Pregovaranje u Internetu stvari

Internet of things as a market, and the number of connected devices in particular is growing very rapidly. Currently, application owners deploy new devices for each application that needs the data. As the number of sensors increases, it will become much more practical to reuse existing sensors for new applications than to deploy new ones. But the problem is that the application owner needs to agree with device owners on conditions under which will the data be made available to applications. Doing this manually is very expensive both in terms of money and time. We implemented a system that does this automatically using negotiating agents. The system was tested on simulated environments and showed that it can mediate between devices and applications with reasonable performance.Internet stvari kao tržište, a posebno broj spojenih uređaja, raste vrlo brzo. Danas vlasnici aplikacija postavljaju nove uređaje za svaku aplikaciju kojoj su potrebni podatci. Kako se povećava broj senzora u upotrebi, postaje sve praktičnije koristiti postojeće senzore nego postavljati nove. Problem predstavlja činjenica da se vlasnik aplikacije mora dogovoriti s vlasnicima uređaja o uvjetima pod kojima će aplikacijama biti dozvoljeno dohvaćanje izmjerenih vrijednosti. Pojedinačno je dogovaranje između vlasnika za svaki uređaj skupo i sporo. Izgradili smo sustav koji automatizira ovaj proces pomoću programskih agenata koji pregovaraju. Sustav je ispitan na simuliranom okruženju i pokazuje da može posredovati između uređaja i aplikacija s razumnim performansama

CloudAnchor Smart Contracts

The CloudAnchor platform allows the negotiation of IaaS Cloud resources for Small and Medium Sized Enterprises (SME), either as resource providers or consumers. This project entails the research, design, and implementation of a solution based on smart contracts, with the goal of permanently recording and managing the contracts on a blockchain network. The usage of smart contracts enables safe contract code execution and raises the level of trust, integrity, and traceability of the platform contracts by keeping the data stored in a decentralised manner. To do so, a method to coordinate and submit transactions to the blockchain network must be implemented. The tests carried out indicate that the solution has been successfully implemented, with contract registration saved in a decentralised and safe manner. As a result, there was an increase in the platform’s execution time, caused by the new transactions made to the blockchain.A plataforma CloudAnchor permite a negociação e contratualização de recursos Cloud do tipo IaaS a pequenas e médias empresas, sejam elas fornecedoras ou clientes. Este trabalho inclui o estudo, projeto e implementação de uma solução baseada em smart contracts, com o objetivo de administrar e registar de forma permanente os contratos celebrados numa rede blockchain. A utilização de smart contracts permite executar o respetivo código de forma segura e aumentar o nível de confiança, integridade e rastreabilidade dos contratos celebrados na plataforma, guardando-os de forma descentralizada. Para tal, é necessário implementar um mecanismo de coordenação e submissão de transações para a rede blockchain. Os testes realizados permitiram concluir que a implementação da solução foi bem sucedida, passando os contratos a ficar guardados de forma descentralizada e segura. Em consequência, verificou-se um aumento do tempo de execução da plataforma provocado pelas novas transações com a blockchain

Asignación de espacios en tiempo real por medio de un modelo basado en agentes (ABM)

Currently, the search for study spaces on a university campus is one of the biggest problems faced by a student because this population, in many cases, exceeds the physical resources of the campuses. Linear models are not feasible to face these types of situations since the large volume of information implied by the study spaces and groups generates data overflow. An alternative to address these situations is a multiagent model which allows assignments dynamically and in real time through interactions between agents, a methodology that due to its characteristics allows progress towards the implementation of a Smart Campus. A model of agents makes possible a “conversation” between agents representing study spaces and groups of students, achieving an assignment similar to what usually happens in real life. It is necessary to highlight that because it is a dynamic model, new groups can be entered even when the model has already been running for a certain time. This implies that, in the long run, the agent model should not be rerun if there is any variation, which is not the case in a linear model. For this reason, this study aimed to implement a multiagent model in the Eclipse development platform for the simulation of different scenarios to assign study spaces to groups of students in real time. To validate the operation of the multiagent model, capacity tests were carried out that sought to know the limitations regarding the amount of data that could be entered, as well as optimality tests in which the objective was to compare the results obtained from an agent model with those of a linear model. Throughout the development of this study, the agent model was compared with a linear model and it was found that this provided results close to the level of a model that could obtain an optimal solution. The results obtained, both in the linear model and in the multiagent model, were subjected to a validator to determine if the results were correct. For a second stage, it was decided to activate the negotiation function, which allowed the groups to give in on their tool requirements in real time. On this occasion, the results were even higher to those of the optimal model, since when input parameters were changed while the model was running, alternative solutions were found that allowed groups to access spaces that were still available, and that in a first instance its assignment was not possible.Ingeniero (a) IndustrialPregrad

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Semantic multi-criteria decision making in autonomous embedded systems

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