Optimization Techniques and Machine Learning Strategies for Small and Dense Energy Efficient LoRa Networks

Abstract

Low-Power Wide-Area Network (LPWAN) technologies, particularly LoRa and Lo- RaWAN, are pivotal in the rapidly expanding Internet of Things (IoT) landscape, en- abling wide-area communication with minimal power consumption. However, these technologies face significant challenges related to energy efficiency, scalability, and secu- rity, especially in dense deployments specially in smart cities and other IoT applications. This research addresses these challenges through a comprehensive investigation into op- timization strategies, machine learning applications, and security measures designed to enhance the performance of LoRa networks. Initially, the study introduces the importance of LoRa (Long Range) and LoRaWAN (Long Range Wide Area Network) in wireless communication, emphasizing the necessity for energy-efficient strategies in wireless sensor networks (WSNs). Common optimiza-tion techniques and machine learning models for energy prediction and detection are discussed, setting the stage for subsequent research aimed at improving LoRaWAN de- ployments in IoT environments. Recognizing the limitations of existing energy-efficient methods, such as Adaptive Data Rate (ADR) and its adaptations, especially for mobile devices within WSNs, the research identifies the impracticality of traditional energy optimization techniques re-quiring frequent updates on channel conditions due to LoRa’s duty cycle constraints. To address this, two novel algorithms are introduced, designed to dynamically opti-mize transmission power and Spreading Factor (SF) based on node distance, thereby minimizing energy consumption. These algorithms offer significant advancements in energy-efficient communication for WSNs, particularly beneficial in scenarios involving mobile devices. The theoretical descriptions are strengthened by the hardware imple- mentation of the algorithms. This practical validation not only confirms the conceptual foundations but also showcases the algorithm’s real-world feasibility and performance. However, the algorithms are developed for dynamic nodes and not generalized for a large scale deployment. The next step is to focus on generalizing the algorithm on a network management level where mathematical optimization models are aimed at opti-mizing communication efficiency by balancing energy consumption and delivery ratios. An integer linear programming model was introduced to offer a structured method for network configuration. By optimizing SF and transmission power settings, the algo- Abstract rithm enhances packet delivery ratios and energy efficiency, ensuring the sustainability and performance of IoT networks in dense deployment scenarios. This two-step ap-proach effectively addresses the objectives of minimizing time on air and transmission power while maintaining robust communication. The integration of machine learning (ML) models are explored, proposing an ML-based system for predicting energy consumption in LPWAN-based WSNs. By training ML models with historical data on transmission parameters, environmental conditions, and energy consumption patterns, the system can have optimal settings for transmission parameters tailored to current environmental factors. Real-time monitoring facilitated by ML models allows dynamic adjustments of parameters in response to changing net- work conditions, ensuring optimal network performance. This system also helps identify excessive energy consumption and greedy behavior, enabling proactive management of network resources. An extensive evaluation of twelve machine learning regression models was conducted to pinpoint the most effective model for predicting energy consumption. Performance metrics were utilized throughout the evaluation process to assess the ac- curacy and reliability of each model. Finally, a detection algorithm and a classification model are employed to distinguish between power greedy and standard nodes in the network. By utilizing data from the network, detection and classification models were devised to differentiate between stan-dard and greedy nodes based on their transmission parameters and energy consumption patterns. They aim to develop a method that can effectively identify nodes consuming excessive energy or operating sub-optimally, enabling better management and optimiza-tion of the network. In conclusion, the key findings and contributions of the research are summarized, emphasizing the significance of the developed strategies in addressing en- ergy constraints and enhancing the sustainability of IoT applications. Potential avenues for future research are outlined, including further refinement of optimization techniques, integration of emerging technologies such as artificial intelligence and edge computing, and expanding the applicability of these solutions to other IoT domains.