Smart Cities and Societies - HPCSaudi...Smart City •A city can be defined as ‘smart’ when •investments in human and social capital • and traditional (transport) and modern

Smart Cities and SocietiesTechnologies, Infrastructure and Applications

Rashid MehmoodKing AbdulAziz University

@ HPC Saudi Conference, KAUST13 March 2017

Outline

Smart Cities – Definition/History/Drivers

Technologies and Infrastructure HPC and Big Data Computational Intelligence Cloud, Fog, Edge Computing Connected and Autonomous Vehicles Data Management (Spatio-Temporal, Data Fusion etc)

Applications UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies Emergency Management System Car-free cities Social Network Analysis

HPC: Sparse Linear Algebra on GPUs

Conclusion

Rashid Mehmood 2Smart Cities and Societies

Smart City

• A city can be defined as ‘smart’ when

• investments in human and social capital

• and traditional (transport) and modern (ICT) communication infrastructure

• fuel sustainable economic development and a high quality of life,

• with a wise management of natural resources,

• through participatory action and engagement.

[Wikipedia]


Smart Cities (Komninos, 2011)

• territories with high capacity for learning and innovation, which is built-in the creativity of their population, their institutions of knowledge creation, and their digital infrastructure for communication and knowledge management.

Rashid Mehmood Smart Cities and Societies 4

Smart Societies (Mehmood, 2017)

• The notion of smart cities can be extended to smart society

– a digitally-enabled, knowledge-based society, aware of and working towards social, environmental and economic sustainability.


Smart Cities

• Smart cities can be identified (and ranked) along six main axes or dimensions

– smart people

– smart governance

– smart mobility

– smart economy

– smart environment

– smart living


Smart Cities Dimensions

Smart People (Social and Human Capital)

• Level of qualification

• Affinity to life long learning

• Social and ethnic plurality

• Flexibility

• Creativity

• Cosmopolitanism/Open mindedness

• Participation in public life

Smart Governance (Participation)

• Participation in decision-making

• Public and social services

• Transparent governance

• Political strategies & perspectives



Smart Mobility (Transport and ICT)

• Local accessibility

• (Inter-)national accessibility

• Availability of ICT-infrastructure

• Sustainable, innovative and safe transport systems

Smart Economy (Competitiveness)

• Innovative spirit

• Entrepreneurship

• Economic image & trademarks

• Productivity

• Flexibility of labour market

• International embeddedness

• Ability to transform



Smart Environment (Natural Resources)

• Attractivity of natural conditions

• Pollution

• Environmental protection

• Sustainable resource management

Smart Living (Quality of life)

• Cultural facilities

• Health conditions

• Individual safety

• Housing quality

• Education facilities

• Touristic attractivity

• Social cohesion


Systems of Systems View [MIT]

• Urban Analysis and Modelling

• Incentives and Governance

• Mobility Networks

• Places of Living and Work

• Electronics and Social Networks

• Energy Networks


A Ubiquitous Converged Infrastructure

• Nanotechnologies• Cloud Computing• Sensor Networks• Internet of Things (IoT)• Vehicular Networks• Big Data, data fusion and data management• Crowdsourcing• Exascale Computing• Convergence and Integration (e.g. Multimedia Networks)• Autonomous and Connected Cars, Entities and Systems• Connected Healthcare


Smart Cities


Smart Cities Drivers

A stage reached in the development of infrastructure

•utilisation of networked infrastructure

A strategy for creating a competitive environment

•business-led urban development

•Local intelligence capacity and knowledge-based economy

An approach to inclusive and sustainable cities

•social sustainability

•co-design and e-participation

•Environmental sustainability


Greenhouse Gas Emissions

• Failure to address GHG Emissions problems could lead to disasters possibly

– “on a scale similar to those associated with the great wars and the economic depression of the first half of the 20th century”.

• To ignore the situation can lead to 20% loss of the global GDP while addressing GHG emissions can cost up to 1% of global GDP.

