Optimizing Near-Field Communication for Industrial IoT: A Biologically-Inspired Approach to Security and Efficiency

As the Industrial Internet of Things (IIoT) continues to expand, the need for robust, efficient, and secure communication mechanisms has become critical. Near-Field Communication (NFC) offers a promising solution for IIoT devices that require low-power, short-range, and secure interactions. However, optimizing NFC for industrial environments presents challenges, particularly in terms of security vulnerabilities and communication efficiency. This paper proposes a biologically-inspired approach to enhancing NFC performance in IIoT systems. Drawing from natural processes such as immune responses, swarm intelligence, and neural network communication strategies, we design adaptive algorithms that dynamically adjust communication protocols and security measures based on contextual factors. The proposed solution leverages decentralized decision-making, self-healing mechanisms, and resource-efficient strategies to improve both the security and efficiency of NFC systems in industrial applications. Through simulation and real-world testing, we demonstrate significant improvements in energy consumption, communication reliability, and resistance to cyber threats. Our results highlight the potential of biologically-inspired approaches for optimizing NFC in IIoT environments, paving the way for more resilient, scalable, and intelligent industrial communication systems.