smart cities: how are they different?
TRANSCRIPT
Smart Cities: How are they different?
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Payam BarnaghiInstitute for Communication Systems (ICS)/5G Innovation Centre University of SurreyGuildford, United Kingdom
2nd EAI International Conference on Software Defined Wireless Networks and Cognitive Technologies for IoTOctober 26, 2015 | Rome, Italy
Desire for innovation
2Driverless Car of the Future (1957)
Image: Courtesy of http://paleofuture.com
“A hundred years hence people will be so avid of every moment of life, life will be so full of busy delight, that time-saving inventions will be at a huge premium…”
“…It is not because we shall be hurried in nerve-shattering anxiety, but because we shall value at its true worth the refining and restful influence of leisure, that we shall be impatient of the minor tasks of every day….”
The March 26, 1906, New Zealand Star :
Source: http://paleofuture.com
4P. Barnaghi et al., "Digital Technology Adoption in the Smart Built Environment", IET Sector Technical Briefing, The Institution of Engineering and Technology (IET), I. Borthwick (editor), March 2015.
Apollo 11 Command Module (1965) had 64 kilobytes of memory operated at 0.043MHz.
An iPhone 5s has a CPU running at speeds of up to 1.3GHzand has 512MB to 1GB of memory
Cray-1 (1975) produced 80 million Floating point operations per second (FLOPS)10 years later, Cray-2 produced 1.9G FLOPS
An iPhone 5s produces 76.8 GFLOPS – nearly a thousand times more
Cray-2 used 200-kilowatt power
Source: Nick T., PhoneArena.com, 2014
Computing Power
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−Smaller size−More Powerful−More memory and more storage
−"Moore's law" over the history of computing, the number of transistors in a dense integrated circuit has doubled approximately every two years.
Internet of Things: The story so far
RFID based solutions Wireless Sensor and
Actuator networks, solutions for
communication technologies,
energy efficiency, routing, …
Smart Devices/Web-enabled
Apps/Services, initial products,
vertical applications, early concepts and
demos, …
Motion sensor
Motion sensor
ECG sensor
Physical-Cyber-Social Systems, Linked-data,
semantics,More products, more
heterogeneity, solutions for control and
monitoring, …
Future: Cloud, Big (IoT) Data Analytics, Interoperability, Enhanced Cellular/Wireless
Com. for IoT, Real-world operational use-cases and
Industry and B2B services/applications,
more Standards… P. Barnaghi, A. Sheth, "Internet of Things: the story so far", IEEE IoT Newsletter, September
2014.
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Cities of the future
8http://www.globalnerdy.com/2007/08/28/home-electronics-of-the-future-as-predicted-28-years-ago/
9Source: BBC News
Source: The dailymail, http://helenography.net/, http://edwud.com/
What are smart cities?
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“An ecosystem of systems enabled by the Internet of Things and information communication technologies.”
“People, resources, and information coming together, operating in an ad-hoc and/or coordinated way to improve city operations and everyday activities.”
Source: Frost and Sullivan via http://raconteur.net/
What does makes smart cities “smart”?
Smart Citizens (more informed and more in control)
Smart Governance (better services and informed decisions)
Smart Environment
Providing more equality and wider reach
Context-aware and situation-aware services
Cost efficacy and supporting innovation
What does makes smart cities “smart”?
How do cities get smarter?
How do cities get smarter?
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Continuous (near-) real-time sensing/monitoringand data collection
Linked/integrated data and linked/integrated services
Real-time intelligence and actionable-informationfor different situations/services
Smart interaction and actuation
Creating awareness and effective participation
How can technology help to make cities smarter?
The role of data
18Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of data driven systems for building, community and city-scale applications, http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
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“Each single data item can be important.”
“Relying merely on data from sources that are unevenly distributed, without considering background information or social context, can lead to imbalanced interpretations and decisions.”?
Data- Challenges
− Multi-modal and heterogeneous− Noisy and incomplete− Time and location dependent − Dynamic and varies in quality − Crowed sourced data can be unreliable − Requires (near-) real-time analysis− Privacy and security are important issues− Data can be biased- we need to know our data!
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“The ultimate goal is transforming the raw data to insights and actionable information and/or creating effective representation forms for machines and also human users, and providing automated services.”
This usually requires data from multiple sources, (near-) real time analytics and visualisation and/or semantic representations.
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“Data will come from various source and from different platforms and various systems.”
This requires an ecosystem of IoT systems with several backend support components (e.g. pub/sub, storage, discovery, and access services). Semantic interoperability is also a key requirement.
