Secure Massive IoT Using Hierarchical Fast Blind Deconvolution

TL;DR

This paper presents a hierarchical blind deconvolution method for secure massive IoT access, enabling collision-resilient communication and shared secret generation without pilot coordination.

Contribution

It introduces a novel fast blind deconvolution algorithm based on hierarchical compressed sensing for secure IoT communication, allowing collision resolution and secret key derivation.

Findings

Algorithm successfully recovers channels and messages in simulations

Demonstrates collision resolution without pilot coordination

Shows trade-offs between recovery efficiency and secret capacity

Abstract

The Internet of Things and specifically the Tactile Internet give rise to significant challenges for notions of security. In this work, we introduce a novel concept for secure massive access. The core of our approach is a fast and low-complexity blind deconvolution algorithm exploring a bi-linear and hierarchical compressed sensing framework. We show that blind deconvolution has two appealing features: 1) There is no need to coordinate the pilot signals, so even in the case of collisions in user activity, the information messages can be resolved. 2) Since all the individual channels are recovered in parallel, and by assumed channel reciprocity, the measured channel entropy serves as a common secret and is used as an encryption key for each user. We will outline the basic concepts underlying the approach and describe the blind deconvolution algorithm in detail. Eventually, simulations demonstrate the ability of the algorithm to recover both channel and message. They also exhibit the inherent trade-offs of the scheme between economical recovery and secret capacity.