This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer�s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user�s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer�s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller to minimize the household�s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks.

Automated Demand Response from Home Energy Management System under Dynamic Pricing and Power and Comfort Constraints

NOMA and 5G emerging technologies: A survey on issues and solution techniques

Sensors play a vital role in our daily lives and are an essential component for Internet of Things (IoT) based systems as they enable the IoT to collect data to take smart and intelligent decisions. Recent advances in IoT systems, applications, and technologies, including industrial Cyber-Physical Systems (CPSs), are being supported by a wide range of different types of sensors based on artificial intelligence (AI). These smart AI-based sensors are typically characterized by onboard intelligence and have the ability to communicate collaboratively or through the Internet. To achieve the high level of automation required in today’s smart IoT applications, sensors incorporated into nodes must be efficient, intelligent, context-aware, reliable, accurate, and connected. Such sensors must also be robust, safety- and privacy-aware for users interacting with them. Sensors leveraging advanced AI technologies, new capabilities have recently emerged which have the potential to detect, identify, and avoid performance degradation and discover new patterns. Along with knowledge from complex sensor datasets, they can promote product innovation, improve operation level, and open up novel business models. We review sensors, smart data processing, communication protocol, and artificial intelligence which will enable the deployment of AI-based sensors for next-generation IoT applications.

Artificial Intelligence-Based Sensors for Next Generation IoT Applications: A Review

The use of Blockchain technology has recently become widespread. It has emerged as an essential tool in various academic and industrial fields, such as healthcare, transportation, finance, cybersecurity, and supply chain management. It is regarded as a decentralized, trustworthy, secure, transparent, and immutable solution that innovates data sharing and management. This survey aims to provide a systematic review of Blockchain application to intelligent transportation systems in general and the Internet of Vehicles (IoV) in particular. The survey is divided into four main parts. First, the Blockchain technology including its opportunities, relative taxonomies, and applications is introduced; basic cryptography is also discussed. Next, the evolution of Blockchain is presented, starting from the primary phase of pre-Bitcoin (fundamentally characterized by classic cryptography systems), followed by the Blockchain 1.0 phase, (characterized by Bitcoin implementation and common consensus protocols), and finally, the Blockchain 2.0 phase (characterized by the implementation of smart contracts, Ethereum, and Hyperledger). We compared and identified the strengths and limitations of each of these implementations. Then, the state of the art of Blockchain-based IoV solutions (BIoV) is explored by referring to a large and trusted source database from the Scopus data bank. For a well-structured and clear discussion, the reviewed literature is classified according to the research direction and implemented IoV layer. Useful tables, statistics, and analysis are also presented. Finally, the open problems and future directions in BIoV research are summarized. 

Blockchain Technology for Intelligent Transportation Systems: A Systematic Literature Review

In this paper, an area-time efficient hardware implementation of modular multiplication over five National Institute of Standard and Technology (NIST)-recommended prime fields is proposed for lightweight elliptic curve cryptography (ECC). A modified radix-2 interleaved algorithm is proposed to reduce the time complexity of conventional interleaved modular multiplication. The proposed multiplication algorithm is designed in hardware and separately implemented on Xilinx Virtex-7, Virtex-6, Virtex-5, and Virtex-4 field-programmable gate array (FPGA) platforms. On the Virtex-7 FPGA, the proposed design occupies only 1151, 1409, 1491, 2355, and 2496 look up tables (LUTs) and performs single modular multiplication in 0.93 μs, 1.18 μs, 1.45 μs, 2.80 μs, and 4.69 μs with maximum clock frequencies of 207.1 MHz, 190.7 MHz, 177.3 MHz, 137.6 MHz, and 111.2 MHz over five NIST prime fields of size 192, 224, 256, 384, and 521 bits, respectively. The hardware implementations on the Virtex-6, Virtex-5, and Virtex-4 FPGAs also show that the proposed design is highly efficient in terms of hardware resource utilization and area-delay product compared with other designs for modular multiplication.

