Clustering-Based Cooperative Computation Offloading Game for Dependent Tasks in Industrial Internet of Things Systems

With the expansion of connected devices used for industrial purposes, the Industrial Internet of Things (IIoT) has emerged as a specific branch of the Internet of Things (IoT) for Industry 4.0. Its applications in industrial domains include monitoring, smart manufacturing, and virtual and augmented reality. However, the huge amount of data generated by IIoT devices with limited computing resources makes it challenging to guarantee the different required quality of service (QoS) of the applications while minimizing the computation cost in terms of energy consumption. especially in terms of latency. In this paper, we opt for the offloading of dependent computation-intensive tasks to edge and cloud servers with more powerful computation capacities. Our proposed model aims to minimize the energy consumption of each IIoT device while respecting the maximal tolerant deadline of task completion. We propose to cluster the IIoT devices that have dependent tasks together to better handle this dependency. Moreover, we propose a distributed cooperative game that allows each device to decide whether it is beneficial for it and for its cluster, in terms of task completion and energy consumption, to offload its task or execute it locally. We prove that the Nash Equilibrium exists by proving that our game is a weighted potential game. Finally, we propose a practical distributed offloading algorithm to implement the cooperative game. The performance evaluation results show that the proposal optimizes energy consumption whilst increasing the number of tasks completed on time.

I. Introduction

Industrial Internet of Things [1] refers to the integration of IoT in industrial environments, also known as industry 4.0. Its applications vary from simple monitoring to critical control. The overwhelming growth of IIoT-connected devices, generating a huge amount of data, makes it more complicated to analyze and process these data to exploit them. For instance, the limited computation resources devices cannot achieve the degree of QoS, especially in terms of latency, required by different IIoT applications. It will be interesting to exploit more powerful nodes to execute the computation-intensive tasks. Hence, solutions based on powerful cloud and edge servers have emerged to offer these additional powerful resources. In these solutions, a task can be computed locally or offloaded to the cloud or the edge servers deployed in the proximity of IIoT devices. The offloading strategy depends on the requirements of the application in terms of QoS, energy consumption, available resources, rental prices, etc.