A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.This work has received funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELK ARTEK programs. D. Camacho also acknowledges support from the Spanish Ministry of Science and Education under PID2020-117263GB-100 grant (FightDIS), the Comunidad Autonoma de Madrid under S2018/TCS-4566 grant (CYNAMON), and the CHIST ERA 2017 BDSI PACMEL Project (PCI2019-103623, Spain)

Power Modeling and Resource Optimization in Virtualized Environments

The provisioning of on-demand cloud services has revolutionized the IT industry. This emerging paradigm has drastically increased the growth of data centers (DCs) worldwide. Consequently, this rising number of DCs is contributing to a large amount of world total power consumption. This has directed the attention of researchers and service providers to investigate a power-aware solution for the deployment and management of these systems and networks. However, these solutions could be bene\ufb01cial only if derived from a precisely estimated power consumption at run-time. Accuracy in power estimation is a challenge in virtualized environments due to the lack of certainty of actual resources consumed by virtualized entities and of their impact on applications\u2019 performance. The heterogeneous cloud, composed of multi-tenancy architecture, has also raised several management challenges for both service providers and their clients. Task scheduling and resource allocation in such a system are considered as an NP-hard problem. The inappropriate allocation of resources causes the under-utilization of servers, hence reducing throughput and energy e\ufb03ciency. In this context, the cloud framework needs an e\ufb00ective management solution to maximize the use of available resources and capacity, and also to reduce the impact of their carbon footprint on the environment with reduced power consumption. This thesis addresses the issues of power measurement and resource utilization in virtualized environments as two primary objectives. At \ufb01rst, a survey on prior work of server power modeling and methods in virtualization architectures is carried out. This helps investigate the key challenges that elude the precision of power estimation when dealing with virtualized entities. A di\ufb00erent systematic approach is then presented to improve the prediction accuracy in these networks, considering the resource abstraction at di\ufb00erent architectural levels. Resource usage monitoring at the host and guest helps in identifying the di\ufb00erence in performance between the two. Using virtual Performance Monitoring Counters (vPMCs) at a guest level provides detailed information that helps in improving the prediction accuracy and can be further used for resource optimization, consolidation and load balancing. Later, the research also targets the critical issue of optimal resource utilization in cloud computing. This study seeks a generic, robust but simple approach to deal with resource allocation in cloud computing and networking. The inappropriate scheduling in the cloud causes under- and over- utilization of resources which in turn increases the power consumption and also degrades the system performance. This work \ufb01rst addresses some of the major challenges related to task scheduling in heterogeneous systems. After a critical analysis of existing approaches, this thesis presents a rather simple scheduling scheme based on the combination of heuristic solutions. Improved resource utilization with reduced processing time can be achieved using the proposed energy-e\ufb03cient scheduling algorithm

Choose the Appropriate Cluster Head for Decrease Energy Consume in Wireless Sensor Networks Based on Gravitational Emulation Local Search Algorithm

New Wireless Sensor Networks (WSN), is the new generation of real-time embedded systems with limited computation, energy and storage which have variety usage, especially when that is not possible using traditional networks. Given that, in this networks energy problem is important major challenge, using Clustering model can be considered as a solution to overcome this problem. In this instruction, sensor nodes grouped in a set of cluster and pick out a central node for Cluster Head (CH) node. Choose the appropriate cluster, reduce energy consumption in these networks, as a result increase networks lifetime. Hence, in this study, unlike previous studies, used Gravitational Emulation Local Search Algorithm (GELS), for clustering and select appropriate CH. This method is based on three descriptors of energy, dispersion and centrality of nodes, and simulations indicate, where the CH only selected based on local data set, significantly increase network lifetime

PROCSIM: an energy community simulator to develop and evaluate load balancing schemes

