Real-Time Location Systems: A Moving Target for IoT

During the first e-commerce era, package location tracking arose as a showcase application. In 1994, FedEx.com’s success making such tracking information available online showed what the web could do, and it quickly got wide attention.

Today, GPS-oriented tracking systems such as Amazon and Uber have again redefined users’ expectations for efficient fleet systems. 

Internet of Things (IoT) technologies can meet the demand for location systems capable of real-time, complex event processing, but there are issues to overcome at each link of the chain. As GPS-based location system processing becomes more precise and immediate, there are a bewildering variety of new big data tools to process that data.

The pieces are plentiful, and freight and related businesses face difficult decisions when it comes to elements in real-time location systems, which represent a market expected to reach $7.5 billion by 2022, growing at 31.4% (CAGR) from 2016 to 2022, according to Allied Market Research. 

Tracking information flow

“Today, there is ‘Uberization’ and ‘Amazonization’ of the freight experience. It’s changed the way that people expect to interact with the freight provider,” said Ben Wiesen, CEO at Carrier Logistics, maker of FACTS transportation management software. 

Today, expectations are that people can see their freight orders as pegs moving on a digital map, delivery is second-day, and – often, as in the case of Amazon – is free, he added.

As a result, the trucking industries have made investments in information flows that collect data from IoT sensors in their network, said Wiesen, a 30-plus-year trucking management veteran. He said businesses will have to “skill-up”  to handle the incoming information not only as data but also as events that initiate steps in operations. 

Real-Time Data Collection

Front-end data collection for IoT has its challenges, and that doesn’t change even as, in more and more cases, the data acquired is ultimately fed to cloud computing centers. The front-end processing challenge is one of the drivers behind interest in edge computing.

That’s according to Ian Skerritt, marketing adviser at HiveMQ, a maker of MQTT message brokering and other software for IoT fleet management, logistics and related implementations. He emphasized that IoT fleet systems navigate rough environments where connections can be shaky. 

“The key challenge fleet systems have is that the vehicles are moving around,” Skerritt said. “Each vehicle typically connects over a cellular or satellite network.” But, as the vehicle moves around, connections between the vehicle and cloud can be lost. 

Lost connections create two issues for location tracking, he continued. One, real-time data may be ready to be sent but no network connection is available; and, two, systems must reestablish the vehicle-to-cloud connection as quickly as possible once the network is available again. 

HiveMQ’s MQTT software is designed to quickly reconnect communications sessions that have been interrupted and work “agnostically” with various types of data feeds, Skerrett said. Protocols competitive with MQTT include AMQP, HTTP and LwM2M as well as proprietary systems.