internet of things presentation to los angeles cto forum
TRANSCRIPT
The Internet of Things ���Why Software and Data is Eating the World
Fred ThielMay 6, 2015
LA CTO Forum
• Cisco forecasted in 2013 the expected value at stake from the Internet of Everything (IoE) will be $14.4T for the private sector and $4.6T for the public sector.
• In the private sector: • Asset utilization ($2.5 trillion)• Employee productivity ($2.5 trillion)• Supply chain and logistics ($2.7 trillion)• Customer experience ($3.7 trillion)• Innovation, including reducing time to market ($3.0 trillion)
• In the public sector: • Employee productivity ($1.8 trillion)• Connected militarized defense ($1.5 trillion)• Cost reductions ($740 billion)• Citizen experience ($412 billion)• Increased revenue ($125 billion).
Here are some stats…..
95% of executives worldwide expect their company to use IoT within 3 years and 63% believe IoT offers a competitive advantage – The Economist
Vision of the Future• Imagine a world where…….things think….
Manufacturing IoT investment will reach $140B over the next five years
Early Adopters & Use Cases
IoT Will Deliver Significant ROI
IoT Benefits for Companies
IoT Will Change Business Models
IoT Formula for Success
THING IT [HW | SW]
THING-‐BASED FUNCTION [Local | Business models known]
IT-‐BASED SERVICE
[Global | Business models required]
Example SERVICE: Send ambulance in case of accident (detected by sensors)
Example FUNCTION: Drive from A to B
A B
Source: University of St. Gallen, Prof. Dr. Elgar Fleisch
Evolution of Product to System of Systems
TRACTORS
FARM EQUIPMENT
SYSTEM
TILLERS
PLANTERS
COMBINE HARVESTERS
FARM EQUIPMENT
SYSTEM
WEATHER DATA
SYSTEM
SEED OPTIMIZATION
SYSTEM
IRRIGATION SYSTEM
FARM MANAGEMENT
SYSTEM
WEATHER MAPS
RAIN, HUMIDY, TEMPERATURE SENSORS
WEATHER FORECASTS
WEATHER DATA APPLICATION
FARM PERFORMANCE DATABASE
SEED DATABASE
SEED OPTIMIZATION APPLICATIONS
IRRIGATION APPLICATION
FIELD SENSORS
IRRIGATION NODES
1. PRODUCT
2. SMART PRODUCT
3. SMART, CONNECTED PRODUCT
4. PRODUCT SYSTEM
5. SYSTEM OF SYSTEMS
“How Smart, Connected Products are Transforming CompeWWon” – HBR November 2014
IoT Network Design
Autonomous Smart Systems• Autonomous smart systems incorporate functions of sensing, actuation, and
control in order to describe and analyze a situation, and make decisions based on the available data in a predictive, cognitive or adaptive manner (system learns), based on closed loop control, using a rules based system, with no need for human intervention or control.
Thermostat senses and learns the owner’s habits and preferences and adapts to changes, operaWng autonomously
A SmartHome will generate ~1GB of data each week
Collaborative Autonomous Smart Systems• Collaborative Autonomous Smart Systems (CASS) communicate with each other,
sharing/leveraging each other’s telemetry data and actuating and controlling each other’s “things”.
HVAC system leverages telemetry from security system
LighWng system leverages telemetry from security system
Complex Autonomous Systems• How much and how fast do we give up control?
Autonomous Smart Grid
SmartGrid Event Processing
Smart City System of Systems
• The convergence of collaborative systems and machine communications implies a total paradigm-shift for IT suppliers and users.
• IT systems still resemble their mainframe ancestors• Information about events is collected, stored, queried, analyzed, and reported upon…but all after
the fact.• Very different from feeding real-time inputs of billions of tiny peer-to-peer “state machines” into
systems that continually compare machine-states and complex events to sets of rules, and then do something on that basis
• It’s a shift from knowing “what happened” to knowing “what is happening”—all the time—and then automatically controlling systems with that knowledge.
• New thinking and new reference architectures for distributed “edge-driven” integration and collaboration are required.
• Software to aggregate and analyze schema-less data, as well as effective user/system interaction designs, must improve to the point where huge volumes of data can be absorbed by smart systems and by human decision-makers more appropriately
A Massive Paradigm Shift
What Does This Mean?
Growing Faster then you think…
IoT and Big Data
Browser, web server
Java, XML
Web Service
Evolution of the InternetConnecting documents Connecting companies Connecting people Connecting functions Agent Based
• Basic Web Pages • Digitization
• Databases • Digital Exhibitions • Online Text-Editing • Crowd-sourced data • Mapping
• Social networks • Blogs & wikis • Media sharing & remixing
• Web browser oriented computing
• GIS/Ambient/local overlay
• Corpus Analysis • Machine learning • Systems of systems • MQTT Publish/Subscribe
• Artificial Intelligence/Turing Test
• Semantic tags • Ontology • Complex Algorithms • Autonomous machines
MQTT / RDF / HTTP, XMPP / IPv6 / HRI
Internet of Things & Services
A new mindset and technology is required for IoT
IoT and Big Data
A changing approach to databases in the Internet of Things
Key Characteristics of IoT Applications• Complexity
– IoT applications are often very sophisticated, including complex event processing and data analysis.– IoT applications need the ability to draw on many different data sources
• Flexibility
– IoT applications will evolve over time, demanding flexibility in terms of architecture, technologies and business models.
