Hybridization of metaheuristic algorithms for resource scheduling in distributed robotic control system

Abstract

This paper presents a novel Distributed Blockchain-Assisted Secure Data Aggregation (Block-DSD) scheme designed to enhance data security, energy efficiency, and scalability in Mobile Ad-hoc Networks (MANETs) for disaster-resilient communication systems (DRCS). The proposed framework integrates an Artificial Neuro-Fuzzy Inference System (ANFIS) for dynamic cluster head selection, ensuring adaptive decision-making based on residual energy, trust value, and centrality metrics. Additionally, the Improved Elephant Herd Optimization (IEHO) algorithm is employed for optimal route selection, leveraging genetic operators to enhance exploration and exploitation capabilities. Blockchain technology is utilized to secure data aggregation through a Secure Two-Step (STS) method and Elliptic Curve Cryptography (ECC), ensuring tamper-proof and reliable data transmission. Simulations conducted using ns-3.25 demonstrate superior performance, with a 97% Packet Delivery Ratio (PDR), 20% reduced energy consumption, and minimal latency of 0.0012 s for emergency data compared to existing methods. The Block-DSD scheme provides a robust solution for secure and efficient data aggregation in highly dynamic and resource-constrained MANET environments, making it suitable for critical applications such as disaster management, military operations, and remote monitoring. Future directions include enhancing blockchain scalability and integrating real-world datasets for further validation.

Article Highlights

Resource scheduling in distributed robotic control system is presented in this work.

The proposed distributed system ensuring the balanced computational load and high reliability across various scenarios.

Proposed system provides a better performance when comparing with existing methods.