ReMEC: Reliability-aware scheduling of mixed-criticality IoT tasks in DVFS-enabled Multi-tier Edge Computing

The Internet of Things (IoT) has witnessed significant growth, driving innovation across a wide range of application domains. Many IoT applications are characterized by their high resource demands and stringent latency requirements. Multi-tier edge computing has emerged, addressing these needs, where the application is scheduled across IoT devices, edge servers, and the cloud. However, ensuring reliable application performance remains a key challenge, particularly in transient IoT device failures caused by electromagnetic interference, battery depletion, hardware failures, or software crashes. In this work, we consider task execution reliability by incorporating failure of the user device, while the previous work primarily focuses on server-side reliability and overlooks user-centric limitations. We also include the user budget constraint while enhancing the task execution reliability by task replication. Additionally, we consider mixed criticality tasks in our application model, reflecting real-world scenarios more accurately, an aspect largely overlooked in existing works. To achieve task execution reliability while ensuring user budget and task latency deadline, we introduce ReMEC, a fuzzy logic-based reliable hybrid task offloading framework that relies on a distributed message queuing strategy to preserve execution state during device failures, and a fixed-point iterative method for optimizing DVFS frequencies to improve energy efficiency without violating task deadlines or compromising reliability. Our comprehensive benchmarking, which rigorously compares ReMEC against two state-of-the-art strategies (RMEAC and FP-TOSM) and three baseline approaches (BR-greedy, LE-greedy, and Random-RR), demonstrates that ReMEC outperforms all of them, achieving average improvements of 26.19 % in latency, 31.49 % in energy consumption, and 72.16 % in application failure rate, thereby demonstrating its practical applicability in real-world IoT scenarios.