Council Post: The Future Of Machines: Data’s Potential To Improve Numerous Industry Processes

Parthsarathi (Parth) Trivedi is CEO and co-founder of Skylo.

From drones to delivery bots to autonomous vehicles, machines already far outnumber humans. Putting these machines to work and effectively managing their data means understanding that their network connectivity needs are quite different. They use less bandwidth in most instances than humans but are more consistent and predictable in their communications needs. They can be pre-programmed and scheduled. They often operate in rural, and sometimes hostile geographies devoid of humans. In general, when successful, they produce more data than they consume. 

By necessity, machine data networks will be far more sentient than human data networks. They’ll need to take immediate and automated action or derive insights and raise alarms from continuous feeds of data. Think of a boat out on the open sea communicating an SOS message if it detects being capsized or the need for monitoring the constant temperature of a vaccine during its journey. 

Making machines smart and transparent so that decisions can be made depends on a new kind of connectivity layer. It’s one that offers broad and affordable coverage, which is always on, real-time and dependable.

Networks For Machines: ‘Smart Pipes’

Today’s most extensive deployments of terrestrial networks aren’t sufficient to meet the needs for machines and sensors operating autonomously and in remote regions of the world. Therefore, global networks will need new capabilities that no network providers today are equipped with, and given the level of need in rural and remote areas, they’ll also need to be notably affordable and accessible. 

The networks enabling the full range of consumer experiences get the big headlines, but equally essential is the emergence of standards such as Narrowband Internet of Things, or NB-IoT, a communications standard associated with 5G that operators are now rolling out across the world. This dedicated machine connectivity layer delivers the potential to highlight all kinds of applications that can make a big difference in people’s lives, like elevating farmland with data-driven remote actions, improving global food delivery and securing vaccine transportation. 

From water and fuel-level sensors to vibration, shock and temperature monitoring, low-cost sensors are giving rise to machine monitoring and perception at a faster rate than previously imagined. But to be used to the fullest potential, the boats, trucks, trains, farm equipment and other machines equipped with such sensors require a “full-stack” approach to combine seamless connectivity with context-specific analytics and remote action.

Machine data networks require built-in functions that developers can easily call on to provision devices remotely, fetch or push commands using software API queries across thousands of machines at once. They can couple that with deep, domain-specific data processing layers that simultaneously organize sensor data and draw results or patterns from it. Those insights can then trigger other actions across the network.   

Machine Networks That Transform Lives

Transporting a vaccine to hospitals while ensuring a constant temperature. Matching demand and supply of trucks in real-time, and instructing those trucks to be re-routed if needed. Monitoring and intervening on crop health for a sustainable food supply. Monitoring daily, even hourly, train suspension systems to predict and plan for service downtime. The potential exists to give every industrial application that needs it the end-to-end connectivity to manage their most essential data to drive key decisions, save time and money, and even save lives. 

Applied to aviation, for instance, this kind of connectivity has improved efficiency while streamlining expenses. For instance, sensors on aircraft en-route relay data on atmospheric conditions to the rest of the fleet that are about to undertake their journey. This, in turn, allows the most optimal route and altitude for a safe and smooth passenger experience. With similar capabilities emerging across sectors from agriculture to renewable energy production, this goes beyond basic competitive advantage and offers an industry-wide rise in possibilities across entire economies. 

And it’s a process that’s only just beginning. In my opinion, machine data networks will grow in scale and number of applications at a rapid pace in the years to come, with faster activations than the shift to smartphones. It will be a world where machine, sensor and device connectivity is transformative for entire industries, even retrofitting those in traditionally analog industries that have not yet benefited from digital innovations or modern connectivity.

Ultimately, the problem isn’t only about knowing where a truck or a tractor is at any given point; it’s about aligning the rest of the supply chain in tandem with other parts of the fleet, unlocking new business models that were previously impossible or allowing risky or costly business processes to be automated to drive throughput. Such capabilities should be available to all industries at a reasonable cost, not just those with the highest value assets such as in defense or aerospace. Once we recognize that, the types of use cases that we can bring online and benefit from are truly endless.

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