## 1 Introduction  
The global wireless sensor market is projected to reach USD 27.79 billion by 2025 and is expected to grow to approximately USD 150.43 billion by 2034, reflecting a compound annual growth rate (CAGR) of 20.71% between 2025 and 2034. In North America, the market size was estimated at USD 8.93 billion in 2024 and is anticipated to expand at a CAGR of 20.75% during the forecast period. The market estimates and projections are based on revenue (USD million/billion), with 2024 as the base year [1]. This growth is attributed to the increasing adoption of industrial automation, advancements in wireless communication technologies, and the rising demand for real-time monitoring solutions. Industries are leveraging these networks to enhance operational efficiency, reduce costs, and improve predictive maintenance capabilities. The widespread deployment of Wireless Sensor Networks is also fueled by their scalability and flexibility, making them a critical component in the evolution of Industry 4.0 [2]. While integrating Wireless Sensor Networks (WSNs) into IoT ecosystems offers significant benefits, several challenges hinder optimal performance. A key issue is power consumption, as sensor nodes rely on limited energy sources. In applications like environmental monitoring and smart agriculture, frequent battery replacements are impractical. Energy-efficient protocols and solar-powered sensors are being explored to extend network longevity. Data processing is another challenge, as WSNs generate vast amounts of real-time data. In smart city traffic monitoring, excessive data transmission can strain network bandwidth and drain energy. Edge computing and data aggregation techniques help reduce transmission overhead while maintaining efficiency. Security vulnerabilities further threaten WSN reliability, especially in healthcare, where sensitive patient data must be protected. Traditional encryption is often too computationally demanding, making WSNs susceptible to attacks. Lightweight encryption schemes and blockchain-based security models offer potential solutions. Addressing these challenges through energy-efficient designs, optimized data handling, and robust security mechanisms is crucial for ensuring the long-term sustainability of WSN-enabled IoT applications [3]. The convergence of WSNs and IoT technologies has unlocked numerous opportunities across various industries; however, addressing energy efficiency and security challenges is crucial for their long-term viability. Since WSN nodes rely on limited power sources, efficient energy management is essential, particularly in scenarios where battery replacement is challenging. Researchers are investigating methods such as energy-efficient protocols and energy harvesting techniques to prolong operational life. Additionally, security remains a pressing issue due to the absence of standardized frameworks, prompting the adoption of lightweight encryption, blockchain solutions, and trust-based models. Advancements in these areas are vital to developing scalable, reliable, and energy-efficient IoT-driven WSN systems [4]. In WSNs, clustering is crucial for improving efficiency and extending network life. Clustering groups sensor nodes into clusters led by a cluster head (CH), which collects data from nodes and sends them to the base station, reducing communication and saving energy. Various algorithms optimize CH selection to maximize network lifetime by minimizing energy consumption and balancing load distribution. Notable algorithms include LEACH, which uses random CH rotation [5]; HEED, focusing on residual energy and neighbor density [6]; PEGASIS, forming a sensor chain to reduce energy use [7]; and DEEC [8] and TEEN [9], offering innovative CH selection strategies based on energy and thresholds. In WSNs, swarm algorithms significantly optimize network performance. Inspired by natural behaviors, these algorithms regulate node interactions within clusters, enhancing data collection and transmission while reducing energy consumption.