Slotted ALOHA Overlay on LoRaWAN: a Distributed Synchronization Approach

LoRaWAN is one of the most promising standards for IoT applications. Nevertheless, the high density of end-devices expected for each gateway, the absence of an effective synchronization scheme between gateway and end-devices, challenge the scalability of these networks. In this article, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA (S-ALOHA) instead of the classic ALOHA approach used by LoRa. The implementation is an overlay on top of the standard LoRaWAN; thus no modification in pre-existing LoRaWAN firmware and libraries is necessary. Our method is based on a novel distributed synchronization service that is suitable for low-cost IoT end-nodes. S-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput.Comment: 4 pages, 8 figure

Security Implications of Fog Computing on the Internet of Things

Recently, the use of IoT devices and sensors has been rapidly increased which also caused data generation (information and logs), bandwidth usage, and related phenomena to be increased. To our best knowledge, a standard definition for the integration of fog computing with IoT is emerging now. This integration will bring many opportunities for the researchers, especially while building cyber-security related solutions. In this study, we surveyed about the integration of fog computing with IoT and its implications. Our goal was to find out and emphasize problems, specifically security related problems that arise with the employment of fog computing by IoT. According to our findings, although this integration seems to be non-trivial and complicated, it has more benefits than the implications.Comment: 5 pages, conference paper, to appear in Proceedings of the ICCE 2019, IEEE 37th International Conference on Consumer Electronics (ICCE), Jan 11- 13, 2019, Las Vegas, NV, US

Cyber-Physical Manufacturing Metrology Model (CPM3) for Sculptured Surfaces - Turbine Blade Application

Cyber-Physical Manufacturing (CPM) and digital manufacturing represent the key elements for implementation of Industry 4.0 framework. Worldwide, Industry 4.0 becomes national research strategy in the field of engineering for the following ten years. The International Conference USA-EU-Far East-Serbia Manufacturing Summit was held from 31st May to 2nd June 2016 in Belgrade, Serbia. The result of the conference was the development of Industry 4.0 Model for Serbia as a framework for New Industrial Policy - Horizon 2020/2030. Implementation of CPM in manufacturing systems generates " smart factory". Products, resources, and processes within smart factory are realized and controlled through CPM model. This leads to significant advantages with respect to high product/process quality, real-time applications, savings in resources consumption, as well as, lower costs in comparison with classical manufacturing systems. Smart factory is designed in accordance with sustainable and service-oriented best business practices/models. It is based on optimization, flexibility, self-adaptability and learning, fault tolerance, and risk management. Complete manufacturing digitalization and digital factory are the key elements of Industry 4.0 Program. In collaborative research, which we carry out in the field of quality control and manufacturing metrology at University of Belgrade, Faculty of Mechanical Engineering in Serbia and at Department of Mechanical Engineering, University of Texas, Austin in USA, three research areas are defined: (a) Digital manufacturing - towards Cloud Manufacturing Systems (as a basis for CPS), in which quality and metrology represent integral parts of process optimization based on Taguchi model, and (sic) Cyber-Physical Quality Model (CPQM) - our approach, in which we have developed and tested intelligent model for prismatic parts inspection planning on CMM (Coordinate Measuring Machine). The third research area directs our efforts to the development of framework for Cyber-Physical Manufacturing Metrology Model (CPM3). CPM3 framework will be based on integration of digital product metrology information through metrology features recognition, and generation of global/local inspection plan for free-form surfaces; we will illustrate our approach using turbine blade example. This paper will present recent results of our research on CPM3

Mixture distribution modelling of the sensitivities of a digital 3-axis MEMS accelerometers large batch