[Lord Stern, Former UK Government and World Bank Chief Economist]


ICT enabling effect

• The direct carbon footprint of the ICT sector

• The quantifiable emissions reductions that can be enabled through ICT applications in other sectors of the economy

• The new market opportunities for ICT and other sectors associated with realising these reductions


ICT: The Global Carbon Footprint & Enabling Effect


Transitions from Industrial to Smart

Industrial Economy/Society

Knowledge Economy/Society

Digital Economy/Society

Intelligent Economy/Society

Smart Economy/Society


TECHNOLOGIES


HPC Market & Return on Investments

• A few years back the UK government reported 25 GBP return on every pound spent on HPC

• IDC reported recently, $551 in revenues were generated for every dollar investment, on average (Nov 2016) – and $52 in profits– based on data from 673 HPC installations in 14 countries

• The HPC market grew in 2015, with a total purchases of $11.4 billion at a growth rate of more than 10%

• The exascale race is on, internationally, and provides another great opportunity for developments in next generation computing


Return on Investments (Nov 2015)


Big Data

• Big Data Market is growing and has become amajor driver for HPC market growth


Computational Intelligence

• Computational Intelligence is increasingly becoming a major driver for HPC and Big Data technologies


Computational Intelligence V3


HPC, Big Data and AI

• Convergence of HPC, Big Data, and AI is creating many opportunities

• IDC reported (Nov 2016) four verticals where these three technologies have shown major market growth– Fraud and anomaly detection– Marketing– Business intelligence– Other commercial verticals including management of large IT infrastructure,

Internet-of-Things (IoT) infrastructures, and precision Medicine

• Core sectors where these technologies will generate major growth include– Smart Cities– Personalised Healthcare– Mobility and Transport– Smart Energy


HPDA


Connected Mobility


Vehicular Self-Managing NetworksIntermediate Delivery Hops


Autonomous Connected Mobility

• traditional and intelligent transportation landscape is being transformed into – futuristic looking transportation

• Olli, the 3D printed smart autonomous vehicle (AV)– made by IBM and an automotive start-up company, Local Motors– incorporates Watson, an IBM supercomputer– Watson acts as an interface between Olli and its passengers– analyses data collected from over 30 embedded sensors of Olli– IBM calls this integration of Watson with vehicles as the IBM

Watson Internet of Things (IoT) for Automotive

• a promising example for transportation system enabler based on AVs


Olli – 3D Printed Car with IBM Watsonhttps://localmotors.com/olli/

Olli is a self-driving electric vehicle designed to streamline shared transportation systems around the world. As long as you have a smartphone, wherever you are is a bus stop. And wherever you’re going is the next stop.


https://localmotors.com/olli/

25% of all transportation in Dubai will be smart and driverless by 2030http://gulfnews.com/news/uae/transport/25-of-all-transportation-in-dubai-will-be-smart-and-driverless-by-2030-mohammad-bin-rashid-1.1810896“The strategy will help increase traffic efficiency, productivity, reduce traffic congestions and pollution & save millions of driving hours”

HH Sheikh Mohammed


http://gulfnews.com/news/uae/transport/25-of-all-transportation-in-dubai-will-be-smart-and-driverless-by-2030-mohammad-bin-rashid-1.1810896

BMW Autonomous Vehicle


Trust Level in Driverless Cars


Internet of Vehicles: Savings


Autonomous Vehicles: Savings


Safer Roads, Cleaner Air: Autonomous Cars Could Drive a Better Future

http://www.nvidia.com/object/autonomous-cars.htmlFueled by advances in artificial intelligence, cars are getting smart enough to begin to drive themselves. But autonomous vehicles will do more than change how we get around. They have the potential to dramatically reduce the number of car crashes, shrink carbon emissions, and provide mobility to people who can’t drive.


http://www.nvidia.com/object/autonomous-cars.html

NVIDIA mapping technology for self-driving carshttp://www.nvidia.com/object/drive-px.htmlNVIDIA offers an end-to-end mapping technology for self-driving cars, designed to help automakers, map companies, and startups rapidly create HD maps and keep them updated. This state-of-the-art technology uses an NVIDIA DRIVE PX 2 AI supercomputer in the car, coupled with NVIDIA Tesla GPUs in the data center, to create highly detailed maps.


