Integrating IoT with Adaptive Beamforming for Enhanced Urban Sensing in Smart Cities

Enahoro, Sunday, Ekpo, Sunday Cookey ORCID: https://orcid.org/0000-0001-9219-3759, Uko, Mfonobong, Elias, Fanuel and Alabi, Stephen (2025) Integrating IoT with Adaptive Beamforming for Enhanced Urban Sensing in Smart Cities. IEEE Access, 13. pp. 96120-96134. ISSN 2169-3536 (In Press)

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Abstract

The exponential growth of Internet of Things (IoT) devices in smart city environments presents significant challenges, including spectrum congestion, interference, energy inefficiency, and scalability. To address these issues, we propose SmartBeam+, a novel adaptive beamforming algorithm designed to optimize the communication performance in dense IoT networks. SmartBeam+ dynamically adjusts the beamforming weights using real-time Channel State Information (CSI) to maximize the Signal-to-Interference-and-Noise Ratio (SINR), minimize the bit error rate (BER), and suppress interference effectively. Extensive simulations compare SmartBeam+ with conventional algorithms such as Zero-Forcing (ZF), MVDR, and Least Mean Squares (LMS) across key performance metrics: SINR, BER, energy efficiency, reliability, throughput, and latency. Results show that SmartBeam+ consistently performs better, with SINR values ranging from 20 to 13 dB, BER as low as 0.0001, and 30 dB interference suppression. SmartBeam+ maintains 20 bps/W energy efficiency and achieves a reliability of 92%, significantly outperforming other methods, especially under high node density and interference levels. Furthermore, the network lifetime is prolonged up to 90 hours, and the latency is reduced to 5 ms, making SmartBeam+ ideal for time-sensitive applications such as disaster management and urban sensing. These results highlight SmartBeam+ as a scalable, energy-efficient, and robust solution for next-generation IoT networks in smart cities.