Huge quantities of low-cost analogue or digital MEMS sensors, in the order of millions per week, are produced by manufacturers. Their use is broad, from consumer electronic devices to Industry 4.0, Internet of Things and Smart Cities. In many cases, such sensors have to be calibrated by accredited laboratories to provide traceable measurements. However, at present, such a massive number of sensors cannot be calibrated and large-scale calibration systems and procedures are still missing. A first step to implementing these methods can be based on the distribution of the sensitivities of the large batches produced. Such distribution is also useful for sensor network end-users who need a single sensitivity, with the associated uncertainty, to be attributed to the whole network. Recently, a large batch of 100 digital 3-axis MEMS accelerometers was calibrated with a primary calibration system developed at INRiM and suitable for 3-axis accelerometers. Distributions of their sensitivities as a function of axis and frequency were analyzed and their non-normal behaviour was shown. However, in the preliminary phase of the study, the calibration uncertainties were not considered in these distributions. Therefore, in this paper, a mixture distribution modelling, based on Monte Carlo simulations and aimed at including the calibration uncertainties in the sensitivity distributions, is implemented and the resulting distributions are compared to the previous ones in histogram form. These distributions are also fitted with Johnson's unbounded and bimodal functions to get continuous distributions. This paper represents a further step towards the development of large-scale statistical calibration methods

Cyber-Physical Manufacturing Metrology Model (CPM3) - Big Data Analytics Issue

Internet of Things (IoT) is changing the world, and therefore the application of ICT (Information and Communication Technology) in manufacturing. As a paradigm based on the Internet, IoT utilizes the benefits of interrelated technologies/smart devices such as RFID (Radio Frequency Identification) and WSAN (Wireless Sensor and Actuator Networks) for the retrieval and exchange of information thus opening up new possibilities for integration of manufacturing system and its cyber representation through Cyber-Physical Manufacturing (CPM) model. On the other hand, CPM and digital manufacturing represent the key elements for implementation of Industry 4.0 and backbone for "smart factory" generation. Interconnected smart devices generate huge databases (big data), so that Cloud computing becomes indispensable tool to support the CPM. In addition, CPM has an extremely expressed requirement for better control, monitoring and data management. Limitations still exist in storages, networks and computers, as well as in the tools for complex data analysis, detection of its structure and retrieval of useful information. Products, resources, and processes within smart factory are realized and controlled through CPM model. In this context, our recent research efforts in the field of quality control and manufacturing metrology are directed to the development of framework for Cyber-Physical Manufacturing Metrology Model (CPM3). CPM3 framework will be based on: 1) integration of digital product metrology information obtained from big data using BDA (big data analytics) through metrology features recognition, and 2) generation of global/local inspection plan for CMM (Coordinate Measuring Machine) from extracted information. This paper will present recent results of our research on CPM3 - big data analytics issue

Development of a Reference Wafer for On-Wafer Testing of Extreme Impedance Devices

This paper describes the design, fabrication, and testing of an on-wafer substrate that has been developed specifically for measuring extreme impedance devices using an on-wafer probe station. Such devices include carbon nano-tubes (CNTs) and structures based on graphene which possess impedances in the κ Ω range and are generally realised on the nano-scale rather than the micro-scale that is used for conventional on-wafer measurement. These impedances are far removed from the conventional 50- reference impedance of the test equipment. The on-wafer substrate includes methods for transforming from the micro-scale towards the nano-scale and reference standards to enable calibrations for extreme impedance devices. The paper includes typical results obtained from the designed wafer

Cyber Physical Manufacturing Metrology

The Cyber Physical Manufacturing Metrology (CP2M) is based on integration of the Cyber Physical Manufacturing (CPM) and connection between Internet of Things (IoT) and Cloud technology (CT). These are high-level methodologies for development of new generation manufacturing metrology systems, which are more intelligent, flexible and self-adaptable. CP2M generates Big Data, horizontally by integration (network of machines/CMMs, processes and sensors) and vertically by control (usually defined over five levels) which should be analytically processed and managed by the CP2M. In this paper was given, a detailed analysis of the current framework of development the CP2M. A brief overview of the concept CP2M research, particularly in Serbia is given as well