Thermal comfort in indoor environments has an enormous impact on the health, well-being, and performance of occupants. Given the focus on energy efficiency and Internet of Things enabled smart buildings, machine learning (ML) is being increasingly used for data-driven thermal comfort (TC) prediction. Generally, ML-based solutions are proposed for air-conditioned or HVAC ventilated buildings and the models are primarily designed for adults. On the other hand, naturally ventilated (NV) buildings are the norm in most countries. They are also ideal for energy conservation and long-term sustainability goals. However, the indoor environment of NV buildings lacks thermal regulation and varies significantly across spatial contexts. These factors make TC prediction extremely challenging. Thus, determining the impact of building environment on the performance of TC models is important. Further, the generalization capability of TC prediction models across different NV indoor spaces needs to be studied. This work addresses these problems. Data is gathered through month-long field experiments conducted in 5 naturally ventilated school buildings, involving 512 primary school students. The impact of spatial variability on student comfort is demonstrated through variation in prediction accuracy (by as much as 71%). The influence of building environment on TC prediction is also demonstrated through variation in feature importance. Further, a comparative analysis of spatial variability in model performance is done for children (our dataset) and adults (ASHRAE-II database). Finally, the generalization capability of thermal comfort models in NV classrooms is assessed and major challenges are highlighted.

Indoor thermal comfort in smart buildings has a significant impact on the health and performance of occupants. Consequently, machine learning (ML) is increasingly used to solve challenges related to indoor thermal comfort. Temporal variability of thermal comfort perception is an important problem that regulates occupant well-being and energy consumption. However, in most ML-based thermal comfort studies, temporal aspects such as the time of day, circadian rhythm, and outdoor temperature are not considered. This work addresses these problems. It investigates the impact of circadian rhythm and outdoor temperature on the prediction accuracy and classification performance of ML models. The data is gathered through month-long field experiments carried out in 14 classrooms of 5 schools, involving 512 primary school students. Four thermal comfort metrics are considered as the outputs of Deep Neural Networks and Support Vector Machine models for the dataset. The effect of temporal variability on school children’s comfort is shown through a “time of day” analysis. Temporal variability in prediction accuracy is demonstrated (up to 80%). Furthermore, we show that outdoor temperature (varying over time) positively impacts the prediction performance of thermal comfort models by up to 30%. The importance of spatio-temporal context is demonstrated by contrasting micro-level (location specific) and macro-level (6 locations across a city) performance. The most important finding of this work is that a definitive improvement in prediction accuracy is shown with an increase in the time of day and sky illuminance, for multiple thermal comfort metrics.

The global spread of coronavirus has sparked a considerable interest in technologies that facilitate seamless communication between users which are physically or spatially distant. Using current remote collaboration systems that utilize 3D sensing with LiDAR and depth cameras, point cloud streaming, and MR/VR devices, distant users can communicate with each other as if they did in person. However, these systems may violate users' privacy since they can share information of their entire personal space with other users. In addition, although various point cloud compression methods have been proposed, remote transmission of 3D scenes still requires significant bandwidth. This paper proposes a 3D spatial data sharing system based on the paradigm of “semantic communication”, i.e., controlling communication in the units of semantic objects. Our system understands the semantics of the scene and leverages point cloud streaming, thereby enabling users to assert fine-grained control over their privacy. Further, the system adaptively controls the size of the data frame based on network capacity and scene context. The experimental results show that the network delay can be reduced by 96%. We have also tested our system in a commercial 4G network, showing that 3-D spatial sharing with point clouds over severe networks is possible.

To meet the rising data-offloading demands, IEEE 802.11-based WiFi networks have undergone consistent densification. The unlicensed spectrum has also been harnessed through LTE-WiFi coexistence. However, in dense and ultradense networks (DNs/UDNs), the network capacity is even more adversely impacted by the endemic interference. Yet, the precise nature of Capacity Interference Relationship (CIR) in DNs/UDNs and LTE-WiFi coexistence remains to be studied. Densification also exacerbates the challenges to network optimization. The conventional approaches to simplify the complex SINR-Capacity constraints lead to high convergence times in DN/UDN optimization. We investigate the CIR in dense and ultra-dense WiFi (IEEE 802. 11a) and LTE-WiFi (LTULAA) networks through real-time experiments. We then subject the empirical data to linear and polynomial regression to determine the nature of CIR and demonstrate that strong linear correlations may exist. We also study the impact of predictor variables, topology, and radio access technology on CIR. Most importantly, we propose CIRNO, a CIR-inspired network optimization approach, wherein the empirically determined CIR equation replaces the theoretically assumed SINR-Capacity constraints in optimization formulations. We evaluate CIRNO by implementing three recent works on optimization. We demonstrate the relevance of CIR and CIRNO in DNs/UDNs through a significant reduction in convergence times (by over 50%) while maintaining high accuracy (over 95%). To the best of our knowledge, this is the first work to statistically analyze CIR in DNs/UDNs and LTE-WiFi heterogeneous networks (HetNets) and to use CIR regression equations in network optimization.

A multitude of Channel Assignment (CA) schemes have created a paradox of plenty, making CA selection for Wireless Mesh Networks (WMNs) an onerous task. CA performance prediction (CAPP) metrics are novel tools that address the problem of appropriate CA selection. However, most CAPP metrics depend upon a variety of factors such as the WMN topology, the type of CA scheme, and connectedness of the underlying graph. In this work, we propose an improved Channel Assignment Link-Weight Metric (iCALM) that is independent of these constraints. To the best of our knowledge, iCALM is the first universal CAPP metric for WMNs. To evaluate iCALM, we design two WMN topologies that conform to the attributes of real-world mesh network deployments, and run rigorous simulations in ns-3. We compare iCALM to four existing CAPP metrics, and demonstrate that it performs exceedingly well, regardless of the CA type, and the WMN layout.