Optimizing the energy envelope in the Internet of Things using predictive cubic splines

The paradigm of Internet of Things (IoT) is the result of rapid advances in the development of small low powered computing devices with wireless connectivity. This has given rise to a plethora of new applications, including, but not limited to monitoring, sensing, intrusion detection and others. Due to the nature of these applications, IoT devices are often burdened with receiving and transmitting a large volume of data that is time and delay sensitive. Furthermore, since most of these devices are battery powered, a data deluge often renders parts of the network depleted of energy, causing that part to go dark. In this work, we propose an energy optimization framework based on predictive cubic splines that anticipate a sudden increase in the bandwidth and minimize energy consumed by these devices while still maintaining an optimum degree of availability and reducing bottlenecks in the data flow.