Blockchain integrated multi-objective optimization for energy efficient and secure routing in dynamic wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) form the backbone of many key use cases, from environmental monitoring to healthcare to smart cities. But their use case is limited in terms of energy, latency, scalability, and security. To combat such problems, the paper suggests a new algorithm, the Energy-based Multi-Objective Donkey Smuggler Optimization Algorithm (EM-DSOA). This approach combines multi-aspect optimization and a thin blockchain protocol, making it a one-stop shop to optimize WSN’s efficiency, security, and stability. EM-DSOA as proposed optimizes energy utilization with dynamic clustering and adaptive routing with safe data transfer via blockchain integration. The approach is compared against current best practices like Multi Weight Chicken Swarm Based Genetic Algorithm (MWCSG) and Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization (AHCS-GWO) by simulation examples of different network densities. The results are marked by significant improvement with energy efficiency of 99.13 %, packet loss reduction of 91 percent and throughput increase of 1000 %. The model likewise has very low end-to-end latency, which is perfect for real-time workloads. The study points out that EM-DSOA can be scalable and flexible, with a high performance across diverse and changing scenarios. With an eye towards energy efficiency, low latency and secure communications in the one, the proposed model takes WSN optimization to a new level of knowledge. This is a work that’s not only up to the challenge of technology now but it also serves as a solid basis for future IoT and smart city deployments and will provide long-term, secure networks.