Blockchain networks are based on cryptographic notions that include asymmetric-key encryption, hash functions and consensus protocols. Despite their popularity, mainstream protocols, such as Proof of Work or Proof of Stake still have drawbacks. Efforts to enhance these protocols led to the birth of alternative consensus protocols, catering to specific areas, such as medicine or transportation. These protocols remain relatively unknown despite having unique merits worth investigating. Although past reviews have been published on popular blockchain consensus protocols, they do not include most of these lesser-known protocols. Highlighting these alternative consensus protocols contributes toward the advancement of the state of the art, as they have design features that may be useful to academics, blockchain practitioners and researchers. In this paper, we bridge this gap by providing an overview of alternative consensus protocols proposed within the past 3 years. We evaluate their overall performance based on metrics such as throughput, scalability, security, energy consumption, and finality. In our review, we examine the trade-offs that these consensus protocols have made in their attempts to optimize scalability and performance. To the best of our knowledge, this is the first paper that focuses on these alternative protocols, highlighting their unique features that can be used to develop future consensus protocols.
In this paper, a new chaos-based hash function is proposed based on a recently proposed structure known as the deterministic chaotic finite state automata (DCFSA). Out of its various configurations, we select the forward and parameter permutation variant, $DCFSA_{FWP}$ due to its desirable chaotic properties. These properties are analogous to hash function requirements such as diffusion, confusion and collision resistance. The proposed hash function consists of six machine states and three simple chaotic maps. This particular structure of DCFSA can process larger message blocks (leading to higher hashing rates) and optimizes its randomness. The proposed hash function is analyzed in terms of various security aspects and compared with other recently proposed chaos-based hash functions to demonstrate its efficiency and reliability. Results indicate that the proposed hash function has desirable statistical characteristics, elevated randomness, optimal diffusion and confusion properties as well as flexibility.
14 November 2022 in IEEE Internet of Things Journal
Blockchain is a distributed ledger in which participating users with varying levels of trust agree on the ledger’s content using a consensus mechanism called consensus protocols. There has been a rising interest in the design of consensus protocols since they play a central role in blockchain architecture. However, many recently proposed consensus protocols lack experimental verification which hampers the possible deployment of these protocols in real-world blockchain networks. In this paper, we propose a simple tool called SIM-P that can accurately simulate the behavior of these consensus protocols with ease. It is an agent-based stochastic simulator that relies on the sequential Monte Carlo method to model how block publishers are selected. The likelihood of each node (represented as agents) being selected as a block publisher is represented by independent trials in a binomial experiment. We provide a base SIM-P model that simulates Proof of Work (PoW) for benchmarking purposes. The PoW model also serves as the basic structure of the simulator that can be adapted to other protocols. We showcase the flexibility of SIM-P by proposing two additional simulation models for– Proof of Reputation-X and Proof of Contribution, both of which lack experimental verification in their original design specifications. We show how the simulator can be used to produce vital metrics such as throughput, resistance against the 51% attack, and energy consumption. We verify the accuracy of SIM-P by comparing PoW’s simulated results with theoretical estimates and historical Bitcoin data.
10 February 2025 in Sustainable Energy, Grids and Networks