Internet of Things devices being used now expose limitations that prevent their proper use in healthcare systems. Interoperability and security are especially impacted by such limitations. In this paper, we discuss today's issues, including benefits and difficulties, as well as approaches to circumvent the problems of employing and integrating Internet of Things devices in healthcare systems. We present this discussion in the context of the REMOA project, which targets a solution for home care/telemonitoring for patients with chronic illnesses.

Network Intrusion Detection Systems (NIDS) play an important role as tools for identifying potential network threats. In the context of ever-increasing traffic volume on computer networks, flow-based NIDS arise as good solutions for real-time traffic classification. In recent years, different flow-based classifiers have been proposed using Machine Learning (ML) algorithms. Nevertheless, classical ML-based classifiers have some limitations. For instance, they require large amounts of labeled data for training, which might be difficult to obtain. Additionally, most ML-based classifiers are not capable of domain adaptation, i.e., after being trained on an specific data distribution, they are not general enough to be applied to other related data distributions. And, finally, many of the models inferred by these algorithms are black boxes, which do not provide explainable results. To overcome these limitations, we propose a new algorithm, called Energy-based Flow Classifier (EFC). This anomaly-based classifier uses inverse statistics to infer a statistical model based on labeled benign examples. We show that EFC is capable of accurately performing binary flow classification and is more adaptable to different data distributions than classical ML-based classifiers. Given the positive results obtained on three different datasets (CIDDS-001, CICIDS17 and CICDDoS19), we consider EFC to be a promising algorithm to perform robust flow-based traffic classification.

Heterogeneous cloud radio access networks incorporate the heterogeneous network and cloud radio access network concepts for next generation cellular networks. H-CRANs exploit the heterogeneity of macro and small cells from HetNets, enabling cellular networks to achieve higher spectral efficiency. Meanwhile, concepts from C-RANs involving baseband units and remote radio heads enable H-CRANs to insert a centralized point of processing for cellular networks, reducing capital and operational expenditures. In this article, we investigate resource sharing in H-CRANs at three levels: spectrum, infrastructure, and network. For each level, we discuss the benefits and challenges, highlighting key enabling technologies that make resource sharing feasible in H-CRANs, such as software defined radio, virtualization, network function virtualization, and software defined networking. Through these technologies, H-CRANs can be virtualized in an overlay network capable of achieving enhanced infrastructure and spectrum sharing.

Abnormal behavior in mobile cellular networks can cause network faults and consequent cell outages, a major reason for operational cost increase and revenue loss for operators. Nonetheless, network faults and cell outages can be avoided by monitoring abnormal situations in the network and acting accordingly. Thus, anomaly detection is an important component of self-healing control and network management. Network operators may use the detected abnormal behavior to quantify numerically their intensity. The quantification of abnormal behavior assists the characterization of potential regions for infrastructure updates and to support the creation of public policies for local connectivity enhancements. We propose an unsupervised learning solution for anomaly detection in mobile networks using Call Detail Records (CDR) data. We evaluate our solution using a real CDR data set provided by an Italian operator and compare it against other state-of-the-art solutions, showing a performance improvement of around 35%. We also demonstrate the relevance of considering the distinct traffic patterns of diverging geographic areas for anomaly detection in mobile networks, an aspect often ignored in the literature.