Integrating AI, ML, and LoRaIoT for Enhanced LoRaWAN Performance and SDG Achievement.pptx

Low power wide area technology, LoRaWAN, officially recognized as an ITU (International Telecommunication Union) open standard in 2021, has gained widespread adoption due to massive IoT proliferation. This research paper explores the seamless integration of Low Power Wide Area technology LoRaWAN, IoT with Artificial Intelligence (AI) and Machine Learning (ML) to address Sustainable Development Goals (SDGs). Investigating the transformative potential through technical analyses, case studies, and real-world applications, the study emphasizes the synergy between LoRaWAN, IoT, and AI-ML to enhance performance in dense deployments. Additionally, it discusses LoRaWAN radio parameters, performance indicators, and AI-ML algorithms. The paper highlights COP28's role in climate action, proposes a P2P LoRaIoT solution for cost-effective greenhouse monitoring, and conducts a pilot study on a LoRaWAN-enabled smart city data set to analyze the behavior of radio parameter so as to carry out future research. This integration propels smart cities toward sustainable urbanization, showcasing technology's catalytic role in global progress.
Keywords: LoRaWAN, SDG, A, ML algorithms, IoT, Environment sustainability, Smart City.

1. Empowering Sustainable Development: Integrating AI, ML, and LoRaIoT for Enhanced LoRaWAN Performance and SDG Achievement Author: Shaista Tarannum Research Scholar, JSSATE, Bengaluru, India Co-Authors: Usha S M, Associate Professor, JSSATE, Bengaluru, India G.F. Ali Ahammed , Professor, VTU Department Of PG Studies Mysuru, India Moeen Fathima, Vidyaashram first grade college Saraswatipuram Mysuru, India

2. Contents Introduction LoRaWAN and IoT Fundamentals Understanding Sustainable Development Goals AI and ML Integration in LoRaWAN Literature Review and Case Studies Methodology and Technical Aspects Use Cases: Navigating Global Climate Challenges and IoT Innovations in Agriculture LoRa Smart City Dataset Analysis Results and Discussions Conclusion and Future Directions Q&A Session

3. Rise of IoT- 75 billion IoT connected devices by 2025[1] . Introduction Sustainable Development through AI , ML , and LoRaIoT is crucial for achieving the SDGs . This Conference paper presentation will explore the potential of AI, ML, and LoRaIoT Integration for leveraging Sustainable Development Goals (SDGs) . LPWAN Technologies-LoRa-Recognised by ITU in 2021 1.5 billion LoRa devices. Artificial Intelligence ( AI ) and Machine Learning ( ML ) are revolutionizing industries and driving innovation

4. IoT Fundamentals Smart Building Home automation, smart heating,HVAC, alarms (security, smoke detectors) Smart Cities Smart lighting, waste disposal, patrolling, parking Agriculture Climate/agriculture monitoring, livestock tracking Enormous IoT Verticals Utilities Smart metering, Leakage Detection, smart grid management Industrial Automation, Process monitoring and optimization Environment Food monitoring/alerts, Climate change, environmental monitoring IoT Layers

5. Sustainable Development Goals (SDGs) and LoRaWAN Applications SDG 9: Industry, Innovation, and Infrastructure Low-Cost Connectivity: LoRaWAN provides affordable, long-range connectivity for diverse IoT devices, driving innovation. Infrastructure Monitoring: Enables real-time monitoring of critical infrastructure for safety and efficient maintenance. SDG 11: Sustainable Cities and Communities Smart City Applications: LoRaWAN supports waste management, traffic optimization, street lighting, and environmental monitoring. Public Safety: Enhances public safety through features like smart street lighting and emergency response systems. SDG 12: Responsible Consumption and Production Supply Chain Optimization: Enables tracking and monitoring throughout the supply chain, reducing wastage. Smart Agriculture: Optimizes irrigation, monitors soil quality, and enhances production efficiency for sustainable farming. SDG 13: Climate Action Environmental Monitoring: Facilitates air quality sensors, weather stations, and flood detection systems for climate research. Precision Agriculture: Reduces environmental impact by optimizing resource usage and minimizing chemical use. SDG 15: Life on Land Wildlife Conservation: Monitors wildlife habitats, tracks animal movements, and collects ecological data for biodiversity conservation. Deforestation Prevention: Detects and alerts authorities about illegal logging activities, preserving natural habitats. SDG 16: Peace, Justice, and Strong Institutions Security and Surveillance: Enhances public security and supports law enforcement agencies. Asset Tracking: Reduces theft and improves the security of valuable assets, supporting law and order.

