Introducing an AI-Powered Framework for Energy Optimization in WSNs
With the rapid expansion of IoT-based Wireless Sensor Networks (WSNs), managing energy use and detecting anomalies in real-time has become critical. Traditional solutions often fail to process data dynamically or identify evolving threats like blackhole attacks. To overcome these issues, we introduce LEGO-WSN—an AI-powered framework combining Long Short-Term Memory (LSTM), an Attention Mechanism, and Genetic Algorithm (GA). This hybrid system enhances real-time anomaly detection and optimizes energy consumption in IoT-driven WSNs.
Why Energy Optimization and Real-Time Detection Matter
Wireless Sensor Networks consist of compact, low-power sensor nodes that monitor and transmit data wirelessly. These nodes are typically deployed in areas where battery replacement is not feasible, making energy efficiency vital. However, WSNs face issues such as network congestion, node failures, and security threats like blackhole attacks, which can severely impact performance.
Challenges in Current WSN Systems
Many traditional anomaly detection systems are not lightweight enough for real-time deployment. Routing protocols often neglect energy efficiency, leading to reduced network lifespan. Additionally, high false-positive rates in intrusion detection systems reduce reliability, especially in complex IoT environments.
LEGO-WSN: An Intelligent Solution
LEGO-WSN addresses these limitations by integrating LSTM for learning temporal data patterns, Attention Mechanism for prioritizing critical features, and Genetic Algorithm for optimizing routing paths. This results in:
- 20% reduction in energy consumption
- 99% accuracy in anomaly detection
- High adaptability to dynamic network conditions
Data Collection and Preprocessing
The framework uses a real-world dataset from Kaggle, including over 370,000 entries labeled as “Normal” or “Blackhole” attacks. Preprocessing involves filling missing values, normalizing features like energy consumption and distance to cluster heads, and splitting data into 80% training and 20% testing sets. This ensures the model can generalize well and maintain high detection accuracy.
Deep Learning for Blackhole Detection
LSTM networks are particularly effective in capturing long-term dependencies in time-series sensor data. The Attention Mechanism further improves accuracy by focusing on features such as energy drops and routing anomalies. Together, these tools enable the model to identify blackhole attacks in real time with minimal delay.
Genetic Algorithm for Energy Efficiency
GA begins with a population of routing paths, evaluates their efficiency based on energy use and data transmission quality, and refines them through crossover and mutation. This iterative process ensures the selection of optimal paths, reducing power consumption while maintaining communication integrity.
Integrated and Scalable Solution
The integration of LSTM and GA modules provides a robust, scalable solution suitable for small- and large-scale IoT deployments. Even in highly dynamic environments, LEGO-WSN adapts to changes, detects anomalies swiftly, and maintains energy efficiency.
A Glimpse Into the Future
As IoT continues to evolve, frameworks like LEGO-WSN offer a promising path toward intelligent, self-optimizing sensor networks. These systems will play a crucial role in smart cities, healthcare, and industrial automation—where real-time decisions and efficient energy use are non-negotiable.
