Distributed Estimation in Blockchain-aided Internet of Things in the Presence of Attacks

TL;DR

This paper develops a performance guarantee framework for distributed estimation in blockchain-enabled IoT systems under malicious attacks, using a relaxation method to handle non-convex optimization problems and derive analytical solutions.

Contribution

It introduces a novel approach to quantify estimation performance in blockchain-aided IoT under attacks by transforming a non-convex problem into a convex one and deriving its analytical solution.

Findings

Derived an analytical expression for the optimal solution of the relaxed problem.

Provided a performance guarantee for distributed estimation under attack scenarios.

Developed a relaxation method to handle non-convex optimization in secure IoT estimation.

Abstract

Distributed estimation in a blockchain-aided Internet of Things (BIoT) is considered, where the integrated blockchain secures data exchanges across the BIoT and the storage of data at BIoT agents. This paper focuses on developing a performance guarantee for the distributed estimation in a BIoT in the presence of malicious attacks which jointly exploits vulnerabilities present in both IoT devices and the employed blockchain within the BIoT. To achieve this, we adopt the Cramer-Rao Bound (CRB) as the performance metric, and maximize the CRB for estimating the parameter of interest over the attack domain. However, the maximization problem is inherently non-convex, making it infeasible to obtain the globally optimal solution in general. To address this issue, we develop a relaxation method capable of transforming the original non-convex optimization problem into a convex optimization problem. Moreover, we derive the analytical expression for the optimal solution to the relaxed optimization problem. The optimal value of the relaxed optimization problem can be used to provide a valid estimation performance guarantee for the BIoT in the presence of attacks.