The extensive growth in Industrial Internet of Things (IIoT) applications has tremendously increased the demands for low latency and resource-sensitive computing to accomplish critical industrial automations. This has leveraged the use of some proficient computing paradigms such as multiaccess edge computing (MEC), which facilitates a low latency and scalable solution for the execution of industrial workloads. However, continual generation of industrial data has imposed a substantial amount of stress on the resource-constrained MEC systems. In this perspective, our study proposes a consolidated stochastic computation offloading (CSCO) framework to address the increasing computational demands of MEC-based IIoT systems. The proposed framework efficiently handles industrial workloads by modeling them as stochastic processes to observe the number of data packets denied service due to the finite number of busy MEC servers. We provide an analytical solution corresponding to the loss probability of data packets denied service at the MEC servers. This leads to the development of a computation offloading mechanism for time-critical tasks. Furthermore, we provide the expression for conditional waiting time (CWT) and unconditional waiting time (UWT) of the data packets waiting to be offloaded to the remote cloud servers. Through extensive numerical simulations, it is inferred that the proposed CSCO framework provides promising results in characterizing the stochastic behavior of MEC-based IIoT systems, thereby providing a low latency, and resource sensitive solution for the considered system.

The Industrial Internet of Things (IIoT) has been introduced in an era of increasingly broad potentials in the medical industry. In recent years, IIoT-based healthcare applications have grown in popularity, with the majority of them relying on Wireless Body Area Network (WBAN) for flexibility. There have been a few recent works that have investigated SDN-based fog architecture for constructing smart healthcare systems. However, the best fog node from the fog layer must be identified and limit the transmission of unnecessary data. To address this issue, the Intelligent Software-defined Fog Architecture (i-Health) is developed in this work. Based on the prior data pattern of each patient, the controller will decide whether to send the data to the fog layer. Furthermore, we introduced the Fog Ranking Service (FRS) and Fog Probing Service (FPS) to select the best fog node. The performance comparison reveals that the proposed i-Health outperforms existing benchmark approaches.