Blockchain PoS and PoW Consensus Algorithms for Airspace Management Application to the UAS-S4 Elecatl +
Footnote †: This paper is an Extended Version of the Conference Paper Published in the AIAA AVIATION 2023 Forum (San Diego, California & Online, USA, 12-16 June 2023). the allocation of safe airspace for UASs emerges as a pivotal undertaking that necessitates the reliability of a well-defined algorithm [5]. Many strategies for allocating airspace in UTM have been designed and used [6]. Airspace allocation can be framed as an optimization challenge with the aim of minimizing a cost function that takes into account safety, energy consumption, and time-related aspects [7]. The significance attributed to each factor within the cost function reflects the priorities for the ecosystem. This study concretely concerns safety in scenarios involving multiple aircraft sharing the same airspace, especially in the context of swarm flight zones [8]. In response to these safety considerations, the UTM problem has been reconfigured using Swarm Dynamic Agents (SDAs) as the foundational framework for the most secure management methodology [9]. The management of Swarm Dynamic Agents involves coordinating a group of autonomous agents (vehicles) towards a common goal. Key aspects include agent communication [10], local decision-making [11], proper movements [12], task allocation [13], adaptability, and optimization. Agents communicate and make decisions based on their perception of the environment and interact to move properly. Tasks are assigned based on agent capabilities, and the system must adapt and recover from changes. Optimization and control ensure efficient performance. This approach revolutionized unmanned vehicle movement management, enabling the decentralized accomplishment of complex tasks. Management methodologies employed for SDAs have been successfully utilized for allocating spaces to various moving vehicles [14]. These methodologies have found application across Unmanned Underwater Systems (UUS) [15], Unmanned Aerial Systems (UAS) [16], Byzantine robots [17], and unmanned cars [18], and their remarkable ability to ensure safety in dynamic environments has been revealed. With respect to predetermined trajectories for each UAV, dynamic path planning [19] is needed to solve the SDA problem, as the trajectory of each vehicle can affect the trajectory of the others due to uncertainties. Consequently, there is a need for a consensus algorithm to analyze trajectory for path planning, all the while minimizing the probabilities of conflicts [20] among vehicles. The consensus algorithms that have achieved the greatest success have been established through deterministic, randomized, leader-free, and leader-based methodologies [21]. Consensus algorithms are widespread and used for various purposes. They are frequently applied for data aggregation, clock synchronization, leader election, etc. [22; 23]. In cases of asynchronous environments, deterministic strategies [24] might prove inadequate in resolving consensus challenges, particularly when faced with a solitary communication breakdown (link failures between communicating entities). Randomized methodologies are risky for critical tasks such as traffic management due to their association with significant safety concerns [25; 26]. A leader-based methodology is more vulnerable to cybersecurity attacks than other methodologies but outperforms other methodologies when security is the main concern. Blockchain provides the most secure framework for the execution of a leader-free consensus algorithm [27]. The P2P blockchain topology is the most reliable coordination methodology that makes decisions based on its decentralized architecture [28]. As the UTM ecosystem requires a consensus mechanism for the reliable allocation of airspaces, the P2P blockchain can perform this task proficiently. The Linear Consensus Protocol (LCP) is a simple algorithm used in distributed systems [29].