ArticleSensors (Basel, Switzerland)2020
Beamforming Optimization in Internet of Things Applications Using Robust Swarm Algorithm in Conjunction with Connectable and Collaborative Sensors.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Optimization Method for Wide Beam Sonar Transmit Beamforming.Sensors (Basel, Switzerland) · 2022Article
- Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems.Sensors (Basel, Switzerland) · 2022Article
- A Study on the Application Model of Blended Teaching in English Language Teaching in Colleges and Universities under the Ecological and Internet Perspectives.Journal of environmental and public health · 2022Article
- Low-Cost Beamforming Concept for the Control of Radiation Patterns of Antenna Arrays Installed onto UAVs.Sensors (Basel, Switzerland) · 2021Article
- Gaussian Approach for the Synthesis of Periodic and Aperiodic Antenna Arrays: Method Review and Design Guidelines.Sensors (Basel, Switzerland) · 2021Article
- An Efficient Broadband Adaptive Beamformer without Presteering Delays.Sensors (Basel, Switzerland) · 2021Article
- Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis-Taguchi System.Sensors (Basel, Switzerland) · 2020Article
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2 authors.
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Abstract
The integration of the Internet of Things (IoT) with Wireless Sensor Networks (WSNs) typically involves multihop relaying combined with sophisticated signal processing to serve as an information provider for several applications such as smart grids, industrial, and search-and-rescue operations. These applications entail deploying many sensors in environments that are often random which motivated the study of beamforming using random geometric topologies. This paper introduces a new algorithm for the synthesis of several geometries of Collaborative Beamforming (CB) of virtual sensor antenna arrays with maximum mainlobe and minimum sidelobe levels (SLL) as well as null control using Canonical Swarm Optimization (CPSO) algorithm. The optimal beampattern is achieved by optimizing the current excitation weights for uniform and non-uniform interelement spacings based on the network connectivity of the virtual antenna arrays using a node selection scheme. As compared to conventional beamforming, convex optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the proposed CPSO achieves significant reduction in SLL, control of nulls, and increased gain in mainlobe directed towards the desired base station when the node selection technique is implemented with CB.
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