Business Process Model for IOT Based Systems Operations

The internet of things (IoT) is an innovative and advanced high-level IT development that provides the connection between a large network of devices equipped with numerous computing capabilities, actuation, and sensing with the help of internet connection, consequently providing multifarious novel services regarding smart systems. All around the globe the attractive big data analytics and IoT services are allowing initiatives regarding smart systems. Business processes are commonly executed inside the application systems where computers, objects of IoT as well as humans participate. However, for the system-supported processes, the use of IoT technology is still facing the problem of the absence of a standard system architecture that is essential to manage the coordination in a smart IoT environment. Business process management (BPM) is regarded as a substantial technique for designing, controlling, and improving the processes of a system. This article introduces a BPM modeling approach for IoT-based systems operation exploits IoT using BPM by adopting an IoT framework architecture and considering IoT data for interaction in a defined process model. The methodology has been carried out on top of current BPM modeling notions and system techniques for formal representations of the system and also to get through the challenges of collaboration and connection

Literature review on the ‘Smart Factory’ concept using bibliometric tools

The objective of this paper is to depict a landscape of the scientific literature on the concept of the ‘Smart Factory’, which in recent years is gaining more and more attention from academics and practitioners because of significant innovations in the production systems within the manufacturing sector. To achieve this objective, a dynamic methodology called "Systematic Literature Network Analysis (SLNA)" has been applied. This methodology combines the Systematic Literature Review approach with the analysis of bibliographic networks. The adopted methodology allows complementing traditional content-based literature reviews by extracting quantitative information from bibliographic networks to detect emerging topics, and by revealing the dynamic evolution of the scientific production of a discipline. This dynamic analysis allowed highlighting research directions and critical areas for the development of the "Smart Factory". At the same time, it offers insights on the fields on which companies, associations, politicians and technology providers need to focus in order to allow a real transition towards the implementation of large-scale Smart Factory

Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning

Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

How the internet of things technology enhances emergency response operations

The Internet of Things (IoT) is a novel paradigm that connects the pervasive presence around us of a variety of things or objects to the Internet by using wireless/wired technologies to reach desired goals. Since the concept of the IoT was introduced in 2005, we see the deployment of a new generation of networked smart objects with communication, sensory and action capabilities for numerous applications, mainly in global supply chain management, environment monitoring and other non-stress environments. This paper introduces the IoT technology for use in the emergency management community. Considering the information required for supporting three sequential and distinct rhythms in emergency response operations: mobilization rhythm, preliminary situation assessment rhythm, and intervention rhythm, the paper proposes a modified task-technology fit approach that is used to investigate how the IoT technology can be incorporated into the three rhythms and enhance emergency response operations. The findings from our research support our two hypotheses: H1: IoT technology fits the identified information requirements; and H2: IoT technology provides added value to emergency response operations in terms of obtaining efficient cooperation, accurate situational awareness, and complete visibility of resources. © 2012 Elsevier Inc

How the Internet of Things Technology Enhances Emergency Response Operations

The Internet of Things (IoT) is a novel paradigmthat connects the pervasive presence around us of a variety of things or objects to the Internet by using wireless/wired technologies to reach desired goals. Since the concept of the IoT was introduced in 2005, we see the deployment of a new generation of networked smart objects with communication, sensory and action capabilities for numerous applications, mainly in global supply chain management, environment monitoring and other non-stress environments. This paper introduces the IoT technology for use in the emergency management community. Considering the information required for supporting three sequential and distinct rhythms in emergency response operations: mobilization rhythm, preliminary situation assessment rhythm, and intervention rhythm, the paper proposes a modified task-technology fit approach that is used to investigate how the IoT technology can be incorporated into the three rhythms and enhance emergency response operations. The findings from our research support our two hypotheses: H1: IoT technology fits the identified information requirements; and H2: IoT technology provides added value to emergency response operations in terms of obtaining efficient cooperation, accurate situational awareness, and complete visibility of resources

Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment

In the last decade, integrated logistics has become an important challenge in the development of wireless communication, identification and sensing technology, due to the growing complexity of logistics processes and the increasing demand for adapting systems to new requirements. The advancement of wireless technology provides a wide range of options for the maritime container terminals. Electronic devices employed in container terminals reduce the manual effort, facilitating timely information flow and enhancing control and quality of service and decision made. In this paper, we examine the technology that can be used to support integration in harbor's logistics. In the literature, most systems have been developed to address specific needs of particular harbors, but a systematic study is missing. The purpose is to provide an overview to the reader about which technology of integrated logistics can be implemented and what remains to be addressed in the future

Performance Evaluation of Variable Bandwidth Channel Allocation Scheme in Multiple Subcarrier Multiple Access

Multiple Subcarrier Multiple Access (MSMA) enables concurrent sensor data streamings from multiple wireless and batteryless sensors using the principle of subcarrier backscatter used extensively in passive RFID. Since the interference cancellation performance of MSMA depends on the Signal to Interference plus Noise Ratio of each subcarrier, the choice of channel allocation scheme is essential. Since the channel allocation is a combinatorial problem, obtaining the true optimal allocation requires a vast amount of examinations which is impracticable in a system where we have tens of sensor RF tags. It is particularly true when we have variable distance and variable bandwidth sensor RF tags. This paper proposes a channel allocation scheme in the variable distance and variable bandwidth MSMA system based on a newly introduced performance index, total contamination power, to prioritize indecision cases. The performance of the proposal is evaluated with existing methods in terms of average communication capacity and system fairness using MATLAB Monte Carlo simulation to reveal its advantage. The accuracy of the simulation is also verified with the result obtained from the brute force method