Khan Irfanullah, Zedadra Ouarda, Guerrieri Antonio, Spezzano Giandomenico
ICAR-CNR, Institute for High Performance Computing and Networking, National Research Council of Italy, Via P. Bucci 8/9C, 87036 Rende, Italy.
DIMES Department, University of Calabria, Via P. Bucci, 87036 Rende, Italy.
Sensors (Basel). 2024 May 21;24(11):3276. doi: 10.3390/s24113276.
In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.
在当今世界，全球大量能源用于建筑物。不幸的是，其中许多能源被浪费了，因为电器没有得到正确或高效的使用。减少这种浪费的一种方法是检测、了解和预测建筑物内人员的存在情况。要做到这一点，建筑物需要变得“智能”和“有认知能力”，并使用现代技术来感知人员何时以及如何占用建筑物。通过利用这些信息，建筑物可以根据最近开发的方法做出明智的决策。在本文中，我们全面概述了物联网（IoT）技术的最新进展，这些技术已被设计并用于监测建筑物内的室内环境状况。使用这些技术对于收集有关室内环境的数据以及确定居住者的数量和存在情况至关重要。此外，本文批判性地审视了每种技术在预测居住者行为方面的优势和局限性。此外，它还探讨了处理这些数据和进行未来居住预测的不同方法。此外，我们强调了一些挑战，例如确定传感器和雷达的最佳数量和位置，并对这些挑战进行了详细解释和深入分析。此外，本文还探讨了可能的未来发展方向，包括居住者数据的安全性以及推广节能做法，如确定居住者在建筑物内的位置并监测他们的活动。与其他关于类似主题的调查工作相比，我们的工作旨在既涵盖最新的传感方法，又回顾文献中用于估计居住情况的方法。
