Due to the growing number of devices accessing the Internet through wireless networks, the radio spectrum has become a highly contended resource. The availability of low cost radio spectrum monitoring sensors enables a geographically distributed, real-time observation of the spectrum to spot inefficiencies and to develop new strategies for its utilization. The potentially large number of sensors to be deployed and the intrinsic nature of data make this task a Big Data problem. In this work we design, implement, and validate a hardware and software architecture for wideband radio spectrum monitoring inspired to the Lambda architecture. This system offers Spectrum Sensing as a Service to let end users easily access and process radio spectrum data. To minimize the latency of services offered by the platform, we fine tune the data processing chain. From the analysis of sensor data characteristics, we design the data models for MongoDB and Cassandra, two popular NoSQL databases. A MapReduce job for spectrum visualization has been developed to show the potential of our approach and to identify the challenges in processing spectrum sensor data. We experimentally evaluate and compare the performance of the two databases in terms of application processing time for different types of queries applied on data streams with heterogeneous generation rate. Our experiments show that Cassandra outperforms MongoDB in most cases, with some exceptions depending on data stream rate.

Serverless computing, especially implemented through Function-as-a-Service (FaaS) platforms, has recently been gaining popularity as an application deployment model in which functions are automatically instantiated when called and scaled when needed. When a warm start deployment mode is used, the FaaS platform gives users the perception of constantly available resources. Conversely, when a cold start mode is used, containers running the application’s modules are automatically destroyed when the application has been executed. The latter can lead to considerable resource and cost savings. In this paper, we explore the suitability of both modes for deploying Internet of Things (IoT) applications considering a low resources testbed comparable to an edge node. We discuss the implementation and the experimental analysis of an IoT serverless platform that includes typical IoT service elements. A performance study in terms of resource consumption and latency is presented for the warm and cold start deployment mode, and implemented using OpenFaaS, a well-known open-source FaaS framework which allows to test a cold start deployment with precise inactivity time setup thanks to its flexibility. This experimental analysis allows to evaluate the aptness of the two deployment modes under different operating conditions: Exploiting OpenFaaS minimum inactivity time setup, we find that the cold start mode can be convenient in order to save edge nodes limited resources, but only if the data transmission period is significantly higher than the time needed to trigger containers shutdown.

Cyber-physical systems (CPSs) are a new class of engineered systems based on interactions between cyber and physical components, by integrating three main components: communications, control, and computing. When these systems are brought to the nanoscale, some design and implementation issues arise. A high level of complexity is due to the use of biological components in a CPS, such as engineered cells, which may play the role of sensors, actuators, or even controllers. In this paper, we study the effectiveness of control solutions implemented through the usage of molecular communications in a biological nanoscale cyber-physical system, where a biological nanomachine plays the role of actuator, that releases drug molecules, and another acts as both sensor and controller. The goal of the proposal is to control the release rate, so that target cells can receive the desired amount of drug in a given time, by limiting potential side effects. Basically, we aim to limit congestion, which can arise when large amounts of molecules are released toward a target. To this aim, we propose a simple congestion detection scheme, and compare different rate control algorithms used to throttle the molecules release rate at the transmitter upon the reception of a feedback signal sent by the receiver. We validate the proposed techniques against delivery efficiency and delivery time of molecules by means of an extensive simulation campaign.

The hyperviscosity syndrome consists of an excessive viscosity of the blood. It has to be timely detected, since it significantly increases the risk of many cardiovascular diseases. It can manifest essentially in two different ways, depending on the capability of the cardiocirculatory system to compensate the increased resistance experienced by the blood in vessels. In case of compensation, the blood flow velocity remains nearly unaltered; otherwise a significant decrease can occur. In this paper, we propose a blood viscosity monitoring system based on molecular communications. It could be used in risky patients for continuous monitoring and can detect blood hyperviscosity both in case of compensation, even partial, and in case of uncompensated cardiocirculatory systems. This proposal results to be both simple and effective for real time estimating the blood viscosity level, without any need of resorting to invasive blood analyses. The main idea is to leverage the statistics of particle dispersion and absorption in a blood vessel as a function of blood viscosity. Viscosity is estimated by processing the statistics of the received signal molecules, which are easy to collect. These features open the way to the implementation of the proposed solution in a simple and cheap device.

Convolutional neural networks (CNNs) are becoming increasingly popular in medical Image Segmentation. Among them, U-Net is a widely used model that can lead to cutting-edge results for 2D biomedical Image Segmentation. However, U-Net performance can be influenced by many factors, such as the size of the training dataset, the performance metrics used, the quality of the images and, in particular, the shape and size of the organ to be segmented. This could entail a loss of robustness of the U-Net-based models. In this paper, the performance of the considered networks is determined by using the publicly available images from the 3D-IRCADb-01 dataset. Different organs with different features are considered. Experimental results show that the U-Net-based segmentation performance decreases when organs with sparse binary masks are considered. The solution proposed in this paper, based on automated zooming of the parts of interest, allows improving the performance of the segmentation model by up to 20% in terms of Dice coefficient metric, when very sparse segmentation images are used, without affecting the cost of the learning process.