Smart Cities Operating System

One important element of implementing a smart city is the city become the platform and the platform is able to learn and have an embodied cognitive replica. A platform that apart from:
  • The connectivity provided by the ICT infraestruture;
  • The applications that can be used to request, use or monitor services;
  • The underlying data in the city data lake – enabling understanding about the interactions produced, the patterns and the behaviors, how to optimize traffic flow or waste management.

Have the ability o manage with formal and non-formal forms of human and machines interaction – operational technology and the pervasive communication infrastructure – nevertheless, still requires a number of considerable advancements, in particular, in the domain of understanding human behavior [1] as an important data source of the city planning and service evolution.

 

city os

City OS framework

Cognitive cities may be constituted with [2] many richly interacting adaptive components that include human beings and other entities with sufficient awareness – sensors – reconfigurability, learning, autonomy and cooperation capabilities at materialized in a City operating system. However, the value of the City OS is if it scales across multiple cities in an ecosystem. In this sense, the objective is to create a Smart City operating system, an “universal-platform” for cities. While they acknowledge the competitive nature and that each city has its own character, identity, tax system or unique capabilities (healthcare or education) there are more commonalities than there are differences. Apart of common services like energy or waste management, the approach is how the ecosystem City OS, connected to other cities can evolve, for example in areas like traffic management, mobility as a service, safety, once the algorithms will be trained in larger data sets or sharing a common capability like person digital identity or resilience (cyber-attacks, geopolitical conflicts, diseases, resources shortages).

References:

[1] Cognition Digital Twins for Personalized Information Systems of Smart Cities: Proof of Concept – Jing Du, Qi Zhu2, Yangming Shi, Qi Wang, Yingzi Lin and Daniel Zhao

[2] Cognitive cities and intelligent urban governance – Ali Mostashari, Friedrich Arnold, Mo Mansouri, and Matthias Finger

Smart Mobility

Governments are increasingly committed to shrinking the environmental footprint of the urban transport sector. The European Union has set a 2050 target of zero carbon emissions from transport in cities. Lisbon is planning an extensive area at downtown with zero emissions, others like Copenhagen and Stockholm, becoming more aggressive in terms of banning combustion engines circulation. Singapore, aims in 2040 that the majority of vehicles, public and private will be electric. This will also contribute to improve the livability of the communities, that become also safer with reduced accident indexes, walkable, were people can cycle, promoting a healthier way of life, as well as, democratize access to a destinations via multiple modes of transport that matches the individual preferences.

Smart mobility can be perceived only as a perfect advanced combination of infrastructure & intermodal transport, taking individuals between two points as fast as possible. Smart mobility is much beyond that.

Smart mobility is what about by better communities. Better communities are safe, walkable, healthy places that promote sustainable and access to destinations by multiple modes and keep together human interaction and connections in a community. Promoting a healthier lifestyle, can be also achieved under the principle of government agencies cooperate under the principle of ecosystems. I’ve been working with some healthcare providers that are promoting a wellness education program. Mobility that invite walking and cycling that can help reverse the trend toward sedentary urban living. Connections are vital to the inner of a community, of a family. People need convenient access to schools, offices, and shopping, leisure areas, to go about their lives. Environmentally progressive cities with world-class public transport and cycling infrastructure, leading the change in decrease carbon footprint, which the majority of the households (75%) to be close to a carbon free public transport in 10 minutes.

Smart mobility can become a reality via the combination of the scenarious as follows, based on a spectrum of government agencies I am working with. At the heart of the concept of smart mobility is data. Data that is going to allow the citizens, a better commute and be on the forefront in terms of influencing the next stage of smart mobility in terms of public policy design.

