Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data”, understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context”, or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context aware computing”, and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this “big data”. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context aware computing studies. Finally, we review learning and “big data” studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.

Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This work seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a 3-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pre-training to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks (CNN) and autoencoders (AE), as well as conventional classifiers employing pre-defined features, such as support vector machines (SVM), random forest (RF) and extreme gradient boosting (Xgboost). Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar - 17.3% improvement over SVM.

The success of convolutional neural networks in the field of computer vision has attracted the attention of many researchers from other fields. One of the research areas in which neural networks is actively used is financial forecasting. In this paper, we propose a novel method for predicting stock price movements using CNN. To avoid the high volatility of the market and to maximize the profit, ETFs are used as primary financial assets. We extract commonly used trend indicators and momentum indicators from financial time series data and use these as our features. Adopting a sliding window approach, we generate our images by taking snapshots that are bounded by the window over a daily period. We then perform daily predictions, namely, regression for predicting the ETF prices and classification for predicting the movement of the prices on the next day, which can be modified to estimate weekly or monthly trends. To increase the number of images, we use numerous ETFs. Finally, we evaluate our method by performing paper trading and calculating the final capital. We also compare our method's performance to commonly used classical trading strategies. Our results indicate that we can predict the next day's prices with 72% accuracy and end up with 5:1 of our initial capital, taking realistic values of transaction costs into account.