Blockchain-IoT peer device storage optimization using an advanced time-variant multi-objective particle swarm optimization algorithm

Abstract

The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements. A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time-variant Multi-objective Particle Swarm Optimization Algorithm (AT-MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time-variant weights for the velocity of the particle swarm optimization and the non-dominated sorting and mutation schemes from NSGA-III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA-II), and the Pareto Envelope-based Selection Algorithm with region-based selection (PESA-II), and NSGA-III. The proposed AT-MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT-MOPSO achieved 52% energy efficiency compared to NSGA-III. To show how this algorithm can be applied to a real-world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT-MOPSO can be used with existing Blockchain systems and the benefits it provides.

Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

“…Some works partition the blockchain network into storage units, in which component peers donate storage resources to store the entire ledger and query within their storage unit for blocks they do not store locally [23]- [25]. Another intuitive concept discussed in depth in this study is optimizing blockchain storage via cloud storage [13], [14]. Several works have recommended evolutionary algorithms to supplement this approach to ensure that cloud-based block storage maintains optimal blockchain performance.…”

“…However, their approach had a comparatively high run-time compared to the benchmarks. To improve the work of [13], Nartey et al [14] introduced the AT-MOPSO algorithm. The AT-MOPSO algorithm had a significantly shorter run-time and greater energy efficiency than the NSGA-C.…”

“…The authors of [13] devised the nondominated sorting genetic algorithm with clustering (NSGA-C) to discover the ideal number of blocks to move to the cloud. Nartey et al [14] addressed the problem of block selection by presenting the advanced time-variant multiobjective particle swarm optimization (AT-MOPSO) algorithm. The algorithms proposed in [13] and [14] belong to a class of nature-inspired meta-heuristic algorithms known as multi-objective evolutionary algorithms (MOEAs), which are commonly employed to solve complex multi-objective optimization problems.…”

“…Nartey et al [14] addressed the problem of block selection by presenting the advanced time-variant multiobjective particle swarm optimization (AT-MOPSO) algorithm. The algorithms proposed in [13] and [14] belong to a class of nature-inspired meta-heuristic algorithms known as multi-objective evolutionary algorithms (MOEAs), which are commonly employed to solve complex multi-objective optimization problems. Issues such as lengthy run-times and a lack of sensitivity to variations hinder the performance of these algorithms [15].…”

A Survey on Network Optimization Techniques for Blockchain Systems

“…Therefore, most existing solutions to this scalability problem use lighter and faster consensus algorithms to achieve high transactional throughput [13][14][15][16][17]. Other existing solutions approach blockchain scalability from a storage perspective and propose storage optimization schemes to reduce the storage requirement of peers [18][19][20][21][22][23][24][25][26][27][28].…”