance of the proposed algorithm using QualNet network simulator and compare it with the performance of the algorithm that minimizes the end-to-end delay. Our numerical results show that the path survival ratio of the proposed algorithm is approximately two times higher when large number of communication links fails. In addition, we apply the global recovery and local recovery schemes to observe the increase in the overhead message exchanges. Compared to the global recovery scheme, local recovery scheme requires six times less control messages to establish the substitute path when a small number of link failures occur, whereas more than 90% communication paths survive.