TY - JOUR Y1 - 2024/// A1 - Umer, A. A1 - Ali, M. A1 - Jehangiri, A.I. A1 - Bilal, M. A1 - Shuja, J. ID - scholars19738 TI - Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics IS - 8 KW - Ant colony optimization; Computation offloading; Decision making; Delay-sensitive applications; Fault tolerance; Fog; Hierarchical systems; Internet of things; Response time (computer systems) KW - Analytical Hierarchy Process; Computation-intensive task; Delay sensitive; Delay-sensitive task; Energy-consumption; Fault-tolerant; Fault-tolerant manager; IoT task offloading & scheduling; Smart transportation of logistic; Task offloading; Task-aware; Task-aware scheduler; Tasks scheduling KW - Energy utilization KW - adult; algorithm; analytic hierarchy process; ant colony optimization; article; bandwidth; controlled study; decision making; diagnosis; energy consumption; female; human; internet of things; male; reaction time; sensor; simulation; traffic and transport N1 - cited By 0 JF - Sensors VL - 24 N2 - IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodesâ?? priority for task allocation and scheduling. The AHP decides the fog nodesâ?? priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7 reduction in response time, 16 reduction in energy consumption, and 22 reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin. © 2024 by the authors. AV - none UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191409032&doi=10.3390%2fs24082381&partnerID=40&md5=7793b14af524c47ced1060ae2d5ed501 ER -