Energy Modeling for Improved Security of IoT Sensors

Abstract

The Internet of Things (IoT) is an increasingly ubiquitous technology that provides boundless opportunity for connection. Whether that entails communication between a human and a device, between two or more devices, or between some network of humans and devices, the commercialization of IoT comes with growing concerns of threats to both privacy and security. Therefore, improvements to the security of these systems are imperative to the future growth and widespread use of this technology. However, where security improvements are made, it is also important to consider the resulting cost to energy consumption as devices become more lightweight and energy constrained. In our research, we place specific emphasis on the category of IoT devices known as Wearable Medical Sensors (WMSs). Due to their size and design, it is especially important that WMSs are not only secure in the data and information that they are collecting and transmitting, but also energy-efficient. Should these features be neglected, the results could be detrimental to the user's private information and quality of life. In this paper, we develop a methodology for energy modeling of IoT devices that can be used for further on-sensor security advancements. Not only will it aid in future IoT research, but it will also aid in the validation and analysis of a proposed system for increased security and energy-efficiency through compressive sensing. Currently, the energy model breaks device energy down into sensing and transmission energy with a focus on BLE at an average of 3.3% error and ZigBee with an average of 13.47% error. Note, the terms sensor and device may be used interchangeably as some WMSs may contain a single sensor while others may contain multiple sensors.