A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

/pdf/deep-learning-in-mobile-and-wireless-networking-a-survey-ct8cy2kuqi.pdf

Deep Learning in Mobile and Wireless Networking: A Survey

Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Space-Air-Ground Integrated Network: A Survey

A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT

As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Mobile-edge computing (MEC) is a in demand method for improving the quality of computation experience on mobile devices (MD) since it helps MD’s to offload computing activities to MEC servers, which provide strong computing capabilities. However, there are certain unresolved concerns in present computation-offloading works: 1) safety issue; 2) joint computation offloading; and 3) flexible optimization. To solve safety and privacy concerns, we use Federated Learning-based blockchain technology, which provides data accuracy and irreversibility in MEC systems. Federated Learning (FL) is a promising technique towards effective machine learning while protecting privacy in dispersed situations such as the Internet of Things (IoT) and MEC. FL’s effectiveness is dependent on a network of participant nodes contributing their data and computational resources to the collective training of a globally model. As a result, preventing malicious nodes from interfering with model training while rewarding trustworthy nodes to assist to the learning process is critical for improved FL security and performance. We created an efficient resource allocation technique that optimizes computational offloading using a Blockchain-based Federated Learning (FL) method in order to add to the literature. The experimental findings show’s that the recommended FLBCPS technique improve system latency while maintaining consensus security.

FLBCPS: Federated Learning based Secured Computation Offloading in Blockchain-Assisted Cyber-Physical Systems

Mobile-edge computing (MEC) is a popular method for increasing the quality of computing experience for consumers since it allows them to offload computing operations to MEC servers, which have significant processing capabilities. However, safety and information security are major challenges that must be addressed. We employ Federated Learning-based Cyber-Physical Systems (CPS) to address safety and information security in MEC systems, which offers data accuracy and un-sustainability. Security of UAV assisted CPS against cyber-attacks yet an another challenging problem. Because most cyber-attacks occur in unpredictable ways, it is difficult to define them in a structured manner. Instead of developing a unique cyber-attack model, we focus on exploring the dynamic behavior of the system to cyber-attacks throughout this article. Attacks that repeat themselves by interfering with system components or data are insignificant if they can be quickly identified by the system's control mechanism. Intelligent cyber attackers remain undetected by the tracking system by carefully designing cyber-attacks. Our primary goal is to examine the effectiveness of such cyberattacks from the aspect of the CPS and overcome them using Federated Learning (FL).

Security Reassessing in UAV-Assisted Cyber-Physical Systems Based on Federated Learning

/pdf/a-comprehensive-survey-on-software-defined-networking-for-240jqzsx.pdf

A comprehensive survey on software‐defined networking for smart communities

The fog computing paradigm has gained significant popularity in meeting the emerging demand for delay-sensitive applications. A fog computing paradigm provides low latency, reduces communication delay, and increases system energy efficiency. Like cloud computing, the fog computing framework also relies on virtualization. In a fog-enabled virtualization environment, challenges of system failure, hardware maintenance and load balancing can be addressed by facilitating the migration of fog-virtualized components. To address this issue, in this study, we first proposed an experimental testbed to facilitate the migration of running nodes using container-based virtualization technology. Furthermore, we discussed recent research challenges and trends for fog-enabled container-based virtualization technology.

Container-based Migration Technique for Fog Computing Architecture

Industry 4.0 is characterized by digitalized production facilities, where a large volume of sensors collect a vast amount of data that is used to increase the sustainability of the production by e.g. optimizing process parameters, reducing machine downtime and material waste, and the like. However, making intelligent data-driven decisions under timeliness constraints requires the integration of time-sensitive networks with reliable data ingestion and processing infrastructure with plug-in support of Machine Learning (ML) pipelines. However, such integration is difficult due to the lack of frameworks that flexibly integrate and program the networking and computing infrastructures, while allowing ML pipelines to ingest the collected data and make trustworthy decisions in real time. In this paper, we present AIDA - a novel holistic AI-driven network and processing framework for reliable data-driven real-time industrial IoT applications. AIDA manages and configures Time-Sensitive networks (TSN) to enable real-time data ingestion into an observable AI-powered edge/cloud continuum. Pluggable and trustworthy ML components that make timely decisions for various industrial IoT applications and the infrastructure itself are an intrinsic part of AIDA. We introduce the AIDA architecture, demonstrate the building blocks of our framework and illustrate it with two use cases. 

Rajat Chaudhary

Papers

FLBCPS: Federated Learning based Secured Computation Offloading in Blockchain-Assisted Cyber-Physical Systems

Security Reassessing in UAV-Assisted Cyber-Physical Systems Based on Federated Learning

A comprehensive survey on software‐defined networking for smart communities

Container-based Migration Technique for Fog Computing Architecture

AIDA - A holistic AI-driven networking and processing framework for industrial IoT applications