<![CDATA[The rapid growth of the Internet of Things (IoT) has significantly increased the number of connected devices, generating massive volumes of data and placing substantial demands on edge and fog computing infrastructures. Traditional resource management approaches often overlook task dependencies, which can lead to inefficient resource utilization, increased execution delays, reduced reliability, and potential security risks in distributed IoT environments. To address these challenges, this paper proposes an improved dependency-aware task scheduling framework designed to operate between edge devices and edge servers. The framework employs directed acyclic graph (DAG) modeling to represent task dependencies and execution order, trust-aware node selection to avoid malicious, overloaded, or unreliable nodes, and Particle Swarm Optimization (PSO) to support adaptive resource allocation under dynamic and heterogeneous workloads. Experimental results demonstrate that the proposed framework achieves an average latency of 50 ms, throughput of approximately 500 transactions per second (tps), and a task completion rate of 98%. These findings indicate that the proposed approach outperforms conventional scheduling methods by improving latency, throughput, reliability, security, and overall task execution efficiency in IoT-enabled edge computing environments.]]>
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