Edge Computing for Real-Time IoT Applications: Architectures and Case Studies
DOI:
https://doi.org/10.15662/IJARCST.2024.0705002Keywords:
Edge computing, real-time IoT, streaming video analytics, DRL scheduling, container orchestration, FogBus2, edge architecture, greenhouse IoT, resource efficiencyAbstract
Edge computing has emerged as a transformative approach for real-time Internet of Things (IoT) applications by overcoming limitations of centralized cloud processing—namely latency, bandwidth constraints, and privacy issues. In 2023, studies have advanced both architectural frameworks and practical implementations across domains. For example, a survey analyzing over 30 edge-based streaming video analytics systems—spanning surveillance and distributed inference—highlighted how edge deployment reduces latency, enhances privacy, and lowers bandwidth demand MDPI. Agricultural IoT case studies demonstrated that multi-tier edge-cloud architectures yield over 30% water savings and up to 80% nutrient uptake improvements in greenhouse environments ar5iv. On the methodology front, a Deep Reinforcement Learning (DRL)-based scheduler (DRLIS) deployed within FogBus2 effectively minimized response time, balanced server load, and cut IoT task cost by up to 50% arXiv. Another study showed that lightweight container orchestration using k3s and FogBus2 improved real-time IoT application response times by around 29% with minimal node overhead arXiv. These findings guide the design of hierarchical edge architectures—spanning device-edge, micro-edge (e.g., gateways), and regional edge layers—to meet stringent requirements of latency, privacy, and resource efficiency. We propose a unified evaluation framework based on real-time streaming analytics, intelligent scheduling, and scalable orchestration in heterogeneous environments. Results from comparative analysis underscore the merits of adaptive scheduling and lightweight container orchestration in improving responsiveness and resource utilization. We conclude with recommendations for future deployments leveraging DRL-driven scheduling and micro-service orchestration, along with opportunities in expanding application domains such as smart agriculture and edge-based video streaming.
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