04858nam 2200529 450 991059505200332120230209133205.01-4842-8276-010.1007/978-1-4842-8276-2(MiAaPQ)EBC7088323(Au-PeEL)EBL7088323(CKB)24846071800041(NjHacI)9924846071800041(OCoLC)1346254297(OCoLC-P)1346254297(CaSebORM)9781484282762(PPN)264960025(EXLCZ)992484607180004120230209d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierIoT system testing an IoT journey from devices to analytics and the edge /Jon Duncan HagarNew York, New York :Apress Media LLC,[2022]©20221 online resource (326 pages)Print version: Hagar, Jon Duncan IoT System Testing Berkeley, CA : Apress L. P.,c2022 9781484282755 Includes bibliographical references and index.Part I: Getting Started -- Chapter 1: The Internet of Things, V and V, and Testing -- Chapter 2: IoT Technology in Time and Space -- Chapter 3: Big Picture Lessons Learned in IoT Project Test Planning -- Chapter 4: Factors Driving IoT Testing/V and V Selection and Planning -- Chapter 5: Beginner Keys for Starting IoT Test Planning -- Part II: IoT Planning, Test, Strategy and Architecture - Team Leadership -- Chapter 6: IoT Test Plan: Strategy and Architecture Introductions -- Chapter 7: IoT Test Planning and Strategy for Hardware and Software -- Chapter 8: Planning for the IoT Tester on Environments and Testing Details -- Chapter 9: System Engineering Concepts in IoT Test Planning -- Part III: IoT Test Designs and Security Assessments -- Chapter 10: IoT Test Design: Frameworks, Techniques, Attacks, Patterns, and Tours -- Chapter 11: Classic IoT V and V/Test Concepts, Techniques, and Practices -- Chapter 12: Test Approaches and Quality Assessments for IoT Agile/DevOps -- Chapter 13: IoT Software Security Test Attacks and Designs -- Chapter 14: OWASP IoT Information Pointer, and Logging Events -- Chapter 15: Internal Security Team Penetration Test Process -- Chapter 16: IoT Test Environment Introduction -- Part IV: IoT Architectures, Environments, and Integrated Independent Testing -- Chapter 17: Architectures Critical to Project Success -- Chapter 18: Overview of IoT Software Architectures: Products and Testing Support -- Chapter 19: IoT STA System: Software Integration Lab (SIL) Environments -- Chapter 20: Tools for the Software-System Integration Lab (SIL) -- Chapter 21: Environments for Independent Testing and V and V on Large IoT Systems -- Chapter 22: Self-Organizing Data Analytics (SODA): IoT Data Analytics, AI, and Statistics -- Appendix A: IoT Supporting Interface, Hardware, Platform, and Protocol Standards -- Appendix B: Careers in IoT Testing -- Appendix C: IoT Testing Start-up Checklist.To succeed, teams must assure the quality of IoT systems. The world of technology continually moves from one hot area to another; this book considers the next explosionof IoTfrom a quality testing viewpoint. You'll first gain an introduction to the Internet of Things (IoT), V&V, and testing. Next, you'll be walked through IoT test planning and strategy over the full life cycle, including the impact of data analytics and AI. You will then delve deeper into IoT security testing and various test techniques, patterns, and more. This is followed by a detailed study of IoT software test labs, architecture, environments and AI. There are many options for testing IoT qualities based on the criticality of the software and risks involved; each option has positives, negatives, as well as cost and schedule impacts. The book will guide start-up and experienced teams into these paths and help you to improve the testing and quality assessment of IoT systems. You will: Understand IoT software test architecture and planning Master IoT security testing and test techniques Study IoT test lab automation and architectures Review the need for IoT security, data analytics, AI, Neural Networks and dependability using testing and V&V.Internet of thingsSecurity measuresTestingComputer securityPenetration testing (Computer security)Internet of thingsSecurity measuresTesting.Computer security.Penetration testing (Computer security)005.8Hagar Jon Duncan1235754MiAaPQMiAaPQMiAaPQBOOK9910595052003321IoT System Testing2915780UNINA03700nam 22006375 450 991095763920332120200424112023.09780226416502022641650X10.7208/9780226416502(CKB)3710000000907323(StDuBDS)EDZ0001588080(MiAaPQ)EBC4532277(DE-B1597)524037(OCoLC)960879928(DE-B1597)9780226416502(Perlego)1850791(EXLCZ)99371000000090732320200424h20162016 fg engur|||||||||||rdacontentrdacontentrdamediardacarrierData-Centric Biology A Philosophical Study /Sabina LeonelliChicago : University of Chicago Press, [2016]©20161 online resource illustrations (black and white)Previously issued in print: 2016.9780226416472 022641647X 9780226416335 022641633X Includes bibliographical references and index.Frontmatter -- Contents -- Introduction -- 1. Making Data Travel: Technology and Expertise -- 2. Managing Data Journeys: Social Structures -- 3. What Counts as Data? -- 4. What Counts as Experiment? -- 5. What Counts as Theory? -- 6. Researching Life in the Digital Age -- 7. Handling Data to Produce Knowledge -- Conclusion -- Acknowledgments -- Notes -- Bibliography -- IndexIn recent decades, there has been a major shift in the way researchers process and understand scientific data. Digital access to data has revolutionized ways of doing science in the biological and biomedical fields, leading to a data-intensive approach to research that uses innovative methods to produce, store, distribute, and interpret huge amounts of data. In Data-Centric Biology, Sabina Leonelli probes the implications of these advancements and confronts the questions they pose. Are we witnessing the rise of an entirely new scientific epistemology? If so, how does that alter the way we study and understand life-including ourselves? Leonelli is the first scholar to use a study of contemporary data-intensive science to provide a philosophical analysis of the epistemology of data. In analyzing the rise, internal dynamics, and potential impact of data-centric biology, she draws on scholarship across diverse fields of science and the humanities-as well as her own original empirical material-to pinpoint the conditions under which digitally available data can further our understanding of life. Bridging the divide between historians, sociologists, and philosophers of science, Data-Centric Biology offers a nuanced account of an issue that is of fundamental importance to our understanding of contemporary scientific practices.BiologyData processingPhilosophyBiologyResearchPhilosophyKnowledge, Theory ofBiologyResearchSociological aspectsResearchPhilosophyBiologyData processingPhilosophy.BiologyResearchPhilosophy.Knowledge, Theory of.BiologyResearchSociological aspects.ResearchPhilosophy.570.285WC 3420SEPArvkLeonelli Sabina, authttp://id.loc.gov/vocabulary/relators/aut761717DE-B1597DE-B1597BOOK9910957639203321Data-Centric Biology4358285UNINA