<![CDATA[Edge computing has emerged as a pivotal paradigm for optimizing performance, privacy, and deployment within Internet of Things (IoT) ecosystems. This narrative review aims to synthesize the latest scholarly insights into how edge computing addresses key challenges in latency reduction, data security, and resource orchestration. Drawing on a structured literature search from major academic databases, the review analyzed empirical and theoretical contributions spanning various edge-IoT implementations. The findings indicate that edge computing enhances system responsiveness by relocating data processing to proximity of data sources, leading to improved latency and throughput. In applications such as smart cities and remote healthcare, this shift enables more efficient bandwidth usage and timely decision-making. Moreover, privacy-centric technologies including federated learning, blockchain, and zero-trust architectures have proven effective in mitigating data security risks across distributed environments. Despite these advantages, systemic challenges persist, particularly regarding policy, infrastructure, and organizational readiness. Deployment in developing countries often encounters limitations due to regulatory ambiguity and insufficient digital capacity. Successful strategies observed globally emphasize the importance of hybrid cloud-edge-fog architectures and localized deployment models aligned with regional capabilities. This study underscores the need for collaborative public-private innovation, policy reform, and inclusive digital infrastructure development to fully realize the benefits of edge computing in diverse IoT contexts.]]>
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