Abstract
The rapid growth of Industrial Internet of Things (IIoT) has demanded
adaptable and efficient edge computing architectures. Traditional edge
nodes have suffered from limited flexibility and high energy
consumption, which has restricted their deployment in dynamic
industrial environments. Existing systems have struggled to balance
computational performance with resource constraints, particularly
under heterogeneous workloads. This study has proposed a Dynamic
Adaptive Reconfigurable SoC (DAR-SoC) method, which has
integrated hardware reconfiguration with intelligent workload
allocation. The architecture has incorporated programmable logic,
embedded processors, and adaptive memory modules that have enabled
runtime reconfiguration. The method has utilized a task-aware
scheduling mechanism that has optimized resource utilization under
varying industrial conditions. The proposed DAR-SoC framework has
demonstrated improved latency, throughput, and energy efficiency
compared to conventional fixed architectures. Experimental results
have shown that the system has achieved a reduction in power
consumption by 28%, while computational efficiency has increased by
35%. The architecture has also maintained stability under real-time
industrial workloads, which has validated its robustness. The proposed
DAR-SoC demonstrates significant performance improvements across
all evaluation metrics. The system achieves a latency reduction of
41.6%, while throughput increases by 50% compared to conventional
architectures. Energy consumption decreases by 35.3%, which ensures
efficient operation. Resource utilization improves to 93%, which
reflects optimal hardware usage. Additionally, the reconfiguration
overhead reduces by 60%, which enhances system adaptability. These
results confirm that the proposed architecture provides a scalable and
efficient solution for Industrial IoT edge environments.
Authors
P.T. Kalaivaani
Vivekanandha College of Engineering for Women, India
Keywords
Reconfigurable SoC, Industrial IoT, Edge Computing, Adaptive Architecture, Energy Efficiency