AoI-Bounded Scheduling for Industrial Wireless Sensor Networks

Abstract

Age of information (AoI) is an emerging network metric that measures information freshness from an application layer perspective. It can evaluate the timeliness of information in industrial wireless sensor networks (IWSNs). Previous research has primarily focused on minimizing the long-term average AoI of the entire system. However, in practical industrial applications, optimizing the average AoI does not guarantee that the peak AoI of each data packet is within a bounded interval. If the AoI of certain packets exceeds the predetermined threshold, it can have a significant impact on the stability of the industrial control system. Therefore, this paper studies the scheduling problem subject to a hard AoI performance requirement in IWSNs. First, we propose a low-complexity AoI-bounded scheduling algorithm for IWSNs that guarantees that the AoI of each packet is within a bounded interval. Then, we analyze the schedulability conditions of the algorithm and propose a method to decrease the peak AoI of nodes with higher AoI requirements. Finally, we present a numerical example that illustrates the proposed algorithm step by step. The results demonstrate the effectiveness of our algorithm, which can guarantee bounded AoI intervals (BAIs) for all nodes.

AoI-Aware Resource Scheduling for Industrial IoT with Deep Reinforcement Learning

“…Consider the long-term average system performance; our purpose is finding a scheduling strategy to maximize the throughout and minimizing the AoI of data. Based on the above, the objective can be described as maximizing the system utility and formulated as: 1), (5), ( 6), (7) Obviously, P1 is a nonlinear programming problem which is complicated to solve using traditional optimization methods such as game-theoretic and heuristic methods. Recent research has shown that DRLs have great performance in long-term decision problems.…”

“…In the Industrial Internet of Things, there are strict requirements for the freshness of sampled data. Optimizing the average AoI alone cannot completely prevent data from exceeding a predetermined AoI threshold [7]. Therefore, it is important to consider both average AoI and AoI violation rate optimization to ensure system stability.…”

Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

“…Currently, AoI has gained popularity as a metric to assess data timeliness in IoT networks [10][11][12][13][14][15]. It underscores the "freshness" of data within target nodes.…”

“…Chen et al [11] introduced an algorithm from a charging perspective, addressing the optimization of information delay in wireless-powered edge networks, concurrently reducing both average and maximum peak AoI. Pu et al [12] proposed a novel AoI-bounded scheduling algorithm, ensuring that the peak age of AoI for each data package is maintained within a finite range. To optimize average AoI, Zhao et al [13] studied the impact of interference on information delay in large-scale wireless networks and presented a novel method.…”