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    Hybrid BLE–LoRa architectures for energy-efficient and resilient wireless sensor networks: Experimental validation and adaptive clustering strategies
    (Springer Science and Business Media LLC, 2026-06-03)
    Del-Valle-Soto, Carolina
    ;
    Briseño, Ramon A.
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    Valdivia, Leonardo J.
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    Varela Aldas, José  
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    Visconti, Paolo
    Wireless Sensor Networks are increasingly deployed in mission-critical scenarios where resilience and energy efficiency are paramount. This paper presents a hybrid architecture combining Bluetooth Low Energy (BLE) and Long Range (LoRa) technologies to enhance both robustness and energy-aware performance in adversarial environments characterized by reactive jamming attacks. A comprehensive experimental testbed was developed, integrating BLE and LoRa nodes, a dual-protocol gateway, and a reactive jammer emulator. We introduce an adaptive clustering algorithm that performs energy-aware role assignment and jamming mitigation based on signal anomaly detection and multi-metric routing. To validate its effectiveness, we conducted an extensive time-series analysis on energy consumption, retransmission rates, and signal resilience under both mitigated and non-mitigated conditions across BLE-only, LoRa-only, and hybrid BLE-LoRa networks. The results show protocol-dependent performance trade-offs under the proposed mitigation algorithm. While LoRa-only and hybrid BLE–LoRa networks exhibit consistent reductions in energy consumption, retransmissions, and variability, the BLE-only configuration demonstrates improved resilience at the cost of a moderate increase in energy usage and retransmission activity due to clustering and control overhead. Notably, the BLE-LoRa architecture balances delivery assurance and energy efficiency while maintaining communication even under interference. Furthermore, we provide statistical modeling and hypothesis testing confirming the mitigation algorithm’s significant impact. These findings offer a critical empirical contribution to the design of resilient and energy-aware heterogeneous WSNs and demonstrate the viability of real-time adaptive mitigation strategies for emerging smart environments
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    Advanced Customer Segmentation in the Natural Supplements Industry to Enhance Marketing Strategies Using Big Data Tools
    (2026)
    Sánchez Montero, Ivanna Karina  
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    SOTAMINGA CINILIN, MARCELO JAVIER  
    This study underscores the critical role of Big Data and Business Intelligence tools in modern corporate decision-making, particularly in the natural supplements industry, where understanding customer behavior through advanced segmentation can significantly enhance marketing strategies and overall business performance. In this research, two years of purchase records from a company in the natural supplements industry were analyzed using a combination of segmentation algorithms and RFM (Recency, Frequency, and Monetary value) analysis to identify distinct customer profiles. Leveraging Python, three clustering algorithms—K-means, DBSCAN, and Agglomerative Hierarchical—were implemented and evaluated, with the Silhouette Score identifying K-means as the most effective approach. This model categorized customers into four key segments: high-value customers, potential growth customers, at-risk customers, and occasional buyers. The insights derived from this segmentation process were fundamental in designing targeted marketing strategies, optimizing resource allocation, and improving customer retention. Furthermore, this study highlights how businesses that embrace data-driven decision-making gain a competitive edge by personalizing customer interactions, enhancing efficiency, and increasing the return on investment in marketing. The findings suggest that the integration of predictive modeling and intelligent data analysis supports more precise segmentation and enables organizations to anticipate trends, mitigate risks, and drive sustainable growth. The application of Big Data and Business Intelligence in this context allows companies to transform large datasets into actionable insights, fostering proactive and informed decision-making in dynamic and competitive markets. Ultimately, this research provides strong evidence that leveraging advanced analytics can lead to substantial economic benefits, positioning businesses for long-term success in data-driven environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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