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3.7 Auto_ID — Value Creation from Big Data and Serialization 189 now. The biologics industry has been a pioneer in re-establishing data linkages between pharmaceutical manufacturers and clinical service providers in the context of developing stratified medicine and autologous cell based therapies which require matching the right cells with the right patients. We can see this linkage of product and patient outcomes at the center of the “7 Flows” model proposed by Genzyme executive Laurent Boer, VP Global Distribution & Logistics and General Manager of the Genzyme Northborough Operations Center, as a framework to think about the kinds of data that are required for managing pharmaceutical industry processes [25 Boer, 2013]. Fig. 3.19 The 7 flows of supply chain information — Laurent Boer, VP global distribution & logistics, Genzyme — Sanofi [32]. In the following section we will review these (7) data protocols. The connection between a product and a patient outcome is an area of great promise for pharmaceutical manufacturers developing stratified medicine therapeutics. 3.7.1.2 Tracking serialized products In the healthcare industry there is no question that both regulatory agencies and pharmaceutical product manufacturers would be better off with identification systems for tracking products, producers, production sites as well as individual
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Internet of Things: Converging Tech
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Internet of Things: Converging Tech
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Dedication “A rock pile ceases to
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Editors Biography Dr. Ovidiu Vermes
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Foreword The Bright Future of the I
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Contents Foreword The Bright Future
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Contents xiii 5 Security and Privac
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1 Driving European Internet of Thin
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1.2 Time for Convergence 3 • Only
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1.3 Towards the IoT Universe(s) 5
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2 Internet of Things Strategic Rese
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2.1 Internet of Things Vision 9 Fig
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2.1 Internet of Things Vision 11 Im
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2.1 Internet of Things Vision 13 Fi
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2.1 Internet of Things Vision 15 Fi
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2.2 IoT Strategic Research and Inno
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comprising a number of components s
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2.3 IoT Applications 39 be possible
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2.3 IoT Applications 41 Fig. 2.17 A
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2.3 IoT Applications 43 Fig. 2.18 S
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2.3 IoT Applications 45 Fig. 2.20 E
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2.3 IoT Applications 47 Energy (IoE
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2.3 IoT Applications 49 momentary t
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2.3 IoT Applications 51 Fig. 2.23 S
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2.3 IoT Applications 53 new range o
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2.3 IoT Applications 55 Fig. 2.24 E
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2.3 IoT Applications 57 • Data ne
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2.3 IoT Applications 59 Fig. 2.26 I
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2.4 Internet of Things and Related
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2.5 Infrastructure 69 tiny chips. T
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2.5 Infrastructure 71 knowledge, e.
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2.6.1.1 Complexity of the Networks
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2.6 Networks and Communication 75 s
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2.6 Networks and Communication 77 2
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2.7 Processes 79 based on events th
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2.8 Data Management 81 centrally, i
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2.8 Data Management 83 Device event
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2.8 Data Management 85 computing pl
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2.8 Data Management 87 open platfor
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2.8 Data Management 89 • The comb
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2.8 Data Management 91 expected res
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2.9 Security, Privacy & Trust 93 So
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2.10 Device Level Energy Issues 95
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2.11 IoT Related Standardisation 10
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2.11.4 Interoperability in the Inte
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2.12 Recommendations on Research To
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Internet of Things Timelines Table
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Table 2.1. (Continued ) Development
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Table 2.2. Internet of Things Resea
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Table 2.2. (Continued ) Research ne
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Table 2.2. (Continued ) Research ne
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References 145 [21] H. Grindvoll, O
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References 147 [57] A. S. Rao, M. P
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References 149 [101] C.A.R. Hoare,
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References 151 [143] INSPIRE, EU FP
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3 IoT Applications — Value Creati
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3.2 IoT Applications for Industry
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Page 354: 3.3 Future Factory Concepts 163 Ima
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Page 408: 190 IoT Applications — Value Crea
Page 444: 208 Internet of Things Privacy, Sec
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214 Internet of Things Privacy, Sec
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226 Security and Privacy Challenge
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246 A Common Architectural Approach
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260 Standardisation — Status, Req
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278 Simpler IoT Word(s) of Tomorrow
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316 Semantic as an Interoperability
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344 Index convergence of informatio
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346 Index manufacturing industry, 1
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348 Index user data, 240 UWB, 35, 9
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