Though the strength of the Internet of Things (IoT) stems from its ability to be more than the sum of its parts, its devices have long been lone rangers.
Hardware, software, middleware, cloud, storage, security, integration platforms and connectivity all need to combine to produce an effective end to end solution. This, of course, demands collaboration and partnerships rather than isolation. Yet when it comes to the nuts and bolts of how devices do their magic, little comment has passed on the merits of a more collective approach; until now.
The rise in scope and diversity of IoT devices in our daily lives is prompting some fundamental questions and has certainly led to a greater awareness of the value of applications operating en masse. Everyone now understands how mass collaboration between machines, programmed to act autonomously on our behalf, can help us live more productively and work less wastefully.
How we have reached this latest chapter can in part be attributed to the growing role of machine learning and artificial intelligence, making smart devices even smarter, acting on their own intuition and initiative, in some cases negating the role of human intervention completely. While connected cars have long been the headline-grabbing example, the traction runs wider in scope beyond the traditional domains of transport and the smart home arena. Patient monitoring and smart meters are just two examples of a long list of applications gradually infiltrating many aspects of our daily life.
This growing ubiquity, along with enhanced technical capabilities, is why scaling up the power and impact of IoT devices through their numbers has becomes the next logical progression. It’s a move evident in the area of drone development, where code-driven unmanned aircraft systems that can work together under one operator to find, track, identify and engage targets, are set to provide considerably more bang for their buck.
Machine learning must be supervised – primed to predict responses on the device
Yet with remote components and a need for speed, it’s a development that once again draws attention to the edge network and underlines the value of optimal agility and flexibility when deploying and managing IoT devices.
It presents an interesting dilemma. One deep-rooted concern that has limited the ambitions of many an IoT project manager is the perceived loss of control. They fear they will sacrifice their ability to monitor device activity if it takes place on the network periphery away from the core of the digital business. However, proponents of collaborative autonomy argue that enabling multiple devices to work together improves the security and operation of the IoT by enhancing accuracy and convenience.
When combining more sophisticated and intricate algorithms with the use of continuous data streams of IoE its clear we have a major opportunity. However, we must be mindful that reaping optimal benefits will depend on seamless integration, a challenge heightened by machine learning and the huge data transfers and connectivity requirements that this brings.
As such, machine learning must be supervised, primed to predict responses on the device. This calls for the agile capabilities of the very lightest integration engine to enable integration applications to be built and deployed directly on edge devices. As a result, connection challenges will be simplified, while making the deployment of artificial intelligence and machine learning an anywhere, everywhere reality.
Each tiny and remote device shoulders a responsibility that is both massive and of central importance. Their collective success hinges on their technical ability to monitor and understand a continually evolving set of conditions. The devil is going to be in the detail.