Area-Time Efficient Hardware Implementation of Modular Multiplication for Elliptic Curve Cryptography

Developing a high-speed elliptic curve cryptographic (ECC) processor that performs fast point multiplication with low hardware utilization is a crucial demand in the fields of cryptography and network security. This paper presents field-programmable gate array (FPGA) implementation of a high-speed, low-area, side-channel attacks (SCAs) resistant ECC processor over a prime field. The processor supports 256-bit point multiplication on recently recommended twisted Edwards curve, namely, Edwards25519, which is used for a high-security digital signature scheme called Edwards curve digital signature algorithm (EdDSA). The paper proposes novel hardware architectures for point addition and point doubling operations on the twisted Edwards curve, where the processor takes only 516 and 1029 clock cycles to perform each point addition and point doubling, respectively. For a 256-bit key, the proposed ECC processor performs single point multiplication in 1.48 ms, running at a maximum clock frequency of 177.7 MHz in a cycle count of 262 650 with a throughput of 173.2 kbps, utilizing only 8873 slices on the Xilinx Virtex-7 FPGA platform, where the points are represented in projective coordinates. The implemented design is time-area-efficient as it offers fast scalar multiplication with low hardware utilization without compromising the security level.

/pdf/fpga-implementation-of-high-speed-area-efficient-processor-5cprayxztn.pdf

FPGA Implementation of High-Speed Area-Efficient Processor for Elliptic Curve Point Multiplication Over Prime Field

In the coming future, it is obvious that the wireless networks will be congested with massive amounts of data traffic with the increasing number of users. Current multiple access techniques will certainly not have the capability to efficiently serve in the massively congested scenarios. In recent times, nonorthogonal multiple access (NOMA) has been recognized as an immensely potential technique for 5G and beyond communications that can increase spectral efficiency to a greater extent serving a vast number of users. However, several circumscriptions are observed in NOMA such as the requirement of a perfect channel state information in the transmitter and high computational complexity in the receiver. The use of deep learning (DL) techniques is a great resolution to deal with the challenges. This paper discusses the applications of the DL methods in NOMA for 5G and beyond communications. Firstly, the deep neural networks that are employed in NOMA are listed up and discussed. After that, their functions are studied specifically focusing on how to improve the NOMA performance. Finally, the possible future challenges and research issues are identified at the end of the paper.

The Role of Deep Learning in NOMA for 5G and Beyond Communications

With the advancement of communication technology, Internet of Things (IoT) enabled smart home (SH) applications have engrossed substantial attention nowadays. However, a few meter range coverage and higher implementation cost are the main limitations of existing SH systems based on other cellular networks or short distance technologies. In this paper, a long range (LoRa) based SH system is proposed for remote monitoring and maintenance of IoT sensors and devices using artificial intelligence (AI) concept. A brief overview of what tasks LoRa can perform in SH networking are conferred. An AI-based data flow system for IoT server and cloud is also presented in this paper.

An Overview of AI-Enabled Remote Smart- Home Monitoring System Using LoRa

With the swift evolution of wireless technologies, the demand for the Internet of Things (IoT) security is rising immensely. Elliptic curve cryptography (ECC) provides an attractive solution to fulfill this demand. In recent years, Edwards curves have gained widespread acceptance in digital signatures and ECC due to their faster group operations and higher resistance against side-channel attacks (SCAs) than that of the Weierstrass form of elliptic curves. In this paper, we propose a high-speed, low-area, simple power analysis (SPA)-resistant field-programmable gate array (FPGA) implementation of ECC processor with unified point addition on a twisted Edwards curve, namely Edwards25519. Efficient hardware architectures for modular multiplication, modular inversion, unified point addition, and elliptic curve point multiplication (ECPM) are proposed. To reduce the computational complexity of ECPM, the ECPM scheme is designed in projective coordinates instead of affine coordinates. The proposed ECC processor performs 256-bit point multiplication over a prime field in 198,715 clock cycles and takes 1.9 ms with a throughput of 134.5 kbps, occupying only 6543 slices on Xilinx Virtex-7 FPGA platform. It supports high-speed public-key generation using fewer hardware resources without compromising the security level, which is a challenging requirement for IoT security.

https://www.mdpi.com/1424-8220/20/18/5148/pdf

Md. Mainul Islam

Papers

FPGA Implementation of High-Speed Area-Efficient Processor for Elliptic Curve Point Multiplication Over Prime Field

Area-Time Efficient Hardware Implementation of Modular Multiplication for Elliptic Curve Cryptography

The Role of Deep Learning in NOMA for 5G and Beyond Communications

An Overview of AI-Enabled Remote Smart- Home Monitoring System Using LoRa

Design and Implementation of High-Performance ECC Processor with Unified Point Addition on Twisted Edwards Curve.