Global Convergence Detection in Decentralized IoT Networks based on Epidemic Approach

Abstract

The concept of decentralization has involved in the varieties of the research area including computer science, management, politics. Bitcoin is the first cryptocurrency that indicates the power of decentralization from blockchain, the emergence of blockchain has established a new solution to solve the problem in centralization. The technology avoids the centralized controller and creates a trusted network in which all participants have a right to verify the information that flows all over the network. Considering the network layer of system architecture, an important aspect of any decentralized distributed systems including blockchain is the epidemic (gossip-based) protocols which maintain the network consistency properties namely, robustness, scalability, convergence speed, and accuracy, across the distributed systems. Epidemic protocols are bio-inspired paradigm that provides randomized communication and computation for extreme-scale networked systems. However, one of the drawback of Epidemic protocol is that each node receives multiple duplicated data with high traffic in the data transmission of data can cause high bandwidth in the network. The ability to extract relevant data from an enormous amount of data which is distributed in the network is necessary. In previous research, local convergence detection is proposed to detect global convergence for a given approximation error of the aggregation estimation. This work adapts the concept of local convergence detection to epidemic aggregation protocols to the scenario of decentralized IoT network architecture and evaluates with the standard benchmark. The results show that the adapted protocols can adjust themselves to be capable of dynamic conditions regarding global convergence.