Climate change is one of the biggest challenges of the present millennium. The energy sector is the biggest contributor to this problem with approximately 25% of the global emissions. In order to mitigate this problem, one of the main solutions concerns to the use of energy from renewable sources. It is important to begin taking better advantage of the renewable resources more effectively and more often. In this sense, it is very important to develop mechanisms to balance the demand and supply, with the goal of minimizing, as much as possible, the use of energy from non renewable sources. For this reason, Renewable Energy Communities (RECs) started to emerge. They allow the sharing of the resources, contributing to a better management of them. However, these are not problem free. There are two main challenges that need to be solved: avoid a bad management of the renewable resources, hence avoiding the need to acquire energy from outside the community, and guarantee a fair distribution of the resources. In this regard, many researchers are focusing their attentions in load shifting approaches (adapt the appliances running time to better balance the load). Nevertheless, most of them use implicit approaches through the use of incentives (such as tariffs and dynamic pricing), which can be considered unfair approaches since richer people tend to benefit (which is not supposed, because ideally all community members should benefit the same). Based on this, in this work it is suggested an explicit load shifting approach based on the distribution of the timeslots, using the Multiple Knapsack combinatorial optimization problem. Although there are some literature which demonstrate the applicability of Knapsack in a variety of real world problems, the same does not happen in the energy field. Furthermore, since a large quantity of data is required to test and evaluate multiple scenarios in this load balancing scheme, and taking in consideration that only two energy community datasets were found on the literature, in this thesis it is also proposed an energy community simulator that allows to create different Energy Community (EC) datasets and evaluate the impact of the optimization, considering only Photovoltaics (PV) production (other types of renewable sources as well as batteries are not considered). Finally, in order to evaluate the impact of the developed load balancing strategy, the developed sim ulator was used in three different experiments: variation in bin size, variation in community size and variation in flexibility. The results were positive and showed that this strategy can provide a better man agement of the PV resources once it increased the PV use, decreased the PV waste and also decreased the use of energy from the grid.As alterações climáticas são um dos maiores desafios do presente milénio. O sector da energia é o que mais contribui para este problema com aproximadamente 25% das emissões globais. A fim de mitigar este problema, uma grande solução está relacionada com a utilização de energia proveniente de recursos renováveis. É importante começar a tirar um melhor partido das fontes reno váveis de forma mais eficaz e mais frequente. Neste sentido, é muito importante o desenvolvimento de mecanismos para equilibrar a procura e a oferta, com o objectivo de minimizar, tanto quanto possível, a utilização de energia proveniente de fontes não renováveis. Por esta razão, RECs começaram a surgir. Elas permitem a partilha dos recursos, contribuindo para uma melhor gestão dos mesmos. No entanto, elas não estão isentas de problemas. Dois dos desafios mais importantes a resolver são: evitar uma má gestão dos recursos renováveis, evitando assim a necessidade de adquirir energia de fora da comunidade, e garantir uma distribuição justa dos recursos. A este respeito, muitos investigadores estão a concentrar as atenções em abordagens load shifting (adaptar o tempo de funcionamento dos aparelhos para melhor equilibrar a carga). No entanto, a maioria deles utiliza abordagens implícitas através do uso de incentivos (tais como tarifas e preços dinâmicos), o que pode ser considerado injusto, uma vez que as pessoas mais ricas serão beneficiadas (o que não é suposto, pois idealmente todos os membros da comunidade devem beneficiar o mesmo). Com base nisto, neste trabalho é sugerida uma abordagem explícita de load shifting baseada na distribuição de timeslots, utilizando o problema de otimização combinatorial Multiple Knapsack. Embora haja alguma literatura que demonstra a aplicabilidade do Knapsack numa variedade de problemas do mundo real, o mesmo não acontece no campo da energia. Além disso, uma vez que é necessária uma grande quantidade de dados para testar e avaliar múlti plos cenários neste esquema de load balancing, e tendo em consideração que apenas dois datasets de comunidades energéticas foram encontrados na literatura, nesta tese é também proposto um simulador de comunidades energéticas que permite criar diferentes datasets de EC e avaliar o impacto da opti mização, considerando apenas a produção fotovoltaica (não são considerados outros tipos de fontes renováveis, bem como baterias). Finalmente, com o intuito de avaliar o impacto da estratégia de load balancing desenvolvida, o simu lador desenvolvido foi utilizado em três experiências diferentes: variação no tamanho do bin, variação no tamanho da comunidade e variação na flexibilidade. Os resultados foram positivos e mostraram que esta estratégia pode proporcionar uma melhor gestão dos recursos do PV uma vez que aumenta a utilização do PV, diminui o desperdício do PV e também diminui a utilização de energia da rede