This chapter explores particle swarm optimization (PSO) in the rapidly evolving landscape of biomedical technologies. The study begins by introducing the fundamental principles of PSO, emphasizing its advantages in addressing complex optimization problems common in biomedical applications. The authors delve into innovative uses of PSO in various biomedical fields, including image enhancement, data clustering, and drug development, highlighting how PSO contributes to more accurate diagnoses, efficient treatment plans, and streamlined research methodologies. Significantly, this chapter identifies emerging opportunities where PSO can be further leveraged, particularly in personalized medicine and predictive health analytics, suggesting a roadmap for future research and development. By combining theoretical insights with practical examples, this work aims to provide a comprehensive overview of PSO's role in advancing biomedical technologies, offering valuable perspectives for researchers, practitioners, and policymakers in the field.

Wireless sensor networks are limited by the vast majority of goods with limited resources. Power consumption, network longevity, throughput, routing, and network security are only a few of the research issues that have not yet been addressed in sensor networks based on the Internet of Things. Prior to becoming widely deployed, sensor networks built on the Internet of Things must overcome a variety of technological obstacles as well as general and specific hazards. In order to address the aforementioned problems, this research sought to improve rogue node detection, reduce packet latency/packet loss, increase throughput, and lengthen network lifetime. Wireless energy harvesting is suggested in the proposed three-layer cluster-based wireless sensor network routing protocol to extend the energy lifespan of the network. For the purpose of recognising and blacklisting risky sensor node behaviour, a three-tier clustering architecture with an integrated security mechanism is suggested. This clustering approach is cost-based, and the sink node selects the cluster and grid heads based on the cost function’s value. With its seemingly endless potential across a wide range of industries, including intelligent transportation, the Internet of Things (IoT) has gained prominence recently. To analyse the nodes and clustering strategies in IoT, the suggested method PSO is applied. A plethora of new services, programmes, electrical devices with integrated sensors, and protocols have been produced as a result of the Internet of Things’ explosive growth in popularity.

Wireless network becoming an integral part of daily existence, and it is expected to become much more so in the future. The dual-band frequency-reconfigurable H-shaped microstrip antenna fed by two coaxial probes is presented. The two modes for textile applications, the antenna is symmetrically filled with back-to-back attached zener diodes. The first mode’s tuneable range is between 2 GHz and 2.5 GHz, while the second mode’s range is between 1.5 GHz and 4.8 GHz. The suggested antenna’s radiation can be switched from two orthogonal linear polarizations in the 1.5 GHz to 2.5 GHz range by adjusting the capacitance of the zener diodes. The HFSS is used for designing and simulating the antenna and antenna performs well in terms of Voltage Standing Wave Ratio [VSWR], Return Loss, directivity, and cross-polarization. The antenna is constructed on a 70 mm × 50 mm with FR4 epoxy dielectric substrate; switch scale of 0.8 mm × 1.6 mm and 3.58 is relative permittivity with substratum height of 1.6 mm.

Wireless sensor network [WSN] energy efficiency is the most critical task of researchers. Power regulation is a very effective technique for interference minimizing in WSN and energy consumption. WSN with several nodes linked to the network, including transmitting power to measure network output. The energy usage is connected to node size and weight. Aggregation data secure in WSN is a highly challenging activity. In this research paper suggested stable aggregation of data using fuzzy logic, based on clustering techniques. The clustering method is done in a network. The distance power consumed and faith value are measured for each cluster. The stable data aggregation using fuzzy logic techniques (FLT) is based on these parameters. The proposed research work would minimize energy usage to increase the lifespan of WSN.

In this paper proposes a broad band circular strip antenna based on numerical techniques. For the operating frequencies, a circular patch antenna is installed on FR-4 substrate material 4.2. The proposed antenna uses the finite element method to design an operating frequency from 2GHz to 10GHz. Substrate height is 1.6mm. The circular patch has a circumference of 8.82mm. The antenna displays good output below -16dB is the return loss value and the antenna gain is obtained in the range from 2.4dBi to 10.02dBi. The proposed circular patch antenna is designed and analysed the various parameters like VSWR, Gain, Return Loss, Directivity and Radiation pattern.