Evaluating the more suitable ISM frequency band for iot-based smart grids: a quantitative study of 915 MHz vs. 2400 MHz

IoT has begun to be employed pervasively in industrial environments and critical infrastructures thanks to its positive impact on performance and efficiency. Among these environments, the Smart Grid (SG) excels as the perfect host for this technology, mainly due to its potential to become the motor of the rest of electrically-dependent infrastructures. To make this SG-oriented IoT cost-effective, most deployments employ unlicensed ISM bands, specifically the 2400 MHz one, due to its extended communication bandwidth in comparison with lower bands. This band has been extensively used for years by Wireless Sensor Networks (WSN) and Mobile Ad-hoc Networks (MANET), from which the IoT technologically inherits. However, this work questions and evaluates the suitability of such a "default" communication band in SG environments, compared with the 915 MHz ISM band. A comprehensive quantitative comparison of these bands has been accomplished in terms of: power consumption, average network delay, and packet reception rate. To allow such a study, a dual-band propagation model specifically designed for the SG has been derived, tested, and incorporated into the well-known TOSSIM simulator. Simulation results reveal that only in the absence of other 2400 MHz interfering devices (such as WiFi or Bluetooth) or in small networks, is the 2400 MHz band the best option. In any other case, SG-oriented IoT quantitatively perform better if operating in the 915 MHz band.This research was supported by the MINECO/FEDER project grants TEC2013-47016-C2-2-R (COINS) and TEC2016-76465-C2-1-R (AIM). The authors would like to thank Juan Salvador Perez Madrid nd Domingo Meca (part of the Iberdrola staff) for the support provided during the realization of this work. Ruben M. Sandoval also thanks the Spanish MICINN for an FPU (REF FPU14/03424) pre-doctoral fellowship

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

An Efficient NVoD Scheme Using Implicit Error Correction and Subchannels for Wireless Networks

Implicit Error Correction (IEC) is a near Video-on-Demand (nVoD) scheme that trades bandwidth utilization for initial playback delay to potentially support an infinite number of users. Additionally, it provides error protection without any further bandwidth increase by exploiting the implicit redundancy of nVoD protocols, using linear combinations of the segments transmitted in a given time slot. However, IEC packet loss protection is weaker at the beginning of the playback due to the lack of implicit redundancy and lower decoding efficiency, resulting in worse subjective playback quality. In tackling this issue, this paper contributes with an extension of the original nVoD architecture, enhancing its performance by adding a new element namely, subchannels. These subdivisions of the original channels do not provide further packet loss protection but significantly improve the decoding efficiency, which in turn increases playback quality, especially at the beginning. Even for very high packet loss probabilities, subchannels are designed to obtain higher decoding efficiency which results in greater packet loss protection than that provided by IEC. The proposed scheme is especially useful in wireless cooperative networks using techniques such as network coding, as content transmissions can be split into different subchannels in order to maximize network efficiency.This work was supported by the AEI/FEDER, UE Project AIM under Grant TEC2016-76465-C2-1-R

The IEEE 1906.1 Standard: nanocommunications as a new source of data

Nanoscale communications is a new paradigm encompassing all those concerns related to the exchange of information among devices at the nanometer scale. A network infrastructure consisting of a huge amount of nano-devices is envisaged to ensure robust, reliable and coordinated data transmission. This will enable a plethora of forthcoming applications and services in many different research fields, such as personalized medicine, synthetic biology, environmental science or industry, which will lead to outstanding and unprecedented advances. The IEEE P1906.1 standard provides a conceptual and general framework to set the starting point for future developments in nanoscale communication networks. This paper reviews the latest IEEE P1906.1 recommendations, observing their main features when applied to the electromagnetic (EM) nanocommunication area. We contribute by identifying and discussing the principal shortcomings of the standard, to which further research efforts must be devoted. We also provide interesting guidelines for focusing the object of future investigations.This work has been supported by the project AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE)

Simultaneous data rate and transmission power adaptation in V2V communications: A deep reinforcement learning approach

