Nanotechnology is enabling the development of a new generation of devices which are able to sense, process and communicate, while being in the scale of tens to hundreds of cubic nanometers. Such small, imperceptible devices enhance not only current applications but enable entirely new paradigms especially for in-body environments. This paper introduces a localization and tracking concept for bionanosensors floating in the human bloodstream to detect anomalies in the body. Besides the nanoscale sensors, the proposed system also comprises macroscale anchor nodes attached to the skin of the monitored person. To realize autonomous localization and resource-efficient wireless communication between sensors and anchors, we propose to exploit inertial positioning and sub-terahertz backscattering. The proposed system is a first step towards early disease detection as it aims at localizing body regions which show anomalies. Simulations are conducted to enable a systematical evaluation on the feasibility of the approach.

The human body can be considered a complex natural network due to the variety of interconnections between the different body regions. One example is the network of blood vessels, where artificial communication channels can be rendered using nanosensors that travel in the bloodstream as collectors and carriers of information. Further advancing this vision, in this work we investigate the detection and localization capabilities of flowing nanosensors in the blood flow to report abnormalities in the human body. Specifically, we target the detection of quorum sensing molecules and provide a methodology to evaluate its performance. The methodology consists of modeling the traveling path of nanosensors along the vessels through a Markov chain, and the use of machine learning (ML) models to compute their transition probabilities. We illustrate the resulting distribution of nanosensors in the body, which evidences a close match to expected results. We also evaluate their detection and localization capabilities in different body regions revealing their effectiveness to determine the presence of abnormalities in the human vessels

Nanotechnology enables the development of a new generation of devices at the scale of a few cubic micrometers that can sense, process, and communicate. Such small, imperceptible devices will revolutionize healthcare applications and enable new possibilities for in-body environments. This paper studies the intra-body communication channel between nanosensors flowing in the bloodstream and gateways attached to the skin using the terahertz (THz) spectrum. The channel model considers three layers through which the waveform travels: skin, tissue, and blood. To optimize the communication performance, this work investigates the impact of noise and mobility, and subsequently derives the trade-off between them. We illustrate the achievable bit error rate (BER) for THz intra-body channels considering communication through human tissue layers, including noise and random mobility of nanosensors in the blood system.

The interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially in the last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modeling the problem. However, it is often applied to abstractions of the original problem, thus leaving to next development phases the integration of RL solutions to the actual systems. This choice may not guarantee the overall optimal performance of the implemented system. In this survey, we analyze the literature on Multi-UAV applications that utilize reinforcement learning, with particular attention to works that consider realistic communication channels. We focus on identifying the key variables that influence communication and whether these variables are integrated within the RL framework or considered externally. Additionally, we identify key trends, challenges, and future directions in the field, providing a comprehensive overview for researchers and practitioners interested in the practical deployment of RL-based Multi-UAV systems.

Today, visual sensor networks (VSNs) are pervasively used in smart environments such as intelligent homes, industrial automation or surveillance. A major concern in the use of sensor networks in general is their reliability in the presence of security threats and cyberattacks. Compared to traditional networks, sensor networks typically face numerous additional vulnerabilities due to the dynamic and distributed network topology, the resource constrained nodes, the potentially large network scale and the lack of global network knowledge. These vulnerabilities allow attackers to launch more severe and complicated attacks. Since the state-of-the-art is lacking studies on vulnerabilities in VSNs, a thorough investigation of attacks that can be launched against VSNs is required. This paper presents a general threat model for the attack surfaces of visual sensor network applications and their components. The outlined threats are classified by the STRIDE taxonomy and their weaknesses are classified using CWE, a common taxonomy for security weaknesses.

Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.