RESUMO
The resource constrained nature of IoT devices set about task offloading over the Internet for robust processing. However, this increases the Turnaround Time (TAT) of the IoT services. High TATs may cause catastrophe in time-sensitive environments such as chemical and steel industries, vehicular networks, healthcare, and others. Moreover, the unreliable Internet in rural parts of underdeveloped and developing countries is unsuitable for time-critical IoT systems. In this work, we propose a framework for continuous delivery of IoT services to address the issue of high latency/TAT with poor/no-internet coverage. The proposed framework guarantees service delivery in such areas. To demonstrate the proposed framework, we implemented an IoT-based mobile patient monitoring system. It predicts the patient's criticality using actual sensor data. When the sensed parameters exceed the pre-set threshold in the rule-base, it initiates data transfer to the fog or cloud server. If fog or the cloud is unreachable, it performs onboard predictions. Thus, the framework ensures essential service delivery to the user at all times. Our test-bed-based evaluation demonstrates edge CPU and RAM load reduction of 16% and 26%, respectively, in the ML model's test phase. Also, the results confirm continuous service delivery, reduced latency, power and computing resource consumption.