Data-centric scheduling for minimizing queue length in wireless machine-to-machine networks
Journal
2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
Date Issued
2019
Author(s)
Abstract
In this paper, we consider the problem of minimizing the queue length of wireless sensors involved in data gathering. Unlike conventional approaches, we focus on the received data quality at the collector for optimizing scheduling design. While meeting the minimum data fidelity requirement from the application, we leverage correlation among gathered data to allow proactive data dropping before queue is full and reduce radio resource usage. We first formulate the problem and then transform the time-average resource allocation problem into a scheduling problem based on the Lyapunov optimization framework. To solve the problem, we investigate two heuristic algorithms called allocation-first and drop-first algorithms in addition to the optimal algorithm. Evaluation results show that data-centric algorithms can effectively reduce resource usage and support more sensors compared to the conventional scheduling algorithm while meeting the requirement on data quality. ? 2019 IEEE.
Subjects
Heuristic algorithms; Optimization; Queueing theory; Scheduling; Scheduling algorithms; Conventional approach; Data centric algorithms; Evaluation results; Machine to machines; Optimal algorithm; Optimization framework; Resource allocation problem; Scheduling problem; Machine-to-machine communication
SDGs
Type
conference paper