6. LPWAN Technologies for IoT

7. LoRaWAN Fundamentals LoRaIoT (Long Range Internet of Things) technology provides long-range, low-power connectivity for a wide range of IoT applications. Its ability to penetrate obstacles and cover long distances makes it ideal for diverse IoT deployments, from smart cities to agricultural monitoring. LoRaWAN Architechture LoRa Protocol Stack

8. LoRa Radio Resource Parameters

9. LoRa Performance Metrics

10. AI Model Classification

11. Machine Learning Types

12. Literature Review [5] Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario [6] Green IoT: A Review and Future Research Directions the economic and environmental impact of IoT need for energy-efficient solutions [7] Exploratory approach for network behavior clustering in LoRaWAN a real LoRaWAN dataset, focusing on 2169 devices The k-means Clustering algorithm to study impact of radio condition on packets [8] Green Bear smart city platform in Coimbra, Portugal, using LoRaWAN nodes to monitor green spaces, bike lanes, and recycling depots prototype's assessment to promote sustainability in urban environments [10] authors also explains the advantages of NB-IoT in terms of delay and service quality The authors of  paper proposed the concept of using two independent learning methods to allocate radio resource parameter SF,TP to devices using combination of reinforcement learning and supervised machine learning techniques, this demonstrated significant improvement in both network level goodput and energy consumption, The paper authors test the performance of  LoRaDRL under large scale frequency and jamming attacks using DDQN based algorithm, demonstrates adaptiveness to changes in environment,  the PDR improves significantly compared to learning based techniques ADR

13. Methodology: Futuristic Smart Cities: Integrating LoRaWAN , IoT, and AI/ML Technologies ML empowers systems to learn and improve from experience, enabling efficient resource allocation, waste reduction, and environmental conservation. [SDG 3,4,6,7, 11,13,17] . Enhancing LoRaWAN performance by optimizing data analysis, predictive maintenance, resource allocation, network scaling, and management this integration offers a sustainable approach to urban development, aligning with various SDGs such as clean energy, efficient transportation, and sustainable urban infrastructure. Illustration of the combination of LoRaWAN , IoT and AI ML for futuristic smart city

14. M achine Learning empowers systems to learn and improve from experience, enabling efficient resource allocation, waste reduction, and environmental conservation. Illustration of the Integration LoRaWAN , IoT and AI ML LoRaIoT and AI-ML Integration for Sustainable Solutions Predictive Maintenance: Algorithms: Regression Analysis, Time Series Forecasting (e.g., ARIMA), Deep Learning (e.g., LSTM) Network Optimization: Algorithms: Reinforcement Learning, Genetic Algorithms, Particle Swarm Optimization Energy Management: Algorithms: Markov Decision Processes, Genetic Algorithms, Q-Learning Anomaly & Fault Detection: Algorithms: Isolation Forest, One-Class SVM, Autoencoders (Deep Learning), Decision Trees, Random Forest, k-Nearest Neighbors (k-NN) Traffic Prediction, Load Balancing, Data Analytics: Algorithms: Decision Trees, Time Series Forecasting, Deep Learning (e.g., CNN, RNN) Security: Algorithms: Deep Learning for Intrusion Detection, Support Vector Machines (SVM), Random Forest Quality of Service (QoS) Improvement Algorithms: Reinforcement Learning, Genetic Algorithms, Particle Swarm Optimization Resource Allocation: Algorithms : Q-Learning, Deep Q-Networks (DQN), Multi-Armed Bandit

