Smart measuring instruments for cyber-physical systems

2016;
: pp. 3-16
1
Lviv Polytechnic National University, Ukraine
2
Lviv Polytechnic National University
3
Lviv Polytechnic National University, Ukraine
4
Lviv Polytechnic National University

Smart measuring instruments are the prerequisite for CPS design as they constitute the essential units of information-measuring subsystems. There is a set of smart measurement instruments which is divided into the following subsets: smart sensors, smart transducers, their grids etc. that can be joined together in modern wireless sensor networks. The emerging field of cheap and easily deployed sensors offers an unprecedented opportunity for a wide spectrum of various applications. When combined, they offer numerous advantages over traditional networks. These include a large-scale flexible architecture, high-resolution data, and application-adaptive mechanisms as well as a row of metrological specific features and performance (self-check, self-validation, self-verification, self-calibration, self-adjustment).
Milestones in everyday work aiming to ensure reliable wireless sensors networks operation lie in the direction of functional and probabilistic verifications. We provide the software and middleware development aiming to reach predetermined behavior. The easiest way to achieve this may be demonstrated on the example of widespread wireless fire detector networks. They are characterized by a number of special algorithms directed on as fast as possible and accurate triggering and actuating the automation of higher level. So, it becomes necessary to research and implement the original operation algorithms for fire sensors and also check algorithms for periodic real-time software examination. Considering their structural complexity (presence of smoke and heat sensitive elements, various principles of elaboration of the received signals, their drift of characteristics, and pollution of translucent elements, etc.) the development of such algorithms is a daunting task. Herein, human life may be the price for a bug. Equally important seems to be probabilistic verification that is to boost the probability of reaching wireless sensors network declared goals (estimation of their chances being achieved).
Each network consists structurally of a large number (up to 103) of nodes which are individual sensors able to radio communicate with one or several neighboring units. The most common wireless sensors network is the fire alarm sensors network each branch of which has up to 26 sensors which was caused by limiting the length of microcontroller register. Topology of every network may differ: star, cluster tree, mesh, up to advanced multi-hop mesh network. Propagation technique between hops of network can be
routing or flooding. Nowadays, problem arises to adapt traditional network topologies to contemporary communicating conditions.
If a centralized architecture is used in a sensor network and the central node fails, then the entire network will collapse, however the reliability of sensor network can be increased by using distributed control architecture. Distributed control is used in such networks for the following reasons: sensor nodes are prone to failure; for better collection of data; to provide nodes with backup in case of the central node failure; resources have to be self -organized.
Aiming at the substantial development of Cyber-Physical systems, which are becoming a key element of everyday life, the smart measuring instruments are considered below as an indispensable part of entire systems. Verification of the metrological subsystems for parameters determining the controlled equipment and processes through the development, implementation and realization of specific metrology and standardization methods, instruments, that is successfully described by the terms “metrological hardware, software, and middleware”.
Smart sensors are supplied with digital information transmissive means by equipping them with built-in digital controllers to match the universal network interface or by combining technology of analogue and digital transmission in a single measuring channel. According to the structure all smart sensors are divided into 4 groups: sensors of centralized and decentralized types, as well as sensors with digital and analogue buses. According to correction methods the analogue interfaces with smart sensors are divided into the groups: with manual error correction, with auto correction of errors in analogue-digital form, and with digital correction of errors.
Specific measurement consists in evaluating MIs performance reliability, trueness, and other metrological properties, due to the quality of a certain kind of metrological software, or the software linked to metrological features of MIs.
MI software metrological verification raises the problem of appropriate methods choice of software and middleware assessing, testing, and certifying. The metrological validation must result in confirmation or discarding of the studied ware following the requirements indicated in normative documents. Procedures and methods of checking software, and determining its disadvantages are considered below. Software study includes first of all the fulfilling the procedures of inambiguity ensuring the operating functions for generated data. Selection of the procedures is determined by regulation requirements, as well as by the software developer or the user’s desires to confirm its compliance with the target specification.

