Code of Paper: VoI-Guaranteed Task Computing for Massive IoT under Demand and Resource Uncertainties

Abstract—In massive Internet of Things (IoT) deployments,the efficient allocation of computing resources to IoT deviceswhile preserving devices’ data poses a significant challenge.This paper proposes a new online probabilistic model to addressuncertainties in demand and resource allocation for IoTnetworks, where the task computing of requesting devices isaddressed by serving devices. The model incorporates uncertaintyand formulates an optimization problem, concerning availablecomputing resources, aimed at minimizing the number of devicesused for task offloading. In addition, the Value of Information(VoI) is considered to ensure that the utility of informationcontained in the data associated with each device remains abovea certain threshold. The problem is solved using a heuristicalgorithm, where if there is no server available to respond,the tasks are stored in a buffer for the next time slot, andtheir waiting time is considered. This approach is based on binpacking algorithms, which have demonstrated their effectivenessin optimizing resource utilization and task allocation. Furthermore,feasibility analyses are provided to assess the performanceof the optimal algorithm in the worst case scenarios, therebyensuring the best possible outcomes. In addition, we investigatetwo strategies for addressing the buffered tasks, First in First Out(FIFO) and Last in First Out (LIFO), to model the task response,where the adoption of FIFO reduces the average waiting time bya factor of two. In general, the proposed framework delivers ahigh level of task computing service reliability, with each servingdevice accommodating an average of four requesting devices.Index Terms— Online task offloading, Distributed resource allocation,Demand uncertainty, Resource uncertainty, and Resourcemanagement.