Middleware architectures for the smart grid: A survey on the state-of-the-art, taxonomy and main open issues

The integration of small-scale renewable energy sources in the smart grid depends on several challenges that must be overcome. One of them is the presence of devices with very different characteristics present in the grid or how they can interact among them in terms of interoperability and data sharing. While this issue is usually solved by implementing a middleware layer among the available pieces of equipment in order to hide any hardware heterogeneity and offer the application layer a collection of homogenous resources to access lower levels, the variety and differences among them make the definition of what is needed in each particular case challenging. This paper offers a description of the most prominent middleware architectures for the smart grid and assesses the functionalities they have, considering the performance and features expected from them in the context of this application domain

Dynamic Hierarchical Cache Management for Cloud RAN and Multi- Access Edge Computing in 5G Networks

Cloud Radio Access Networks (CRAN) and Multi-Access Edge Computing (MEC) are two of the many emerging technologies that are proposed for 5G mobile networks. CRAN provides scalability, flexibility, and better resource utilization to support the dramatic increase of Internet of Things (IoT) and mobile devices. MEC aims to provide low latency, high bandwidth and real- time access to radio networks. Cloud architecture is built on top of traditional Radio Access Networks (RAN) to bring the idea of CRAN and in MEC, cloud computing services are brought near users to improve the user’s experiences. A cache is added in both CRAN and MEC architectures to speed up the mobile network services. This research focuses on cache management of CRAN and MEC because there is a necessity to manage and utilize this limited cache resource efficiently. First, a new cache management algorithm, H-EXD-AHP (Hierarchical Exponential Decay and Analytical Hierarchy Process), is proposed to improve the existing EXD-AHP algorithm. Next, this paper designs three dynamic cache management algorithms and they are implemented on the proposed algorithm: H-EXD-AHP and an existing algorithm: H-PBPS (Hierarchical Probability Based Popularity Scoring). In these proposed designs, cache sizes of the different Service Level Agreement (SLA) users are adjusted dynamically to meet the guaranteed cache hit rate set for their corresponding SLA users. The minimum guarantee of cache hit rate is for our setting. Net neutrality, prioritized treatment will be in common practice. Finally, performance evaluation results show that these designs achieve the guaranteed cache hit rate for differentiated users according to their SLA

Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems

The recent advances in cloud services technology are fueling a plethora of information technology innovation, including networking, storage, and computing. Today, various flavors have evolved of IoT, cloud computing, and so-called fog computing, a concept referring to capabilities of edge devices and users' clients to compute, store, and exchange data among each other and with the cloud. Although the rapid pace of this evolution was not easily foreseeable, today each piece of it facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation, and smart homes. As most current cloud, fog, and network services run simultaneously in each scenario, we observe that we are at the dawn of what may be the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, embedding capacities such as storage or processing, as well as new functionalities, such as decision making, data collection, forwarding, and sharing, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This article introduces a layered F2C architecture, its benefits and strengths, as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented, and a comparative performance analysis, albeit conceptual, all clearly show the way forward toward a new IoT scenario with a set of existing and unforeseen services provided on highly distributed and dynamic compute, storage, and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs, as well as conventional clouds.Peer ReviewedPostprint (author's final draft

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