The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delay-sensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT.

Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a quite challenging task because of the complexity associated with them. SC is modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration as well as service costs. It also improves system efficiency and reduces the associated security risks. However, SCs are well encouraging the new technological reforms in Industry 4.0, but still, various security and privacy challenges need to be addressed. In this paper, a survey on SC security vulnerabilities in the software code that can be easily hacked by a malicious user or may compromise the entire BC network is presented. As per the literature, the challenges related to SC security and privacy are not explored much by the authors around the world. From the existing proposals, it has been observed that designing a complex SCs cannot mitigate its privacy and security issues. So, this paper investigates various Artificial Intelligence (AI) techniques and tools for SC privacy protection. Then, open issues and challenges for AI-based SC are analyzed. Finally, a case study of retail marketing is presented, which uses AI and SC to preserve its security and privacy.

Imperceptible latency, uninterrupted communication, and the availability of inexhaustible bandwidth are conceptualized as essential milestones to revolutionize the modes by which societies generated, circulate, receive, and perceive information. The exponential increase in wireless data traffic has raised concerns to investigate suitable bands in the radio spectrum to satisfy the intensifying user’s data rate requirements. Overall the wireless infrastructure needs development and exploitation to synchronize with the massive capacity and connectivity demands. The Terahertz (THz) frequency band (0.1-10 THz) is considered as a pivotal solution to fulfill the needs of applications and devices requiring the high speed transmission, and have received noticeable attention from the research community. Technologies in this spectrum are facing rapid development and hold high potentials in applications like ultra-fast short-range wireless communications, remote sensing, biological detection, and basic material research. The antenna is one of the critical components to support the THz systems and require a considerable attention in terms of precision. Compact high-gain antennas are desirable for low latency and high data rate THz wireless communication systems, specifically for applications having space limitation, for example, in the high speed interlink inside the high density wireless communication base station (BS). Nevertheless, there still exist many challenges, while designing the antenna for THz communications requiring innovative solutions. This paper serves an introductory guideline to address the challenges and opportunities, while designing a THz enabled antenna.

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques—BERT's deep contextual understanding and the generative capabilities of GPT—to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively.