Internet of Things (IoT) technology relies on wireless communication between devices through the Internet. Specialized antennas are essential for enabling seamless communication between sensors, smart devices, and network access points. Compact, high-performing antenna designs are becoming more and more in demand as smart services continue to penetrate daily life, especially in situations where wired connections are either impossible or unviable. This study provides a thorough analysis of various IoT-specific antenna designs, highlighting advancements that enhance important variables including gain, bandwidth, efficiency, and isolation. In addition to comparing existing antenna models, it looks at their strengths across different IoT applications. Furthermore, this research investigates developments in antenna design techniques, tackling crucial elements including environmental adaptability, multi-band functioning, and miniaturization to accommodate developing 5G-IoT systems. Additionally, the study examines current research gaps, ongoing challenges, and major market trends impacting the development of future 5G-IoT antenna designs. The information offered is intended to help engineers and designers choose the best antenna options and performance-boosting strategies to successfully satisfy a range of IoT communication needs.

A compact high‐isolation, four‐port MIMO antenna that is optimized for 5G‐IoT applications and works at the 28 and 39 GHz frequency bands is presented in this paper. The single antenna having a compact size of 6.6 × 6.6 × 0.254 mm ³ is a slot‐etched patch antenna fabricated from Rogers RT Duroid 5880 substrate, which is low loss and designed to carry adequate radiation properties. These properties include a good radiation efficiency of up to 86% and 87% and a suitable comparative gain of up to 7.1 and 6.8 dBi for 28 and 39 GHz, respectively. A compact four‐port orthogonally aligned MIMO antenna with a compact total size of 15.2 × 15.2 × 0.254 mm ³ is proposed after a thorough investigation and exploitation of the single‐antenna element. A simple geometric parasitic element is loaded among the MIMO elements to reduce mutual interaction between antenna elements. The performance of MIMO diversity is thoroughly assessed in terms of many important metrics, such as the envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), channel capacity loss (CCL), and total active reflection coefficient (TARC), where strong performance is revealed. The proposed design is significantly more suitable for the 5G‐IoT systems and superior to many other existing designs as a consequence of the results obtained.

Due to the easy accessibility of various image editing tools, image authentication has become a demanding area of research in multimedia technology. An image hash can be used for the authentication of an image that could be sensitive to content changes and invariant to perceptually similar images. The main problem in image hashing is when a system is designed to address the issues of perceptual robustness and discrimination simultaneously, as they are both inverse to each other. To address this problem, we exploited the phase spectrum of the Fourier transform (PFT), a visual attention model, to reveal the saliency map of the pre-processed test image. After that, the first-level discrete wavelet transform (DWT) is applied to obtain the four subbands, out of which the low low (LL) subband, which is located at the top left corner, comprises the lower-frequency components of the DWT and has a quite small variation because of suitable manipulations, similar to JPEG compression, and any malicious tampering in a particular portion of the image results in clear variations in the resultant coefficients of the LL subband. These LL subband coefficients are further exploited using the discrete cosine transform (DCT), as it has an energy compaction property. The energy of the image is gathered into a few DCT coefficients. Among these coefficients, the first one is the DC, which has the main information of the image, and the other ones are AC coefficients, which have the detailed information of the data. The hamming distance is used as a similarity metric among different hashes. Experimental validation is performed, focusing on malicious tampering detection, perceptual robustness, discrimination, hit rate, error rate, and computational complexity, confirming the effectiveness and efficiency of the proposed algorithm. Compared with other eminent algorithms, our proposed algorithm achieves good performance in terms of both hash length and execution time.