5G-Driven Smart Sensor Networks: Integrating Software- Defined Radios and AI-Based Spectrum Optimization for IoT Security

Abstract

The rapid expansion of Internet of Things (IoT) applications—ranging from environmental monitoring, industrial automation, healthcare,
to smart cities—places ever greater demand on wireless spectrum, low latency, high reliability and strong security. 5G networks
promise to deliver on high throughput, low latency, massive device connectivity, and with network slicing, ultra‐reliable lowlatency
communication (URLLC) and massive machinetype communications (mMTC). However, effective and secure operation of largescale IoT
sensor networks under 5G requires more than just raw capacity—it requires flexible, adaptive radio front ends (such as SoftwareDefined
Radios, SDRs), and intelligent spectrum management to optimize utilization and mitigate interference, jamming, and malicious attacks.
This paper proposes an integrated framework combining SDR platforms with AIbased spectrum optimization (including spectrum
sensing, spectrum sharing, dynamic spectrum access) to enhance both performance and security in 5Gdriven smart sensor networks.
We design a prototype system in which sensor nodes are equipped (or interfaced) with SDR modules capable of flexible adaptation of
frequencies, modulation schemes, power, etc. On the AI side, we employ a two‐layer model: (i) a local spectrum sensing and anomaly
detection module using machine learning to detect spectrum holes, interference, or suspicious behaviour; (ii) a centralized optimization
module using reinforcement learning (or metaheuristic algorithms) to allocate spectrum, adjust radio parameters, schedule sensor
transmissions, and manage spectrum sharing among nodes and primary users. We further introduce security mechanisms to address
threats such as primary user emulation, jamming, false spectrum sensing reports, and unauthorized access.
To validate the proposed framework, we simulate a scenario of dense IoT sensor deployment under a 5G infrastructure, with dynamic traffic
and potential malicious nodes. Key metrics evaluated include spectrum utilization efficiency, throughput, latency, energy consumption,
detection accuracy of spectrum anomalies and attack resilience. Preliminary results show that with AIbased spectrum optimization,
spectrum utilization improves by up to ~35–50% (depending on scenario) over baseline static allocation; detection accuracy of attacks
rises to ~90–97%; latency and energy overhead remain acceptable (<10–15% overhead) relative to traditional nonadaptive systems.
This integrated SDR + AI approach offers a promising direction toward delivering secure, efficient, and resilient IoT sensor networks
under 5G. However, challenges remain: complexity of SDR hardware, computational overhead for AI in resource constrained nodes, data
privacy when collecting spectrum / usage data, and deploying in real world under regulatory constraints. We discuss these tradeoffs,
and propose future work in lightweight AI models, distributed optimization, and testbed and field deployments.