A nanoscale communication network scheme and energy model for a human hand scenario

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.This work has been supported by the project AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE). Sebastian Canovas-Carrasco also thanks the Spanish MECD for an FPU (ref. FPU16/03530) pre-doctoral fellowship

Intra-Body Communications for Nervous System Applications: Current Technologies and Future Directions

The Internet of Medical Things (IoMT) paradigm will enable next generation healthcare by enhancing human abilities, supporting continuous body monitoring and restoring lost physiological functions due to serious impairments. This paper presents intra-body communication solutions that interconnect implantable devices for application to the nervous system, challenging the specific features of the complex intra-body scenario. The presented approaches include both speculative and implementative methods, ranging from neural signal transmission to testbeds, to be applied to specific neural diseases therapies. Also future directions in this research area are considered to overcome the existing technical challenges mainly associated with miniaturization, power supply, and multi-scale communications.Comment: https://www.sciencedirect.com/science/article/pii/S138912862300163

Energy Management in RFID-Sensor Networks: Taxonomy and Challenges

Ubiquitous Computing is foreseen to play an important role for data production and network connectivity in the coming decades. The Internet of Things (IoT) research which has the capability to encapsulate identification potential and sensing capabilities, strives towards the objective of developing seamless, interoperable and securely integrated systems which can be achieved by connecting the Internet with computing devices. This gives way for the evolution of wireless energy harvesting and power transmission using computing devices. Radio Frequency (RF) based Energy Management (EM) has become the backbone for providing energy to wireless integrated systems. The two main techniques for EM in RFID Sensor Networks (RSN) are Energy Harvesting (EH) and Energy Transfer (ET). These techniques enable the dynamic energy level maintenance and optimisation as well as ensuring reliable communication which adheres to the goal of increased network performance and lifetime. In this paper, we present an overview of RSN, its types of integration and relative applications. We then provide the state-of-the-art EM techniques and strategies for RSN from August 2009 till date, thereby reviewing the existing EH and ET mechanisms designed for RSN. The taxonomy on various challenges for EM in RSN has also been articulated for open research directives

Performance Analysis and Learning Algorithms in Advanced Wireless Networks

Over the past decade, wireless data traffic has experienced an exponential growth, especially with multimedia traffic becoming the dominant traffic, and such growth is expected to continue in the near future. This unprecedented growth has led to an increasing demand for high-rate wireless communications.Key solutions for addressing such demand include extreme network densification with more small-cells, the utilization of high frequency bands, such as the millimeter wave (mmWave) bands and terahertz (THz) bands, where more bandwidth is available, and unmanned aerial vehicle (UAV)-enabled cellular networks. With this motivation, different types of advanced wireless networks are considered in this thesis. In particular, mmWave cellular networks, networks with hybrid THz, mmWave and microwave transmissions, and UAV-enabled networks are studied, and performance metrics such as the signal-to-interference-plus-noise ratio (SINR) coverage, energy coverage, and area spectral efficiency are analyzed. In addition, UAV path planning in cellular networks are investigated, and deep reinforcement learning (DRL) based algorithms are proposed to find collision-free UAV trajectory to accomplish different missions. In the first part of this thesis, mmWave cellular networks are considered. First, K-tier heterogeneous mmWave cellular networks with user-centric small-cell deployments are studied. Particularly, a heterogeneous network model with user equipments (UEs) being distributed according to Poisson cluster processes (PCPs) is considered. Distinguishing features of mmWave communications including directional beamforming and a detailed path loss model are taken into account. General expressions for the association probabilities of different tier base stations (BSs) are determined. Using tools from stochastic geometry, the Laplace transform of the interference is characterized and general expressions for the SINR coverage probability and area spectral efficiency are derived. Second, a distributed multi-agent learning-based algorithm for beamforming in mmWave multiple input multiple output (MIMO) networks is proposed to maximize the sum-rate of all UEs. Following the analysis of mmWave cellular networks, a three-tier heterogeneous network is considered, where access points (APs), small-cell BSs (SBSs) and macrocell BSs (MBSs) transmit in THz, mmWave, microwave frequency bands, respectively. By using tools from stochastic geometry, the complementary cumulative distribution function (CCDF) of the received signal power, the Laplace transform of the aggregate interference, and the SINR coverage probability are determined. Next, system-level performance of UAV-enabled cellular networks is studied. More specifically, in the first part, UAV-assisted mmWave cellular networks are addressed, in which the UE locations are modeled using PCPs. In the downlink phase, simultaneous wireless information and power transfer (SWIPT) technique is considered. The association probability, energy coverages and a successful transmission probability to jointly determine the energy and SINR coverages are derived. In the uplink phase, a scenario that each UAV receives information from its own cluster member UEs is taken into account. The Laplace transform of the interference components and the uplink SINR coverage are characterized. In the second part, cellular-connected UAV networks is investigated, in which the UAVs are aerial UEs served by the ground base stations (GBSs). 3D antenna radiation combing the vertical and horizontal patterns is taken into account. In the final part of this thesis, deep reinforcement learning based algorithms are proposed for UAV path planning in cellular networks. Particularly, in the first part, multi-UAV non-cooperative scenarios is considered, where multiple UAVs need to fly from initial locations to destinations, while satisfying collision avoidance, wireless connectivity and kinematic constraints. The goal is to find trajectories for the cellular-connected UAVs to minimize their mission completion time. The multi-UAV trajectory optimization problem is formulated as a sequential decision making problem, and a decentralized DRL approach is proposed to solve the problem. Moreover, multiple UAV trajectory design in cellular networks with a dynamic jammer is studied, and a learning-based algorithm is proposed. Subsequently, a UAV trajectory optimization problem is considered to maximize the collected data from multiple Internet of things (IoT) nodes under realistic constraints. The problem is translated into a Markov decision process (MDP) and dueling double deep Q-network (D3QN) is proposed to learn the decision making policy

