VoI-Guaranteed Task Computing for Massive IoT Under Demand and Resource Uncertainties

In large-scale Internet of Things (IoT) deployments, efficiently allocating computing resources to IoT devices, while preserving the integrity and utility of their data, remains a critical challenge. This paper introduces a novel online probabilistic model designed to handle uncertainties in both demand and resource availability within IoT networks, where the computing tasks of requesting devices (RDs) are fulfilled by serving devices (SDs). The proposed model integrates stochastic elements and formulates an optimization problem that aims to minimize the number of active serving devices required for task offloading, subject to the constraints of available computing resources. To further enhance decision-making, the model incorporates the concept ofValue of Information (VoI)to ensure that the informational utility of each device’s data remains above a predefined threshold during task processing. The optimization problem is addressed using a heuristic algorithm. In scenarios where no serving device is immediately available, tasks are temporarily stored in a buffer and deferred to the next time slot, with their waiting time being tracked. This task allocation process is inspired by bin-packing algorithms, which are known for their efficiency in resource management and task scheduling. Moreover, the paper evaluates the performance of the proposed solution under worst-case conditions through feasibility analysis, thereby demonstrating its robustness. Two buffering strategies, First-In First-Out (FIFO) and Last-In First-Out (LIFO), are also examined to model task retrieval and execution behavior. Results show that adopting the FIFO strategy can reduce the average waiting time by approximately 50%. Overall, the proposed framework provides a reliable and scalable task computing service, with each serving device capable of supporting, on average, four requesting devices under typical operating conditions.