Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures

Healthcare applications are distinguished by being critical and time sensitive. Multiple healthcare applications have been implemented through Internet of Things (IoT) technology due to its capability of improving the quality and efficiency of treatments and accordingly improving the health of the patients. This paper contributes to the domain by proposing efficient IoT architecture, mobility-aware scheduling and allocation protocols for healthcare. The proposed approach supports the mobility of the patients through an adaptive Received Signal Strength (RSS) based handoff mechanism. The proposed architecture allows the dynamic distribution of healthcare tasks among computational nodes whether cloud devices or fog devices through an implementation of a mobility-aware heuristic based scheduling and allocation approach (MobMBAR). It dynamically balances the distribution of task execution according to the movements of patients and the temporal/spatial residual of their sensed data. The objective of the proposed approach is the minimization of the total schedule time through utilizing task features such as critical level and the maximum response time of the task during the ranking and reallocation phases. We validate the performance of the proposed approach by simulation and compare against other existing solutions. The simulation results have shown that missed tasks range doesn't exceed one thousandths percent, and is proven to be 88% lower than state-of-the-art solutions in terms of Makespan and 92% lower in terms of energy consumption. The paper also includes a realistic simulation for evaluating MobMBAR in an indoor hospital building in Chicago, and it has demonstrated acceptable performance.