Real-time monitoring of medical test parameters as well as biological and chemical substances inside the human body is an aspiration which might facilitate the control of pathologies and would ensure better effectiveness in diagnostics and treatments. Future Body Area NanoNetworks (BANN) represent an ongoing effort to complement these initiatives, although due to its early stage of development, further research is required. This paper contributes with a hierarchical BANN architecture consisting of two types of nanodevices, namely, nanonodes and a nanorouter, which are conceptually designed using technologically available electronic components. A straightforward communication scheme operating at the THz band for the exchange of information among nanodevices is also proposed. Communications are conducted in a human hand scenario since, unlike other parts of the human body, the negative impact of path loss and molecular absorption noise on the propagation of electromagnetic waves in biological tissues is mitigated. However, data transmission is restricted by the tiny size of nanodevices and their extremely limited energy storing capability. To overcome this concern, nanodevices must be powered through the bloodstream and external ultrasound energy harvesting sources. Under these conditions, the necessary energy and its management have been thoroughly examined and assessed. The results obtained reveal the outstanding ability of nanonodes to recharge, thus enabling each pair of nanonode–nanorouter to communicate every 52 min. This apparently long period is compensated by the considerably high number of nanonodes in the network, which satisfies a quasi-constant monitoring of medical parameter readings.

Electromagnetic nanocommunications, understood as the communication between electronic nanoscale devices through electromagnetic waves in the terahertz band, has attracted increasing attention in recent years. In this regard, several solutions have already been proposed. However, many of them do not sufficiently capture the significance of the limitations in nanodevice energy-gathering and storing capacity. In this paper, we address key factors affecting the energy consumption of nanodevices, highlighting the effect of the communication scheme employed. Then, we also examine how nanodevices are powered, focusing on the main parameters governing the powering nanosystem. Different mathematical expressions are derived to analyze the impact of these parameters on its performance. Based on these expressions, the functionality of a nanogenerator is evaluated to gain insight into the conditions under which a wireless nanosensor network (WNSN) is viable from the energetic point of view. The results reveal that a micrometer-sized piezoelectric system in high-lossy environments (exceeding 100 dB/mm) becomes inoperative for transmission distances over 1.5 mm by its inability to harvest and store the amount of energy required to overcome the path loss.

Nanotechnology is an emerging scientific area whose advances, among many others, have a positive direct impact on the miniaturization of electronics. This unique technology enables the possibility to design and build electronic components as well as complete devices (called nanomachines or nanodevices) at the nano scale. A nanodevice is expected to be an essential element able to operate in a nanonetwork, where a huge number of them would coordinate to acquire data, process the information gathered, and wirelessly transmit those data to end-points providing innovative services in many key scenarios, such as the human body or the environment. This paper is aimed at studying the feasibility of this type of device by carefully examining their main component parts, namely the nanoprocessor, nanomemory, nanoantenna, and nanogenerator. To this end, a thorough state-of-the-art review is conveyed to discuss, substantiate, and select the most suitable current technology (commercial or pre-commercial) for each component. Then, we further contribute by developing a complete conceptual nanodevice layout taking into consideration its ultra-small size (similar to a blood cell) and its very restricted capabilities (e.g., processing, memory storage, telecommunication, and energy management). The required resources as well as the power consumption are realistically estimated.

Terahertz-based nano-networks are emerging as a groundbreaking technology able to play a decisive role in future medical applications owing to their ability to precisely quantify figures, such as the viral load in a patient or to predict sepsis shock or heart attacks before they occur. Due to the extremely limited size of the devices composing these nano-networks, the use of the Terahertz (THz) band has emerged as the enabling technology for their communication. However, the characteristics of the THz band, which strictly reduce the communication range inside the human body, together with the energy limitations of nano-nodes make the in-body deployment of nano-nodes a challenging task. To overcome these problems, we propose a novel in-body flow-guided nano-network architecture consisting of three different devices: i) nano-node, ii) nano-router, and iii) bio-sensor. As the performance of this type of nano-network has not been previously explored, a theoretical framework capturing all its particularities is derived to properly model its behavior and evaluate its feasibility in real medical applications. Employing this analytical model, a thorough sensitivity study of its key parameters is accomplished. Finally, we analyze the terahertz flow-guided nano-network design to satisfy the requirements of several medical applications of interest.

By empowering the Internet of Things (IoT) with nanoscale communications, thousands of small devices, called nano-nodes, are enabled to monitor complex environments in a nonintrusive way, giving rise to the Internet of Nano-Things (IoNT) paradigm. It is in one of these very complex and critical environments, the human cardiovascular system, where IoNT might stand out. However, the supervision of human health poses significant challenges: first, to reduce costs and allow in vivo monitoring nano-sensor networks, the number of deployed nano-routers must be limited, which could cause nano-nodes to undergo long out-of-coverage periods. Lastly, nano-nodes rely on harvested energy from the medium, which severely reduces the number of potential transmissions. To overcome these two problems, smart policies that direct devices on how to proceed with sensed events are crucial. In this line, we propose a generic Markov decision process (MDP) model which can be exploited to derive optimal transmission policies that are easily employed by nano-nodes. These policies maximize nano-node throughput and cope with the energy and coverage problems. We have also run a set of simulations to validate our proposal and to compare it to other alternative policies. Results reveal that: 1) our policy systematically outperforms the rest of policies by a large margin and 2) precision in placing nano-routers and average distance between them strongly affect the nano-network performance. The Python code for the MDP model and simulations has been programmed to be easily adaptable to researchers' needs, making it easier for future IoNT works to add intelligence to the network.

The IoT is the cornerstone of many innovating processes such as those behind Smart Cities and Smart Industries. As more and more wireless IoT devices are deployed, a newer, more congestion-resilient communication infrastructure is required to absorb the traffic from the 50 billion IoT nodes expected by the year 2020. Although 5G is said to be a key technology for the future IoT, it is not a silver bullet. Therefore, providing nodes with different Radio Access Technologies (RAT) would allow them to reap the various benefits offered by each RAT. However, the process of determining which technology should be used at any given time should not be based on uninformed intuition, but on mathematically educated choices. By making use of the mathematical framework of Reinforcement Learning, we have allowed IoT nodes to learn from previous real world data in order to derive optimal RAT-selection policies. These policies, which are implemented as Artificial Neural Networks (ANN), maximize a predefined reward closely related to classic throughput, while maintaining power consumption and operational costs below a certain limit. To allow hardware-constrained IoT nodes to use these ANNs, we have proposed the application of a quantization technique that reduces computation and memory requirements and have validated it by its implementation in a real IoT device. Finally, to evaluate the proposal, we have simulated a network of 1000 devices deployed in the city of Chicago. The obtained results are compared to those achieved with other intuitive policies to further highlight the benefits of our proposal.