Speaker: Nik Spirin, University of Illinois at Urbana-Champaign
Abstract: The Internet of Things (IoT) is rapidly gaining popularity and promises to disrupt multiple industries via automation and data-driven analytics. In turn, it drives new requirements for machine-to-machine (M2M) communication networks and data transmission devices. Different from the networks serving people, two key characteristics await radical improvements in the case of M2M communication — energy efficiency and sensor deployment/maintenance cost.
Taking into account these requirements, we have developed a new radio protocol exclusively for devices and large distributed wireless telemetry networks. By properly modulating the signal, we guarantee very high signal-to-noise ratios. Therefore, the sensors connected to our transmitters (operating in the unlicensed radio spectrum band) can send and receive information over very large distances (up to 7 miles in an urban area and up to 30 miles in open terrain). At the same time, our protocol is very energy efficient and, hence, our transmitters could operate autonomously without maintenance more than 10 years from a standard AA battery. The amortized communication, deployment, and maintenance cost for a transmitter and a base station configuration is orders of magnitude lower compared to the state-of-the-art GPRS/3G/4G/LTE approaches.
We will consider three real-world cases that demonstrate how this new networking technology could help optimize food supply chain. First, we will focus on smart irrigation systems and show how to increase crop yield while decreasing water usage by providing the right amounts of water to the plants taking into account the data collected from various IoT sensors (moisture, soil content, plant type, weather conditions, etc.). Second, we will touch upon the problem of safe agricultural produce storage, which requires maintenance of optimal temperature, moisture, and CO2 concentration levels in agricultural silos, and show how to reduce the risk of produce rot by automatically adjusting the temperature (think of “Nest” for vegetables/seeds) and activating the ventilation system on the basis of reliable data. Third, we will show how to detect the cattle breeding period by mounting an accelerometer enhanced with our transmitter to a cow and analyzing its motion patterns in the cloud. Now the cow will send you a message when it is the right time!