1. A. Platzer. Carnegie Mellon University. Logical Foundations of Cyber-Physical Systems. http://symbolaris.com/logic/lfcps.html.. 2. C. Mathas, Smart Sensors – Not Only Intelligent, but Adaptable, Contributed By Electronic Products, 2011-09-29. 3. S. Y. Yurish, Sensors: Smart vs. Intelligent, Sensors & Transducers, Vol. 114, Issue 3, , pp. 1-6, March 2010. 4. B.Deb, S.Bhatnagar , B.Nath. “A Topology Discovery Algorithm for Sensor Networks with Applications to Network Management”, CiteSeerX, 2002 5. Wayne W. Manges, Oak Ridge National Laboratory. 6. Ad-hoc Sensor Networks http://www.brunel.ac.uk/cedps /electronic-computer-engineering/research-activities/ wncc/ad-hoc-sensor-networks 7. Sensor technology handbook, Editor-in-Chief Jon S.Wilson, SA, 2005. 8. D.Wobschall, Smart Sensors for Smart Grid, Sensor Tech Forum 2011. 9. What is smart grid sensor? - Definition from WhatIs.com. internetofthingsagenda. techtarget.com/definitio... 10. Change into UA. Start-up in energetics, 2015. 11. H.Salem, M.Nader, Middleware: “Middleware Challenges and Approaches for Wireless Sensor Networks”, IEEE DISTRIBUTED SYSTEMS ONLINE 1541-4922, Published by the IEEE Computer Society, Vol. 7, No. 3, March 2006. 12. "What is Middleware?", Middleware.org. Defining Technology, 2008, Retrieved 2013-08-11. 13. S. Hadim, N. Mohamed, “Middleware challenges and approaches for wireless sensor networks”, IEEE Distributed Systems Online, vol.7, Issue, 2006, Retrieved March 4, 2009 from IEEE Distributed Systems. 14. M. Kazahaya, “A Mathematical Model and Error Analysis of Coriolis Mass Flowmeters”, IEEE Transactions on Instrum. and Measurement, Vol. 60, Issue 4, pp. 1163 – 1174, 2011. 15. D. Spitzer, The Consumer Guide to Coriolis Mass Flowmeters, Seminar. Spitzer and Boyes, LLC, 2004. 16. The procedure for certification of software of measuring instruments. Access to information:: http://www.uazakon.com/. 17. WELMEC 7.1, Edition 2 Information document. Development of software requirements, Vienna, 48 p., 2005. 18. MI 3286 – 2010. Testing of software protection, with the determination of its level at the measuring instruments tests in order to type approval, Moscow, 33 pp., 2010, (in Russian). 19. Microsoft Secure Software Development, The 3-rd Conference on minimization of software vulnerabilities in its development, www.microsoft.com/ru-ru/.../mssdcon2013/. 20. What is CSSLP (certified secure software lifecycle. Searchsecurity.techtarget.com/.../CSSLP-certifie... 21. ISSECO, the International Secure Software Engineering Council. www.isseco.org/ 22. G.V. Zlygosteva, SV Muravyev, The generalized model of a test procedure of measuring software, Bulletin of the Tomsk Polytechnic. Univ, Vol.318, No.4, pp.62-67, 2011 (in Russian). 23. E.V.Kovalevskaya, Metrology, software quality and certification, Moscow, 2004 (in Russian). 24. O. Oleskiv, I.Kunets. I.Mykytyn, Review of Techniques and Methods of Software Verification of Metrological Means, Lviv, 2014, Publishing House of Lviv Politechnic, No.75, 2014, http://vlp.com.ua/node/12707 (in Ukrainian). 25. S. Yatsyshyn, B.Stadnyk, Ya.Lutsyk, L.Buniak. Handbook of Thermometry and Nanothermometry, IFSA Publishing, Spane, 2015. 26. Measurement Science Roadmap for Metal-Based Additive Manufacturing, May 2013, sponsored by NIST. 27. S. Ko, H. Pan, C. Grigoropoulos, et al. “All-inkjet-printed flexible electronics fabrication on a polymer substrate by low-temperature high-resolution selective laser sintering of metal nanoparticles”, IOP Publishing, Nanotechnology, Vol.18, 345202, 8 pp., 2007 ,8. Concept Laser Introduces QA Tool for In Process AM: QMmeltpool 3D Available Next Year, July 2, 2015.