The designers of Artificial Immune Systems (AIS) had been inspired from the properties of natural immune systems: self-organization, adaptation and diversity, learning by continual exposure, knowledge extraction and generalization, clonal selection, networking and meta-dynamics, knowledge of self and non-self, etc. The aim of this chapter, along its sections, is to describe the principles of artificial immune systems, the most representational data structures (for the representation of antibodies and antigens), suitable metrics (which quantifies the interactions between components of the AIS) and their properties, AIS specific algorithms and their characteristics, some hybrid computational schemes (based on various soft computing methods and techniques like artificial neural networks, fuzzy and intuitionistic-fuzzy systems, evolutionary computation, and genetic algorithms), both standard and extended AIS models/architectures, and AIS applications, in the end.
Recently, ubiquitous approaches are more and more used during training/learning sessions. Based on smart and intelligent devices all kind of professions can benefit from the usage of e-learning models for partially mastering both scientific and engineering topics. Recent developments in augmented reality, Internet of Things technology, and the availability of smart devices useful in efficient mobile learning and for mobile virtual laboratories make possible the developments of reliable cloud or fog infrastructures for e-learning. This paper is based on [1] and [2] when claims about the increasing reliability of modern cloud/fog architectures. In authors' vision, smart devices are electronic devices, connectable to other devices or networks supporting autonomous or interactive operation, including sensors capable 'to feel' the physical world, as considered in [2]. Mobile laboratories can be used not only to measure/diagnose, maintain, or repair various entities, but also for training and/or examination. Virtual laboratories, based currently on cloud computing, could benefit of fog computing developments. The proposal is a service oriented architecture, as shown by [3] and [4], extended to support the fog computing paradigm [1, 2]. References: 1. Bonomi F., Milito R., Zhu J. & Addepalli S. "Fog Computing and its Role in the Internet of Things", Proceedings of MCC'12, August 17, 2012, Helsinki, Finland, pp. 13-15, 2012. 2. Madsen, Henrik; Burtschy, Bernard; Albeanu, G.; Popentiu-Vladicescu, Fl., Reliability in the utility computing era: Towards reliable Fog computing, 2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2013. p. 43-46. 3. H. Madsen, G. Albenu, R.C. Tarca, Fl.Popentiu-Vladicescu, Service-Oriented Reliability Analysis For Collaborative Mechatronic Laboratories Involved In Virtual Training, ESREL 2010, Annual Conference, 5-9 September 2010, Rhodes, Greece. 4. R.D. Albu, R.C. Tarca, Fl.Popentiu-Vladicescu, Ildiko Margit Pasc, Designing Reliable Web-Based Virtual Laboratory Architectures, The 6th International Scientific Conference eLSE "eLearning and Software for Education", pp. 403-409, Bucharest, April 15-16 2010.
The large number of sensors in the field, and the usage of IoT based equipment generate big collections of data, some of them being useful to address maintenance policies, replacement requirements, calibration, research on new data analysis models etc. Both industrial and social systems are increasing in complexity due to new technologies applied to information processing. Recent developments in embedding and integration offered new opportunities to collect, filter, analyse, and interpret huge collections of data generated by large populations of sensor, special devices, or people, and named "Big Data". This work emphasizes on conceptual issues and methodological aspects related to data registration, filtering, smoothing, analysing in order to predict important indicators of the quality of life. The systems reliability engineering field is revisited taking into account both data sources and the new methodologies used for reliability data. Software reliability of applications for smart cities is also addressed. The following frameworks are considered: Systems of Systems (SoS), Big Data, System Operating/Environmental (SOE) data, and Smart cities reliability. The SoS reliability engineering depends on its nature: virtual - based on resource sharing, collaborative - based on agreements, acknowledged - based on collaborative management through a well defined interface, and directed SoS - based on centralized management. The SoS reliability is estimated differently depending on the specific architecture and particular reliability requirements. When the reliability is considered in context Big data, both technologies are considered: batch processing (based on analytics on "data at rest") or stream processing (analytics on "data in motion"). The adequacy of existing reliability models to the Big data reliability concerns, taking into account the 'curse of dimensionality" is considered in the last section.
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