There has been a large amount of work on smart classrooms spanning over a wide range of research areas including information communication technology, machine learning, sensor networks, mobile computing, and hardware. Consequently, there have been several disparate reviews on various aspects of smart classrooms. Such piecemeal development is not sufficient for a pragmatic smart classroom solution. This article complements the literature by providing a consolidated review of interdisciplinary works under a common nomenclature and taxonomy. This multi-field review has exposed new research opportunities and challenges that need to be addressed for the synergistic integration of interdisciplinary works.

Current sensor technologies facilitate device-free and non-invasive monitoring of target activities and infrastructures to ensure a safe and inhabitable environment. Device-free techniques for sensing the surrounding environment are an emerging area of research where a target does not need to carry or attach any device to provide information about its motion or the surrounding environment. Consequently, there has been an increasing interest in device-free sensing. Seismic sensors are extremely effective tools for gathering target motion information. In this paper, we provide a comprehensive overview of the seismic sensor-based device-free sensing process and highlight the key techniques within the research field. We classify the existing literature into three categories, viz., (i) target detection, (ii) target localization, and (iii) target identification, and activity recognition. The techniques in each category are divided into multiple subcategories in a structured manner to comprehensively discuss the details. We also discuss the challenges associated with contemporary cutting-edge research and suggest potential solutions.

Privacy is a big concern in current video surveillance systems. Due to privacy issues, many strategic places remain unmonitored leading to security threats. The main problem with existing privacy protection methods is that they assume availability of accurate region of interest (RoI) detectors that can detect and hide the privacy sensitive regions such as faces. However, the current detectors are not fully reliable, leading to breaches in privacy protection. In this paper, we propose a privacy protection method that adopts adaptive data transformation involving the use of selective obfuscation and global operations to provide robust privacy even with unreliable detectors. Further, there are many implicit privacy leakage channels that have not been considered by researchers for privacy protection. We block both implicit and explicit channels of privacy leakage. Experimental results show that the proposed method incurs 38% less distortion of the information needed for surveillance in comparison to earlier methods of global transformation; while still providing near-zero privacy loss.

When detecting anomalies in audio, it can often be necessary to consider concept drift: the distribution of the data may drift over time because of dynamically changing environments, and anomalies may become normal as time elapses. We propose to use dynamic Huffman coding for anomaly detection in audio with concept drift. Compared with the existing method of adaptive Gaussian mixture modeling (AGMM), dynamic Huffman coding does not require a priori information about the clusters and can adjust the number of clusters dynamically depending on the amount of variation in the audio. To control the size of the Huffman tree, we propose to merge clusters that are close to each other instead of replacing rare clusters with new data. This reduces redundancy in the Huffman tree while ensuring that it never forgets past information. On audio datasets with concept drift which we have curated ourselves, our proposed method achieves a higher area under the curve (AUC) compared with AGMM and fixed-length Huffman trees.