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Virtual Multiple‐Input Multiple‐Output–Based Optimized Cross‐Layer Design for Wireless Sensor Networks.

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Title: Virtual Multiple‐Input Multiple‐Output–Based Optimized Cross‐Layer Design for Wireless Sensor Networks.
Authors: Prajapati, Monali R.1 (AUTHOR) monalimandli79@gmail.com, Joshi, Jay M.2 (AUTHOR), Joshi, Maulin M.3 (AUTHOR), Dalal, Upena Devang4 (AUTHOR)
Source: International Journal of Communication Systems. 1/10/2026, Vol. 39 Issue 1, p1-26. 26p.
Subjects: Wireless sensor networks, Cross layer optimization, MIMO systems, Mathematical optimization, Energy consumption, K-means clustering, Quality of service, Data transmission systems
Abstract: Wireless sensor networks (WSNs) essentially aid in numerous medical and networking applications. However, they face challenges, such as the availability of limited energy resources and the need to satisfy varying service quality requirements with efficient data transmission. Most of the traditional methods used to deal with these challenges often consume significant energy, limiting the operational lifespan of the sensor nodes. Hence, our research attempts to overcome the limitations in the existing WSN solutions by proposing a cross‐layer design (CLD) with a novel algorithmic optimization, focusing more on energy efficiency and communication optimization. The novel beluga whale–adapted Namib beetle optimization (BWANBO) presented here effectively addresses the complex optimization challenges in WSNs by balancing multiple conflicting objectives, such as maximizing data rates, minimizing energy consumption, and ensuring reliable communication under varying network conditions. Additionally, k‐means clustering is employed to form the clusters of nodes, and BWANBO uses parameters, like node energy, quality of service (QoS), improved trust, and security, to select the cluster head (CH). Integrating the novel virtual multiple‐input multiple‐output (MIMO) technology with the optimized CLD framework, this research simultaneously aims to reduce the transmission energy, boost the data rates, and improve the communication among diverse protocol layers. The resultant expression is a significant extension in the operational lifespan of the sensor nodes. Finally, the outcomes reveal that the BWANBO algorithm effectively optimizes the bit error rate (BER) performance, under the consideration of end‐to‐end (ETE) throughput and latency. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Wireless sensor networks (WSNs) essentially aid in numerous medical and networking applications. However, they face challenges, such as the availability of limited energy resources and the need to satisfy varying service quality requirements with efficient data transmission. Most of the traditional methods used to deal with these challenges often consume significant energy, limiting the operational lifespan of the sensor nodes. Hence, our research attempts to overcome the limitations in the existing WSN solutions by proposing a cross‐layer design (CLD) with a novel algorithmic optimization, focusing more on energy efficiency and communication optimization. The novel beluga whale–adapted Namib beetle optimization (BWANBO) presented here effectively addresses the complex optimization challenges in WSNs by balancing multiple conflicting objectives, such as maximizing data rates, minimizing energy consumption, and ensuring reliable communication under varying network conditions. Additionally, k‐means clustering is employed to form the clusters of nodes, and BWANBO uses parameters, like node energy, quality of service (QoS), improved trust, and security, to select the cluster head (CH). Integrating the novel virtual multiple‐input multiple‐output (MIMO) technology with the optimized CLD framework, this research simultaneously aims to reduce the transmission energy, boost the data rates, and improve the communication among diverse protocol layers. The resultant expression is a significant extension in the operational lifespan of the sensor nodes. Finally, the outcomes reveal that the BWANBO algorithm effectively optimizes the bit error rate (BER) performance, under the consideration of end‐to‐end (ETE) throughput and latency. [ABSTRACT FROM AUTHOR]
ISSN:10745351
DOI:10.1002/dac.70313