http://www.nvidia.com/object/drive-px.html

Nvidia DRIVE PX 2 FOR FULLY AUTONOMOUS DRIVING


Multiple fully configured DRIVE PX 2 systems can be integrated in a single vehicle to enable autonomous driving



Intel to Team With Delphi and Mobileye for Self-Driving CarsMobileye is an Israeli company that specializes in vision systems that have been used in some of the autonomous-driving systems made by Tesla Motors

http://www.nytimes.com/2016/11/29/business/intel-to-team-with-delphi-and-mobileye-for-self-driving-cars.html?_r=0


http://www.nytimes.com/2016/11/29/business/intel-to-team-with-delphi-and-mobileye-for-self-driving-cars.html?_r=0

Google Car


Autonomous Systems

• Autonomous Vehicles• Drones

– Shopping Delivery– Agriculture

• Autonomous Ships• Robots

– e.g. in manufacturing

• Androids– Sociotechnical systems– Social machines– Human-agent collectives


Social and Connected AVs

• AVs will have to interact with the environment for security, safety, service, and social reasons

• Social on behalf of the passengers

• also social at personal level?


Personalised Healthcare System

• Healthcare is now considered the largest global industry with an increasing ICT penetration rate.

• ICT is paving the way for integrated healthcare systems which will enable the healthcare stakeholders to seamlessly coordinate their activities with each other, provide personalized and preventive healthcare to citizens, and improve systems and operational efficiencies.

• Patients particularly will benefit from this convergence because they can play a more proactive role in managing their health by conveniently producing and accessing data relevant to their health, and making more informed decisions about their well-being.

• Indeed with the emerging concepts in urban developments such as smart cities, a coordinated healthcare approach can be extended to city, country, or global levels.


Connected healthcare

http://www.slideshare.net/AlainvanGool/2015-0914-precision-medicine-2015-london-alain-van-gool-52881885


3D and immersive Environments

• 3D modelling, simulations and visualizations– Understanding scientific phenomena– Engineering prototypes– Marketing products– Consumers would increasingly demand 3D visualizations of

products• A future building or dress• How would a product interact with its environment in the future• How does a dress (including color) fit/look on different people in

real time

• Immersive environments– Virtual reality etc


Cloud Computing

• The National Institute of Standards and Technology (NIST) defines:

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.


The NIST CC Reference Architecture


Mobile Cloud Computing

• “Mobile Cloud Computing at its simplest, refers to an infrastructure where both the data storage and the data processing happen outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile phones and into the cloud, bringing applications and mobile computing to not just smartphone users but a much broader range of mobile subscribers.”

[Mobile Cloud Computing Forum]

• “A new paradigm for mobile applications whereby the data processing and storage are moved from the mobile device to powerful and centralized computing platforms located in clouds. These centralized applications are then accessed over the wireless connection based on a thin native client or web browser on the mobile devices.” [AEPONA]


Edge or Fog Computing

• Distributes some of the resources and services of computation, communication, control, and storage away from Cloud and closer to devices and gateways


Fog Computing (Marcus)


Data Management

• Spatio-Temporal

• Data as a Service

• Data Fusion


Pervasive & Ubiquitous Computing

• Mark Weiser (1991) vision of the next-generation computer technologies that "weave themselves into the fabric of everyday life until they are indistinguishable from it."

• Anytime, Anywhere, computing disappears in the environment

• Eventually we will have services that will respond in real-time to information provided either by humans, sensors and/or machines


APPLICATIONS


System Architecture


City X (Conventional)

54

• Google Map removed due to confidentiality

Rashid Mehmood Smart Cities and Societies

City X (Smart)

55

• Google Map removed due to confidentiality

Rashid Mehmood Smart Cities and Societies

UTiLearn

• A Personalised Ubiquitous Teaching and Learning System for Smart Societies

• The education industry around the globe is undergoing major transformations.

• Organisations such as Coursera are advancing new business models for education.

• A number of major industries have dropped degrees from the job requirements.

• While the economics of higher education institutions are under threat in a continuing gloomy global economy, digital and lifelong learners are increasingly demanding new teaching and learning paradigms from educational institutions.

• There is an urgent need to transform teaching and learning landscape in order to drive global economic growth.


UTiLearn

• The use of distance eTeaching and eLearning (DTL) is on the rise among digital natives alongside our evolution towards smart societies.

