A new tool for food industrial plant simulation and IoT control

The general objective of this research is to create a Simulation Software allowing the Optimization and Control of an IoT system for food industry applications. An industrial plant is today considered a complex system as it is often composed of many types of machines characterized by a strong temporal variation of the productions. The three missing factors to accomplish what above are: • Simulation; • Implementation of communicating objects also called IoT (Internet of Things) systems. • Intelligent control; In this paper, we show the tools developed and implemented in the Simulation software used to model a physical industrial process by mean of a user-friendly graphic interface allowing interactively defining different plant configurations, through the selection of basic graphic elements and their appropriate connection. The software has been developed in Unity, a cross-platform engine used to create three-dimensional, twodimensional, virtual reality, and augmented reality, as well as simulations. The software consists of the following main sections: • library; • creation and management of the process scheme; • simulation. The "library" section contains the elements (blocks) used in the process scheme, to be completed with functions describing their specific behaviour. These elements represent physical components and logical connectors that allow connecting the different blocks generating an oriented graph.The "creation and management of the process scheme" section is the graphical interface through which new configurations can be created. In particular, the connectors correspond to a flow of information from one block to another and contain the output variables determined by the function of the starting block. These values are used as input in the arrival block. The "simulation" section allows simulating of previously designed configurations

Towards convergence of AI and IoT for energy efficient communication in smart homes

The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) promotes the energy efficient communication in smart homes. Quality of Service (QoS) optimization during video streaming through wireless micro medical devices (WMMD) in smart healthcare homes is the main purpose of this research. This paper contributes in four distinct ways. First, to propose a novel Lazy Video Transmission Algorithm (LVTA). Second, a novel Video Transmission Rate Control Algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission) and standard deviation is investigated while comparing the LVTA, VTRCA, and Baseline approaches. Experimental results demonstrate that the reduction in encoding (32%, 35.4%) and transmission (37%, 39%) energy drains, PMR (5, 4), and standard deviation (3dB, 4dB) for VTRCA and LVTA, respectively, is greater than that obtained by Baseline during video streaming through WMMD

Digital twin reference model development to prevent operators' risk in process plants

In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems