Mobile Providers: Are Smart Cities Your Next Big Customer

For mobile WiFi providers, the old real estate adage of Location, location, location is taking on new meaning with the advent of location-based services. Thanks to the growing availability of device GPS and the ubiquity of mobile access points, a world of new-business opportunity is opening as fast as vendors – everyone from innovative startups to wireless infrastructure owners – can come up with new applications.

Comcast’s recently announced MachineQ enterprise IoT service has already found customers in health-sector wearables for indoor/outdoor patient tracking, sensor/meter reading, rodent control and business-asset monitoring. In Europe, Orange Business Services, a spin-off of France Telecom SA, is partnering with location-based services provider Pole Star, who markets indoor-positioning, geo-fencing and other solutions to malls, airports, hospitals and other big venues.

Between these extremes are hundreds of other potential applications, many looking for products, that will make use of location-based services, or LBS.

As for big venues, many cities and city suppliers will be aspiring to become smarter in how they put technology to use to create and support improvements across their societal and business realms. These efforts are creating massive new business opportunities for vendors at all levels of the customer-service network stack. Because of their expertise and experience in delivering large-scale wireless services, mobile providers, including cable vendors bundling wireless services, will be well positioned to take advantage of these opportunities.

Smart-City IoT Applications

So called “smart-city” opportunities are seemingly limitless. The San Francisco Municipal Transportation Agency wants to track bike ridership volumes at busy intersections in an effort to reduce motor-vehicle usage. Auto traffic is on the minds of planners in Louisville, Kentucky and Sacramento California, too, which expect to use IoT data to reduce traffic congestion and improve parking availability.

The Lower Colorado River Authority wants to track stream levels in real time so they can warn against floods. And Los Angeles, which already has Internet-connected sensors in nearly 150,000 streetlights and 5,000 intersections, hopes to combine that data with the location information coming from people’s smartphones and watches. Real-time data from sources such as these could be used in a number of applications, from traffic control to early earthquake warning.

One real-time IoT application that likely will see wide use will be geofencing, which can be used by sports stadiums, shopping malls and other large venues to predict attendance levels at certain times of the day. This can be used for growth planning, event management, or dozens of other initiatives that are yet to come.

If It Were Easy…

“If it were that easy, everyone would be doing it,” goes the saying. In the case of building smart-city IoT applications, practically everyone is doing it, or at least trying.

What gives mobile providers the edge is their experience in deploying large-scale wireless services through WiFi or Bluetooth infrastructures. But here is where mobile providers, and in particular cable-offering-wireless companies, must pay close attention. The road to success with smart city applications has two distinct lanes: improving support for current customers, and then preparing their customer-service infrastructure for the new demands of location-based applications.

Why improve customer support? Studies have shown that customers can be fickle, and are justifiably offended by poor customer service. Today’s customers expect an immediate response when they lose service. Real-time analytics technologies are available today that can respond instantly to network failure, and can even diagnose proactively the likelihood of failure. Customer-support reputation will be an important factor in winning large smart-city-related contracts.

Real-time analytics will face even greater challenges from the coming onslaught of location and proximity-based service applications. One challenge is the newness of the phenomenon itself: it’s no one’s guess as to what near-future applications will look like or where they’ll pop up.

A more obvious challenge will come from the scope and volumes of IoT data these applications will produce. Location-based services will be tracking users – and their cars, bicycles and pets – constantly, thus producing limitless streams of data for analysis. Proximity services, such as those used by indoor beacons, will require far greater precision in order to determine whether a mall customer is nearer to the shoe store than the CVS. This will generate exponentially more data.

How IoT Analysis Makes Sense Of The Data

That data, which can be upwards of a million events per second, comes from various probes inserted into network elements and that deliver user and device identification to the analytics ingestion point. The analytics engine takes it from there, adding contest on the subscriber – background, preferences, and so on – and performs time-series analysis and predictive modeling to see where the subscriber, or possible customer, may be heading.

Along the way the analytics add situational context, such as weather information, and assess the likelihood of possible scenarios, all the while tracking that customer’s continued movement and displaying the results. To get this information, the analytics engine is drawing on a number of resources, from data warehouses and data lakes to pattern-matching, hypothesis-testing and other models.

Delivering this level of information within an actionable timeframe will demand ultra-fast performance. The good news is that this performance, and the cloud-IoT performance to go with it, is within the capabilities of some of today’s analytics architectures. The best solutions will have these qualities in common:

  • High-performance data handling – Data coming in from IoT applications will require an analytics engine that can perform in-memory computations at ultra high speeds. This is critical for processing the incoming data and analyzing it in various contexts – and doing it in real time.
  • Flexible end-to-end architecture – An agile analytics infrastructure will help citizen analysts and data scientists respond quickly to the demands of newer IoT applications as they come on board. In the same way, a flexible, building-block development environment will help developers create the predictive and prescriptive solutions that new location-services customers will demand.

How effectively and how quickly mobile operators can put these tools in use could well determine which among them gets the coming contracts.

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