• However, the DTL systems today lack the necessary sophistication due to several challenges including data analysis and management, learner-system interactivity, system cognition, resource planning, agility, and scalability.

• The UTiLearn Framework leverages IoT, big data, supercomputing, and deep learning to provide enhanced development, management and delivery of teaching and learning in smart society settings.

• A proof of concept UTiLearn system has been developed based on the framework.

• A detailed design, implementation and evaluation of the UTiLearn system, including its five components, was carried out using eleven widely used datasets.



UTiLearn Prototype Network Architecture for two cities


Spatio-temporal human activity patterns


0

0.5

1

1.5

2

2.5

3

3.5

4

L96

M96

H96

VH

96

UH

96

L192

M192

H192

VH

192

UH

192

L384

M384

H384

VH

384

UH

384

L768

M768

H768

VH

768

UH

768

L1248

M1248

H1248

VH

1248

UH

1248

Dela

y (

ms)

Video Performance Comparison: IntraCity

Actual Predicted

92

94

96

98

100

102

104

106

108

110

112

114

L96

M96

H96

VH

96

UH

96

L192

M192

H192

VH

192

UH

192

L384

M384

H384

VH

384

UH

384

L768

M768

H768

VH

768

UH

768

L1248

M1248

H1248

VH

1248

UH

1248

Dela

y (

ms)

Video Performance Comparison: InterCity

Actual Predicted

IntraCity Video Delay for Jeddah and Munich

(symmetric): Actual Data vs Predicted by DLANN.

InterCity Video Delay between Jeddah and

Munich: Actual Data vs Predicted by DLANN.


Car-free City Heathcare-Mobility Management System


Transport System Behaviour


The System Pipeline


Geocoded Traffic Related Events


Job and Hiring



Conclusions

• Smart cities provide next generation engineering approaches for urbanization, having evolved from knowledge-based economy, digital economy and intelligent economy.

• Smart cities aim to exploit the intellectual and social capital as its core ingredient for urbanization, in addition to the physical and ICT infrastructure.

• Smart cities are driven by several interdependent trends. These include a pressing need for environmental sustainability, and peoples’ increasing demands for personalization, mobility and higher quality of life.

• Technological developments such as miniaturisation of devices, internet of things (IoT), big data, computational and artificial intelligence, and decreasing costs of computational entities have also accelerated the smart cities developments.


Conclusions

• Smart cities encompass all aspects of modern day life, transportation, healthcare, entertainment, work, businesses, social interactions, governance, etc.

• It is therefore necessary to engineer smart cities as complex systems of systems supported by a converged ubiquitous infrastructure.

• A key challenge in the realisation of smart cities is to create an ecosystem of digital infrastructures that are able to work together and enable dynamic real-time interactions between various smart city subsystems.


Conclusions

• In term of High Performance Computing– Technologies such as big data, pervasive, cloud

and fog computing, as well as the increasingly complex demands of smart cities and societies, are likely to transform the future of high performance computing.

– The trend would be its integration with big data technologies and provision of on-demand service oriented high performance computing together with the required data, AI and other applications


References

• Eric D. Isaacs. (2010, Nov.) Huffpost Chicago. [Online]. http://www.huffingtonpost.com/eric-d-isaacs/why-america-must-win-the_b_785652.html

• Wikipedia. Supercomputer. [Online]. https://en.wikipedia.org/wiki/Supercomputer

• Top500. http://www.top500.org/. [Online]. http://www.top500.org/

• BBC. (2015, Apr.) US nuclear fears block Intel China supercomputer update. [Online]. http://www.bbc.com/news/technology-32247532

• Giles M. B. and Reguly I., "Trends in high-performance computing for engineering calculations. ," Phil. Trans. R. Soc. A, vol. 372, no. 2022, 2014, http://dx.doi.org/10.1098/rsta.2013.0319.