15. LoRaIoT and AI-ML Integration for Sustainable Solutions

16. 1. SDG 1: No Poverty : Implementing smart agricultural systems with LoRaWAN sensors and AI-driven predictive analytics to optimize crop yield, reduce wastage, and enhance agricultural income in rural areas. 2. SDG 2: Zero Hunger Deploying precision agriculture techniques utilizing LoRaWAN -connected sensors and AI algorithms to monitor soil conditions, predict crop diseases, and optimize irrigation, ensuring higher agricultural productivity and food security. 3. SDG 3: Good Health and Well-being Developing IoT-enabled healthcare systems with LoRaWAN for remote patient monitoring and employing AI/ML algorithms for early disease detection, personalized treatment plans, and efficient healthcare delivery, thereby improving health outcomes. 4. SDG 4: Quality Education Creating smart classrooms with LoRaWAN connectivity, integrating AI-based educational tools for personalized learning experiences, intelligent tutoring systems, and adaptive assessments, ensuring quality and accessible education for all. 5. SDG 5: Gender Equality Implementing technology-assisted skill development programs in rural areas utilizing LoRaWAN for connectivity. AI and ML algorithms can tailor learning paths, encouraging women's participation in education, entrepreneurship, and decision-making processes. 6. SDG 6: Clean Water and Sanitation Deploying IoT-based water quality monitoring systems with LoRaWAN sensors and using AI/ML for predictive analysis of water quality parameters. This ensures clean and safe water supply, allowing for preventive measures against contamination. 7. SDG 7: Affordable and Clean Energy Implementing smart grids with LoRaWAN -connected devices and employing AI algorithms for energy consumption prediction, demand-side management, and efficient distribution, leading to affordable and clean energy solutions. Methodology: The Power of LoRaIoT AI ML Integration for SDG Achievement

17. 8. SDG 8: Decent Work and Economic Growth Integrating AI-driven predictive maintenance in industries, enabled by LoRaWAN -connected sensors. This ensures efficient use of resources, reduces downtime, and creates job opportunities in technology-related sectors, fostering economic growth. 9. SDG 9: Industry, Innovation, and Infrastructure : I nnovating smart infrastructure solutions like intelligent transportation, waste management, and public services using LoRaWAN , AI, and ML technologies, promoting sustainable industry practices and innovation. 10. SDG 10: Reduced Inequality : Implementing IoT-based solutions in rural and underserved areas, offering equal access to technology-driven services, education, and healthcare. AI algorithms can ensure personalized services catering to diverse needs, reducing inequality. 11. SDG 11: Sustainable Cities and Communities: Building smart cities with LoRaWAN -enabled infrastructure. AI and ML algorithms can optimize traffic flow, reduce energy consumption, and enhance public safety, creating sustainable and intelligent urban spaces. 12. SDG 12: Responsible Consumption and Production: Implementing IoT systems in supply chains with LoRaWAN connectivity. AI and ML algorithms can optimize production schedules, reduce wastage, and enable responsible consumption by ensuring efficient use of resources. 13. SDG 13: Climate Action: Creating environmental monitoring systems using LoRaWAN sensors. AI algorithms analyze data for climate patterns, pollution levels, and natural disasters, aiding in early warning systems and climate change mitigation efforts. 14. SDG 14: Life Below Water: Developing underwater sensor networks with LoRaWAN technology. AI and ML algorithms analyze marine data, helping in marine conservation, tracking aquatic life, and monitoring water quality, contributing to life below water. 15. SDG 15: Life on Land : Utilizing sensor networks for biodiversity monitoring in forests and wildlife habitats. AI algorithms process data for conservation efforts, helping prevent deforestation, protect wildlife, and promote sustainable land use. 16. SDG 16: Peace, Justice, and Strong Institutions: Implementing smart surveillance systems in public spaces with LoRaWAN -connected cameras. AI algorithms process 17. SDG 17: Partnerships for the Goals: data for security analysis, promoting peace, justice, and strong institutions by enhancing public safety. v Encouraging collaborative research and partnerships between academia, industries, and governmental bodies for integrating LoRaWAN , AI, and ML technologies. Such partnerships drive innovation and sustainable development, fostering global goals' achievement. Cont . The Power of LoRaIoT AI ML Integration for SDG Achievement

