Advancements and artificial intelligence approaches in antennas for environmental sensing

Lalbakhsh, Ali; Simorangkir, Roy B.V.B.; Bayat-Makou, Nima; Kishk, Ahmed A.; Esselle, Karu P.

Abstract

Environmental sensors have come a long way over the last decade, surged in variety and capabilities. Such growth was impossible without developing wireless technologies, particularly antennas, thanks to advanced numerical computation software and artificial intelligence (AI). Sensors have numerous applications in industrial environments for purposes such as safety improvement, data acquisition, and environment and human body monitoring. For wireless sensor networks (WSNs), there may be several antennas to send the sensing data. However, further developments in the invention of planar antennas have opened up an unprecedented direction in the miniaturization of wireless sensors. Consequently, unobtrusive human-centric wireless sensing is becoming far more accessible due to the recent developments of epidermal antennas. Moreover, AI and its integration into antenna designs have resulted in more efficient WSNs. This chapter reviews the printed antennas for WSNs, explains how printed antenna sensors can be used for material characterization, gives an overview of epidermal antenna for unobtrusive human-centric wireless communications and sensing, and finally reviews the recent AI-based approaches in designing antennas.

Citation

Lalbakhsh, A., Simorangkir, R. B., Bayat-Makou, N., Kishk, A. A., & Esselle, K. P. (2022). Advancements and artificial intelligence approaches in antennas for environmental sensing. In Artificial Intelligence and Data Science in Environmental Sensing: Cognitive Data Science in Sustainable Computing (19-38). Elsevier. https://doi.org/10.1016/b978-0-323-90508-4.00004-6

• Online Publication Date: Feb 11, 2022
• Publication Date: 2022
• Deposit Date: Oct 19, 2023
• Publisher: Elsevier
• Pages: 19-38
• Book Title: Artificial Intelligence and Data Science in Environmental Sensing: Cognitive Data Science in Sustainable Computing
• Chapter Number: 2
• DOI: https://doi.org/10.1016/b978-0-323-90508-4.00004-6
• Public URL: https://durham-repository.worktribe.com/output/1791975