In Vehicle-to-Vehicle (V2V) communications, channel load is key to ensuring the appropriate operation of safety applications as well as driver-assistance systems. As the number of vehicles increases, so do their communication messages. Therefore, channel congestion may arise, negatively impacting channel performance. Through suitable adjustment of the data rate, this problem would be mitigated. However, this usually involves using different modulation schemes, which can jeopardize the robustness of the solution due to unfavorable channel conditions. To date, little effort has been made to adjust the data rate, alone or together with other parameters, and its effects on the aforementioned sensitive safety applications remain to be investigated. In this paper, we employ an analytical model which balances the data rate and transmission power in a non-cooperative scheme. In particular, we train a Deep Neural Network (DNN) to precisely optimize both parameters for each vehicle without using additional information from neighbors, and without requiring any additional infrastructure to be deployed on the road. The results obtained reveal that our approach, called NNDP, not only alleviates congestion, leaving a certain fraction of the channel available for emergency-related messages, but also provides enough transmission power to fulfill the application layer requirements at a given coverage distance. Finally, NNDP is thoroughly tested and evaluated in three realistic scenarios and under different channel conditions, demonstrating its robustness and excellent performance in comparison with other solutions found in the scientific literature.This work was supported in part by the AEI/FEDER/UE [Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarrollo Regional (FEDER), and Unión Europea (UE)] under Grant PID2020-116329GB-C22 [ARISE2: Future IoT Networks and Nano-networks (FINe)] and Grant PID2020-112675RB-C41 (ONOFRE-3), in part by the Fundación Séneca, Región de Murcia, under Grant 20889/PI/18 (ATENTO), and in part by the LIFE project (Fondo SUPERA COVID-19 through the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The work of Juan Aznar-Poveda was supported by the Spanish Ministerio de Educación, Cultura y Deporte (MECD) through the Formación de Personal Investigador (FPI) Predoctoral Scholarship under Grant BES-2017-08106

Simone: a dynamic monitoring simulator for the evacuation of navy ships

In this paper, the automation of the evacuation process of a military ship is studied in real time. For this purpose, a scenario is reconfigured to produce a failure or damage. Then, an optimal network of alternative escape routes is computed. The resulting escape route map can be indicated by lighting the appropriate corridors on the ship. Through these corridors, the members of the embarked population and the entire process is monitored so that the crew can reach their lifeboats in the shortest possible time. To undertake this automated process, the dynamic ship evacuation monitoring system (SIMONE, from its acronym in Spanish: Sistema de Monitorización Dinámica de Evacuación de Buques) has been developed. This system integrates a communication gateway with the integrated platform control system (IPCS) and integrated lighting system that will be installed in new Spanish naval constructions.This research was funded by MCIN/AEI/10.13039/501100011033 grant number PID2020-116329GB-C22 and grant number TED2021-129336B-I00. This work is also a result of an internship funded by the Autonomous Community of the Region of Murcia through the Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia (Seneca Foundation—Agency for Science and Technology in the Region of Murcia) and European Union NextGenerationEU program

MDPRP: A Q-learning approach for the joint control of beaconing rate and transmission power in VANETs

Vehicular ad-hoc communications rely on periodic broadcast beacons as the basis for most of their safety applications, allowing vehicles to be aware of their surroundings. However, an excessive beaconing load might compromise the proper operation of these crucial applications, especially regarding the exchange of emergency messages. Therefore, congestion control can play an important role. In this article, we propose joint beaconing rate and transmission power control based on policy evaluation. To this end, a Markov Decision Process (MDP) is modeled by making a set of reasonable simplifying assumptions which are resolved using Q-learning techniques. This MDP characterization, denoted as MDPRP (indicating Rate and Power), leverages the trade-off between beaconing rate and transmission power allocation. Moreover, MDPRP operates in a non-cooperative and distributed fashion, without requiring additional information from neighbors, which makes it suitable for use in infrastructureless (ad-hoc) networks. The results obtained reveal that MDPRP not only balances the channel load successfully but also provides positive outcomes in terms of packet delivery ratio. Finally, the robustness of the solution is shown since the algorithm works well even in those cases where none of the assumptions made to derive the MDP model apply.This work was supported in part by the AIM Project [Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)] under Grant TEC2016-76465-C2-1-R, in part by the Fundación Séneca, Región de Murcia, through the ATENTO Project, under Grant 20889/PI/18, and in part by the LIFE (Fondo SUPERA Covid-19 funded by the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The work of Juan Aznar-Poveda was supported by the Spanish Ministerio de Educación, Cultura y Deporte (MECD) for the FPI Grant BES-2017-081061

