Artificial intelligence (AI), combined with the Internet of Things (IoT), plays a beneficial role in various fields, including intelligent surveillance applications. With IoT and 5G advancement, intelligent sensors, and devices in the surveillance environment collect large amounts of data in the form of videos and images. These collected data require intelligent information processing solutions, help analyze the recorded videos and images to detect and identify various objects in the scene, particularly humans. In this study, an automated human detection system is presented for a complex industrial environment, in which people are monitored/detected from a top view perspective. A top view is usually preferred because it can provide sufficient coverage and enough visibility of a scene. This study demonstrates the applications, efficiency, and effectiveness of deep learning architectures, that is, Faster Region Convolutional Neural Network (Faster R-CNN), Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv3), with transfer learning. Experimental results reveal that with additional training and transfer learning, the performance of all detection architectures is significantly improved. The detection results are also compared using the same data set. The deep learning architectures achieve promising results with maximum true-positive rate of 93%, 94%, and 94% for Faster-RCNN, SSD, and YOLOv3, respectively. Furthermore, a detailed study is performed on output results that highlight challenges and probable future trends.

Advancements in digital technologies, such as the Internet of Things (IoT), fog/edge/cloud computing, and cyber-physical systems have revolutionized a broad spectrum of smart city applications. The significant contributions and rapid developments of advanced artificial intelligence-based technologies and approaches, like, machine learning and deep learning, which are applied for extracting accurate information from extensive data, perform a potential role in IoT applications. Moreover, blockchain technology's fast adoption also contributes a significant role in the development of the new digital smart city ecosystem. Thus, artificial intelligence and blockchain technology convergence revolutionize smart city infrastructures to establish sustainable ecosystems for IoT applications. Nevertheless, these advancements and technological improvements also provide both opportunities and challenges for developing sustainable IoT applications. This paper aims to examine the convergence of blockchain technology and artificial intelligence, a unique driver towards technological transformation in intelligent and sustainable IoT applications. We mainly discussed the advantages of blockchain technology that might promote the advancement and development of sustainable IoT applications. On the basis of the discussion, we introduced a smart and sustainable conceptual framework that leverages cloud computing, IoT devices, and artificial intelligence to process and obtain necessary information. The system provides digital analytics and saves results in decentralized cloud repositories through blockchain technology to promote various applications. Moreover, the layer-based architecture allows a sustainable incentive structure, which can possibly assist secure and protected smart city applications. We reviewed the enhanced solutions, summing up the key points that can be applied for generating various artificial intelligence and blockchain-based systems. Also, we discussed the issues that still remain open and our future research goals; that can introduce new ideas and future guidelines for sustainable IoT applications.