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

Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201

Peer-to-Peer Energy Trading for Networked Microgrids

Considering the limitations of the existing centralized power infrastructure, research interests have been directed to decentralized smart power systems constructed as networks of interconnected microgrids. Therefore, it has become critical to develop secure and efficient energy trading mechanisms among networked microgrids for reliability and economic mutual benefits. Furthermore, integrating blockchain technologies into the energy sector has gained significant interest among researchers and industry professionals. Considering these trends, the work in this thesis focuses on developing Peer-to-Peer (P2P) energy trading models to facilitate transactions among microgrids in a multiagent network. Price negotiation mechanisms are proposed for both islanded and grid-connected microgrid networks. To enable a trusted settlement of electricity trading transactions, a two-stage blockchain-based settlement consensus protocol is also developed. Simulation results have shown that the model has successfully facilitated energy trading for networked microgrids