• The White House, Office of the Press Secretary. Executive Order -- Creating a National Strategic Computing Initiative, 29 July 2015. [Online]. https://www.whitehouse.gov/the-press-office/2015/07/29/executive-order-creating-national-strategic-computing-initiative

• Robert F. Service, "Obama orders effort to build first exascale computer," Science, AAAS, July 2015, http://www.sciencemag.org/news/2015/07/obama-orders-effort-build-first-exascale-computer

• Lawrence Livermore National Laboratory Dona Crawford. (2016, Jan.) The Impact of the U.S. Supercomputing Initiative Will Be Global. [Online]. http://www.top500.org/blog/the-impact-of-the-us-supercomputing-initiative-will-be-global/

• Daniel A. Reed and Jack Dongarra, "Exascale Computing and Big Data," Communications of the ACM, vol. 58, no. 7, pp. 56--68, July 2015, http://doi.acm.org/10.1145/2699414


http://www.huffingtonpost.com/eric-d-isaacs/why-america-must-win-the_b_785652.html

https://en.wikipedia.org/wiki/Supercomputer

http://www.top500.org/

http://www.bbc.com/news/technology-32247532

https://www.whitehouse.gov/the-press-office/2015/07/29/executive-order-creating-national-strategic-computing-initiative

http://www.sciencemag.org/news/2015/07/obama-orders-effort-build-first-exascale-computer

http://www.top500.org/blog/the-impact-of-the-us-supercomputing-initiative-will-be-global/

http://doi.acm.org/10.1145/2699414

References• Niroshinie Fernando, Seng W. Loke, Wenny Rahayu, Mobile cloud computing: A survey, Future Generation Computer Systems, Vol. 29, Issue

1, pp 84–106, 2013.

• Hoang T. Dinh, Chonho Lee, Dusit Niyato, Ping Wang, A survey of mobile cloud computing: architecture, applications, and approaches, Vol 13 Issue 18, 2013.

• Scott Jarr, Fast Data and the New Enterprise Data Architecture. First Edition. October 2014. O’Reilly Media, Inc.

• Bob Marcus, “Data Processing in Cyber-Physical Systems”, January 2016

• Barry Bolding, 5 Predictions for Supercomputing in 2016

• https://www.hpcwire.com/2015/11/18/hpc-roi-invest-a-dollar-to-make-500-plus-reports-idc/

• http://www.enterprisetech.com/2016/11/16/idc-ai-hpda-driving-hpc-high-growth-markets/?eid=328369061&bid=1593803

• Saudi Arabia Invests US$70 Billion in Economic Cities Project, Cisco.

• Technology Holds the Key to Success for Saudi Arabia's Vision 2030, Says IDC, 21 May 2016.

• Big data essential to cancer 'moonshot‘, CIO, 11 May 2016. http://www.cio.com/article/3068571/government/big-data-essential-to-cancer-moonshot.html

• Vice President Biden Says Better Data, Computing Make Cancer Beatable, CIO, 19 September 2016. http://blogs.wsj.com/cio/2016/09/19/vice-president-biden-says-better-data-computing-make-cancer-beatable/

• http://qz.com/811199/apple-aapl-is-scaling-back-its-autonomous-car-ambitions-and-focusing-on-creating-self-driving-software/

• References list to be updated


https://www.hpcwire.com/2015/11/18/hpc-roi-invest-a-dollar-to-make-500-plus-reports-idc/

http://www.enterprisetech.com/2016/11/16/idc-ai-hpda-driving-hpc-high-growth-markets/?eid=328369061&bid=1593803

http://www.cio.com/article/3068571/government/big-data-essential-to-cancer-moonshot.html

http://blogs.wsj.com/cio/2016/09/19/vice-president-biden-says-better-data-computing-make-cancer-beatable/

References

• Giffinger, Rudolf; Christian Fertner, Hans Kramar, Robert Kalasek, Nataša Pichler-Milanovic, Evert Meijers (2007). "Smart cities – Ranking of European medium-sized cities". http://www.smart-cities.eu/. Vienna: Centre of Regional Science.

• Rashid Mehmood and M. Nekovee, Vehicular Ad hoc and Grid Networks: Discussion, Design and Evaluation, In Proc of the 14th World Congress on Intelligent Transport Systems, October 2007

• SMART 2020: Enabling the low carbon economy in the information age. A report by The Climate Group on behalf of the Global eSustainability Initiative (GeSI). 2008.

