Smarter City: Smart Energy Grid based on Blockchain Technology

The Revolution Lean Six Sigma 4.0

The Internet of Things (IoT) as a concept is fascinating and exciting, with an exponential growth just beginning. The IoT global market is expected to grow from 170 billion USD in 2017 to 560 billion USD by 2022. Though many experts have pegged IoT as the next industrial revolution, two of the major challenging aspects of IoT since the early days are having a secure privacy-safe ecosystem encompassing all building blocks of IoT architecture and solve the scalability problem as the number of devices increases. In recent years, Distributed Ledgers have often been referred to as the solution for both privacy and security problems. One form of distributed ledger is the Blockchain system. The aim of this paper consists of reviewing the most recent Blockchain architectures, comparing the most interesting and popular consensus algorithms, and evaluating the convergence between Blockchain and IoT by illustrating some of the main interesting projects in this research field. Furthermore, the paper provides a vision of a disruptive research topic that the authors are investigating: the use of AI algorithms to be applied to IoT devices belonging to a Blockchain architecture. This obviously requires that the devices be provided with adequate computational capacity and that can efficiently optimize their energy consumption.

The analysis of modern business processes implemented as orchestration of software services demands for new approaches that explicitly take into account the inherent complexity and distribution characteristics of such processes. In this respect, Distributed Simulation (DS) offers a viable tool to cope with such a demand, due to the aggregation, scalability, representativeness and load balancing properties that it allows to achieve. However, the use of DS is mostly limited by the specialized technical know-how and the extra-development that DS requires with respect to approaches based on conventional local simulation. This paper proposes a model-driven method that enables the DS-based analysis of business processes by introducing the automated transformation of business process models into analysis models that are specified as Extended Queueing Network (EQN) models and executed as distributed simulations. The paper also presents an example application to a business process for an e-commerce scenario.