Time Slotted Channel Hopping (TSCH), defined among the operating modes in IEEE 802.15.4-2015 standard, was established to offer a guaranteed quality of service for deterministic industrial type applications. However, the standard only provides a framework but it does not mandate a specific scheduling mechanism. In this paper, we formulate the NP-hard scheduling problem in terms of maximizing the throughput with deadline constraints and at the same time satisfying interference constraints in TSCH Networks. In the considered TSCH network, a centralized entity typically called gateway, coordinates the assignment of frequencies and timeslots to the nodes. To solve this NP-hard throughput scheduling problem, a Genetic Algorithm (GA) framework was adopted. Simulation results corroborate that our GA-based approach yields very close performance to the optimal solutions and operates with much lower complexity. In addition, the results also confirmed that GA outperforms other popular scheduling algorithms in the literature in terms of throughput maximization as well minimizing violated deadlines.
Throughput Maximization Scheduling Algorithm in TSCH Networks with Deadline Constraints
Ojo, Mike
;Giordano, Stefano;Portaluri, Giuseppe;Adami, Davide
2017-01-01
Abstract
Time Slotted Channel Hopping (TSCH), defined among the operating modes in IEEE 802.15.4-2015 standard, was established to offer a guaranteed quality of service for deterministic industrial type applications. However, the standard only provides a framework but it does not mandate a specific scheduling mechanism. In this paper, we formulate the NP-hard scheduling problem in terms of maximizing the throughput with deadline constraints and at the same time satisfying interference constraints in TSCH Networks. In the considered TSCH network, a centralized entity typically called gateway, coordinates the assignment of frequencies and timeslots to the nodes. To solve this NP-hard throughput scheduling problem, a Genetic Algorithm (GA) framework was adopted. Simulation results corroborate that our GA-based approach yields very close performance to the optimal solutions and operates with much lower complexity. In addition, the results also confirmed that GA outperforms other popular scheduling algorithms in the literature in terms of throughput maximization as well minimizing violated deadlines.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
