The world is entering a new era, where Internet-of-Things (IoT), smart homes, and smart cities will play an important role in meeting the so-called big challenges. In the near future, it is foreseen that the majority of the world’s population will live their lives in smart homes and in smart cities. To deal with these challenges, to support a sustainable urban development, and to improve the quality of life for citizens, a multi-disciplinary approach is needed. It seems evident, however, that a new, advanced Information and Communications Technology ICT infrastructure is a key feature to realize the “smart” vision. This paper proposes a specific solution in the form of a hierarchical layered ICT based infrastructure that handles ICT issues related to the “big challenges” and seamlessly integrates IoT, smart homes, and smart city structures into one coherent unit. To exemplify benefits of this infrastructure, a complex IoT system has been deployed, simulated and elaborated. This simulation deals with wastewater energy harvesting from smart buildings located in a smart city context. From the simulations, it has been found that the proposed infrastructure is able to harvest between 50% and 75% of the wastewater energy in a smart residential building. By letting the smart city infrastructure coordinate and control the harvest time and duration, it is possible to achieve considerable energy savings in the smart homes, and it is possible to reduce the peak-load for district heating plants.

It is foreseen that billions of IoT devices will be connected to the internet in the near future. Most of these devices will communicate wirelessly in a limited spectrum which means that a substantial amount of interferences will be generated. To overcome these interferences a significant increase in (battery) power consumption is needed to re-transmit packages and to provide a transmit power margin. Hence, this challenge calls for agile methods that can overcome the interferences without wasting power. In this paper the problem is addressed by using a self-adapting machine learning system which uses information from the channel state to predict the transmit power level that is needed to overcome the interferences. This approach predicts the correct transmit power level when a package needs to be sent and thereby avoids wasting power on selecting a wrong transmit power level, i.e. either excess power is wasted or extra power is wasted to retransmit the packet. Extensive simulations based on data from smart homes show that this approach achieves power savings in the range of 42 to 82 percent and a packet receive ratio of at least 92 percent.

Pathological electrocardiogram is often used to diagnose abnormal cardiac disorders where accurate classification of the cardiac beat types is crucial for timely diagnosis of dangerous conditions. However, accurate, timely, and precise detection of arrhythmia-types like premature ventricular contraction is very challenging as these signals are multiform, i.e. a reliable detection of these requires expert annotations. In this paper, a multivariate statistical classifier that is able to detect premature ventricular contraction beats is presented. This novel classifier can be trained with a very sparse amount of expert annotated data. To enable this, the dimensionality of the feature vector is kept very low, it uses strong designed features and a regularization mechanism. This approach is compared to other classifiers by using the MIT-BIH arrhythmia database. It has been found that the average accuracy, specificity, and sensitivity are above 96%, which is superior given the sparse amount of training data.