• Context and Semantic Richness– Combining data sources is critical for IoT.
– Data must be labeled in appropriate and meaningful ways.
• Data Management– IoT involves data sharing and complex analysis, including mashing-up multiple data sources.– Data management capabilities will be paramount, including analytics, ownership, privacy and security.
New Requirements in Enabling Technologies
Evolution from M2M to IoT and Big Data
IoT Data GenerationCisco's large-‐scale data tracking project, the Global Cloud Index (GCI), esWmate of the amount of data generated by Internet of Everything (IoE) devices -‐-‐ which encompasses people-‐to-‐people (P2P), machine-‐to-‐people (M2P), and machine-‐to-‐machine (M2M) connecWons -‐-‐ by 2018:
403ZB (1 Zeeabyte = 1 billion TBs)
That is 47 =mes the es=mated total data center traffic and 267 =mes the es=mated amount flowing between data centers today.
IDC FutureScape Predictions for IoT • IoT and the Cloud. Within the next five years, more than 90% of all IoT data will be hosted on service provider plajorms as cloud compuWng reduces the complexity of supporWng IoT "Data Blending".
• IoT and security. Within two years, 90% of all IT networks will have an IoT-‐based security breach, although many will be considered "inconveniences." Chief InformaWon Security Officers (CISOs) will be forced to adopt new IoT policies.
• IoT at the edge. By 2018, 40% of IoT-‐created data will be stored, processed, analyzed, and acted upon close to, or at the edge, of the network.
• IoT and network capacity. Within three years, 50% of IT networks will transiWon from having excess capacity to handle the addiWonal IoT devices to being network constrained with nearly 10% of sites being overwhelmed.
• IoT and non-‐tradi?onal infrastructure. By 2017, 90% of datacenter and enterprise systems management will rapidly adopt new business models to manage non-‐tradiWonal infrastructure and BYOD device categories.
IDC FutureScape Predictions for IoT • IoT and ver?cal diversifica?on. Today, over 50% of IoT acWvity is centered in manufacturing, transportaWon, smart city, and consumer applicaWons, but within five years all industries will have rolled out IoT iniWaWves.
• IoT and the Smart City. CompeWng to build innovaWve and sustainable smart ciWes, local government will represent more than 25% of all government external spending to deploy, manage, and realize the business value of the IoT by 2018.
• IoT and embedded systems. By 2018, 60% of IT soluWons originally developed as proprietary, closed-‐industry soluWons will become open-‐sourced allowing a rush of verWcal-‐driven IoT markets to form.
• IoT and wearables. Within five years, 40% of wearables will have evolved into a viable consumer mass market alternaWve to smartphones.
IoT Challenges• Security — The increasing digiWzaWon and automaWon of the mulWtudes of devices deployed across different areas of modern urban environments are set to create new security challenges to many industries.
• Enterprise — Significant security challenges will remain as the big data created as a result of the deployment of myriad devices will drasWcally increase security complexity. This, in turn, will have an impact on availability requirements, which are also expected to increase, pusng real-‐Wme business processes and, potenWally, personal safety at risk.
• Consumer Privacy — As is already the case with smart metering equipment and increasingly digiWzed automobiles, there will be a vast amount of data providing informaWon on users' personal use of devices that, if not secured, can give rise to breaches of privacy. This is parWcularly challenging as the informaWon generated by IoT is a key to bringing beeer services and the management of such devices.
• Data — The impact of the IoT on storage is two-‐pronged in types of data to be stored: personal data (consumer-‐driven) and big data (enterprise-‐driven). As consumers uWlize apps and devices conWnue to learn about the user, significant data will be generated.
IoT Challenges• Storage Management — The impact of the IoT on storage infrastructure is another factor contribuWng to the increasing demand for more storage capacity, and one that will have to be addressed as this data becomes more prevalent. The focus today must be on storage capacity, as well as whether or not the business can harvest and use IoT data in a cost-‐effecWve manner.
• Server Technologies — The impact of IoT on the server market will be largely focused on increased investment in key verWcal industries and organizaWons related to those industries where IoT can be profitable or add significant value.
• Data Center Network — ExisWng data center WAN links are sized for the moderate-‐bandwidth requirements generated by human interacWons with applicaWons. IoT promises to dramaWcally change these paeerns by transferring massive amounts of small message sensor data to the data center for processing, dramaWcally increasing inbound data center bandwidth requirements.
