## 4 Discussion
Meta-learning captures historical drift to optimize retraining schedules, while reinforcement learning fine-tunes drift thresholds and the retraining frequency [85]. Dimensionality reduction techniques like PCA help isolate drift-sensitive components, improving detection efficiency [84; 86]. Ethical considerations, long-term deployment issues, and real-world implementation challenges are central to the responsible use of IoT and sensor technologies. One of the foremost ethical concerns is privacy, defined as the individual's right to control how their personal data is collected and used, particularly in systems involving wearables and healthcare digital twins [88]. To address this, developers are encouraged to adopt \"privacy in design\" principles, embedding privacy-enhancing technologies and transparent control mechanisms during the early stages of system development. Data ownership and stewardship also present significant ethical challenges, as the IoT data often flows across devices, cloud services, and international borders, raising complex legal and regulatory issues. Clear definitions of data ownership, robust access controls, and user awareness of who can access and use their data are essential [89]. In contexts such as smart healthcare, systems must also allow users to override automated decisions when they conflict with personal values or clinical best practices [88]. Long-term deployment introduces technical challenges, including the need for interoperable communication protocols that can dynamically adapt to changing environmental conditions [90]. Many IoT devices are resource-constrained, making the implementation of strong cryptographic protections without compromising per formance particularly difficult. Security measures, including intrusion detection systems, must continuously evolve to keep pace with new attack vectors, requiring ongoing updates and vigilance [91]. Real-world implementation also demands clear governance models, especially in multi-user environments. These deployments must be guided by transparent policies regarding data access, user consent, and ownership, with ethical oversight mechanisms ensuring adherence to established standards [92; 93]. The deployment of large-scale IoT networks involves billions of sensors, actuators, and communication modules, each contributing to both operational and embodied energy footprints. Energy challenges are intensified by device distributions in remote settings and the dependence on batteries or energy harvesting technologies. Notably, a significant portion of energy is consumed not during active transmission, but in maintaining the network connectivity, even in idle states [94]. The production and disposal of IoT devices add to embedded energy costs and electronic waste, which further increases their environmental footprint [95]. Complex AI systems--particularly LLMs--require high computational power during both training and inference phases [96], demanding specialized hardware such as GPUs and TPUs. This elevates energy consumption substantially. Data centers supporting these operations face environmental pressure due to their high Power Usage Effectiveness (PUE), with even minor inefficiencies resulting in large-scale carbon impacts [97; 96]. Although virtualization and dynamic resource management have improved efficiency, the reliance on non-renewable energy remains a challenge [96]. Sustainable solutions under exploration include ultra-low-power chips, renewable energy harvesting, neuromorphic processors, and wafer-scale engines--though many remain experimental [94; 98]. Edge computing and distributed cloud infrastructures offer an additional promise by reducing the transmission overhead and bringing computation closer to users, thus improving energy efficiency without compromising performance [97]. The digital divide today extends beyond affordability and includes factors such as digital literacy, cultural norms, geographic isolation, and disability-related access issues [98; 99].