Aug 2026· Engineering, Technology & Applied Science Research· 0 citations· 26 references
TL;DR
A new hybrid bioinspired optimization framework for efficient blockchain mining is presented, integrating Genetic Algorithm, Firefly optimization, and Particle Swarm Optimization into a unified architecture to take advantage of their complementary strengths.
Abstract
Blockchain consensus mechanisms are important to ensure the safe validation of transactions. However, the limitations of high computational complexity, energy consumption, and mining latency restrict the scalability of blockchain in large-scale IP-based and wireless network environments. Current methods mainly rely on single optimization methods without jointly optimizing miner selection and hash computation, resulting in inferior performance under dynamic network conditions. To fill this gap, this study presents a new hybrid bioinspired optimization framework for efficient blockchain mining, integrating Genetic Algorithm (GA), Firefly optimization, and Particle Swarm Optimization (PSO) into a unified architecture to take advantage of their complementary strengths. The proposed method uses both historical and real-time performance metrics to determine the best mining nodes. The Firefly algorithm is used to optimize the selection of hash ranges to reduce CPU workload. PSO is used to select high-performance neighboring nodes for collaborative mining. The model is implemented using the NS-2 simulator and tested over a network of 1000 wireless nodes under different consensus protocols. The experimental results illustrate 4.3% decrease in computational complexity, 4% decrease in energy consumption, and 5% decrease in mining delay. The model further reduces mining complexity by up to 34.2% and reduces the energy utilization from 24.5% to 16.6%, demonstrating its effectiveness for scalable and energy-efficient blockchain deployment.
The proposed OIBTO framework employs a lightweight Proof-of-Authority consensus within a two-tier architecture consisting of a vehicle layer and an edge layer, and proposes an Improved Starfish Optimization Algorithm (ISFOA) that utilizes chaotic mapping and genetic mutation to optimize offloading decisions and task partitioning ratios, aiming to minimize a priority-weighted combination of latency and energy consumption.
Blockchain technology has become a strong backbone to decentralized applications; however, the fast-growing energy usage of consensus mechanisms has become a cause of serious economic, environmental, and sustainability worries. This paper will be a detailed examination of the energy usage in the leading blockchain consensus algorithms, focusing specifically on the question of why some of them are high-energy usage and how their energy imprints might be decreased. The paper has started by conducting a review of the basic architecture of blockchain systems and classifying the different consensus mechanisms according to their working principle and resource needs. To analyze energy-intensive schemes, the specific attention is paid to Proof of Work, which is analyzed in terms of models of mining operations, hardware dependence, difficulty adjustment, and power consumption. Case studies of large-scaled PoW-based blockchains are presented in real-life scenarios to represent the implications of the energy in practical scenarios and network conditions. Moreover, the research also undertakes the systematic examination of optimization methods that will help to decrease the energy consumption without affecting the security or decentralization. These are innovations at the protocol level, hardware-awareness, mining policies based on renewable energy sources, and the use of layer-2 and off-chain transaction processing systems. Trade-offs between security, scalability, and decentralization and energy efficiency are discussed comparatively. The findings show that next-generation consensus mechanisms and hybrid designs are capable of saving a huge amount of energy without compromising the reasonable performance and trust guarantees.
Shivganga Gavhane, Virendrakumar A. Dhotre, Sudheer Kumar et al.· Journal of Intelligent Decis...· 0 citations
An optimal choice of consensus algorithm and type of blockchain, based on security, energy consumption and computing resources is proposed, which will enable the design of microgrid architectures and components that are compatible with decentralized cybersecurity technologies without affecting their operation.
Benedict Djouboussi, Elie Fute Tagne, G. Sèmassou· Journal of Hardware and Syst...· 0 citations
The increasing usage of distributed renewable energy resources has rapidly increased the transition to decentralised smart grids. This is where secure and efficient peer-to-peer (P2P) energy trading is crucial. However, conventional blockchain-based energy trading schemes suffer from high computational overhead, communication latency, and limited scalability, making them unsuitable for resource-constrained Internet of Things (IoT) networks. This research proposes a lightweight blockchain framework that incorporates Hyperledger Fabric with Practical Byzantine Fault Tolerance (PBFT) consensus, ZigbeePro communication, and Long Short-Term Memory (LSTM)-based energy demand forecasting to facilitate secure and intelligent decentralised energy trading. The framework was evaluated using MATLAB/Simulink simulation, NS-3, Hyperledger Fabric, and a Raspberry Pi/ESP32 prototype. The results of the experiment show that the proposed framework achieved an average latency of 48.9 ms, throughput of 185 transactions per second, packet delivery ratio of 97.8 percent, and support for up to 250 IoT nodes while maintaining low energy overhead. The LSTM forecasting model attained an R² of 0.964 with a MAPE of 4.7 percent, delivering accurate demand prediction for intelligent energy allocation. Compared with centralised and Proof-of-Work blockchain models, the proposed framework enhanced communication efficiency, scalability, and security while reducing computational cost. These results demonstrated that integrating lightweight blockchain, low-power communication, and Artificial Intelligence-based forecasting provides a practical and scalable solution for decentralised energy trading for the next-generation smart grids.
Aliyu Musa Kida, C. Ngene, Jafaru Usman et al.· International Journal of Inn...· 0 citations
Simulation results suggest that the proposed EGWO-CS-based approach offers significant improvements in packet delivery ratio, energy efficiency, trust precision, and network lifetime compared to conventional GWO, GWO-CS, and other standard schemes.
Cailing Cheng, Hui Zhang, Li Fei et al.· Computing· 0 citations
Sharding technology divides the blockchain network into multiple parallel-processing subnetworks, achieving high throughput and scalability. However, it also faces challenges, including the risk of 51% attack caused by malicious node clustering and systemic load imbalances. While reputation mechanisms are widely employed to mitigate these risks, existing approaches remain constrained by unidimensional evaluations. Specifically, most studies focus on node behavior and assess node security metrics, neglecting node performance metrics and node heterogeneity. To address these challenges, this paper presents a multi-granularity reputation model to quantify the efficiency and reliability of nodes. This model accounts for the performance and security differences arising from node heterogeneity and behavioral dynamics. Based on this model, we further propose a neighborhood-constrained simulated annealing-based node partition algorithm, NCSA-NP, that achieves balanced security and performance across shards. Experimental results demonstrate that the proposed approach achieves significant improvements in throughput and latency compared to other baselines.
Jie Li, Zhihao Guo, Yibing Li et al.· Annual International Compute...· 0 citations