Device/Data interoperability
23The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
Search on the Internet/Web in the early days
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Accessing IoT data
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“ The internet/web norm (for now) is often to use an interface to search for the data; the search engines are usually information locators – return the link to the information; IoT data access is more opportunistic and context aware”.
The IoT requires context-aware and opportunistic push mechanism, dynamic device/resource associations and (software-defined) data routing and networks.
IoT environments are usually dynamic and (near-) real-time
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Off-line Data analytics
Data analytics in dynamic environments
Image sources: ABC Australia and 2dolphins.com
What type of problems we expect to solve using the IoT and data analytics solutions?
28Source LAT Times, http://documents.latimes.com/la-2013/
A smart City exampleFuture cities: A view from 1998
29Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/
Source: wikipedia
Back to the Future: 2013
Common problems
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Guildford, Surrey
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Applications and potentials
− Analysis of thousands of traffic, pollution, weather, congestion, public transport, waste and event sensory data to provide better transport and city management.
− Converting smart meter readings to information that can help prediction and balance of power consumption in a city.
− Monitoring elderly homes, personal and public healthcare applications.
− Event and incident analysis and prediction using (near) real-time data collected by citizen and device sensors.
− Turning social media data (e.g. Tweets) related to city issues into event and sentiment analysis.
− Any many more…
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EU FP7 CityPulse Project
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Designing for real world problems
101 Smart City scenarios
35http://www.ict-citypulse.eu/scenarios/
Dr Mirko PresserAlexandra Institute Denmark
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Data Visualisation
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Event Visualisation
CityPulse demo
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Deep IoT
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Creating Patterns- Adaptive sensor SAX
40F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data”, IEEE Sensors Journal, 2013.
Data abstraction
41F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
Adaptable and dynamic learning methods
http://kat.ee.surrey.ac.uk/
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https://github.com/UniSurreyIoT/KAT
Website: http://kat.ee.surrey.ac.uk
Real world data
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Analysing social streams
45With
City event extraction from social streams
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Tweets from a city POS Tagging
Hybrid NER+ Event term extraction
Geohashing
Temporal Estimation
Impact Assessment
Event Aggregatio
nOSM
LocationsSCRIBE
ontology
511.org hierarchy
City Event ExtractionCity Event Annotation
P. Anantharam, P. Barnaghi, K. Thirunarayan, A.P. Sheth, "Extracting City Traffic Events from Social Streams", ACM Trans. on Intelligent Systems and Technology, 2015.
Collaboration with Kno.e.sis, Wright State University
Geohashing
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0.6 miles
Max-lat
Min-lat
Min-long
Max-long
0.38 miles
37.7545166015625, -122.40966796875
37.7490234375, -122.40966796875
37.7545166015625, -122.420654296875
37.7490234375, -122.420654296875
437.74933, -122.4106711
Hierarchical spatial structure of geohash for representing locations with variable precision.
Here the location string is 5H34
0 1 2 3 4 5 67 8 9 B C D EF G H I J K L
0 172 3 4
5 6 8 9
0 1 2 3 4
5 6 7
0 1 23 4 5
6 7 8
Social media analysis
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City Infrastructure
Tweets from a city
P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, ACM Transactions on TICS, 2014.
Social media analysis (deep learning – under construction)
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http://iot.ee.surrey.ac.uk/citypulse-social/
Accumulated and connected knowledge?
50Image courtesy: IEEE Spectrum
Reference Datasets
51http://iot.ee.surrey.ac.uk:8080/datasets.html
Importance of Complementary Data
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Users in control or losing control?
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Image source: Julian Walker, Flicker
Data Analytics solutions for smart cities
− Great opportunities and many applications;− Enhanced and (near-) real-time insights;− Supporting more automated decision making and
in-depth analysis of events and occurrences by combining various sources of data;
− Providing more and better information to citizens;− …
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However…
− We need to know our data and its context (density, quality, reliability, …)
− Open Data (there needs to be more real-time data)
− Complementary data − Citizens in control − Transparency and data management issues
(privacy, security, trust, …)− Reliability and dependability of the systems
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In conclusion
−Smart cities are made of informed citizens, smart environments and informed and intelligent decision making and governance.
−Smart cities should promote innovation, equality and wider reach of services to all citizens.
−IoT plays a key role in making cities smarter; openness of data and interconnection and interoperability between different data sources and services is a key requirement.
−Technology alone won’t make cities smart. 56
IET sector briefing report
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Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
CityPulse stakeholder report
58http://www.ict-citypulse.eu/page/sites/default/files/citypulse_annual_report.pdf
Other challenges and topics that I didn't talk about
Security
Privacy
Trust, resilience and reliability
Noise and incomplete data
Cloud and distributed computing
Networks, test-beds and mobility
Mobile computing
Applications and use-case scenarios
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