• Nicholas Stern. Key Elements of a Global Deal on Climate Change, London School of Economics and Political Science. 2008. http://www.lse.ac.uk/collections/climateNetwork/publications/KeyElementsOfAGlobalDeal_30Apr08.pdf

• Nicholas Stern. Executive Summary, Stern Review on the Economics of Climate Change, HM Treasury. 2006.

• http://abhi-carmaniacs.blogspot.co.uk/2012/02/vehicular-ad-hoc-network.html

• http://mubbisherahmed.wordpress.com/2011/11/29/the-future-of-intelligent-transport-systems-its/

• R. Mehmood, Disk-based Techniques for Efficient Solution of Large Markov Chains, School of Computer Science, University of Birmingham, UK, October 2004

• R. Mehmood, J A. Lu, Computational Markovian analysis of large system, In Special issue on Intelligent Management Systems in Operations, Journal of Manufacturing Technology Management, Vol. 22, Issue 6, pp.804 – 817, 2011, DOI: 10.1108/17410381111149657

• Rashid Mehmood, Jie A. Lu. Computational Markovian analysis of large system. In Special issue on Intelligent Management Systems in Operations. Journal of Manufacturing Technology Management, Vol. 22, Issue 6, pp.804 – 817, 2011. DOI: 10.1108/17410381111149657


http://www.lse.ac.uk/collections/climateNetwork/publications/KeyElementsOfAGlobalDeal_30Apr08.pdf

http://abhi-carmaniacs.blogspot.co.uk/2012/02/vehicular-ad-hoc-network.html

http://mubbisherahmed.wordpress.com/2011/11/29/the-future-of-intelligent-transport-systems-its/

References• N. Komninos, “Intelligent cities: Variable geometries of spatial intelligence,” Intell. Build. Int., vol. 3, no. 3, pp. 172–188, 2011

• Rashid Mehmood, Furqan Alam, Nasser N. Albogami, Iyad Katib, Aiiad Albeshri and Saleh M. Altowaijri, UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies, IEEE Access, March 2017

• Zubaida AlAzawi, Omar Alani, Mohmmad B. Abdljabar, Saleh Altowaijri, and Rashid Mehmood, A Smart Disaster Management System for Future Cities, In Proceedings of the ACM Workshop on Wireless and Mobile Technologies for Smart Cities (WiMobCity 2014), in conjunction with the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2014), Philadelphia, USA, August 11-14, pp 1-10, 2014.

• Z. Alazawi, M. Abdljabar, S. Altowaijri, A. M. Vegni and R. Mehmood, ICDMS: An Intelligent Cloud based Disaster Management System for Vehicular Networks, Communication Technologies for Vehicles, Lecture Notes in Computer Science, Vol. 7266/2012, April 2011, DOI: 10.1007/978-3-642-29667-3_4

• Zubaida Alazawi, Mohmmad Abdljabar, Saleh Altowaijri and Rashid Mehmood. Invited Paper: Intelligent Disaster Response System based on Cloud-Enabled Vehicular Networks. 11th International Conference on Intelligent Transportation Systems (ITS) Telecommunications, Saint-Petersburg, Russia, August 2011. DOI: 10.1109/ITST.2011.6060083

• Rashid Mehmood and Jon Crowcroft, Parallel Iterative Solution Method for Large Sparse Linear Equation Systems, Technical Report UCAM-CL-TR-650, Computer Laboratory, University of Cambridge, October 2005, http://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-650.html

• http://cities.media.mit.edu/

• Wikipedia. Smart City. [Online]. https://en.wikipedia.org/wiki/SmartCity

• A. Caragliu, C. Del Bo, and P. Nijkamp, “Smart Cities in Europe,” 3rd Cent. Eur. Conf. Reg. Sci., pp. 45–59, 2009.


http://cities.media.mit.edu/

https://en.wikipedia.org/wiki/SmartCity

Acknowledgement


Furqan Alam Thaha Muhammed

Sugi Miyanto Zubaida Alazawi

Mohammad Abdljabar

Thank you


Documents

Smart Cities and Societies - HPCSaudi...Smart City •A city can be defined as ‘smart’ when •investments in human and social capital • and traditional (transport) and modern