Digital Underpinnings for Smart Cities: Opportunities and Challenges

Nick JenningsProfessor of Artificial Intelligence &

Vice-Provost (Research)

with David Gann, Yi-Ke Guo, Julie McCann and John Polak

Cities Matter• Cities are uniquely important

– Majority of world’s population already live in cities

• this trend is accelerating

– Cities directly and indirectly account for ~70% of CO2 emissions

– Cities are crucibles of economic, institutional and cultural value

• Cities are uniquely vulnerable

– Ageing populations, infrastructure and supply networks

– Regional and global competition

– Hostile threat and natural hazards

• Cities are under enormous pressure to change

– Repair broken functional and governance models (e.g., transport, healthcare, water resource management)

– Energy supply and security

– Adaptation to climate change

70% of world’s population will live in cities by 2050

Challenges of City Life

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The Sustainable Eco-CityThe Well Planned City The Healthy and Safe City

The Cultural or Convention Hub The City of Innovation The City of Commerce

Dr. Guruduth Banavar: Building a Smart Planet

Smart Cities

Cyber-Physical Convergence

• Sensors

• Data

• Connectivity

• Bring data together

• Integrated view

• Inference

• New insights

• Action

• Effectors change state of the world

Digital Technologies: Opportunities

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Energy Transport Health CreativeIntegrative Layer: Data Fusion, Analytics, Modelling

Data Integration: London Data Store

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London Infrastructure Map

7maps.london.gov.uk/ima/

Transport

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Shanghai Metro

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Shanghai Metro

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Normal load per station by line across a day showing where lines get busy on

an average day

Increased load between stations based on model of disrupted system due to a line closure (marked in black)

Geographical heatmap showing entries of people across day, allowing

observation of congested parts of system

Connected Autonomous Vehicles

• Reduce stress of driving

• Fewer accidents

• Reduce social exclusion

• Better use of infrastructure resources

– More efficient routing of vehicles

– Higher road throughput

– Easier parking

– Fewer cars on the road?

from Peter Stone

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Air Quality Monitoring

• Crowd Sourcing vs Static Sensors

– Coverage

– Cost

– Accuracy

• Incentives for participants

• Coordinating readings

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Air Quality Monitoring

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(Zenonos, Stein and Jennings, 2017)

Air Quality Monitoring

• Best-Match 75% better than Greedy

• Best-Match 30x faster than Greedy algorithm

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Generation of urban geometries for air flow

and pollution dispersion simulation

Beyond Sensing

Turbulent flow & traffic modelling in Marlyebone Rd, London –buildings grey, vehicles red, pollution

iso-surface blue and velocity vectors at 1m height from road

Beyond Sensing

Smart Infrastructure Interdependencies

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Electric Mobility• Managing transition to and operation of decarbonised

mobility system• new user behaviours• new infrastructure interdependencies• new value streams and business models

• Types of data• transport and energy network operational data (road links,

charge points)• user behaviour and preference data• energy market data (forward and spot markets)

• Methods• behavioural modelling and characterisation via econometrics, • short term prediction via ML methods, • (distributed) optimisation for management and decision support

• New methods for• real time energy distribution network management • real time dynamic pricing for parking and charging services• charging recommender systems for individual users• charging infrastructure planning and appraisal

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Electric Mobility

• When and how will widespread adoption of electric vehicles occur?

• How will travel patterns be affected by the pricing and charging infrastructure?

• When and where will people charge their vehicles and what will be the appetite for vehicle to grid exchanges?

• How will dynamic transport pricing work with dynamic energy pricing?

• What support do EV owners require with regard to charging policies?

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Transport, Air Quality and Health• Reduce harmful emissions from transport and improve

health of urban populations• transport network modelling• dispersion modelling/atmospheric chemistry• epidemiology.

