###### Abstract  
Wireless sensor networks (WSNs) play a pivotal role in diverse applications such as environmental monitoring, industrial automation, healthcare, and smart cities. The motivation behind the development of WSNs stems from their impact in providing real-time data on various environmental parameters. The challenge for WSNs is to achieve strong security and efficient energy saving together. Traditional methods sought to find solutions either through security or energy. In response, this study proposed a secure and energy-efficient framework for enhancing security measures in WSNs while minimizing the impact on energy resources by using the Enhanced Consumed Energy Leach (ECP-LEACH) protocol and the Enhanced Random Forest Classifier for Low Execution Time (ERF-LET) algorithm for attack detection named Security-Enhanced Energy Conservation with ERF-LET (S-2 EC-ERF). The integration of the detection algorithm at the node level played a pivotal role in fortifying the security posture of individual nodes by detecting and mitigating potential security threats. Leveraging a comprehensive dataset obtained from NS3 simulations, the ERF-LET algorithm demonstrated its proficiency in differentiating between normal and attack packets, thereby laying a strong foundation for subsequent evaluations, where it achieved an accuracy of 98.193%. The proposed methodology was further validated through real-time simulations conducted on the NS3. The results demonstrated the superiority of the proposed S-2 EC-ERF in terms of the packet delivery ratio (PDR), average throughput, end-to-end delay, and mean energy consumption compared to the Security-Enhanced Energy Conservation with Logistic Regression (S-2 EC-LR), Security-Enhanced Energy Conservation with Decision Tree (S-2 EC-DT), and Security-Enhanced Energy Conservation with AdaBoost (S-2 EC-Ada) algorithms. wireless sensor networks (WSN); ECP-LEACH protocol; ERF-LET algorithm; energy efficiency; attack detection; node-level security +
Footnote †: journal: Applied  
Article  
A Security-Enhanced Energy Conservation with Enhanced Random Forest Classifier for Low Execution Time Framework  
[PERSON], M.  
on diverse parameters. This ability to monitor and respond to real-time changes in the physical world positions WSNs as invaluable tools for enhancing situational awareness and decision-making processes. The motivation for this research stems from the dynamic landscape of WSNs, where the demand for robust security and optimal energy efficiency has reached unprecedented levels. Conventional approaches often grapple with challenges in reconciling these two critical aspects, thereby leading to compromises in either security or energy consumption [5]. This study is propelled by the need for a comprehensive and innovative methodology that seamlessly integrates both elements, thereby fostering a symbiotic relationship between security and energy efficiency. Challenges such as energy efficiency, communication protocols, security, and node localization underscore the complexity of WSN design [6]. Ongoing trends involve integration with the Internet of Things (IoT), the utilization of machine learning for data analytics, and the exploration of energy harvesting technologies. WSNs continue to evolve and reshape how to monitor and comprehend the surroundings, with ongoing research aimed at enhancing their capabilities and addressing persistent challenges. However, challenges persist in the realm of WSNs, including the optimization of energy consumption [7], ensuring robust security measures, and addressing scalability concerns. Energy efficiency is a critical consideration, given that many sensor nodes operate in remote or inaccessible locations where battery replacement is impractical. Security is paramount to safeguarding the integrity and confidentiality of the transmitted data, especially in applications such as healthcare [8] and critical infrastructure monitoring. The intricate intersection of security and energy efficiency in WSNs presents a multifaceted challenge.