<p>Low-power sensing Internet of Things (IoT) devices rely on filtering noise and interference in any impedance network applications. The sensing device’s high-performance is identified based on their utilization, maximum interference impedance and signal gain. However, due to large device integration, the above-mentioned features are distorted in IoT platforms. To address this problem, a novel Minimal Impedance Optimization Method (MIOM) is introduced in this paper. The proposed method incorporates high-impedance filters with a half-adder design for filtering noises between different frequency devices. This is exclusive for heterogeneous integrations in IoT for filtering noise and increasing signal gain. The verification of increasing or sustaining gain is computed using Deep Recurrent Learning (DRL) for impedance and utilization. Based on the failures in impedance and utilization (low) the adder design is replaced with alternate gates that handle minimal signal inputs. Therefore, the change in further utilization and interference impedance is validated from the new adder design by the neural learning. This learning output is used for determining the Device-To-Device (D2D) integration and communication modeling in the IoT platform. Besides, the above recurrent verifications are pursued by IoT devices across various applications in a distributed manner.</p>