Rapid advances in industrial information integration methods have spurred tremendous growth in the use of enterprise systems. Consequently, a variety of techniques have been used for probing enterprise systems. These techniques include business process management, workflow management, Enterprise Application Integration (EAI), Service-Oriented Architecture (SOA), grid computing, and others. Many applications require a combination of these techniques, which is giving rise to the emergence of enterprise systems. Development of the techniques has originated from different disciplines and has the potential to significantly improve the performance of enterprise systems. However, the lack of powerful tools still poses a major hindrance to exploiting the full potential of enterprise systems. In particular, formal methods and systems methods are crucial for modeling complex enterprise systems, which poses unique challenges. In this paper, we briefly survey the state of the art in the area of enterprise systems as they relate to industrial informatics.

Enterprise Systems: State-of-the-Art and Future Trends

Recently, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.

IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing

Today's manufacturing environments are very dynamic and turbulent. Traditional enterprise information systems (EISs) have mostly been implemented upon hierarchical architectures, which are inflexible to adapt changes and uncertainties promptly. Next-generation EISs must be agile and adaptable to accommodate changes without significant time delays. It is essential for an EIS to obtain real-time data from the distributed and dynamic manufacturing environment for decision making. Wireless sensor networks (WSNs) and radio-frequency identification (RFID) systems provide an excellent infrastructure for data acquisition, distribution, and processing. In this paper, some key challenges related to the integration of WSN and RFID technologies are discussed. A five-layer system architecture has been proposed to achieve synergistic performance. For the integration of WSN and RFID, one of the critical issues is the low efficiency of communication due to redundant data as redundant data increases energy consumption and causes time delay. To address it, an improved data cleaning algorithm has been proposed; its feasibility and effectiveness have been verified via simulation and a comparison with a published algorithm. To illustrate the capacity of the developed architecture and new data cleaning algorithm, their application in relief supplies storage management has been discussed.

Data Cleaning for RFID and WSN Integration

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this paper, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely, the Internet of Things, robotics, Artificial Intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This paper aims to open up new research opportunities for readers, particularly industrial practitioners.

IEEE transactions on industrial informatics

Easily installable and highly reliable data communications over power line is a requirement to make Smart Grid more convenient and useful than fixed grid systems. Therefore RFID (Radio-Frequency IDentification) over Power Line technology was developed. Unlike conventional RFID carrying data wirelessly by antenna, radio-frequency signals for identification and payment runs through the power line in Smart Grid. The appliances embedded with IC card chips are detected and identified when their electrical plug connectors are inserted into the outlets in the electrical grid equipped with reader/writers. This system has the potential to revolutionize the usage of Power Line Communications with applications such as automatic identification, power control, and payment/ticketing through the power lines.

Applications of RFID over power line for Smart Grid

Automatic identification based on radio frequency identification (RFID) is progressively being introduced into industrial environments, enabling new applications and processes. In the context of communications, RFID rely mostly on Frame Slotted Aloha (FSA) anticollision protocols. Their goal is to reduce the time required to detect all the tags within range (identification time). Using FSA, the maximum identification rate is achieved when the number of contending tags equals the number of contention slots available in the frame. Therefore, the reader must estimate the number of contenders and allocate that number of slots for the next frame. This paper introduces the new MFML-DFSA anticollision protocol. It estimates the number of contenders by means of a maximum-likelihood estimator, which uses the statistical information from several frames (multiframe estimation) to improve the accuracy of the estimate. Based on this expected number of tags, the algorithm determines the best frame length for the next reading frame, taking into account the constraints of the EPCglobal Class-1 Gen-2 standard. The MFML-DFSA algorithm is compared with previous proposals and found to outperform these in terms of (lower) average identification time and computational cost, which makes it suitable for implementation in commercial RFID readers.

V. Bueno-Delgado

Papers

Multiframe Maximum-Likelihood Tag Estimation for RFID Anticollision Protocols