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A Day in the Life

n  Designing the Internet of Things by Adrian McEwen & Hakim Cassimally n  Wake late – alarm has checked train schedule

n  Tablet blinking on bottle lights for reminder mails doctor

n  Umbrella lit up going to rain

n  At station phone notifies family

n  Training shoes update cloud application and doctor

n  Integrates with online shopping to map calories

IASA is

n  a non-profit professional association

n  run by architects

n  for all IT architects

n  centrally governed and locally run

n  technology and vendor agnostic

The use, disclosure, reproduction, modification,

transfer, or transmittal of this work without the written

permission of IASA is strictly prohibited. © IASA 2009

Topics

n  What is Cloud and IoT?

n  What is the relationship between Cloud and IoT?

n  Where is the ‘Smart’ in Smart Cloud and Smart IoT?

n  What is valuable about Cloud and IoT?

n  How to include smart system thinking into designs

n  How to get started with smart tools like inferencing, fuzzy, neural and other technologies

n  When to think smart and when to avoid

n  Possible outcomes to strive for today in preparing your architecture for the age of smart systems.

Cloud and IoT

n  Everything will have identity whether simple or complex n  ‘who is my phone’

n  Everything will be able to act physically or virtually or both n  ‘what can my phone do’

n  Everything will be able to act dependently or independently or both n  ‘what can my phone do with permission or without it’

n  Everything will have more or less responsibility for everything it interacts with n  ‘what does my phone do to other devices|people|systems’

Cloud

n  The umbrella term for anything available over a network

n  Relevant attributes which typify and classify architectures include n  Public or private

n  Virtualized or non-virtualized

n  Service oriented or person oriented

n  Hardware oriented or platform oriented or software oriented

n  Organizationally oriented or personally oriented

n  Secure or unsecure

n  Paid or free

n  Paid by quality attribute or paid by operational attribute

n  Guaranteed or unguaranteed

Internet of Things

n  Identifying all physical and virtual objects on a network

n  Relevant attributes which will typify and classify architectures may include n  Type of IoT identity (hardware, network, software, service,

invoker, agent, intelligent agent, independent intelligent agent, provocateur)

n  Size or scope of object (molecular -> planetary)

n  Data type/volume consumption/production

n  Power consumption/production

n  Location and Mobility

n  Object interaction power in virtual, physical or both

n  Intention and Autonomy

Proposed Hierarchy of IoT Identities n  Provocateur - Intelligent agent with intention (human level)

n  Independent Intelligent Agent - Intelligent agent acting without permission

n  Intelligent Agent – Agent with a degree of reasoning capacity

n  Agent – Invoker which changes addresses in some way

n  Invoker – Service which calls other services

n  Service – Software object which returns a complex response

n  Software – Network object which returns a simple response

n  Network – An object which is addressable over a network

n  Hardware – An object which is identifiable over a network

Concepts and Relationships

n  Cloud is the raw network access mechanism

n  IoT is the type of things accessible

n  Understanding these relationships requires a much more sophisticated ontology and series of reference points

Why Is Smart Required for IoT and Cloud?

n  Volumes of data require more advanced searching, analysis and transformation techniques

n  Automation and availability of physical and virtual services require significantly complex process orchestration and optimization

n  Competitive advantage in business will continue to require more awareness and ability within smaller opportunities

n  All businesses of all sizes are technology businesses

n  Both human and non-human provocateurs will take advantage of less sophisticated provocateurs

How is Smart Implemented Now

n  Advanced Search – Genetic, Graph Theory

n  Inferencing (Deductive, Inductive)

n  Fuzzy Reasoning

n  Optimization

n  Learning

n  Interpreting and Language

n  Negotiation

Searching for Information

n  Our lives and companies are run with information

n  Information has to be constructed from data and context

n  There is more data and information in the world than we can process

n  Intelligent search is key to our ability to make use of information

n  Common applications: business intelligence, lifestyle optimization, interest optimization

The Rules We Live By

n  Most companies have large numbers of commonly modified rules

n  Inferencing allows us to n  deduce new information within context (forward-chaining)

n  induce information from existing data (backward-chaining)

n  Common Applications: Insurance rates and converage, retail pricing and discounts, purchase decisions, lifestyle choices n  “If the train is late let me sleep in”

