Jeremy Lynn Reed’s research while affiliated with The University of Texas at San Antonio and other places

IoT edge computing is a network design model that captures and processes data at the network edge. The results are forwarded to a cloud service or, if additional processing is needed, a middle tier. By processing data at the edge and middle tier, edge networks achieve better load-balancing and improve performance; however, traditional edge network deployments represent a rigid participation model. Edge networks require physical access to an IoT device and often lock the device to a single edge network. These constraints make it difficult to construct the ideal network, as they reject IoT devices deployed at the network edge but not owned by the network administrator. Our goal is to remove these limitations by creating a network protocol that supports broader participation of IoT devices, cryptographically secures network data, and improves network performance by increasing captured data at the network edge. The protocol is named Snap to symbolize the ease of self assembly. Our experimental research focuses on temperature stability and the cycle efficiency of an HVAC system by utilizing a Snap network to combine two existing edge networks and increase the number of temperature measurement points. The additional measurement points improved the efficiency of the HVAC cycle strategy by increasing the square footage of measured building space. The additional temperature capture points supported an adjustment to the HVAC cycle strategy which resulted in reducing the disparity between the requested temperature and the resulting temperatures. Snap networks support a broader range of IoT sensors leading to increased measurement density, sample rate frequency, and coverage of the network edge.

The explosive growth of the Internet of Things (IoT) devices raises serious concerns for a user’s privacy and security because the existing software framework on these devices often support various default features and generate large data sets. Moreover, many IoT devices incorporate a manufacturer-owned cloud-based back-end support to process and store the generated data while simultaneously sharing with third parties. Clearly, in such an industry-driven environment with the desire to use the IoT data as a revenue stream, it is a challenge for users to control IoT data. Device manufacturers utilize an opaque software design where user data is generated and stored with little transparency. Manufacturers use EULAs as a legal construct to protect a manufacturer’s legal standing and to explain a device’s behavior, however this explanation is vague and lacks the necessary details for a user to determine a device’s acceptable use and it has become increasingly difficult for users to secure and maintain their data. Fortunately, as the privacy minded user base of IoT devices grows, the manufacturers will be forced to implement a new framework that can enable users to have more control on the creation of their IoT data, and to store/disseminate such data in a secure and private manner. In this paper, we address this lack of transparency from manufacturers and address the issues of privacy and security by proposing a new framework called Bulwark, for manufacturer use on IoT devices and mobile applications. Proposed framework enables the user to generate and manage a set of data controlling rules, and store the result in their personal cloud account, while providing a dashboard data reporting tool enabling data transparency and supporting good user choices. The user’s ability to access, disseminate and secure IoT generated data, is now available within our proposed framework. Using reverse engineering, simulation and implementation of open source solutions, we demonstrate support for a set of common devices. Each device executed the framework, while communicating with a mobile application and cloud services. Rules were generated for each message and telemetry was returned to the mobile application for dashboard rendering. We stored generated data in the cloud using our own account, while maintaining the free tier for each of the cloud services. Network usage increased between 4% and 9% while storage size grew between 0% and 2% larger, as compared to using the device without the framework. Our framework demonstrates support for a multitude of devices, by either open source or support for similar feature sets. This framework is easy to integrate and we anticipate wide spread adoption.