18. Use Case1: Navigating Global Climate Challenges The WHO states that "wildfires and volcanic activities" from 1998 to 2017 impacted 6.2 million people, causing 2,400 deaths[17], highlighting the rising risks from climate-linked wildfires. Rapid urbanization for 50% of the global population by 2050 poses challenges for resource management, especially in food and freshwater. The analysis acknowledges contributors like CO2, methane and nitrous oxide in climate change discussions[19].

19. Use Case1: Global Climate Challenges K ey reasons for climate change: Burning of Fossil Fuels Wildfires and Volcanic Activities [17] Deforestation Industrial Processes Land Use Changes Waste Management Use of Fertilizers Transportation Emissions Permafrost Thawing Livestock Farming Changes in Energy Production European Commission highlights climate change as a paramount and enduring challenge, stressing the urgency of comprehensive climate action . Impact of Climate change: Rising Temperatures Melting Ice and Rising Sea Levels Extreme Weather Events Ocean Acidification Changes in Precipitation Patterns Loss of Biodiversity Food Security Challenges Health Risks Migration and Displacement Economic Consequences Water Scarcity Threats to Livelihoods Increased Risk of Wildfires COP28: Action Oriented Agenda to Keep 1.5 deg. within reach[16]. Aims to mobilize diverse stakeholders, conduct a Global Stocktake, and formulate pathways to limit warming, enhance resilience, transforming energy Systems, protect natural ecosystem, prioritising human health, inclusive participation and mobilize finance.

20. Use Case1: Navigating Global Climate Challenges Net Zero Tracker Policies in place World in Action

21. Use Case1: Greenhouse G as Monitoring IoT Innovations in Agriculture Environmental monitoring and conservation efforts can lead to real- time insights, early detection of ecological threats, and proactive conservation measures[SDG 13].

22. Lorawan Behavior Analysis at the Edge Dataset ( LoED ): Open dataset with 1,263,001 entries of LoRaWAN packets collected at gateways during a period of time of 4 months, generated by smart city application and research deployment Use Case2: Futuristic Smart Cities: Integrating LoRaWAN , IoT, and AI/ML Technologies Metadata information: time : Time at which the packet was received by the gateway; physical payload : Raw payload contained in the received packet; gateway : Identifier of the gateway that has received the packet; crc status : Physical layer CRC; frequency : Transmission frequency; Spreading Factor : Transmission  SF  of the packet; bandwidth : Bandwidth used by the received packet; code-rate : LoRa coding-rate of the packet; RSSI : Sampled  RSSI  value of packet reception; SNR : Sampled SNR value of packet reception; device-address : Device-address of the device that has sent the packet; mtype : mtype bit fields of the packet; fcnt : Counter value of the packet; fport : Port of the packet

23. Results and Discussion 𝑁 𝐶  = 3,374,952 uplink packets correctly received and we computed  𝑁 𝐿  = 1,333,398 lost packets Histogram representing the number of devices managed by each gateway in the network.

24. Results and Discussion RSSI Histogram SF Histogram

25. The convergence of AI , ML , and LoRaIoT presents unprecedented opportunities to accelerate progress towards the SDGs . Embracing these technologies is essential for unlocking sustainable development and shaping a better future for all. Transforming Cities: Unleashing the Potential of AI, LoRaWAN , and Collaborative Innovation for Sustainable Urban Futures. In future research work, methodology based on machine learning approach for dynamic resource allocation, minimize collision and enhance performance of LoRaWAN for effective massive IoT deployment under secure environment will be adopted Conclusion

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