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

The amalgamation of information and communication technologies in power industry has led to a revolution known as smart grid (SG). The energy consumers interact with the power utility using a bidirectional communication channel for energy trading in SG ecosystem. However, the traditional energy trading mechanisms strongly rely on trusted third parties which act as a single point of failure. Therefore, it is important to equip SG with a decentralized and secure energy trading system which can execute contracts and handle negotiations among various trading parties. Hence, in this paper, EnergyChain, a blockchain model for storing and accessing the data generated by smart homes in a secure manner is proposed. EnergyChain works in following phases: 1) a miner node is selected on the basis of power capacity of various smart homes, 2) a block creation and validation scheme is presented, and 3) a transaction handling mechanism is designed for secure energy trading. After evaluation, the superiority of EnergyChain is validated. The results obtained show that EnergyChain outperforms the traditional scheme in terms of communication costs and computation time.

EnergyChain: Enabling Energy Trading for Smart Homes using Blockchains in Smart Grid Ecosystem

Due to its widespread popularity and usage in many applications (smart transport, energy management, ${e}$ -healthcare, smart ecosystem, and so on), the Internet of Things (IoT) has become popular among end users over the last few years. However, with an exponential increase in the usage of IoT technologies, we have been witnessing an increase in the number of cyber attacks on the IoT environment. An adversary can capture the private key shared between users and devices and can launch various attacks, such as IoT ransomware, Mirai botnet, man-in-the-middle, denial of service, chosen plaintext, and chosen ciphertext. To mitigate these security attacks on the IoT environment, the traditional public key cryptographic primitives are inadequate because of their high computational and communication costs. Therefore, lattice-based public-key cryptosystem (LB-PKC) is a promising technique for secure communication. We discuss the taxonomy of two major problems, namely, the shortest path and the closest path problems with respect to the applicability of lattice-based cryptographic primitives for IoT devices. Moreover, we also discuss various LB-PKC techniques, such as NTRU, learning with errors (LWEs), and ring-LWE (R-LWE) which are often used to solve shortest path and lattice NP-hard problems in a polynomial time. We further classify the R-LWE into three categories, namely identity-based encryption, homomorphic encryption, and secure authentication key exchange. We describe the operations and algorithms adopted in each of these encryption mechanisms. Finally, we discuss the challenges, open issues, and future directions for applying LB-PKC in the IoT environment.

Lattice-Based Public Key Cryptosystem for Internet of Things Environment: Challenges and Solutions

With the evolution of Internet of Things (IoT), there has been an overwhelming increase in the number of connected devices in recent years. Due to this, generation of massive amounts of data is inevitable from these enormous number of devices in IoT environment, especially in Internet of Vehicles (IoV). In such an environment, there is a need of a paradigm shift from traditional host-centric approach to a more flexible content-centric networking approach. The existing TCP/IP-based congestion control mechanisms can not be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput which most of the existing TCP variants (Tahoe, Reno, NewReno and TCP Vegas) may not be able to provide. So, in this paper, a deep learning- based content centric data dissemination approach for IoV is presented by taking into account the mobility of vehicles and type of content shared among vehicles. The proposed scheme works in three phases: 1) In the first phase, an energy estimation scheme is designed to identify the vehicles which can participate in data dissemination. 2) In the second phase, connection probability of these vehicles is computed to identify stable and reliable connections using Weiner process model. 3) In the last phase, a convolutional neural network (CNN)-based scheme for estimating the social relationship score among vehicle-to-vehicle pairs is designed. CNN is used to identify the ideal vehicle pairs, which can share data to ensure minimum delay and high data availability. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained proves the efficacy of the proposed scheme with respect to performance metrics such as-content disseminated, energy, and social score.

Deep Learning-Based Content Centric Data Dissemination Scheme for Internet of Vehicles

Steganography is the art and science of covered or hidden writing. The purpose of steganography is covert communication to hide the existence of a message from an intermediary. Digital Steganography algorithms have been developed by using texts, images and audio etc as the cover media. In this approach, a security model is proposed which imposes the concept of secrecy over privacy for text messages. In the recent years, we have plenty of security tools which are developed to protect the transmission of multimedia objects. But approaches for the security of text messages are comparatively less. In this approach we present a new approach on text steganography through the null space in the cover message for hiding the secret message. In this method, hiding bits of secret message is done through adding extra null spaces in the plaintext of the cover file. These null spaces placed, when the binary bit of secret message is equal to 1 in plaintext of the cover file. And null space remains unchanged when the binary bit of the secret message is equal to 0.

/pdf/a-novel-approach-of-text-steganography-based-on-null-spaces-tddtv3lkga.pdf

A Novel Approach of Text Steganography based on null spaces

Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 40 ecosystem In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move However, there are various challenges for the design of patient driven healthcare recommender systems Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes

/pdf/dlrs-deep-learning-based-recommender-system-for-smart-zp059kul5v.pdf

Rajat Chaudhary

Papers

EnergyChain: Enabling Energy Trading for Smart Homes using Blockchains in Smart Grid Ecosystem

Lattice-Based Public Key Cryptosystem for Internet of Things Environment: Challenges and Solutions

Deep Learning-Based Content Centric Data Dissemination Scheme for Internet of Vehicles

A Novel Approach of Text Steganography based on null spaces

DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem