Gossip-based data aggregation in hostile environments

In the last decade, several gossip-based algorithms have been introduced for data aggregation in distributed systems. The main and common advantage of these algorithms is their robustness in dynamic and fault-prone environments. However, gossip-based algorithms are not robust in hostile environments. In such environments, some malicious nodes intentionally violate the normal execution of the algorithm to distort the aggregate value. In this paper, we focus on gossip-based averaging and try to improve the security of these algorithms. First, we revise existing gossip-based averaging algorithms to present a more secure algorithm called “Pull-Adjust.” Then, we develop and examine a light, transparent, and fully distributed protection system to push back malicious nodes. The simulation results show that the proposed system considerably improves the performance of the Pull-Adjust algorithm in hostile environments.