The rapid development forms a new transition of information technologies to offer an intelligent manufacturing. The manufacturer has revolutionized the stages of product lifecycle including process planning and maintenance for the early detection of potential system failures and proactive management. Technological advancements including big data, the cloud, and the Internet of Things have applied digital-twin for industrial practice. It has low-power wireless-enabled devices to play a vital role in various industrial automation systems such as industry logistics, portable equipment, and intelligent wireless monitoring. It is evident that industrial manufacturers are nowadays aiming to transform the machine into fully automated systems that not only control the operation of the equipment but also try to meet the demand of future markets effectively. One of the challenging issues in the automation of the machinery process is the deployment of reliable systems to analyze the machinery condition such as fault diagnosis. Thus, this article proposes a digital-twin-assisted fault diagnosis using deep transfer learning to analyze the operational conditions of machining tools. Moreover, this proposed system has developed an intelligent tool-holder that integrates a k-type thermocouple and cloud data acquisition system over the WiFi module. The analytical study proves that this intelligent tool-holder provides better accuracy to demonstrate the optimization of milling and drilling operations of cutting tools.

Internet-of-Things (IoT) enabled cyber-physical systems (CPS) is a system in which communication between the physical devices and the cyber environment runs independently without any user interaction. Several optimization algorithms have been used for determining the optimal solutions that can reduce the production cost and/or enhance the production efficiency with in limited time-periods. However, existing optimization approaches have failed to solve the issues in the complex manufacturing process. To overcome this issue, a novel technique called directed acyclic graph theory based multiobjective oppositional learnt artificial ant colony resource optimization (DAGT-MOLAACRO) technique has been introduced in this study for solving the complex manufacturing process in the industry. Initially, IoT devices are used in the industrial sector for sensing and collecting data. Then the collected data is sent to the cyberspace of the CPS system with the least latency. Then, the CPS system collects the data generated from the industrial IoT devices that is stored in cyberspace with lesser memory consumption. MOLAACRO is applied to find the optimal solution among the population that satisfies the resource constraints by constructing the directed acyclic graph. In this way, the DAGT-MOLAACRO technique reduces the time complexity with minimal latency and computation overhead. For verification purposes, our experimental work has been carried out using different performance metrics such as data latency, time complexity, and computation overhead with respect to the number of IoT devices and the amount of data collected. The results show that the DAGT-MOLAACRO technique has better performance with reductions in terms of time complexity by 10%, latency by 17%, and the computation overhead by 11% against the existing works in literature.

Real-estate advertisements through electronic and print media are bringing considerable fortune to the global real-estate sector. However, innovative advertisement methods must be adopted if real estate aims to transform into smart real estate. The current study, which is based on a systematic literature review of 58 articles published in the last decade, identifies key media for real-estate advertisements as print media (e.g., magazines, brochures, newspapers, and digests), electronic media (e.g., websites, social media, and other digital tools), and mixed methods (e.g., billboards, signs and banners, and personalized messaging). This study takes the case of Kingsford suburb in the eastern Sydney area, and investigates the performance of the Australian real-estate industry in general and lists the key dynamics of properties in Kingsford and its prominent real-estate agencies. An unmanned aerial vehicle (UAV)-based smart real-estate advertisement material delivery system is proposed to deliver advertisement materials and gifts to the potential customers of these agencies. The system paths are optimized through four Java-run algorithms: greedy, interroute, intraroute, and Tabu. Results based on six cases, three each for rent and sales with varying numbers of customers and UAVs and an 8-h operating time, indicate that the Tabu algorithm provides the best-optimized paths in all cases, followed by the interroute, intraroute, and greedy algorithms. However, the inter- and intraroute algorithms show superior performance in terms of computation speed. The proposed framework is a practical approach in disrupting the real-estate advertising sector, thereby helping this sector transform into a smart real estate consistent with industry 4.0 goals.