Exploring design methodologies for next-generation IoT sensors

Rafael Mena, Texas Instruments
embedded.com (March 15, 2016)

The paradigm of ‘big data’ stems from an increasingly connected world through the rise of social media and business transactions. The prominence of the Internet of Things (IoT) will continue to strain the resources of the internet by a convergence of wireless sensor networks that generate massive amounts of data. It is projected that by the year 2020 there will be 20 billion devices connected to the internet with each individual having an average of 6.58 connected devices [1].

The IoT sensor backplane is increasingly expected to monitor the system under test on a real-time basis. This is true for IoT sensor solutions monitoring body area networks, safety and security solutions, industrial factory and process automation solutions, and building automation solutions to name a few. This gives rise to a new paradigm tied to the data collected by the connected devices, that of 'big data sensing.'

Big data sensing drives a rethinking of the way this data is managed. The concept of edge computing tries to address these issues by processing the data at the point where the connected device uploads the data to the network. This fails to consider the system as a whole where in addition to minimizing the amount of data on the network, the overall power consumption of the wireless sensor network needs to be minimized in order to maintain acceptable battery life. In industrial IoT solutions for example, battery life of 10 years is typically expected for the connected device. Requiring the connected device to stream data real time to the network drives resources from the end node, which reduces the battery life of the device.

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