The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.

Human-centered data collection is typically costly and implicates issues of privacy. Various solutions have been proposed in the literature to reduce this cost, such as crowd-sourced data collection, or the use of semi-supervised algorithms. However, semi-supervised algorithms require a source of unlabeled data, and crowd-sourcing methods require numbers of active participants. An alternative passive data collection modality is fingerprint-based localization. Such methods use received signal strength (RSS) or channel state information (CSI) in wireless sensor networks to localize users in indoor/outdoor environments. In this paper, we introduce a novel approach to reduce training data collection costs in fingerprint-based localization by using synthetic data. Generative adversarial networks (GANs) are used to learn the distribution of a limited sample of collected data and, following this, to produce synthetic data that can be used to augment the real collected data in order to increase overall positioning accuracy. Experimental results on a benchmark dataset show that by applying the proposed method and using a combination of 10% collected data and 90% synthetic data, we can obtain essentially similar positioning accuracy to that which would be obtained by using the full set of collected data. This means that by employing GAN-generated synthetic data, we can use 90% less real data, thereby reduce data-collection costs while achieving acceptable accuracy.

Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs. These applications require intelligent decision-making that relies on human sensing. Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy. Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns. Sensor-based techniques refer to a prospective method that employs various combinations of sensors. These solutions are static and necessitate costly equipment installation and maintenance. Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions. Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering). Due to its low cost and non-intrusive nature, wireless-based human activity detection has received substantial attention and become a key topic of study. This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems. We classify the reviewed research studies based on the technical measures and applications they employ. In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.

Missing data imputation is a technique to deal with incomplete datasets. Since many models and algorithms cannot be applied to data containing missing values, a pre-processing step needs to be performed to remove incomplete data or to estimate the missing values. This is a well-known problem referred to as the data imputation problem . Several approaches have been designed for data imputation. These algorithms can be divided into two main categories: statistical and machine learning-based algorithms. As machine learning algorithms are optimized, they usually have better performance compared with statistical ones. In this paper, we review the most recent literature related to missing data imputation based on generative adversarial networks (GANs) that have gained tremendous attention in dealing with missing values. We examine the structures of GANs for missing data imputation and discuss the commonly used datasets and metrics for evaluation. We also cover the influence of the missing datatype, the effect of the missing data fraction, and the algorithm-related problems on data imputation performance. We conduct experiments on two publicly available datasets and evaluate the performance of GAIN , a missing data imputation algorithm to that of existing state-of-the-art approaches, demonstrating that the GAN-based algorithm outperforms the others in terms of RMSE and FID.

The vehicle routing problem (VRP) and its variants have been intensively studied by the operational research community. The existing surveys and the majority of the published articles tackle traditional solutions, including exact methods, heuristics, and meta-heuristics. Recently, machine learning (ML)-based methods have been applied to a variety of combinatorial optimization problems, specifically VRPs. The strong trend of using ML in VRPs and the gap in the literature motivated us to review the state-of-the-art. To provide a clear understanding of the ML-VRP landscape, we categorize the related studies based on their applications/constraints and technical details. We mainly focus on reinforcement learning (RL)-based approaches because of their importance in the literature, while we also address non RL-based methods. We cover both theoretical and practical aspects by clearly addressing the existing trends, research gap, and limitations and advantages of ML-based methods. We also discuss some of the potential future research directions.