The role of intuitive networks in building smart city visions

Built for digital business and the Internet of Things, intuitive networks promise to transform public sector organisations struggling with legacy networks and looking for a stepping stone towards a much bigger picture.

By 2030 the number of connected devices and objects will reach an estimated 500 billion. This rapid growth in networks and devices has meant that the connected world is now too big and too complex for humans to administer effectively. Networks of tomorrow will therefore not be manually administered. They will be too complex, too cumbersome, and too complicated, to effectively allow manual administration. Traditional networking models simply do not scale and perform to meet the expectations of this digital era.

Businesses now need a new networking framework, that is simplified and more secure to use. IDC indicates that businesses that have invested in modern networks, have improved their rate of growth in revenue, customer retention and profit, by a factor of two to three times. For digital organisations, the network is the foundation of their business and success.

From the field of analytics, machine learning can be used to build complex models and algorithms within networks that are capable of generating forward-looking trends. Analytical models in-built inside networks can produce reliable and repeatable decisions and can uncover hidden insights through learning from historical relationships embedded in data. Machine learning can give networks the ability to learn without being programmed. This approach has evolved from pattern recognition and learning theory in artificial intelligence.

Networks with such in-built algorithms can learn and make predictions from data. With the help of these algorithms, networks can overcome the limitation of static programming and can make data-driven predictions and decisions, through building a model from data inputs. In the past, data mining has been used to discover new trends in wide arrays of collected data. However, in the case of such intuitive networks built on machine learning, algorithms discover known trends that are prevalent in data as it is aggregated.

Networks with in-built machine learning and complex algorithms can establish a pattern of baseline behavior and can successfully flag deviations without supervision. One of the immediate benefits is the ability to successfully build models of optimal network behaviour and proactively react without intervention to anomalies and intrusions within.

Intuitive networks shift from the traditional manual, time-intensive, static mode of operation, towards one that is capable of continuously learning from the data that it manages for an organisation. The more volume of data it manages, the more it is capable of learning through analytics and adapting for automatic and efficient response. The intuitive network automates the edge of the network and embeds machine learning and analytics at a foundational level. Both of these characteristics make them well suited for smart city use case applications.

Dubai recently announced its entry into the adoption of latest digital technologies through its Smart Dubai vision. The vision encompasses advanced technologies like intuitive networks, machine learning, artificial intelligence and analytics to solve real life chronic problems, achieving sustainable development, maintain economic competitiveness, as well as providing high levels of quality of life for its inhabitants.

With Dubai's residents experiencing some of the highest technology adoption rates in the region, matching their expectations on living, personal development and their quality of life requires the city to adopt the latest digital technologies as well.

A key aspect of meeting these objectives is to have better digital integration amongst significant citizen facing players inside the emirate. The Smart Dubai initiative therefore looks at strategic partnerships between 11 government entities operating within Dubai. These strategic partners are expected to drive use case initiatives using a number of enabling digital transformation technologies.

These include IoT sensory systems, advanced analytics, artificial intelligence, 3D printing, drones, wearable devices, robotics, driverless vehicles and virtual reality. Other initiatives that are making progress include Dubai's Open Data Law, Dubai's Happiness Meter and public-private partnerships.

The objective of the Smart Dubai journey is to radically change how governance is practiced, how business is done and how society as a whole and people as individuals live in the city of the future. Highly-intelligent digital and networking technologies are expected to play a key role in meeting these objectives.

Where does all this lead? The first is, an intuitive network will gain the trust of public and private sector organisations wanting to select a platform to build their digital business models of tomorrow. This will be on the basis of its machine learning capabilities, that it is constantly learning and evolving to become highly secure and provide insights. And the second is the intuitive network is just the stepping stone to a much bigger vision of creating intuitive technology infrastructure. But for now, the network remains the accelerator and enabler towards this exciting end-game.