The Internet of Things (IoT) and artificial intelligence (AI) enabled IoT is a significant paradigm that has been proliferating to new heights in recent years. IoT is a smart technology in which the physical objects or the things that are ubiquitously around us are networked and linked to the internet to deliver new services and enhance efficiency. The primary objective of the IoT is to connect all the physical objects or the things of the world under a common infrastructure, allowing humans to control them and get timely, frequent updates on their status. These things or devices connected to IoT generate, gather and process a massive volume of binary data. This massive volume of data generated from these devices is analyzed and learned by AI algorithms and techniques that aid in providing users with better services. Thus, AI-enabled IoT or artificial IoT (AIoT) is a hybrid technology that merges AI with IoT and is capable of simplifying complicated and strenuous tasks with ease and efficiency. The various machine learning (ML) and deep learning (DL) algorithms in IoT are necessary to ensure the IoT network’s improved security and confidentiality. Furthermore, this paper also surveys the various architectures that form the backbone of IoT and AIoT. Moreover, the myriad state-of-the-art ML and DL-based approaches for securing IoT, including detecting anomalies/intrusions, authentication and access control, attack detection and mitigation, preventing distributed denial of service (DDoS) attacks, and analyzing malware in IoT, are also enlightened. In addition, this work also reviews the various emerging technologies and the challenges associated with AIoT. Therefore, based on the plethora of prevailing significant works, the objective of this manuscript is to provide a comprehensive survey to draw a picture of AIoT in terms of security, architecture, applications, emerging technologies, and challenges.

In this paper, we propose a novel scheme termed layered orthogonal frequency division multiplexing with index modulation (L-OFDM-IM) to increase the spectral efficiency (SE) of OFDM-IM systems. In L-OFDM-IM, all subcarriers are first divided into multiple layers, each determining the active subcarriers and their modulated symbols. The index modulation (IM) bits are carried on the indices of the active subcarriers of all layers, which are overlapped and distinguishable with different signal constellations so that the number of the IM bits is larger than that in traditional OFDM-IM. A low-complexity detection is proposed to alleviate the high burden of the optimal maximum-likelihood detection at the receiver side. A closed-form upper bound on the bit error rate, the achievable rate, and diversity order are derived to characterize the performance of L-OFDM-IM. To enhance the diversity performance of L-OFDM-IM, we further propose coordinate interleaving L-OFDM-IM (CI-L-OFDM-IM), which interleaves the real and imaginary parts of the modulated symbols over two different subchannels. Computer simulations verify the theoretical analysis, and results show that L-OFDM-IM outperforms the conventional OFDM-IM scheme. Moreover, it is also confirmed that CI-L-OFDM-IM obtains an additional diversity order in comparison with L-OFDM-IM.

In this letter, we propose a new secrecy enhancement scheme for spatial modulation systems by exploiting the knowledge of the legitimate channel state information to rotate the indices of the transmit antennas and the constellation symbols, which are mapped into spatial and constellation bits. A secrecy rate analysis shows that the eavesdropper cannot recover any information carried by spatial and constellation bits. The numerical results on the secrecy rate and the bit error rate verify the effectiveness and robustness of the proposed scheme.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) transmits additional information bits through the indices of active subcarriers. In this paper, we explore the potential of an improvement of OFDM-IM by reusing inactive subcarriers to transmit signals for a secondary system. We consider a cognitive radio network assisted by OFDM-IM, where the primary user (PU) transmits its signal to the primary receiver, and the secondary user (SU) senses the idle spectrum and transmits its own signal to the secondary receiver via those idle subcarriers. By either combining the signal transmitted from SU or not, we propose two different detection strategies with different trade-offs between the computational complexity and system performance. An asymptotically tight upper bound on the bit error rate is derived to evaluate the error performance for PU and SU.

In this paper, we propose Euclidean geometric schemes to construct the codebooks for the space-time block coded spatial modulation (STBC-SM) system. The new codebook constructing schemes are more efficient than the previous exhaustive searching scheme. The result system is referred to as Euclidean geometry based space-time block coded spatial modulation (EG-STBC-SM) system. To achieve transmit diversity, two different schemes are introduced to construct sub-codebooks for the EG-STBC-SM system. In the first scheme, active transmits are selected based on the points and μ-flats of $d$ -dimensional Euclidean geometry over the Galois field $GF(p^s)$ . Furthermore, the extended orthogonal space-time block codes (EO-STBCs) are used to support more than two active transmit antennas (TAs) in the EG-SM-STBC system. In the second scheme, the flats of a Euclidean geometry are grouped into cyclic structure, which makes the encoding of EG-STBC-SM scheme very easy. In addition, a simple maximum-likelihood (ML) decoder is derived in this paper. BER performance of the EG-SM-STBC system is evaluated via computer simulation and theoretical upper bound. It is shown that the proposed EG-SM-STBC system outperforms various existing multiple-input multiple-output (MIMO) transmission counterparts of the same or lower spectral efficiencies.

For a given base graph, the lifted graph can be obtained by a copy-and-permute procedure. If the permutation is cyclic, the lifted graph corresponds to a quasi-cyclic (QC) protograph low-density parity-check (LDPC) code. The girth of the QC protograph LDPC code is determined by the girth of the base graph and the permutation shifts. In this paper, we first derive a lower bound on the lifting degree to achieve a large girth lifted graph. Then, motivated by the cycle searching and girth maximizing features of the progressive edge-growth (PEG) algorithm, we introduce the permutation shifts determining (PSD) PEG algorithm, which can construct large girth base graph and determine the optimal permutation shifts, simultaneously. It is shown that the computational complexity of PSD-PEG algorithm is much lower than that of the PEG algorithm and the PEG-QC algorithm for the same codeword length. Furthermore, we show that the PSD-PEG algorithm can also be used to construct nonbinary QC protograph LDPC codes without low weight codes. Simulation results show that the binary and nonbinary QC protograph LDPC codes constructed by the PSD-PEG algorithm have good bit error rate performance and frame error rate performance over the additive white Gaussian noise channel.