Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This article analyses the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analysed. Conclusions show that operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.Comment: 18 pages, 5 figure

Rebel Foods’ Cloud Kitchen Technologies: Food for Thought?

This case study examines the India based cloud kitchens and food services provider Rebel Foods’ technology platforms. We document the development of the company from its foundation in 2004 and the role played by technology in enabling its various lines of business. We describe in detail the technology stack that drives the operations at Rebel Foods. We also present various emerging technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), blockchain and augmented reality (AR) that may be utilized by Rebel Foods to increase efficiency, build customer engagement and improve sales growth and profitability. We critically examine Rebel Foods’ current approach to technology and analyze the various technology options that the company may consider to drive its future strategy

A novel multi-level and community-based agent ecosystem to support customers dynamic decision-making in smart grids

Electrical systems have evolved at a fast pace over the past years, particularly in response to the current environmental and climate challenges. Consequently, the European Union and the United Nations have encouraged the development of a more sustainable energy strategy. This strategy triggered a paradigm shift in energy consumption and production, which becoming increasingly distributed, resulted in the development and emergence of smart energy grids. Multi-agent systems are one of the most widely used artificial intelligence concepts in smart grids. Both multi-agent systems and smart grids are distributed, so there is correspondence between the used technology and the network's complex reality. Due to the wide variety of multi-agent systems applied to smart grids, which typically have very specific goals, the ability to model the network as a whole may be compromised, as communication between systems is typically non-existent. This dissertation, therefore, proposes an agent-based ecosystem to model smart grids in which different agent-based systems can coexist. This dissertation aims to conceive, implement, test, and validate a new agent-based ecosystem, entitled A4SG (agent-based ecosystem for smart grids modelling), which combines the concepts of multi-agent systems and agent communities to enable the modelling and representation of smart grids and the entities that compose them. The proposed ecosystem employs an innovative methodology for managing static or dynamic interactions present in smart grids. The creation of a solution that allows the integration of existing systems into an ecosystem, enables the representation of smart grids in a realistic and comprehensive manner. A4SG integrates several functionalities that support the ecosystem's management, also conceived, implemented, tested, and validated in this dissertation. Two mobility functionalities are proposed: one that allows agents to move between physical machines and another that allows "virtual" mobility, where agents move between agent communities to improve the context for the achievement of their objectives. In order to prevent an agent from becoming overloaded, a novel functionality is proposed to enable the creation of agents that function as extensions of the main agent (i.e., branch agents), allowing the distribution of objectives among the various extensions of the main agent. Several case studies, which test the proposed services and functionalities individually and the ecosystem as a whole, were used to test and validate the proposed solution. These case studies were conducted in realistic contexts using data from multiple sources, including energy communities. The results indicate that the used methodologies can increase participation in demand response events, increasing the fitting between consumers and aggregators from 12 % to 69 %, and improve the strategies used in energy transaction markets, allowing an energy community of 50 customers to save 77.0 EUR per week.Os últimos anos têm sido de mudança nos sistemas elétricos, especialmente devido aos atuais desafios ambientais e climáticos. A procura por uma estratégia mais sustentável para o domínio da energia tem sido promovida pela União Europeia e pela Organização das Nações Unidas. A mudança de paradigma no que toca ao consumo e produção de energia, que acontece, cada vez mais, de forma distribuída, tem levado à emergência das redes elétricas inteligentes. Os sistemas multi-agente são um dos conceitos, no domínio da inteligência artificial, mais aplicados em redes inteligentes. Tanto os sistemas multi-agente como as redes inteligentes têm uma natureza distribuída, existindo por isso um alinhamento entre a tecnologia usada e a realidade complexa da rede. Devido a existir uma vasta oferta de sistemas multi-agente aplicados a redes inteligentes, normalmente com objetivos bastante específicos, a capacidade de modelar a rede como um todo pode ficar comprometida, porque a comunicação entre sistemas é, geralmente, inexistente. Por isso, esta dissertação propõe um ecossistema baseado em agentes para modelar as redes inteligentes, onde vários sistemas de agentes coexistem. Esta dissertação pretende conceber, implementar, testar, e validar um novo ecossistema multiagente, intitulado A4SG (agent-based ecosystem for smart grids modelling), que combina os conceitos de sistemas multi-agente e comunidades de agentes, permitindo a modelação e representação de redes inteligentes e das suas entidades. O ecossistema proposto utiliza uma metodologia inovadora para gerir as interações presentes nas redes inteligentes, sejam elas estáticas ou dinâmicas. A criação de um ecossistema que permite a integração de sistemas já existentes, cria a possibilidade de uma representação realista e detalhada das redes de energia. O A4SG integra diversas funcionalidades, também estas concebidas, implementadas, testadas, e validadas nesta dissertação, que suportam a gestão do próprio ecossistema. São propostas duas funcionalidades de mobilidade, uma que permite aos agentes mover-se entre máquinas físicas, e uma que permite uma mobilidade “virtual”, onde os agentes se movem entre comunidades de agentes, de forma a melhorar o contexto para a execução dos seus objetivos. É também proposta uma nova funcionalidade que permite a criação de agentes que funcionam como uma extensão de um agente principal, com o objetivo de evitar a sobrecarga de um agente, permitindo a distribuição de objetivos entre as várias extensões do agente principal. A solução proposta foi testada e validada por vários casos de estudo, que testam os serviços e funcionalidades propostas individualmente, e o ecossistema como um todo. Estes casos de estudo foram executados em contextos realistas, usando dados provenientes de diversas fontes, tais como comunidades de energia. Os resultados demonstram que as metodologias utilizadas podem melhorar a participação em eventos de demand response, subindo a adequação entre consumidores e agregadores de 12 % para 69 %, e melhorar as estratégias utilizadas em mercados de transações de energia, permitindo a uma comunidade de energia com 50 consumidores poupar 77,0 EUR por semana

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.publishedVersio

A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

© 2018 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. In this paper, we present a novel distributed framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy

A review of the meanings and the implications of the Industry 4.0 concept

The global industrial landscape has changed deeply in the last few years due to successive technological developments and innovations in manufacturing processes. The Industry 4.0 concept has emerged and the academic literature has paid an increased attention to this topic, which remains non-consensual or ill defined. In this research, a literature review is made to understand this concept in its technological dimension, and to comprehend its impacts. This new industrial paradigm brings together the digital and physical worlds through the Cyber-Physical Systems enhanced by Internet of Things and it is expected that this novel has consequences on industry, markets and economy, improving production processes and increasing productivity, affecting the whole product lifecycle, creating new business models, changing the work environment and restructuring the labor market. Therefore, this paper focuses on Industry 4.0 concept and contributes for its clarification and further understanding about the importance and implications of this complex technological system.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Sviluppo, Deployment e Validazione Sperimentale di Architetture Distribuite di Machine Learning su Piattaforma fog05

Ultimamente sta crescendo sempre di più l'interesse riguardo al fog computing e alle possibilità che offre, tra cui la capacità di poter fruire di una capacità computazionale considerevole anche nei nodi più vicini all’utente finale: questo permetterebbe di migliorare diversi parametri di qualità di un servizio come la latenza nella sua fornitura e il costo richiesto per le comunicazioni. In questa tesi, sfruttando le considerazioni sopra, abbiamo creato e testato due architetture di machine learning distribuito e poi le abbiamo utilizzate per fornire un servizio di predizione (legato al condition monitoring) che migliorasse la soluzione cloud relativamente ai parametri citati prima. Poi, è stata utilizzata la piattaforma fog05, un tool che permette la gestione efficiente delle varie risorse presenti in una rete, per eseguire il deployment delle architetture sopra. Gli obiettivi erano due: validare le architetture in termini di accuratezza e velocità di convergenza e confermare la capacità di fog05 di gestire deployment complessi come quelli necessari nel nostro caso. Innanzitutto, sono state scelte le architetture: per una, ci siamo basati sul concetto di gossip learning, per l'altra, sul federated learning. Poi, queste architetture sono state implementate attraverso Keras e ne è stato testato il funzionamento: è emerso chiaramente come, in casi d'uso come quello in esame, gli approcci distribuiti riescano a fornire performance di poco inferiori a una soluzione centralizzata. Infine, è stato eseguito con successo il deployment delle architetture utilizzando fog05, incapsulando le funzionalità di quest'ultimo dentro un orchestratore creato ad-hoc al fine di gestire nella maniera più automatizzata e resiliente possibile la fornitura del servizio offerto dalle architetture sopra