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

From RF-Microsystem Technology to RF-Nanotechnology

The RF microsystem technology is believed to introduce a paradigm switch in the wireless revolution. Although only few companies are to date doing successful business with RF-MEMS, and on a case-by-case basis, important issues need yet to be addressed in order to maximize yield and performance stability and hence, outperform alternative competitive technologies (e.g. ferroelectric, SoS, SOI,…). Namely the behavior instability associated to: 1) internal stresses of the free standing thin layers (metal and/or dielectric) and 2) the mechanical contact degradation, be it ohmic or capacitive, which may occur due to low forces, on small areas, and while handling severe current densities.The investigation and understanding of these complex scenario, has been the core of theoretical and experimental investigations carried out in the framework of the research activity that will be presented here. The reported results encompass activities which go from coupled physics (multiphysics) modeling, to the development of experimental platforms intended to tackles the underlying physics of failure. Several original findings on RF-MEMS reliability in particular with respect to the major failure mechanisms such as dielectric charging, metal contact degradation and thermal induced phenomena have been obtained. The original use of advanced experimental setup (surface scanning microscopy, light interferometer profilometry) has allowed the definition of innovative methodology capable to isolate and separately tackle the different degradation phenomena under arbitrary working conditions. This has finally permitted on the one hand to shed some light on possible optimization (e.g. packaging) conditions, and on the other to explore the limits of microsystem technology down to the nanoscale. At nanoscale indeed many phenomena take place and can be exploited to either enhance conventional functionalities and performances (e.g. miniaturization, speed or frequency) or introduce new ones (e.g. ballistic transport). At nanoscale, moreover, many phenomena exhibit their most interesting properties in the RF spectrum (e.g. micromechanical resonances). Owing to the fact that today’s minimum manufacturable features have sizes comparable with the fundamental technological limits (e.g. surface roughness, metal grain size, …), the next generation of smart systems requires a switching paradigm on how new miniaturized components are conceived and fabricated. In fact endowed by superior electrical and mechanical performances, novel nanostructured materials (e.g. carbon based, as carbon nanotube (CNT) and graphene) may provide an answer to this endeavor. Extensively studied in the DC and in the optical range, the studies engaged in LAAS have been among the first to target microwave and millimiterwave transport properties in carbon-based material paving the way toward RF nanodevices. Preliminary modeling study performed on original test structures have highlighted the possibility to implement novel functionalities such as the coupling between the electromagnetic (RF) and microelectromechanical energy in vibrating CNT (toward the nanoradio) or the high speed detection based on ballistic transport in graphene three-terminal junction (TTJ). At the same time these study have contributed to identify the several challenges still laying ahead such as the development of adequate design and modeling tools (ballistic/diffusive, multiphysics and large scale factor) and practical implementation issues such as the effects of material quality and graphene-metal contact on the electrical transport. These subjects are the focus of presently on-going and future research activities and may represent a cornerstone of future wireless applications from microwave up to the THz range