MAAS

Scenarios that enable smart mobility

Mobility as a service

The concept of mobility as a service (MaaS) is not about using an application to show travel options and itineraries. Carrying 10 different mobile applications one for each transport mode is not for certain the objetive. 
The concept of MaaS is simple: bundling different transport modes, public and private to end-customer. This breaks traditional paradigms of owning a car, that is one of the biggest outcomes that a transport authority wants to influence. Hence, the development of MaaS is fostered by a new way of thinking that does not considers traffic flow as an isolated function but linked to society. And society is about people. And people have preferences. Hence MaaS is about incorporating a digital assistant that recognizes our preferences as an individual. Who we are.
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That matchmaking process ensures the captures and learns all preferences of the traveler and his travel conditions in a mobility profile. A digital assistant demonstrates that the options combined his preferences concerning comfort, available budget, and time made available to meet business partners and share lunch or dinner, travel with the family to the park or go the airport. It will learn if and individual have more time to travel and want to be environmental friendly.
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Traffic Accident Prevention / Traffic Flow Management
Proactive traffic enforcement and intervention should be based on an analysis on the collision data available to identify leading causes of accidents, the most prone locations, as well as, to predict the conditions for collision occurrence, data such as weather, geospatial information and social events data that can be obtained via a combination of datasets with existing sensor technology. Traffic collision should also be integrated into traffic flow management in order to ensure smooth travels and road safety.
Using a machine learning model is possible to aggregate historical data sets about regarding collisions, traffic intersection volumes, social events – national day, sports events – geospatial road segments, weather conditions. This way, it is possible to estimate collision probability map per road segment. The model is updated in real time and regenerates itself with real live data.
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This information can be proactively shared with drivers, law enforcement authorities to redirect them in order to escape to points where there is a higher probability of accident and at the same time support decisions in terms of changes in traffic flow direction of infraestruture configuration. In the future with the introduction of autonomous vehicles (buses, automobiles) managed like a device with with traffic management systems, the value realization is much higher.
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Asset Integrity
Asset integrity is one of the areas that evolved very much in the last 2 years. Despite the fact anomaly prediction models are well know, the key factor that enable the change is a combination of new hardware: drones with cameras, mixed reality devices like HoloLens, sensors and be able to run applications on real time using edge or fog technologies, that makes possible to localize issues, predict failure and integrate end to end the maintenance supply chain, spare parts, automatic routing based on issue taxonomy, crew availability based on estimarted risk.

 

 

Ecosystems and Personal Healthcare

To reach a stage where healthcare will be personal, there is a fundamental step: designing a medical treatment considering individual variability. For such purpose it is necessary to combine and curate to existing EMR records with data about the patient’s societal environment, lifestyle, and their genomic data. This can assist the doctors in identifying which approaches, treatments, and preventions will be effective for the patients, what also can be called personalized healthcare, on the prevention side, would also allow identification of genetic variants that increase a patient’s chances of developing diseases. Treatment could begin earlier, helping reduce risk through behavior modification (healthier diet or increased exercise) or leading to placement of patients on drug therapies.

Such an outcome relies of connecting and sharing data within a particular ecosystem. The ecosystem of me (ties back to the earlier concept of the internet of me). On one hand phone, wearables, health sensors, connected clothing—and combining that with clinical tests, scans, and check-ups—will allow much-improved analysis and monitoring and focus on health optimization. On the other hand, sharing such data in medical doctor community, academia, healthcare think tanks will improve healthcare foresight and shape healthcare public policy design, but most importantly will support via the existence of such dataset to create models of detecting effectively symptoms, understating treatment impact, find new ways of treatment and assist medical doctors with assisted reasoning.

 

Personalized healthcare is about shifting from volume to value. Revealing the unknown and creating a healthcare ecosystem continuum. The purpose of the transformation journey is to shift from normalized healthcare to personalized healthcare.

 

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Personal Healthcare Ecosystem

 

With this type of engagement, patients are likely to be more cooperative, and comply with procedures and treatment plans. There are of course other kind of benefits, like ease to schedule an appointment and track medication time, decrease healthcare costs, however, the biggest benefit and value of such an approach allows patients and citizens to look after themselves better and have better quality of life, because healthcare become, personal, anchored on a personal health record, which data sharing consent is managed by the individual.