Fuzzy Reasoning and Controllers

n  Humans and business work on ‘fuzzy definitions’ which is simply that most things are both true and not true n  “It is cold in Sweden” may be true to a Texan but not an Eskimo! n  “A cup is also a bowl” can be more or less true n  “That hotel is extremely expensive” for me but Bill Gates?

n  Allows our devices to be more precise and selective in decision making and reasoning n  “Pre-heat the car when it is very cold” n  “We buy very high quality business supplies”

n  Common Applications: Energy utilization, mechanical controllers, human definitional input

Optimization

n  Business processes, graph navigation, optimal path traversal, and business integration all involve process optimizations

n  Multi-processes integration beyond the simplicity of a single service (physical or virtual) control much of our lives

n  Utilization of embedded process engines and optimization allows for maximum flexibility of physical and virtual agents

n  Common Applications: multi-partner business transactions, automated delivery systems, personal travel itineraries, multi-device automation

Learning

n  More and more data and choice is available to system software

n  As automation and autonomy become ubiquitous training in desired outcomes is necessary for personal and business

n  The vast amount of data and information requires grouping, characterizing and classifying

n  Neural networks and decision trees

n  Common applications: Food, travel and personal preferences, natural language processing, optimal energy input/output, security threat detection

Thing to Thing Communication

n  Language, dialect, grammar, vocabulary and pronunciation are all relevant in IoT communications and configuration

n  Knowledge and language ontology and dictionary will be essential to self-configuration (and therefore adoption)

n  This may be the single most difficult task in the IoT n  Even humans struggle with this constantly n  ‘Molecular’ data element combinations are not solidified (what is

an address, a name, a birthday)

n  Common applications: Thing configuration and communication, business analytics, service orchestration, personal identity management (pay for use)

Negotiation

n  As systems begin to represent us there is more and more conflict n  “What is the best price we can get for pencils for employees”

n  Using negotiation techniques to avoid conflict with game theory

n  Common applications: Device resource allocation and utilization, purchasing

Considering Value and Risk

n  Value to Who? n  Individuals

n  Governments and NGOs

n  Vendors and Service Integrators

n  For Profit – non-vendor

n  What type of Value n  Lifestyle|Social Value

n  Financial Value

n  Customer|Operational Value

n  Societal|Human Value

n  Risk to Who? n  Individual

n  Corporation

n  Governments

n  What type of Risk? n  Physical

n  Financial

n  Societal

How Smart Becomes Value

n  There is a world of ‘new’ objects to sell to the world

n  There is an unlimited number of ways to incorporate new inventions into multiple channels, services and ‘products’ n  Learning about your customers and partners

n  Dynamically allocating resources and processes

n  Optimized pathing

n  Planning and forecasting

n  Configuration management and ease of use

n  Human interaction and reasoning

Architecture Value

•  Profitability •  Constituent Value •  Reuse •  Grow Market Size •  Grow Market Quality

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

Value Questions

n  Financial Value n  How do our customers buy from us?

n  When does a person ‘have’ to be involved?

n  How do our partners supply us?

n  When do our customers have to think?

n  When do our employees have to use a best guess or experience?

n  Are there times we ‘diagnose’ a problem?

n  How can our systems interact on long-lasting complex transactions?

What does Smart Mean Tomorrow

n  We must begin to consider systems as more than software services n  Autonomy – the degree to which systems can act without

permission

n  Power (to influence) – the amount of influence or size of outcomes a system can achieve

n  Resources (to command and use) – the size and makeup of objects a system may use

n  Motivation – as systems gain more power and autonomy we will need to understand

n  Combat – when systems with autonomy, power and resources disagree about outcomes

Resources

n  Books n  Designing the Internet of Things

n  Practical Artificial Intelligence Programming with Java

n  Rethinking the Internet of Things

n  IoT – Global Technological and Societal Trends

n  Tools/Frameworks n  Drools

n  Weka

n  JFuzzyLogic

n  Fuzzylite

n  Gambit

Skill Taxonomy

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Questions