OPTIMIZED ENERGY HARVESTING ANTENNA DESIGN FOR WIRELESS SENSOR NETWORKS INTEGRATION WITH GENETIC ALGORITHM APPROACH

Abstract

Energy harvesting technologies offer a sustainable solution to power constraints in Wireless Sensor Networks (WSNs). This paper presents a genetic algorithm (GA) optimization framework for designing energy harvesting antennas tailored specifically for WSN applications. The proposed multi-objective GA simultaneously optimizes harvesting efficiency, radiation pattern, size, and bandwidth while satisfying practical WSN constraints. The optimized dual-band antenna operates at 915 MHz and 2.45 GHz, facilitating energy harvesting from multiple ambient RF sources. Simulation results demonstrate significant performance improvements over conventional designs, with the optimized antenna achieving energy harvesting efficiencies of 42% at 915 MHz and 38% at 2.45 GHz—representing improvements of 180% and 25% respectively compared to conventional patch antennas. The GA-optimized design features an unconventional geometry with asymmetric slots and parasitic elements that enable superior bandwidth and impedance matching characteristics while maintaining a 40% size reduction. Simulations indicate that the optimized antenna can extend WSN node lifetime by up to 280% in typical urban RF environments. Comparative analysis against state-of-the-art designs confirms the effectiveness of the proposed approach for addressing energy limitations in WSN deployments. Keywords: Energy harvesting, Genetic algorithm, Microstrip antenna, RF energy harvesting, Wireless sensor networks.