Technology and healthcare always had an uneasy relationship, and the latest announcements that Apple and Google did entering into healthcare may bring the argument of massive surveillance, however, countries like Singapore, are aggressively progressing towards the creation of a shared nationwide healthcare data lake, by law. It should give us some thoughts about how healthcare is fast becoming personal.

Ecosystems and Capabilities as a Service

7 years ago, I got an invitation from Prof. Wil Van der Aalst to participate in a research project lead by the Fraunhofer university, related with using process mining on process event logs, from different companies. The idea was to benchmark and compare how the a similar process was being executed (e.g.: order to cash) in a way that all the companies involved could learn how its process was behaving vis-à-vis the others. There was also another technical research challenge related on data preparation that allow balanced, not biased, process performance comparison. Data sources were being managed and stored by ERP’s and even on the case the ERP used was the same, challenges related with version, database schema or process flow design were also relevant in the research domain.

In those days, companies were not prepared to share such kind of data, it was considered a loss of competitive advantage to expose and compare performance with competitors. Digital transformation was a strategic concept that was not born yet, that resides on how value chain separation fades and starts to intersect with other value chains of different industry sectors, that enables new business models that were not even possible (in todays environment a oil company can provide electricity poles to recharge electrical vehicles, from distributed energy source provided by an utility company) . To another extent, a company is no longer as a member of a single industry sector but as part of a business ecosystem that crosses a variety of industries.

I am working lately with e-commerce companies in Asia. Despite they still pursue a growth strategy in terms of average customer spending, merchants onboarding, sales increase, they are also looking to transform themselves into a software company. For example an e-commerce company wants to stop to transport an order to the customer, to evolve the shipping capability to streamlining sourcing, planning, execution, settlement and end-to-end transport optimization and introduce responsive packaging systems (critical for food safety) or Just in Time supply integration via real time condition monitoring, meaning, the e-commerce company can start exploring direct supply integration with electronics companies or automotive industry. The more they penetrate into new industries by the evolution of the capability, that can include among others: Collaborative Commerce, Product accountability, Supplier Risk Management, Demand Forecasting, Warehouse Management, Smart Contracts, they are able to grow outside their core business and can start providing Retail-as-a-Service for companies that want to enter in the market they operate, connecting local factories where the products are produced to the front end commerce portals and the underlying delivery to the consumers. The capability is not a property and competitive advantage of the e-commerce company is now shared and sold as a service to other companies.

Other area that I see looming is to re-use technologies that were crafted and used in multiple companies. Some start-ups that operate in the artificial intelligence arena are making of their business model to keep the IP and redeploy it in other customers, in particular A.I. models. Like in the example of the Fraunhofer research project, the objective is to improve the models used – for example: for credit risk scoring, fraud, asset integrity management. Customers require that data keeps private, but they do not mind to reuse models that are being evolved in other companies if they fit and they will seek to substitute for the next version as it progressed and continue to deliver better results.

In this kind ecosystems, companies coevolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations, as ecosystem evolve and originate other ecosystems, consumers or business users can interact, transact,  for a wide range of offerings, without leaving the ecosystem. For the companies that operate in the ecosystem, this means having access to new revenue sources. To the companies that are served via these new kind of ecosystems, it is also a way of lower cost of ownership, speed to market and access more evolved technologies to operate.

 

Blogs worth reading 2018

Here it is the anual list of blogs which I normally consume on a random basis for research purposes and to be inspired by others point of view.

On Digital Transformation at large:

  • Cape Gemini blog – A good source to look for trends on digital transformation.
  • Column 2 – Independent source on digital transformation by Sandy Kemsley.
  • IEEE Spectrum – Future technologies brought by IEEE.
  • Jim Sinur – Digital transformation trends by Jim Sinur.
  • PeterVan – A 1.0 version type of blog that curates interesting articles from Peter Vander Auwera.
  • On Web Strategy – Dion Hinchcliffe writes about the human side of digital transformation.
  • Internet of Things and Services Blog – By Bosch Software on IoT.

On Complexity:

 

If you are interested in 2017 list click here.