• Types of data• transport network operational data (e.g., vehicle type and

condition, speed/acceleration, traffic control) • build environment data (e.g., building form)• metrological data• health status and health outcome data

• Methods• modelling workflow/stacks integrating traffic simulation• CFD• sensor fusion• trajectory re-construction• behavioural modelling

• New methods for• integrated modelling stacks (from emission to exposure)• transport network management concepts

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Transport, Air Quality and Health

• What sort of environmental sensor networks are needed to capture emissions?

• How do we predict future emissions ad their impact on air quality?

• How can we support city-scale design to address poor air quality?

• How can formal and informal urban developments enhance the health and wellbeing of their inhabitants?

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Digital Technologies: Challenges

Cyber Security: A Real Problem Today

• Central to ensuring society can function effectively and make most of digital technologies

• In UK, GCHQ reports– 60 serious cyber-attacks per month

– 65% of large companies reported breach in 2016

• In the US– Office of Personnel Management breach of 4 million US government workers

– Alleged Russian involvement in the recent US election

• Cyber attacks can have physical effects– Stuxnet destroyed Iranian centrifuges at Uranium

enrichment plant (2007)

– German steel mill suffered “massive damage” following a cyber-attack (2015)

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Connectivity Offers More Opportunities

• Ever more systems networked– physical and digital systems integration, highly-connected

systems of systems

• On us– Digital personal assistants

– Wearables and implantables

• In our homes– Smart meters, smart TVs, smart alarms– Smart meter indicates house unoccupied and connected

home security network opens door to intruders

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In Our Cities!!!

Already Happening on Critical Infrastructure• 2007 attack on Estonia targeted banks, government

networks, police and broadcasters

• Utilities

– 2001: man jailed for hacking Queensland waste management systems and releasing millions of litres of raw sewage

– 2015: attack on Ukraine power grids saw 225,000 customers lose power

• Health

– 2016: healthcare sector experienced highest number of attacksof all sectors in US

– NHS trusts affected by ransomware

• Joining of many components and organisations vastly increases attack surface

– Ability to hop from less well-defended systems to more sensitive ones

– Target network compromised in 2015

o Compromised air-conditioning computer systems, stole credentials of sub-contractor who had worked at Target

o Using these credentials, uploaded software to steal payment card details and offload these to overseas systems 26

Possible Cyber Attacks on a Smart City

• Data blackout

– Turn off sensors and cameras in a given area

• Denial of service attack

– No electricity or water

– No financial transactions

– No emergency services

– Transport gridlock

• Physical effects

– Alter traffic control systems

– Cause widespread panic through misinformation

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What do we need to do?

• Think about security from the start

– Cannot be bolted-on afterwards

• Ensure sensitive data is secure

– Personal data

– Data on connectivity and capabilities of buildings and infrastructure

• Build security into individual services and components

– Continuously monitor networks

• Consider inter-dependencies and make it difficult to hop between systems

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Conclusions

• Digital technologies have great potential to positively impact cities of the future

– Bring together data from many sources to develop new services and insights on city life

– Connectivity helps break down silos

• Need to think about cyber security from the beginning

– Cannot be an after thought

– Scope for harm is enormous

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SPARE SLIDES

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The Sustainable Eco-CityThe Well Planned City The Healthy and Safe City

The Cultural or Convention Hub The City of Innovation The City of Commerce

Dr. Guruduth Banavar: Building a Smart Planet

Smart Cities

• Productive: Efficiently achieve life goals

• Predictive : Able to plan and optimise the future

• Personalised: Fit to individual’s requirement

• Participatory: Be a part of the city life & development

City Infrastructure

• Working with Smart London Board to determine where existing infrastructure is located:

– Stops multiple utilities digging up same street

– Helps with planning of new infrastructure

Smart Water Lab

Now•Sensing• Is “X” working?

“Strain gauge A43 is too

high”

Next•Predicting• When will “X” fail?

“Strain on gauge A43 willbe too high”

So What•Implications• What happens if

“X” fails?

“The bridge will collapse if the wind is over

50mph”

Causes• Understanding

• Interventions

“Cable A31 has degraded –replace it”

Increasing Value

Extracting the Value of Data