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@article{205969,
        author = {Zuha Biyababani and Sugandha Nandedkar},
        title = {Machine Learning Approaches for Adaptive Geofencing and GPS-Based Location Validation: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {9007-9011},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205969},
        abstract = {Geofencing has become a fundamental component of modern location-based services, supporting applications such as attendance management, asset tracking, logistics monitoring, and security systems. Traditional geofencing techniques primarily rely on fixed geographic boundaries and predefined radius thresholds for location verification. However, these approaches are often affected by GPS signal inaccuracies, positional drift, and variations in user mobility patterns, which can reduce classification reliability. This paper presents a comprehensive review of recent advancements in adaptive geofencing and GPS trajectory analysis. The survey examines the application of spatial clustering techniques, distance-based geospatial modelling, and machine learning algorithms for intelligent location validation. Existing studies employing density-based clustering methods, trajectory mining approaches, and supervised learning models are analysed and compared. The review further discusses the role of adaptive boundary generation in improving spatial classification performance and reducing the limitations of conventional rule-based systems. Research gaps, current challenges, and future directions in adaptive geofencing are identified. The findings suggest that integrating spatial intelligence with machine learning offers significant potential for developing more robust, scalable, and context-aware location validation systems.},
        keywords = {Geofencing, GPS Trajectory Analysis, Spatial Clustering, Machine Learning, Adaptive Geofencing, Location Validation, Spatial Classification},
        month = {June},
        }