Approximate reinforcement learning to control beaconing congestion in distributed networks

In vehicular communications, the increase of the channel load caused by excessive periodical messages (beacons) is an important aspect which must be controlled to ensure the appropriate operation of safety applications and driver-assistance systems. To date, the majority of congestion control solutions involve including additional information in the payload of the messages transmitted, which may jeopardize the appropriate operation of these control solutions when channel conditions are unfavorable, provoking packet losses. This study exploits the advantages of non-cooperative, distributed beaconing allocation, in which vehicles operate independently without requiring any costly road infrastructure. In particular, we formulate the beaconing rate control problem as a Markov Decision Process and solve it using approximate reinforcement learning to carry out optimal actions. Results obtained were compared with other traditional solutions, revealing that our approach, called SSFA, is able to keep a certain fraction of the channel capacity available, which guarantees the delivery of emergency-related notifications with faster convergence than other proposals. Moreover, good performance was obtained in terms of packet delivery and collision ratios.This research has been supported by the projects AIM, ref. TEC2016-76465-C2-1-R, ARISE2 “Future IoT Networks and Nano-networks (FINe)” ref. PID2020-116329GB-C22, ONOFRE-3, ref. PID2020-112675RB-C41 [Agencia Estatal de Investigación (AEI), European Regional Development Fund (FEDER), European Union (EU)], ATENTO, ref. 20889/PI/18 (Fundación Séneca, Región de Murcia), and LIFE [Fondo SUPERA Covid-19, funded by Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC), Universidades Españolas and Banco Santander]. J.A.P. thanks the Spanish MECD for an FPI grant ref. BES-2017-081061. Finally, the authors acknowledge Laura Wettersten for her contribution in reviewing the grammar and spell of the manuscript

The hepatocyte nuclear factor 4 (HNF-4) represses the mitochondrial HMG-CoA synthase gene

We have recently shown that the gene for the mitochondrial HMG-CoA synthase is a target for PPAR and that this receptor mediates the induction of this gene by fatty acids. With the aim of gaining further insight into the function and regulation of this gene we examined the effect of other members of the nuclear hormone receptor superfamily on its expression. We previously identified a regulatory element in the mitochondrial HMG-CoA synthase gene promoter that confers transcriptional regulation by PPAR, RXR and the orphan nuclear receptor COUP-TF, In this study we demonstrate a trans-repressing regulatory function for HNF-4 at this same nuclear receptor response element (NRRE). HNF-4 binds to the mitochondrial HMG-CoA synthase NRRE, and, in cotransfection assays in HepG2 cells, it represses PPAR-dependent activation of a reporter gene linked to the mitochondrial HMG-CoA synthase gene promoter. These results suggest that the mitochondrial HMG-CoA synthase gene is subject to differential regulation by the interplay of multiple members of the nuclear hormone receptor superfamily

Nanorouter awareness in flow-guided nanocommunication networks

Flow-guided electromagnetic nanonetworks will enable innovative medical applications for monitoring, information gathering, and data transmission inside the human body. These nanonetworks will have to operate under extreme computational and powering-related constraints, and in very hostile environments inside human vascular systems. Under these circumstances, successful transmissions between in-body nanonodes and an on-body nanorouter rarely occur, thus requiring new approaches to improve the network throughput in this scenario. Along this view, in classical flow-guided nanonetworks the nanonodes are envisioned to transmit packets if they have enough energy for the transmission, regardless of their vicinity to the nanorouter. In this paper, we propose a nanorouter awareness model that can provide significant throughput gains compared to the baseline based on blind transmissions, facilitating the roll-out of nanocommunication-supported medical applications.This work was supported by project “AriSe2: Future IoT Networks and Nano-networks (FINe)”, ref. PID2020-116329GB-C22 (AEI/FEDER, UE). This work was supported in part by the Fundación Séneca, Región de Murcia, through the ATENTO Project, under Grant 20889/PI/18, and in part by the LIFE project (Fondo SUPERA Covid-19 funded by the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The author Filip Lemic was supported by the EU Marie Curie Actions Individual Fellowship project entitiled “Scalable Localization-enabled In-body Terahertz Nanonetwork” (ScaLeITN), grant nr. 893760