Internet of Things: The Optimal Generation Rates under Preemption Strategy in a Multi-Source Queuing System

Abstract

With the rapid development and wide application of the Internet of Things (IoT), how to provide timely and fresh information for strategic analysis and decision-making has become a key issue. Recent studies have shown that preemption strategies are of great importance to the improvement of information freshness. In view of this, we focus on the multi-source preemptive queuing model and investigate how to control the generation rate of each source to achieve the optimal overall information freshness. Specifically, we consider two typical preemption strategies: self-preemption strategy and global-preemption strategy. Noting that the urgency requirements of the systems on the data of each source are different, we propose the weighted average age of information (AoI) to characterize the overall information freshness of the system. For the self-preemption strategy, we prove that the optimal generation rate allocation is a convex problem and present an efficient algorithm to find the optimal solution. Additionally, we also derive a closed-form approximate optimal solution under light load cases to meet the demands for rapid deployment. For the global-preemption strategy, we directly derive the closed-form optimal solution of the corresponding problem. By comparing the optimized weighted average AoIs, the performance achieved by the global-preemption system was better than that achieved by the self-preemption system in terms of the overall timeliness. The numerical analysis verified the correctness of the theoretical analysis and that the proposed approximate solution had high accuracy not only under light load cases but also under other cases.

Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions

“…Various systems characterized by different queueing models such as M/M/1, D/M/1, M/D/1, M/G/1, and G/G/1/1 adopted AoI to evaluate the freshness of the received data [ 7 , 8 , 9 ]. Authors in [ 10 ] considered the multi-source preemptive queuing model and investigated the optimal generation rate of each source achieving the best information freshness. The optimal generation rate of update which induced the minimal violation probability is found in [ 11 ].…”

Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

“…And the scheduling policy that can effectively improve AoI has been designed. In [4], they investigate a multisource preemptive queuing model as well as how to regulate the generation rate of each source under various preemption strategies to attain the highest level of information freshness overall. In [5], the AoI of a multisource queueing model with an FCFS (first-come, first-served) Poisson arrival strategy is examined.…”

Moment Generating Function of Age of Information in Multisource M/G/1/1 Queueing Systems

“…The authors of [15], [16] derived the average AoI for a multi-source M/M/1 queueing model under various preemptive and non-preemptive packet management policies. The work in [22] considered a multi-source M/M/1/1 queueing model and studied the optimal generation rate of different This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ sources to minimize the weighted average AoI under the globally preemptive and self-preemptive policies.…”

“…Finally, substituting the MGF of the system time of source c derived in (22) and the MGF of the interdeparture time of source c derived in ( 23) into (11) results in the MGF of the AoI under the self-preemptive policy, Mδc (s), given in Theorem 1. In addition, substituting ( 22) and ( 23) into (12) results in the MGF of the peak AoI under the self-preemptive policy, MAc (s), given in Theorem 1.…”