A Rolling Blockchain for a Dynamic WSNs in a Smart City

Blockchain is one of the most popular topics for discussion now. However, most experts still see this technology as only part of Bitcoin, other crypto-currencies or money transfer systems. Often, new solutions, proposed by young researchers, are blocked by reviewers, only because these solutions can not be used for Bitcoins. However, Blockchain technology is more universal and can be used also in other areas, for example, in IoT, WSN and mobile devices. This paper considers the implementation of Blockchain technology in sensor networks as an element of IoT. The concept of "Rolling Blockchain" was proposed, which can be used to build WSN with the participation of Smart Cars, as nodes of the network. The order of block formation and structure in the chain is proposed and a mathematical model is created for it. We estimate the optimal number of WSN nodes, the number of connections between nodes, for specified network reliability values, was performed

Causal Unit of Rotors in a Cardiac System

The heart exhibits complex systems behaviors during atrial fibrillation (AF), where the macroscopic collective behavior of the heart causes the microscopic behavior. However, the relationship between the downward causation and scale is nonlinear. We describe rotors in multiple spatiotemporal scales by generating a renormalization group from a numerical model of cardiac excitation, and evaluate the causal architecture of the system by quantifying causal emergence. Causal emergence is an information-theoretic metric that quantifies emergence or reduction between microscopic and macroscopic behaviors of a system by evaluating effective information at each spatiotemporal scale. We find that there is a spatiotemporal scale at which effective information peaks in the cardiac system with rotors. There is a positive correlation between the number of rotors and causal emergence up to the scale of peak causation. In conclusion, one can coarse-grain the cardiac system with rotors to identify a macroscopic scale at which the causal power reaches the maximum. This scale of peak causation should correspond to that of the AF driver, where networks of cardiomyocytes serve as the causal units. Those causal units, if identified, can be reasonable therapeutic targets of clinical intervention to cure AF.Comment: 19 pages, 9 figures. arXiv admin note: text overlap with arXiv:1711.1012

A review of the applications of the Block-chain technology in smart devices and dis-tributed renewable energy grids

In this paper we make a critical review of the existing technology in the smart cities and smart grid paradigms from the security perspective. First we summarize the findings about the evolution of renewable technology over time and in particular the benefits of a Cost reduction potential for solar and wind power in the period 2015-2025.  Then we build from existing sources to highlight different ways for efficiency improvement in solar panel solutions during 1975-2015. Next we analyze growth of the smart metering and smart grid technology in the world.  Also, the existing Blockchain solutions are critically reviewed in regard to cyber infrastructure security. From these findings we conclude that there is an increasing need for developing new Blockchain solutions in the smart grids ecosystem

On the dynamics of rigid-block structures applications to SDOF masonry collapse mechanisms

This dissertation proposes a novel formulation for the rocking motion (RM) of rigid-blocks when no sliding mechanisms are present. Both theoretical and numerical analyses are included. The results obtained from a new formulation for single block dynamics, which have shown to be extremely powerful, led to the generalization for many degrees of freedom systems presented herein. That generalization was possible through a transformation of the Euclidean configuration space to a complex Riemannian manifold endowed with an Hermitian metric tensor. The present contribution applies also techniques derived from chaos theory to the RM problem for the understanding and quantification of the dynamic stability. The probability of overturn of rocking blocks under random loading is both experimentally and numerically investigated. Through an intensive use of the Poincaré surface of section technique and bifurcation analysis, the underlying structure of the phase space is highlighted. Delay coordinated, recurrence plots and recurrence quantification analysis are used to find the recurrences and patterns present in the dynamics at different time scales. An estimator for the quantification of chaos in the problem of rocking blocks under earthquake loading is proposed. Stochastic analysis is performed through an ensemble of input earthquake samples. The ensemble limits and other stochastic properties of the relevant physical magnitudes are analyzed. A set of experiments at a seismic table on four blue granite stones were conducted to validate the theoretical analyses. The response is investigated under different input actions and block geometries. Several applications to Earthquake Engineering and structural safety assessment are therefore derived. In particular, numerical thresholds for the probability of collapse of slender structures are found by means of different criteria. A model for the probability of collapse based on the stationary solution of the associated Fokker-Planck equation is proposed.Fundação para a Ciência e a Tecnologia (FCT) - This work was partially supported by the Ref. SFRH/BD/9014/200

Disentangling Jenny’s equation by machine learning

The so-called soil-landscape model is the central paradigm which relates soil types to their forming factors through the visionary Jenny’s equation. This is a formal mathematical expression that would permit to infer which soil should be found in a specific geographical location if the involved relationship was sufficiently known. Unfortunately, Jenny’s is only a conceptual expression, where the intervening variables are of qualitative nature, not being then possible to work it out with standard mathematical tools. In this work, we take a first step to unlock this expression, showing how Machine Learning can be used to predictably relate soil types and environmental factors. Our method outperforms other conventional statistical analyses that can be carried out on the same forming factors defined by measurable environmental variablesThis work has been partially supported by the Spanish Ministry of Science, Innovation and Universities, Gobierno de España, under Contract No. PID2021-122711NB-C21. The authors wish to thank Ricardo Pérez-Ochoa (Government of Asturias) and José Gumuzzio, for facilitating access to basic soil information, and Javier Rodríguez Alonso (INIA, Madrid), for managing georeferenced dat

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

[EN] Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints

Reserve costs allocation model for energy and reserve market simulation

This paper proposes a new model to allocate reserve costs among the involved players, considering the characteristics of the several entities, and the particular circumstances at each moment. The proposed model is integrated in the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM), which enables complementing the multi-agent simulation of diverse electricity market models, by including the joint simulation of energy and reserve markets. In this context, the proposed model allows allocating the payment of reserve costs that result from the reserve market. A simulation based on real data from the Iberian electricity market - MIBEL, is presented. Simulation results show the advantages of the proposed model in sharing the reserve costs fairly and accordingly to the different circumstances. This work thus contributes the study of novel market models towards the evolution of power and energy systems by adapting current models to the new paradigm of high penetration of renewable energy generation.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013info:eu-repo/semantics/publishedVersio

Monopolistic and game-based approaches to transact energy flexibility

The appearance of the flexible behavior of end-users based on demand response programs makes the power distribution grids more active. Thus, electricity market participants in the bottom layer of the power system, wish to be involved in the decision-making process related to local energy management problems, increasing the efficiency of the energy trade in distribution networks. This paper proposes monopolistic and game-based approaches for the management of energy flexibility through end-users, aggregators, and the Distribution System Operator (DSO) which are defined as agents in the power distribution system. Besides, a 33-bus distribution network is considered to evaluate the performance of our proposed approaches for energy flexibility management model based on impact of flexibility behaviors of end-users and aggregators in the distribution network. According to the simulation results, it is concluded that although the monopolistic approach could be profitable for all agents in the distribution network, the game-based approach is not profitable for end-users.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Application of artificial immune system to domestic energy management problem

[EN] The connection of devices in a smart home should be done optimally, this helps save energy and money. Numerous optimization models have been applied, they are based on fuzzy logic, linear programming or bio-inspired algorithms. The aim of this work is to solve an energy management problem in a domestic environment by applying an artificial immune system. We carried out a thorough analysis of the different strategies that optimize a domestic environment system, in order to demonstrate the ability of an artificial immune system to find a successful optima that satisfies the problem constraints