The future of software and even hardware is based in ever more complex abilities to adapt to highly dynamic change and input. The Internet of Things brings with it input from billions of sources locally and around the globe and for intelligent architects this represents an opportunity to create deep competitive advantage and customer loyalty. The Japanese have used intelligent systems for years from cars to trains to vacuum cleaners and there will continue to be smarter and smarter systems. Architects around the world must include this thinking into their designs and strategies. Adaptive social networks, individually designed health care, just in time 3d printing are only some of the components of this coming era. How to include smart system thinking into designs How to get started with smart tools like inferencing, fuzzy, neural and other technologies When to think smart and when to avoid Possible outcomes to strive for today in preparing your architecture for the age of smart systems

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2. A Day in the Life

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4. Topics
 What is Cloud and IoT?
 What is the relationship between Cloud and IoT?
 Where is the „Smart‟ in Smart Cloud and Smart IoT?
 What is valuable about Cloud and IoT?
 How to include smart system thinking into designs
 How to get started with smart tools like inferencing, fuzzy, neural and other
technologies
 When to think smart and when to avoid
 Possible outcomes to strive for today in preparing your architecture for the
age of smart systems.

5. Cloud and IoT
 ‟

6. Cloud
 The umbrella term for anything available over a network
 Relevant attributes which typify and classify architectures
include
 Public or private
 Virtualized or non-virtualized
 Service oriented or person oriented
 Hardware oriented or platform oriented or software oriented
 Organizationally oriented or personally oriented
 Secure or unsecure
 Paid or free
 Paid by quality attribute or paid by operational attribute
 Guaranteed or unguaranteed

7. Internet of Things
 Identifying all physical and virtual objects on a network
 Relevant attributes which will typify and classify architectures
may include
 Type of IoT identity (hardware, network, software, service, invoker,
agent, intelligent agent, independent intelligent agent, provocateur)
 Size or scope of object (molecular -> planetary)
 Data type/volume consumption/production
 Power consumption/production
 Location and Mobility
 Object interaction power in virtual, physical or both
 Intention and Autonomy

8. Proposed Hierarchy of IoT Identities
 Provocateur - Intelligent agent with intention (human level)
 Independent Intelligent Agent - Intelligent agent acting without permission
 Intelligent Agent – Agent with a degree of reasoning capacity
 Agent – Invoker which changes addresses in some way
 Invoker – Service which calls other services
 Service – Software object which returns a complex response
 Software – Network object which returns a simple response
 Network – An object which is addressable over a network
 Hardware – An object which is identifiable over a network

9. Concepts and Relationships
 Cloud is the raw network access mechanism
 IoT is the type of things accessible
 Understanding these relationships requires a much more
sophisticated ontology and series of reference points

10. Google Acquires
Deep Mind

11. Why Is Smart Required for IoT and
Cloud?

17. How is Smart Implemented Now
 Advanced Search – Genetic, Graph Theory
 Inferencing (Deductive, Inductive)
 Fuzzy Reasoning
 Optimization
 Learning
 Interpreting and Language
 Negotiation

18. Searching for Information
 Our lives and companies are run with information
 Information has to be constructed from data and context
 There is more data and information in the world than we can
process
 Intelligent search is key to our ability to make use of information
 Common applications: business intelligence, lifestyle
optimization, interest optimization

19. The Rules We Live By
 Most companies have large numbers of commonly modified
rules
 Inferencing allows us to
 deduce new information within context (forward-chaining)
 induce information from existing data (backward-chaining)
 Common Applications: Insurance rates and converage, retail
pricing and discounts, purchase decisions, lifestyle choices
 “If the train is late let me sleep in”

20. Fuzzy Reasoning and Controllers
 Humans and business work on „fuzzy definitions‟ which is
simply that most things are both true and not true
 “It is cold in Sweden” may be true to a Texan but not an Eskimo!
 “A cup is also a bowl” can be more or less true
 “That hotel is extremely expensive” for me but Bill Gates?
 Allows our devices to be more precise and selective in decision
making and reasoning
 “Pre-heat the car when it is very cold”
 “We buy very high quality business supplies”
 Common Applications: Energy utilization, mechanical
controllers, human definitional input

21. Optimization
 Business processes, graph navigation, optimal path
traversal, and business integration all involve process
optimizations
 Multi-processes integration beyond the simplicity of a single
service (physical or virtual) control much of our lives
 Utilization of embedded process engines and optimization
allows for maximum flexibility of physical and virtual agents
 Common Applications: multi-partner business
transactions, automated delivery systems, personal travel
itineraries, multi-device automation

22. Learning
 More and more data and choice is available to system software
 As automation and autonomy become ubiquitous training in
desired outcomes is necessary for personal and business
 The vast amount of data and information requires
grouping, characterizing and classifying
 Neural networks and decision trees
 Common applications: Food, travel and personal
preferences, natural language processing, optimal energy
input/output, security threat detection

23. Thing to Thing Communication
 Language, dialect, grammar, vocabulary and pronunciation are
all relevant in IoT communications and configuration
 Knowledge and language ontology and dictionary will be
essential to self-configuration (and therefore adoption)
 This may be the single most difficult task in the IoT
 Even humans struggle with this constantly
 „Molecular‟ data element combinations are not solidified (what is an
address, a name, a birthday)
 Common applications: Thing configuration and communication,
business analytics, service orchestration, personal identity
management (pay for use)

24. Negotiation
 As systems begin to represent us there is more and more
conflict
 “What is the best price we can get for pencils for employees”
 Using negotiation techniques to avoid conflict with game theory
 Common applications: Device resource allocation and
utilization, purchasing

25. Considering Value and Risk
 Value to Who?
 Individuals
 Governments and NGOs
 Vendors and Service
Integrators
 For Profit – non-vendor
 What type of Value
 Lifestyle|Social Value
 Financial Value
 Customer|Operational
Value
 Societal|Human Value
 Risk to Who?
 Individual
 Corporation
 Governments
 What type of Risk?
 Physical
 Financial
 Societal

26. How Smart Becomes Value
 There is a world of „new‟ objects to sell to the world
 There is an unlimited number of ways to incorporate new
inventions into multiple channels, services and „products‟
 Learning about your customers and partners
 Dynamically allocating resources and processes
 Optimized pathing
 Planning and forecasting
 Configuration management and ease of use
 Human interaction and reasoning

27. Architecture Value
• Profitability
• Constituent Value
• Reuse
• Grow Market Size
• Grow Market Quality

28. What is “creates value”?
What is Good?
suitable or efficient for a purpose
beneficial or advantageous

30. Value Questions
 Financial Value
 How do our customers buy from us?
 When does a person „have‟ to be involved?
 How do our partners supply us?
 When do our customers have to think?
 When do our employees have to use a best guess or experience?
 Are there times we „diagnose‟ a problem?
 How can our systems interact on long-lasting complex transactions?

31. What does Smart Mean Tomorrow
 We must begin to consider systems as more than software
services
 Autonomy – the degree to which systems can act without
permission
 Power (to influence) – the amount of influence or size of outcomes a
system can achieve
 Resources (to command and use) – the size and makeup of objects
a system may use
 Motivation – as systems gain more power and autonomy we will
need to understand
 Combat – when systems with autonomy, power and resources
disagree about outcomes

32. Resources
 Books
 Designing the Internet of Things
 Practical Artificial Intelligence Programming with Java
 Rethinking the Internet of Things
 IoT – Global Technological and Societal Trends
 Tools/Frameworks
 Drools
 Weka
 JFuzzyLogic
 Fuzzylite
 Gambit

33. Skill Taxonomy
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35. Questions