TY - JOUR A2 - Vieira, Marcos A. AU - Dao, Nhu-Ngoc AU - Vu, Duc-Nghia AU - Lee, Yunseong AU - Cho, Sungrae AU - Cho, Chihyun AU - Kim, Hyunbum PY - 2018 DA - 2018/04/04 TI - Pattern-Identified Online Task Scheduling in Multitier Edge Computing for Industrial IoT Services SP - 2101206 VL - 2018 AB - In smart manufacturing, production machinery and auxiliary devices, referred to as industrial Internet of things (IIoT), are connected to a unified networking infrastructure for management and command deliveries in a precise production process. However, providing autonomous, reliable, and real-time offloaded services for such a production is an open challenge since these IIoT devices are assumed lightweight embedded platforms with limited computing performance. In this paper, we propose a pattern-identified online task scheduling (PIOTS) mechanism for the networking infrastructure, where multitier edge computing is provided, in order to handle the offloaded tasks in real time. First, historical IIoT task patterns in every timeslot are used to train a self-organizing map (SOM), which represents the features of the task patterns within defined dimensions. Consequently, offline task scheduling among edge computing-enabled entities is performed on the set of all SOM neurons using the Hungarian method to determine the expected optimal task assignments. In real-time context, whenever a task arrives at the infrastructure, the expected optimal assignment for the task is scheduled to the appropriate edge computing-enabled entity. Numerical simulation results show that the proposed PIOTS mechanism overcomes existing solutions in terms of computation performance and service capability. SN - 1574-017X UR - https://doi.org/10.1155/2018/2101206 DO - 10.1155/2018/2101206 JF - Mobile Information Systems PB - Hindawi KW - ER -