The bakery industry is undergoing a profound digital transformation driven by the increasing need for enhanced energy efficiency, operational resilience, and a commitment to environmental sustainability. Digital Twin (DT) technology, recognized as a fundamental component of Industry 4.0, provides advanced capabilities for intelligent energy management across bakery operations. This paper utilizes a narrative and integrative review approach, conceptually integrating emerging developments in using DT with respect toenergy management in the baking industry, including real-time energy monitoring, predictive maintenance, dynamic optimization of production processes, and the seamless integration of renewable energy sources. The study underscores the transformative benefits of adopting DT technologies, such as improvements in energy utilization, greater equipment reliability, increased operational transparency, and stronger alignment with global sustainability objectives. It also critically examines the technical, organizational, and financial barriers limiting broader adoption, particularly among small and medium-sized enterprises (SMEs). Future research directions are identified, emphasizing the potential of artificial intelligence-driven DTs, the adoption of edge computing, the development of scalable and modular platforms, and the necessity of supportive policy frameworks. By integrating DT technologies, bakeries can shift from traditional reactive energy practices to proactive, data-driven strategies, paving the way for greater competitiveness, operational excellence, and a sustainable future.

Sensors are used in almost all aspects of human life and the usage of sensors has rapidly increased exponentially in the last few decades. The sensor is typically a device that measures any alteration in the target environment or events by detecting physical, chemical, or biological phenomena. Subsequently, this information is transformed into an output signal in digital, analog, or any other desired form. Sensors monitor temperature, pressure, vibration, movement, dimensions, illumination, etc. They are also used for environmental and, mobility applications for safety, production and manufacturing processes, healthcare, and more. This paper emphasizes sensor types with its functionality and the use of different sensors for monitoring the environment and mobility applications. This sensors are categorised and analysed based on the different applications, operating ranges, temperature ranges and power consumption ranges. Some real-time sensors and applications have been included in this manuscript based on the power consumption and other operating parameters like temperature and operating ranges.

Due to the need to replace fossil fuels with cleaner alternatives, the advancement of electric mobility has accelerated. Based on this, the electric train has been successfully developed and reached the condition. However, logistics and commercial vehicles still need to be sufficiently developed due to the electrical machine efficiency, weight, size, and control technology of the machines. This can be overcome by optimizing the dimensions of machines like winding diameter, length, and magnetic properties. In this study, we discussed various loss assessments, optimized winding size, and examined the mechanical properties of machines, including speed and torque. To examine the efficiency, speed, and torque characteristics of a machine, mathematical calculations are first done. The analytical model is validated through COMSOL, later the speed-torque characteristics are validated using a Simulink/MATLAB model.

Wireless sensor networks (WSNs) provide the foundation for the advancement of smart transportation systems (STS) to enable real-time monitoring, maximum traffic management, and improved urban mobility. WSNs make it possible to have permanent data exchange among infrastructure, cars, and control centers, offering support for intelligent transport solutions. However, the maintenance of consistent wireless data transfer in the networks is an enormous challenge from dynamic environmental conditions, increasing network density, and increasing application sophistication. Problematic phenomena such as network saturation, delay, interference, power constraints, and security risks can impair system performance and compromise safety-critical functions. They are fully addressed in this paper, and the full scope of recent technological advances to make reliable wireless communication available in transport environments is surveyed. It focuses on future tools and technologies like 5G and future 6G wireless communication, adaptive routing, and traffic prediction based on AI, low-latency edge computing, and blockchain technology for data reliability and secure identity management. It also focuses on adaptive communication protocols, low-power and long-distance wireless technologies like LoRaWAN, Zigbee, and BLE, and AI-based optimization techniques that enhance scalability, energy efficiency, and network resilience. Besides, the review integrates lessons gained in current deployments, like Singapore and London's smart traffic systems, South Korea and the Netherlands' intelligent highway initiatives, and autonomous vehicle platforms like Waymo and Tesla, all of which rely on high-speed wireless connectivity. A comparison of wireless communication standards utilized in smart transport is also presented to guide future technology adoption. The work highlights the need for scalable, secure, and energy-efficient WSNs to enable heterogeneous and dynamic transportation applications. Through overcoming long-standing transmission issues and tapping into recent advancements, WSNs can greatly improve the efficiency, security, and sustainability of urban mobility systems and thereby contribute toward the vision of intelligent and interconnected smart cities. Sensor networks (WSNs) are foundational to the development of smart transportation systems.