I- Introduction and Contents

II- Prior Art

III- Human Inspiration

IV- System Embodiment

V- Example Application of Data Proxy

VI- Conclusion

VII- Future Work

References

Abstract

The Internet of Things’ (IoT’s) rapid growth is constrained by resource use and fears about privacy and security. A solution jointly addressing security, efficiency, privacy, and scalability is needed to support continued expansion. We propose a solution modeled on human use of context and cognition, leveraging cloud resources to facilitate IoT on constrained devices. We present an architecture applying process knowledge to provide security through abstraction and privacy through remote data fusion. We outline five architectural elements and consider the key concepts of the “data proxy” and the “cognitive layer.” The data proxy uses system models to digitally mirror objects with minimal input data, while the cognitive layer applies these models to monitor the system’s evolution and to simulate the impact of commands prior to execution. The data proxy allows a system’s sensors to be sampled to meet a specified quality of data target with minimal resource use. The efficiency improvement of this architecture is shown with an example vehicle tracking application. Finally, we consider future opportunities for this architecture to reduce technical, economic, and sentiment barriers to the adoption of the IoT.

Introduction & Contents

The  Internet of Things (IoT) is a term describing a system of connected people, devices, and services [1]. The IoT allows computer-interfaced sensors and actuators to facilitate novel products and services by reducing costs, improving efficiency, and enhancing the usability of existing systems. The benefits of connectivity are understood across industries, with Connected Cars and Homes, Smart Factories, Wearable Devices, and Intelligent Infrastructure signaling the widespread adoption of the Internet of Things. Few technical, economic, and social barriers, like support costs [2] and concerns about data privacy and system security, [3] limit this technology’s opportunity space. Today, power and bandwidth consumption challenge IoT’s growth. The desire for rich data and information sharing dominates resource use, particularly challenging battery life and network loading for distributed wireless devices [4], [5]. A simultaneous proliferation of high-value connected devices makes the IoT a desirable attack surface [2], [6] and drives security-related resource requirements, demanding highpowered computation – lest a platform become unfavorable for mission-critical applications. This paper builds upon the author’s dissertation [7] to demonstrate an approach leveraging scalable cloud resources to address efficiency, privacy and security for next-generation IoT. In Section II, we identify a need for IoT architecture improving system-wide efficiency and security, and discuss contemporary research. Then, we consider how people process, share, and protect information in Section III. In Section IV, we present a human-inspired model for data collection, synthesis, distribution, and protection. We develop a parallel IoT architecture utilizing process and measurement knowledge to reduce the cost of sampling sensors and transmitting data. This approach leverages system knowledge to provide security through abstraction and data privacy through remote sensor fusion.