Internet of Things (IoT) is one of the most rapidly used technologies in the last decade in various applications. The smart things are connected in wireless or wired for communication, processing, computing, and monitoring different real-time scenarios. The things are heterogeneous and have low memory, less processing power. The implementation of the IoT system comes with security and privacy challenges because traditional based existing security protocols do not suitable for IoT devices. In this survey, the authors initially described an overview of the IoT technology and the area of its application. The primary security issue CIA (confidentially, Integrity, Availability) and layer-wise issues are identified. Then the authors systematically study the three primary technology Machine learning(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT. In the end, an analysis of this survey, security issues solved by the ML, AI, and Blockchain with research challenges are mention.

Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

Nowadays, 5G is in its initial phase of commercialization. The 5G network will revolutionize the existing wireless network with its enhanced capabilities and novel features. 5G New Radio (5G NR), referred to as the global standardization of 5G, is presently under the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP) and can be operable over the wide range of frequency bands from less than 6GHz to mmWave (100GHz). 3GPP mainly focuses on the three major use cases of 5G NR that are comprised of Ultra-Reliable and Low Latency Communication (uRLLC), Massive Machine Type Communication (mMTC), Enhanced Mobile Broadband (eMBB). For meeting the targets of 5G NR, multiple features like scalable numerology, flexible spectrum, forward compatibility, and ultra-lean design are added as compared to the LTE systems. This paper presents a brief overview of the added features and key performance indicators of 5G NR. The issues related to the adaptation of higher modulation schemes and inter-RAT handover synchronization are well addressed in this paper. With the consideration of these challenges, a next-generation wireless communication architecture is proposed. The architecture acts as the platform for migration towards beyond 5G/6G networks. Along with this, various technologies and applications of 6G networks are also overviewed in this paper. 6G network will incorporate Artificial intelligence (AI) based services, edge computing, quantum computing, optical wireless communication, hybrid access, and tactile services. For enabling these diverse services, a virtualized network slicing based architecture of 6G is proposed. Various ongoing projects on 6G and its technologies are also listed in this paper.

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A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies

To satisfy the expected plethora of demanding services, the future generation of wireless networks (6G) has been mandated as a revolutionary paradigm to carry forward the capacities of enhanced broadband, massive access, and ultrareliable and lowlatency service in 5G wireless networks to a more powerful and intelligent level. Recently, the structure of 6G networks has tended to be extremely heterogeneous, densely deployed, and dynamic. Combined with tight quality of service (QoS), such complex architecture will result in the untenability of legacy network operation routines. In response, artificial intelligence (AI), especially machine learning (ML), is emerging as a fundamental solution to realize fully intelligent network orchestration and management. By learning from uncertain and dynamic environments, AI-/ML-enabled channel estimation and spectrum management will open up opportunities for bringing the excellent performance of ultrabroadband techniques, such as terahertz communications, into full play. Additionally, challenges brought by ultramassive access with respect to energy and security can be mitigated by applying AI-/ML-based approaches. Moreover, intelligent mobility management and resource allocation will guarantee the ultrareliability and low latency of services. Concerning these issues, this article introduces and surveys some state-of-the-art techniques based on AI/ML and their applications in 6G to support ultrabroadband, ultramassive access, and ultrareliable and lowlatency services.

Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service

Recent advancements in radio frequency machine learning (RFML) have demonstrated the use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet, deep learning techniques have been shown, in other applications, to be vulnerable to adversarial machine learning (ML) techniques, which seek to craft small perturbations that are added to the input to cause a misclassification. The current work differentiates the threats that adversarial ML poses to RFML systems based on where the attack is executed from: direct access to classifier input, synchronously transmitted over the air (OTA), or asynchronously transmitted from a separate device. Additionally, the current work develops a methodology for evaluating adversarial success in the context of wireless communications, where the primary metric of interest is bit error rate and not human perception, as is the case in image recognition. The methodology is demonstrated using the well known Fast Gradient Sign Method to evaluate the vulnerabilities of raw IQ based Automatic Modulation Classification and concludes RFML is vulnerable to adversarial examples, even in OTA attacks. However, RFML domain specific receiver effects, which would be encountered in an OTA attack, can present significant impairments to adversarial evasion.

Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications

Trust is a prominent concern with the continued expansion of the Internet of Things (IoT). As new devices enter the market, device security must be a design pillar. In order to trust these devices, they must be identifiable and authenticated before they begin transmitting possible sensitive information, and given the vast number of IoT devices in the future, it may prove difficult to properly trust and authenticate these authorized devices on networks with current methods. Machine learning neural networks (NNs) have the ability to uniquely identify transmitters based on their physical waveform characteristics which could be used to identify and authenticate IoT nodes in large networks with little impact to latency, providing an extra layer of security and trust. This paper presents the groundwork for performing NN-based specific emitter identification (SEI) on resource constrained IoT devices using only raw in-phase and quadrature (IQ) streams, with protocols to secure IoT networks. As proof of concept, an existing NN-based SEI algorithm is executed on both a resource-rich and a more resource-constrained device with low latency, demonstrating the feasibility of using such algorithms on IoT devices now and in the future.

Groundwork for Neural Network-Based Specific Emitter Identification Authentication for IoT

Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency fingerprint. This paper presents an approach for identifying emitters using convolutional neural networks to estimate the inphase/quadrature (IQ) imbalance parameters of each emitter, using only the received raw IQ data as input. Because an emitter’s IQ imbalance parameters will not change as it changes modulation schemes, the proposed approach has the ability to track emitters, even as they change the modulation scheme. The performance of the developed approach is evaluated using simulated quadrature amplitude modulation and phase-shift keying signals, and the impact of signal-to-noise ratio, imbalance value, and modulation scheme are considered. Furthermore, the developed approach is shown to outperform a comparable feature-based approach, while making fewer assumptions and using fewer data per decision.

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Specific Emitter Identification Using Convolutional Neural Network-Based IQ Imbalance Estimators

Specific Emitter Identification (SEI) is the act of matching a received signal to an emitter using a database of radio frequency (RF) features belonging to known transmitters. SEI systems are of vital importance to the military for applications such as early warning systems, emitter tracking, and emitter location, and, more recently, have been used in cognitive radio systems to enforce Dynamic Spectrum Access (DSA) rules [1], [2]. This work investigates using Convolutional Neural Networks (CNNs) as feature learners and extractors, paired with the clustering algorithm DBSCAN, to perform SEI. The process through which emitter-specific features are extracted from raw I/Q data streams is described in detail, including the CNN architecture, design, and training. Extensive performance analysis demonstrates the effectiveness of the proposed approach in identifying emitters, and shows that features extracted from CNNs can be used to differentiate between devices unseen in training.

Clustering Learned CNN Features from Raw I/Q Data for Emitter Identification

While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions. However, while recent works seek to mature machine learning and deep learning techniques in applications related to wireless communications, a field loosely termed radio frequency machine learning, few have demonstrated the use of transfer learning techniques for yielding performance gains, improved generalization, or to address concerns of training data costs. With modifications to existing transfer learning taxonomies constructed to support transfer learning in other modalities, this paper presents a tailored taxonomy for radio frequency applications, yielding a consistent framework that can be used to compare and contrast existing and future works. This work offers such a taxonomy, discusses the small body of existing works in transfer learning for radio frequency machine learning, and outlines directions where future research is needed to mature the field.

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Lauren J. Wong

Papers

Groundwork for Neural Network-Based Specific Emitter Identification Authentication for IoT

Specific Emitter Identification Using Convolutional Neural Network-Based IQ Imbalance Estimators

Clustering Learned CNN Features from Raw I/Q Data for Emitter Identification

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey